Biophysical platform for drug development based on energy landscape

In one aspect, the present invention provides a method of selecting or identifying an agent that inhibits a target protein having an active site. In another aspect, the invention provides a method of selecting an agent that inhibits a target protein having an active site for further optimization. In some embodiments, the methods comprise measuring or predicting stability of an induced fit conformation of an agent contacted to an active site of the protein, wherein the agent is selected if the stability of the induced fit conformation of the agent contacted to the active site of the protein is increased relative to a reference stability.

BACKGROUND OF THE INVENTION

The fundamental importance of protein kinases is indisputable. Their central role in essential physiological processes has provoked extensive studies and has resulted in a wealth of knowledge from biological signaling cascades to atomistic structural details. Kinases are attractive therapeutic drug targets because different signaling cascades can be selectively regulated by inhibiting individual kinases. However, all kinases share a great degree of similarity, thus making it difficult to design inhibitors that are specific for a particular kinase. This complication has hampered progress in drug development and highlights the need for a deeper understanding of the biophysical principles that govern kinase-drug interactions.

The evolution of more than 500 human protein kinases from a few protein kinases in unicellular organisms allowed for the development of complexity via differential regulation. Such regulation can be achieved by autophosphorylation or interactions with other domains or binding partners. While many of the signaling cascades and their in vivo biological effectors have been well characterized, and a wealth of structural information is available, the molecular mechanism whereby kinase activity is modulated is a topic of controversial debate.

Accordingly, new methods of identifying potential protein kinase inhibitors or potential inhibitors of other proteins using an energy landscape providing tight affinity through an induced fit and binding plasticity through a conformational-selection mechanism are urgently required.

SUMMARY OF THE INVENTION

The present invention features methods of selecting or identifying an agent that inhibits a target protein having an active site. The methods comprise measuring or predicting stability of an induced fit conformation (E*-I) of a candidate agent contacted to the active site of the protein.

In one aspect, the invention provides a method of selecting or identifying an agent that inhibits a target protein having an active site, the method comprising measuring or predicting stability of an induced fit conformation (E*-I) of a candidate agent contacted to the active site of the protein, wherein the candidate agent is selected or identified as an inhibitor of the protein if the measured or predicted stability of the induced fit conformation (E*-I) of the candidate agent contacted to the active site is increased relative to a reference stability.

In another aspect, the invention provides a method of selecting or identifying an agent that inhibits a target protein having an active site, the method comprising measuring or predicting a rate of conversion between a primary bound conformation (E-I) and an induced fit conformation (E*-I) of a candidate agent contacted to an active site of the protein, wherein a candidate agent is selected or identified as an inhibitor of the protein if a measured or predicted rate of conversion from the primary bound conformation (E-I) to the induced fit conformation (E*-I) is increased and/or a measured or predicted rate of conversion from the induced fit conformation (E*-I) to the primary bound conformation (E-I) is decreased relative to a reference rate.

In yet another aspect, the invention provides a method of selecting an agent that inhibits a target protein having an active site, the method comprising measuring a structure of an induced fit conformation (E*-I) of a candidate agent contacted to an active site of the kinase, wherein the stability of the induced fit conformation (E*-I) of the candidate agent contacted to the active site of the protein is pre-identified as increased relative to a reference stability.

In various embodiments of any one of the aspects delineated herein, the reference stability is the stability of an induced fit conformation (E*-I) of a pre-selected lead agent, a natural substrate of the protein, or a natural ligand of the protein or an analog thereof contacted to the active site of the protein. In various embodiments, the reference rate is a rate of conversion to or from a primary bound conformation (E-I) to or from an induced fit conformation (E*-I) of a pre-selected lead agent, a natural substrate of the protein, or a natural ligand of the protein or an analog thereof contacted to the active site of the protein.

In another aspect, the invention provides a method of selecting an agent that inhibits a target protein having an active site for further optimization, the method comprising measuring an induced fit step when a first candidate agent is contacted with the protein, wherein the first candidate agent is selected for further optimization if an induced fit step is detected. In various embodiments, the induced fit step is measured by measuring stability of an induced fit conformation (E*-I) of the candidate agent contacted to the active site of the protein relative to a reference stability, by measuring a rate of conversion to or from a primary bound conformation (E-I) to or from the induced fit conformation (E*-I) of the candidate agent contacted to the active site of the protein relative to a reference rate, or by measuring a structure of an induced fit conformation (E*-I) of the candidate agent contacted to an active site of the protein.

In still another aspect, the invention provides a method of selecting an agent that inhibits a target protein having an active site for further optimization, the method comprising measuring stability of an induced fit conformation (E*-I) of a candidate agent contacted to an active site of the protein, wherein the candidate agent is selected for further optimization if the stability of the induced fit conformation (E*-I) is increased relative to a first reference stability. In various embodiments, the further optimization comprises identifying a modified form of the candidate agent having an increased stability of an induced fit conformation of the modified form of candidate agent contacted to the active site of the protein relative to a second reference stability.

In another aspect, the invention provides a method for selecting an agent that inhibits a target protein having an active site, the method comprising (a) measuring stability of an induced fit conformation (E*-I) of a candidate agent contacted to an active site of the protein; (b) measuring a structure of the induced fit conformation (E*-I) if the stability of the induced fit conformation in step (a) is increased relative to a first reference stability; and (c) predicting stability of an induced fit conformation (E*-I) of a modified form of the candidate agent contacted to an active site of the kinase using the structure measured in step (b), wherein the modified form of the candidate agent is selected if the predicted stability is increased relative to a second reference stability.

In various embodiments of any one of the aspects delineated herein, the first reference stability is the stability of an induced fit conformation (E*-I) of a pre-selected lead agent, a natural substrate, or a natural ligand or an analog thereof contacted to an active site of the protein. In various embodiments, the second reference stability is the stability of the induced fit conformation (E*-I) of the modified form of the candidate agent contacted to the active site of the protein. In some embodiments, the modified form of the candidate agent is an analog of the candidate agent. In various embodiments of any one of the aspects delineated herein, the method further comprises measuring a stability of or a rate of conversion to or from any one of a kinetically distinct state selected from the group consisting of a binding incompetent state, binding competent state, a primary bound conformation (E-I), and an induced fit conformation (E*-I).

In various embodiments of any one of the aspects delineated herein, the stability of the induced fit conformation (E*-I) is characterized by measuring a Keqof the equilibrium between the primary bound conformation (E-I) and induced fit conformation (E*-I) or by measuring a rate of conversion from a primary bound conformation (E-I) to the induced fit conformation (E*-I) is increased and/or a rate of conversion from the induced fit conformation (E-I*) to the primary bound conformation (E-I). In various embodiments, the selected agent has an increased affinity for the protein. In particular embodiments, the selected agent has an increased residence time on the protein. In some embodiments, the agent induces a conformation change in the protein during the induced fit step subsequent to the primary binding of the agent to the protein.

In some other embodiments, contacting the protein with the agent results in an equilibrium that is far-shifted to the induced fit step or induced fit conformation. In still other embodiments, the affinity of the selected agent to the protein is increased by at least about 1 kcal/mol, 2 kcal/mol, at least about 3 kcal/mol, at least about 4 kcal/mol, at least about 5 kcal/mol, at least about 6 kcal/mol, at least about 7 kcal/mol, at least about 8 kcal/mol, at least about 9 kcal/mol, or at least about 10 kcal/mol. In other embodiments, the equilibrium is shifted to the induced fit conformation (E*-I) by at least about 1000 fold or at least about 10000 fold.

In various embodiments of any one of the aspects delineated herein, the measuring involves X-ray crystallography, NMR spectroscopy, and/or fast fluorescence binding kinetics, enzyme kinetics, surface plasmon resonance, and molecular dynamics simulation. In some embodiments, the measuring of the structure of the induced fit conformation (E*-I) involves NMR spectroscopy and/or X-ray crystallography. In some other embodiments, the predicting stability of an induced fit conformation (E*-I) of a candidate agent contacted to an active site of the protein involves in silico simulation. In still other embodiments, wherein the induced fit step or induced fit conformation (E*-I) is identified by detecting a rate having a non-linear dependence on agent concentration.

In various embodiments of any one of the aspects delineated herein, the pre-selected lead agent is selected from a conventional screen of a library of agents or from an in silico simulation.

In various embodiments, the agent is a small molecule, polypeptide, peptide, or peptide mimetic.

In some embodiments, the protein is a kinase. In other embodiments, the active site is an ATP binding site. In some other embodiments, the natural ligand is ATP.

In another aspect, the invention provides a method of identifying a functional residue on a target protein, the method comprising (a) identifying a protein related to the target protein by ancestral reconstruction; (b) measuring stability of a conformation of the related protein contacted with the agent; and (c) correlating a sequence of the target protein and/or a sequence of the related protein with the stability of a conformation of the target protein and/or the related protein contacted with the agent to determine a residue that alters stability when the residue is modified, thereby identifying a functional residue on the target protein.

In still another aspect, the invention provides a method of identifying an agent that selectively modulates a kinase, the method comprising (a) identifying on the kinase a functional residue outside of the active site by ancestral reconstruction; (b) detecting or predicting binding of a candidate agent to the functional residue, and (c) detecting kinase activity of the kinase in the presence of the agent; wherein the candidate agent is identified as binding to the functional residue and modulating the kinase function. In various embodiments, the functional residue is not on an active site of the protein. In some embodiments, the agent is a small molecule.

In some other embodiments, the protein is a kinase. In still other embodiments, the active site is an ATP binding site.

In still another aspect, the invention provides a tangible, non-transitory computer readable medium comprising: computer program instructions for implementing a method of identifying or selecting an agent that inhibits a protein comprising predicting stability of an induced fit conformation (E*-I) of a candidate agent contacted to the active site of the protein, wherein the candidate agent is selected or identified as an inhibitor of the protein if the measured or predicted stability of the induced fit conformation (E*-I) of the candidate agent contacted to the active site is increased relative to a reference stability.

Compositions and articles defined by the invention were isolated or otherwise manufactured in connection with the examples provided below. Other features and advantages of the invention will be apparent from the detailed description, and from the claims.

Definitions

As used herein, “activity” or “biological activity” of a polypeptide refers to any biological function or any biological interaction of a polypeptide. Activity of a polypeptide may refer to the polypeptide's enzymatic or catalytic activity (e.g., kinase activity). For example, “kinase activity” of Aurora A kinase refers to Aurora A kinase's phosphorylation of a serine or threonine residue on a substrate polypeptide.

By “active site” is meant an area or portion on a protein where a substrate of the protein binds. For example, if the protein is a kinase, an active site of the kinase is an ATP binding site. A protein may have multiple substrates. Thus, an active site of a kinase may also bind other substrates. For example, another substrate of a kinase is a residue on a polypeptide to which the kinase transfers a phosphate group (i.e., phosphorylates), so an active site on a kinase may be a site on the kinase that binds a residue that the kinase phosphorylates.

By “agent” is meant any small molecule chemical compound, antibody, nucleic acid molecule, peptide, peptide mimetic, polypeptide, or fragments thereof.

By “alteration” is meant a change (increase or decrease) in the binding affinity, expression levels or activity of a gene or polypeptide (e.g., kinase activity) as detected by standard art known methods such as those described herein. As used herein, an alteration includes a 10% change in expression levels, preferably a 25% change, more preferably a 40% change, and most preferably a 50% or greater change in expression levels.

By “analog” is meant a molecule that is not identical, but has analogous functional or structural features. For example, a polypeptide analog retains the biological activity of a corresponding naturally-occurring polypeptide, while having certain biochemical modifications that enhance the analog's function relative to a naturally occurring polypeptide. Such biochemical modifications could increase the analog's protease resistance, membrane permeability, or half-life, without altering, for example, ligand binding. An analog may include an unnatural amino acid.

The terms “binding,” “bind,” “bound” refer to an interaction between two molecules. The interaction may include a covalent or non-covalent bond. The interaction may also be reversible or irreversible depending on the type of interaction, such as covalent bond formation.

“Detect” refers to identifying the presence, absence or amount of the analyte to be detected.

By “detectable label” or “detectable tag” is meant a composition that when linked to a molecule of interest renders the latter detectable, via spectroscopic, photochemical, biochemical, immunochemical, or chemical means. For example, useful labels include radioactive isotopes, magnetic beads, metallic beads, colloidal particles, fluorescent dyes, electron-dense reagents, enzymes (for example, as commonly used in an ELISA), biotin, digoxigenin, or haptens.

By “disease” is meant any condition or disorder that damages or interferes with the normal function of a cell, tissue, or organ. Examples of diseases include cancer.

By “effective amount” is meant the amount of a required to ameliorate the symptoms of a disease relative to an untreated patient. The effective amount of active agent(s) used to practice the present invention for therapeutic treatment of a disease varies depending upon the manner of administration, the age, body weight, and general health of the subject. Ultimately, the attending physician or veterinarian will decide the appropriate amount and dosage regimen. Such amount is referred to as an “effective” amount.

The invention provides a number of targets that are useful for the development of highly specific drugs to treat or a disorder characterized by the methods delineated herein. In addition, the methods of the invention provide a facile means to identify therapies that are safe for use in subjects.

“Hybridization” means hydrogen bonding, which may be Watson-Crick, Hoogsteen or reversed Hoogsteen hydrogen bonding, between complementary nucleobases. For example, adenine and thymine are complementary nucleobases that pair through the formation of hydrogen bonds.

By “induced fit conformation (E*-I)” is meant a conformation formed by a protein-agent complex only after an agent (e.g., a small molecule) is bound to the protein (e.g., after an agent binds to a site, such as an active site, of the protein). The conformational change of the protein-agent (e.g., enzyme-inhibitor) complex happens after initial binding. The induced fit conformation does not exist in a free protein (i.e., a protein not bound or contacted with the agent).

In some embodiments, the induced fit conformation is formed after a “primary bound conformation (E-I)” (i.e., a conformation corresponding to the initial binding or contact of the agent with the site on the protein). The “induce fit step” corresponds to step of transitioning or converting from the primary bound conformation (E-I) to the induced fit conformation (E*-I) Formation of an induced fit conformation is after the binding of an agent to a site (e.g., active site) on the protein generally results in increased affinity of the agent and/or increased residence time of the agent on the protein.

In particular embodiments, the agent is an inhibitor of the protein (e.g., the agent inhibits an activity, such as catalytic or enzymatic activity, of the protein). The agent may inhibit the protein by binding to the active site of the protein (i.e., by competitive binding of the agent to the active site, where the natural substrate binds). Generally, an induced fit conformation may form when a substrate, particularly a natural substrate, is bound to the active site of a protein. However, because a natural substrate must be turned over, the induced fit conformation formed by the natural substrate-protein complex is not extremely stable. An inhibitor of a protein is effective if the inhibitor forms a very stable induced fit conformation, resulting in a highly increased affinity and increased residence time of the inhibitor to the active site of the protein.

In some embodiments, the free energy of binding of an agent at the initial binding step (or primary binding step) is at least about 2 kcal/mol, at least about 3 kcal/mol, at least about 4 kcal/mol, or at least about 5 kcal/mol. In particular embodiments, the induced fit step adds at least about 2 kcal/mol, at least about 3 kcal/mol, at least about 4 kcal/mol, or at least about 5 kcal/mol to the free energy of binding to the energy of binding of the initial binding step. In other embodiments, the overall free energy of binding of the agent to a protein (or to an active site on a protein) is at least about 2 kcal/mol, at least about 3 kcal/mol, at least about 4 kcal/mol, at least about 5 kcal/mol, at least about 6 kcal/mol, at least about 7 kcal/mol, at least about 8 kcal/mol, at least about 9 kcal/mol, or at least about 10 kcal/mol.

Unless otherwise specified, a “polynucleotide encoding an amino acid sequence,” a “polynucleotide encoding a polypeptide,” or a “nucleotide sequence encoding an amino acid sequence,” includes all nucleotide sequences that are degenerate versions of each other and that encode the same amino acid sequence. The phrase nucleotide sequence that encodes a polypeptide or an RNA may also include introns to the extent that the nucleotide sequence encoding the polypeptide may in some version contain an intron(s).

By an “isolated polypeptide” is meant a polypeptide of the invention that has been separated from components that naturally accompany it. The terms “polypeptide” and “protein” are used interchangeably herein. Typically, the polypeptide is isolated when it is at least 60%, by weight, free from the proteins and naturally-occurring organic molecules with which it is naturally associated. Preferably, the preparation is at least 75%, more preferably at least 90%, and most preferably at least 99%, by weight, a polypeptide of the invention. An isolated polypeptide of the invention may be obtained, for example, by extraction from a natural source, by expression of a recombinant nucleic acid encoding such a polypeptide; or by chemically synthesizing the protein. Purity can be measured by any appropriate method, for example, column chromatography, polyacrylamide gel electrophoresis, or by HPLC analysis.

By “kinase” is meant a protein that catalyzes a phosphorylation reaction, i.e., the transfer of a phosphate group from a phosphate-donor molecule (e.g., ATP) to another agent (e.g, a substrate such as a protein residue). In some embodiments, the protein is a kinase. In some embodiments, the active site of the kinase is an ATP binding site.

By “lead agent” or “pre-selected lead agent” is meant an agent (e.g., a small molecule) that has been identified, detected, or predicted to bind a protein (e.g., a target protein). For example, the lead agent may be an initial “hit” from a conventional screen of a library of agents (e.g., a library of compounds). The lead agent may also be an agent predicted to bind the target protein via in silico simulation methods that calculate predicted binding free energy of the agent to the protein based on atoms or residues on the agent and/or protein. In some embodiments, the agent or lead agent is a macrocycle. In some embodiments, the lead agent is selected from a screen of a library of macrocycles. In particular embodiments, the macrocycle or library of macrocycles is synthesized by DNA encoded synthesis. In other embodiments, the macrocycle is based on a peptide bond.

By “marker” is meant any protein or polynucleotide having an alteration in expression level or activity that is associated with a disease or disorder.

By “modulate” is meant increase or decrease a measured parameter. In one embodiment, the parameter is kinase activity, binding affinity or equilibrium. For example, an increase in affinity is by at least about 1 kcal/mol, 2 kcal/mol, at least about 3 kcal/mol, at least about 4 kcal/mol, at least about 5 kcal/mol, at least about 6 kcal/mol, at least about 7 kcal/mol, at least about 8 kcal/mol, at least about 9 kcal/mol, or at least about 10 kcal/mol.

As used herein, “obtaining” as in “obtaining an agent” includes synthesizing, purchasing, or otherwise acquiring the agent.

As used herein, a “peptide mimetic” or “peptidomimetic” is a small, peptide-like molecule having a structure and/or molecular properties that mimic a peptide.

By “phosphorylation rate” or “rate of phosphorylation” is meant the kinetic rate of a phosphorylation reaction catalyzed by a kinase. An exemplary measure of the rate is the value of a rate constant, k. The rate constant may be determined by plotting the concentrations of phosphorylated substrate against time, and fitting a curve or line to the concentration vs. time data. In some embodiments, the rate constant is determined by determining the slope of a line fit to concentrations of phosphorylated kemptide (substrate of Aurora A kinase) or another substrate of Aurora A kinase over time.

By “reduces” is meant a negative alteration of at least 10%, 25%, 50%, 75%, or 100%.

By “reference” is meant a standard or control condition.

A “reference sequence” is a defined sequence used as a basis for sequence comparison. A reference sequence may be a subset of or the entirety of a specified sequence; for example, a segment of a full-length cDNA or gene sequence, or the complete cDNA or gene sequence. For polypeptides, the length of the reference polypeptide sequence will generally be at least about 16 amino acids, preferably at least about 20 amino acids, more preferably at least about 25 amino acids, and even more preferably about 35 amino acids, about 50 amino acids, or about 100 amino acids. For nucleic acids, the length of the reference nucleic acid sequence will generally be at least about 50 nucleotides, preferably at least about 60 nucleotides, more preferably at least about 75 nucleotides, and even more preferably about 100 nucleotides or about 300 nucleotides or any integer thereabout or therebetween.

A “reference stability” is a pre-determined or pre-measured stability used as a basis for stability comparison. For example, without limitation, a reference stability may be the stability of a conformation of a protein (or active site of a protein) contacted with a natural ligand or a natural substrate of the protein. In some embodiments, the conformation is an induced fit conformation (E*-I) of an active site of a protein contacted with a natural ligand or a natural substrate of the protein. The natural ligand of the protein may be ATP, for example, if the protein is a kinase. A reference stability may also be the stability of a conformation of a protein (or active site of a protein) contacted with a pre-selected lead agent. For example, the pre-selected lead agent may be a small molecule that binds the active site of the protein with weak or moderate affinity.

The “stability” of a conformation or a state of a protein (or, a protein in contact with an agent) may be characterized by the ratio of rates of conversion or transition between the conformation to another conformation (e.g., rate of conversion to or from a primary bound conformation E-I to an induced fit conformation E*-I). Stability of a particular conformation is increased when the rate of conversion from another conformation to the particular conformation (i.e., “forward rate”) is increased and/or the rate of conversion from the conformation (i.e., “reverse rate”) is decreased. In particular embodiments, the stability of an induced fit conformation (E*-I) is increased by decreasing a rate of conversion from the induced fit conformation (E*-I) to another conformation. Stability of a particular conformation may also be characterized by the fraction (or concentration) of the particular conformation relative to other conformations at equilibrium. A particular conformation has high stability if the fraction or concentration of that conformation is high relative to the fraction or concentration of other conformations at equilibrium. Conversely, a particular conformation has low stability if the fraction or concentration of that conformation is low relative to the fraction or concentration of other conformations at equilibrium. Concentrations of such conformations at equilibrium may be characterized by measuring an equilibrium constant (Keq). In some embodiments, stability of the induced fit conformation (E*-I) is characterized by measuring a Keqof the equilibrium between the primary bound conformation (E-I) and induced fit conformation (E*-I) or by measuring a rate of conversion from a primary bound conformation (E-I) to the induced fit conformation (E*-I) is increased and/or a rate of conversion from the induced fit conformation (E-I*) to the primary bound conformation (E-I).

Nucleic acid molecules useful in the methods of the invention include any nucleic acid molecule that encodes a polypeptide of the invention or a fragment thereof. Such nucleic acid molecules need not be 100% identical with an endogenous nucleic acid sequence, but will typically exhibit substantial identity. Polynucleotides having “substantial identity” to an endogenous sequence are typically capable of hybridizing with at least one strand of a double-stranded nucleic acid molecule. Nucleic acid molecules useful in the methods of the invention include any nucleic acid molecule that encodes a polypeptide of the invention or a fragment thereof. Such nucleic acid molecules need not be 100% identical with an endogenous nucleic acid sequence, but will typically exhibit substantial identity. Polynucleotides having “substantial identity” to an endogenous sequence are typically capable of hybridizing with at least one strand of a double-stranded nucleic acid molecule. By “hybridize” is meant pair to form a double-stranded molecule between complementary polynucleotide sequences (e.g., a gene described herein), or portions thereof, under various conditions of stringency. (See, e.g., Wahl, G. M. and S. L. Berger (1987) Methods Enzymol. 152:399; Kimmel, A. R. (1987) Methods Enzymol. 152:507).

By “specifically binds” is meant an agent (e.g., a small molecule) that recognizes and binds a polypeptide (or an active site thereof) of the invention, but which does not substantially recognize and bind other molecules in a sample, for example, a biological sample, which naturally includes a polypeptide of the invention. An agent may also “specifically bind” to a particular site on a polypeptide, and not bind to other sites of the polypeptide. In some embodiments, a small molecule (e.g., Gleevec or Danusertib) binds an active site of a protein (e.g., a kinase).

By “subject” is meant a mammal, including, but not limited to, a human or non-human mammal, such as a bovine, equine, canine, ovine, or feline.

DETAILED DESCRIPTION OF THE INVENTION

The invention features methods of selecting or identifying an agent that inhibits a target protein having an active site. The methods comprise measuring or predicting stability of an induced fit conformation (E*-I) of a candidate agent contacted to the active site of the protein. The invention is based, at least in part, on the discovery that agents (e.g., small molecules or drugs) that increased stability of an induced fit conformation (E*-I) (as opposed to stability of other conformations, e.g., primary bound conformation (E-I)) was key to having a high affinity and/or a long residence time of the drug on the protein.

Drug Design Platform

In some aspects, the present invention provides an integrated platform combining NMR, fast kinetics experiments, x-ray structures, MD simulations and ancestral reconstruction to identify the energy landscapes of targets and their optimal use for drug design. Current drug design is primarily based on just considering static structures. It is proposed herein that the conformational dynamics of the targets are the crucial part for high affinity and specificity for inhibitors, and described herein is a novel approach to characterize the dynamic ensemble of targets in the pre-bound states and after the initial binding of the drug with the goal to use this plasticity of the protein for drug binding.

Present technology is based on single static structures. Accordingly, the present invention identifies that the dynamics of the targets are crucial for binding, hence the need to characterize the target dynamics in different states, free and when bound to compounds. Present technology identifies “pockets” from static structures, and uses docking. Herein is provided a method to design better inhibitors by exploiting the dynamic nature of the targets. Second, current technology does not realize the power of efficient induced fit steps. The new technology is focused to deliberately target dynamic parts of the protein for engaging them in induced fit steps.

In some aspects, the current invention designs inhibitors of a target protein with high very high affinity, long life-time of the drugs on the targets and potentially very high specificity. Using this platform, the underlying atomistic mechanism for high affinity and selectivity for Abl was characterized. The mechanism for Gleevec resistance in the most commonly occurring resistance mutation in cancer patients was also solved. Other test examples are active site binders (available inhibitors) binding to Abl and Aurora A. These test data underscore the importance of the induced fit step for selectivity, high affinity, long residence time of the drug on the target, and the power of this new platform.

In one aspect, the invention provides a method of selecting or identifying an agent that inhibits a target protein having an active site, the method comprising measuring or predicting stability of an induced fit conformation (E*-I) of a candidate agent contacted to the active site of the protein, wherein the candidate agent is selected or identified as an inhibitor of the protein if the measured or predicted stability of the induced fit conformation (E*-I) of the candidate agent contacted to the active site is increased relative to a reference stability. In one embodiment, the invention comprises a method of identifying an inhibitor for a target compound, wherein the target compound is a protein, protein kinase or other inhibitable compound.

The process would start with either already known initial hits of compounds, or a first screen for compounds. Thus, in some embodiments, the reference stability is the stability of an induced fit conformation (E*-I) of a pre-selected lead agent or lead compound contacted to an active site of the protein. In other embodiments, the reference stability is the stability of an induced fit conformation (E*-I) of a natural substrate of the protein, or a natural ligand of the protein or an analog thereof contacted to the active site of the protein.

In particular embodiments, the measuring involves fast fluorescence kinetics. Next overall affinities may be measured using fluorescence, ITC or SPR methodology. Next, characterization of the binding kinetics using stopped-flow fluorescence experiments to characterize the individual steps for binding (measurement of association and dissociation kinetics) may be performed. The obtained kinetics traces may be globally fit. These experiments will yield the binding scheme for the compounds and the individual contribution of the different microscopic steps to the overall affinity. Steps include conformational selection steps and induced fit steps. If a number of compounds were found in the initial screen, the comparison between them will be informative for relating differences in compound structure to differences in the energy landscape of binding. The binding of the compounds will then be followed by NMR titrations either using 1H 15N or 1H 13 C HSQC spectra. These experiments deliver information about which parts of the protein experiencing conformational changes in which step of the binding. Accordingly, in some aspects, the invention provides method of selecting an agent that inhibits a target protein having an active site, the method comprising measuring a structure of an induced fit conformation (E*-I) of a candidate agent contacted to an active site of the kinase, wherein the stability of the induced fit conformation (E*-I) of the candidate agent contacted to the active site of the protein is pre-identified as increased relative to a reference stability.

In particular embodiments, the measuring involves X-ray crystallography and/or NMR spectroscopy. The appropriate NMR dynamics experiments may be performed on the free protein and also on the enzyme/drug complex to characterize the flexibility of the target. X-ray structures of the free protein and bound to the compounds will be solved. To successfully obtain crystals, strategies such as seeding, soaking, and ethylation of lysine side chains will be included.

These experiments together with the stopped-flow kinetics data will deliver a description of the energy landscape of the apo protein, the inhibitor binding and the protein bound to the inhibitor. The combined approach between fast kinetics and NMR is novel.

Agents that are selected or identified as binding to or inhibiting a protein may be further optimized to increase affinity and/or residence time of the agent on the protein. To increase affinity and/or residence time of the agent on the protein, a structure of the protein in the induced fit conformation (E*-I) when in contact with the agent may be measured, for example, by X-ray crystallography and/or NMR. The measured structure may then be used to design agents (e.g., small molecule) that better bind or having increased stability in the induced fit conformation (E*-I). Such “optimized” agents may be design by predicting stability in the induced fit conformation (E*-I) of the protein bound or in contact with the optimized agent. Prediction of stability may be done using molecular docking techniques or simulations. Such techniques are well-known in the art. Such techniques typically involve calculations of energies of binding and/or interaction of the agent with the target protein (or active site of the protein) using relevant atoms or residues in the target protein and/or agent and their spacing or distances from each other.

In other aspects, molecular dynamic (MD) simulations in explicit water on the apo protein will deliver information on flexibility on the picosecond to microsecond time scale. Dynamic NMR experiments will reveal motions on this faster time scale, but additionally on the millisecond and slower time scale as well. The combination of NMR and MD characterization will allow identification of the flexible regions in the apo, and the goal is to exploit this plasticity for the drug binding.

In use, the same experiments may be performed in the inhibitor-bound state to exploit conformational flexibility after inhibitor binding that can be exploited for further optimization of the inhibitor via an induced-fit step. Induced fit steps for lead optimization will result in higher affinity and better specificity, because an induced fit step has at least two major advantageous effects for drug development; i) it strengthens the binding and ii) it increases the residence time of the drug on the target. Therefore the detailed characterization of the dynamics of the target when bound to initial hits is a major focus.

For targets where high specificity is a major agenda, such as protein kinases, calculating ancestral sequences using either maximum likelihood methods or Bayesian phylogenetic analysis, and interpreting evolution of amino acid changes may be a powerful additional method to be employed. The characterization of the differences in inhibitor binding between the ancestral nodes will allow to identify the crucial residues for specificity. In analogy, determining differences in dynamics, particularly correlated motions along the different evolutionary trees will narrow down the amino acids differences that can be exploited for specificity. MD simulations as described above can be repeated quite quickly on ancestors and the results be interpreted in respect to differences in energetics. This approach has been successful for (i) the discovery of the residues responsible for Gleevec specificity for Abl vests Src, and (ii) for identifying the allosteric network between the TPX2 biding site and the active site in Aurora A.

For identifying new allosteric sites, correlated motions will be identified by calculating mutual information metrics for backbone and side chains from MD simulations (picosecond to microsecond dynamics) (FIG.37). Such correlation has been successfully identified for Aurora A kinase. Calculating mutual information metrics for backbone and side chains for Aurora A i its inactive and active state revealed a different set of residues with correlated motions.

Implementation in Hardware and/or Software

The methods described herein can be implemented on general-purpose or specially programmed hardware or software. For example, the methods can be implemented by a computer readable medium. Accordingly, the present invention also provides a software and/or a computer program product configured to perform the algorithms and/or methods according to any embodiment of the present invention. It is well-known to a skilled person in the art how to configure software which can perform the algorithms and/or methods provided in the present invention. The computer-readable medium can be non-transitory and/or tangible. For example, the computer readable medium can be volatile memory (e.g., random access memory and the like) or non-volatile memory (e.g., read-only memory, hard disks, floppy discs, magnetic tape, optical discs, paper table, punch cards, and the like). The computer executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are described in, for example Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2nded., 2001).

The present invention may also make use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. (See, U.S. Pat. Nos. 5,593,839, 5,795,716, 5,733,729, 5,974,164, 6,066,454, 6,090,555, 6,185,561, 6,188,783, 6,223,127, 6,229,911 and 6,308,170.) Additionally, the present invention may have preferred embodiments that include methods for providing genetic information over networks such as the Internet as shown in U.S. Ser. Nos. 10/197,621, 10/063,559 (US Pub No 20020183936), Ser. Nos. 10/065,856, 10/065,868, 10/328,818, 10/328,872, 10/423,403, and 60/482,389.

EXAMPLES

Using Ancient Protein Kinases to Unravel a Modern Cancer Drug's Mechanism

Results of studies described in this example show that macromolecular function is rooted in energy landscapes, where sequence determines not a single structure but an ensemble of conformations. Hence, evolution can modify a protein's function by altering its energy landscape. Here the evolutionary pathway between two modern human oncogenes, Src and Abl, was recreated by reconstructing their common ancestors and characterizing the respective ancestral energy landscapes. The evolutionary reconstruction revealed a detailed molecular mechanism for the selectivity of the successful cancer drug Gleevec. While Gleevec had a 3000-fold preference for modern Abl versus Src, their common ancestor had an intermediate affinity for Gleevec. Affinity for Gleevec was gained during the evolutionary trajectory towards Abl and lost towards Src, primarily by shifting an induced-fit equilibrium. The subset of atomic interactions underlying this difference in Gleevec specificity was identified using mutations, guided by X-ray crystal structures of the common ancestor bound to Gleevec. It is further shown that Gleevec resistance in the clinically relevant T315I mutation is caused by disruption of the induced-fit step, and not by steric hindrance of drug binding. This work simultaneously sheds light on the mechanism of Gleevec specificity at atomic resolution while offering insights into how energy landscapes evolve.

The evolution of protein kinases is a key event in the origin of multicellularity (1). This enabled the development of more complex signaling cascades essential for the evolution of higher organisms. The central role of protein kinases in the cell cycle has placed them at the center of cancer drug research. Despite an explosion in diversity in the kinome (2), the catalytic kinase domains have maintained nearly identical structures (2-5). It is therefore surprising that the clinically successful cancer drug Gleevec has such strong selectivity towards Abl versus other tyrosine kinases, including the closely related Src. This is puzzling because the structures of Abl and Src bound to Gleevec are nearly identical, including the N- and C-terminal lobes and the 3000-fold difference in affinity for these two kinases (6). The atomistic determinants of this selectivity, however, are still an open question, and sequence swaps between human Abl and Src ascertained from the x-ray structures (FIG.1A) have failed to answer this question for the past 20 years (3, 7). The differences between Src, Abl and other homologous kinases have evolved over a billion years from their common ancestor—not via amino acid swaps from one modern kinase to another. Sequence swap experiments using modern enzymes have a fundamental shortcoming by neglecting epistasis (the effect of the surrounding amino acid background). However, evolution has already navigated the complex epistatic protein space by producing functional proteins at each stage despite large numbers of accumulated mutations. It was therefore reasoned that it may be essential to exploit current knowledge of the evolution of Src and Abl along its phylogentic branches using ancestral reconstruction to determine the atomistic mechanism of Gleevec selectivity.

Ancestral reconstruction has recently provided a novel way to achieve mechanistic insight into protein function (8-13). Studies described herein elucidate the basis of modern specificity towards Gleevec with atomic resolution by recapitulating the evolution of the Src and Abl catalytic domain from their last common ancestor. Analysis of the ancestral kinases allowed tracking of the evolution of the protein energy landscape (14, 15). The term “energy landscape” is defined herein as a set of free energy and kinetic parameters linking kinetically distinct states that are relevant to biological processes.

Seventy-six modern day sequences spanning the cytosolic tyrosine kinase family (Src/Abl/Tec families) were used in a Bayesian phylogenetic analysis with receptor tyrosine kinases as the out-group (FIG.1B). Since the quality of the ancestral reconstruction strongly depends on the alignment, the tree and alignments were estimated simultaneously. The most probable sequences were inferred for four key ancestral proteins between modern Src and Abl and their last common ancestor (FIG.1B;FIG.4A;FIG.4B;FIGS.6A-6B), and their corresponding proteins were expressed, purified, and characterized. It is noted that although ancestral reconstruction is a well-established method (8, 16) it is still a developing field.

The reconstructed protein corresponding to the last common ancestor of Src and Abl is denoted as ANC-AS. Similarly, on the lineage leading from ANC-AS to the modern Abl, ANC-A1 represents the common ancestor between humans and colonial choanoflagellates, while ANC-A2 corresponds to the common ancestor between humans andC. elegans.On the lineage leading to modern Src, ANC-S1 is the last common ancestor between humans and colonial choanoflagellates/sponges. Despite the fact that the oldest ancestor (ANC-AS) differs by 96 amino acid residues from any modern cytosolic tyrosine kinase, all ancestral kinases reconstructed herein were fully active and thermostable (FIG.1C;FIG.7;FIG.8). Using the activity assay, the specificity of Gleevec towards the ancestral kinases was evaluated by measuring inhibition constants. The last common ancestor's (ANC-AS) inhibition was intermediate between modern Src and Abl. Gleevec affinity increased gradually towards Abl along the evolutionary pathway, while it drastically decreased towards Src (FIG.2A). Direct measurement of Gleevec binding affinity by fluorescence quenching corroborated these results (FIG.2B).

Recently, it has been proposed that Gleevec binding is controlled by and induced-fit step, a protein conformational changes after binding (6). However, Src and Abl differ by 146 amino acids and experiments with the modern kinases could not identify the subset of residues responsible for the changes in dynamics (6). Because the reconstructed kinase ancestors had intermediate Gleevec affinities, the evolution of energy landscapes could be explored. To this end, the changes in the energy landscape from the oldest ancestor (ANC-AS) to modern Src and Abl were characterized by comparing the kinetics of Gleevec binding. All ancestors followed the same kinetic scheme as modern Src and Abl (FIG.2C), but with differences in individual conformational steps. The double exponential binding kinetics (FIG.2D) reflected the physical binding step (identified by the linear dependence of the observed rate on Gleevec concentration, (FIG.2F), followed by the induced fit step with the observed rate approaching a maximum at Gleevec saturation (FIG.2G). The gradual change in these kinetic parameters (kfast and kslow) from the weak binders to the tight binders was clearly visible, while the physical off rates (koff), identified by the intercept inFIG.2Fremained similarly fast. The process reversal to binding, namely dissociation of the inhibitor-enzyme complex, was extremely slow for ancestors ANC-AS, ANC-A1 and ANC-A2, and much faster for ANC-S1 (FIG.2E). However these observed rate constants for dissociation were still much smaller than the physical off-rate, revealing that the rate-limiting step in Gleevec release for all ancestors was a conformational change before dissociation (E*.I→E.I) (FIG.2E) (see methods for details of the kinetic analysis). Strikingly, a systematic shift in the conformational equilibrium from E*.I to E.I when traversing the evolutionary tree from Abl to Src was detected, caused by a gradual decrease in the forward rate (kconf+) (FIG.2G;FIG.3C) and a more dramatic increase in the reverse rate (kconf−) (FIG.2E;FIG.3C;FIG.3E). This conformational step, independently validated previously by a direct visualization of the E.I and E*.I conformers by NMR on the enzyme-drug complex (6), accounted for the major difference in binding energy between the different ancestors and modern Src and Abl, while changes in the drug's binding/dissociation step were nearly negligible (FIGS.3A-3E).

A frequently cited but controversial model for Gleevec selectivity posits a pre-existing equilibrium between two alternative conformations of the fully conserved segment of the activation loop, the DFG-motif (for Asp-Phe-Gly) (3, 6, 7, 17-21). A number of x-ray structures have revealed the sampling of a Gleevec-binding-competent DFG-out position and a binding-incompetent DFG-in position (FIG.3A) (3, 7, 19, 20). Quantification of the equilibrium between these two alternative states has proven elusive, despite direct observation of both states in crystal structures (3) The analysis of the evolutionary trajectory of Src and Abl described herein provides experimental estimates of the relative populations of the in- and out-conformations of the DFG loop and illustrates that this equilibrium plays only a minor role in Gleevec affinity (FIG.3A;FIG.3D).

This unexpected opportunity arises from the time-resolved detection of the binding step. The relative amplitude of the fast binding step reflects the propensity to populate the DFG-out conformation (pDFG-out). As apparent fromFIG.3A, one can indeed “watch” this flip in population from mainly being in DFG-in state for modern Src and ANC-S1 to increasing DFG-out populations in ANC-AS as an intermediate, and to even higher DFG-out populations for ANC-A1, ANC-A2 and Abl (large amplitudes). The DFG-out population is also an intrinsic component of the observed rate konobs(konobs=kon×pDFG-out). Notably, the increase in pDFG-outmeasured from the amplitudes (FIG.3A) was mirrored in the gradual increase in konobs(FIG.3B), implying that the true konrate constants were very similar. The populations of DFG-out in ANC-S1 and Src were too small to allow a quantitative analysis of the fast binding step (FIG.3A). The “thermodynamic Kd” (FIG.2B) agreed well with the Kd calculated from all microscopic rate constants (FIG.9, see discussion of methods herein and (6)), which corroborated the kinetic scheme and the accuracy of the fitted values.

During the evolution of the energy landscape from the last common ancestor (ANC-AS) to the modern tight-binding Abl and the weak-binding Src, the major contribution to increased affinity arose from an induced-fit mechanism (FIG.3E) with a minor but significant contribution from the pre-existing DFG-in/out flip in the free enzymes (FIG.3D). The actual binding/unbinding step, which is commonly used in structure-guided rational drug design (e.g., docking analyses), was very similar between the weak and strong binders.

The sequence differences responsible for the two major changes in the energy landscape, the DFG loop equilibrium and the E.I E*.I equilibrium, were examined. The ancestral reconstruction narrowed down the regions responsible for these changes dramatically. Modern Abl and Src differ at 146 amino acids, yet only 70 differences separate ANC-AS and ANC-A2 and only 42 differences separate ANC-AS and ANC-S1. These sequence changes were distributed throughout the protein, in agreement with NMR observations from Gleevec titrations of Src and Abl (6).

The X-ray crystal structures of ANC-AS bound to AMPPCP (FIGS.10A-10C) and ANC-AS bound to Gleevec (FIGS.4A-4H) illustrated the structural consequences of sequence evolution. As expected, the overall structure of ANC-AS was highly similar to modern Src and Abl with subtle differences in the P-loop, C-helix and 4-5 loop (FIG.4E;FIG.4G;FIGS.10A-10E;FIGS.11A-11C). Ancestral reconstruction identified a subset of 70 residues potentially responsible for the dramatic shift of E.I E*.I conformational equilibrium between ANC-AS and ANC-A2 (FIG.4A), but not all of these residues were necessarily important for the observed increased affinity. To pinpoint the essential residue differences, the ANC-AS-Gleevec structure was analyzed and these 70 residues were divided into four groups using a crude divide-and-conquer approach (FIGS.12A-12B). Constructs containing subgroups of mutations were then tested for activity and Gleevec binding (FIGS.12A-12B). Remarkably, changing only 15 amino acids in the core of the ANC-AS N-terminal lobe to the Abl sequence (named AS(+15)) drastically increased Gleevec affinity to a level similar to Abl (FIGS.4C-4D). This drastic increase in affinity is rooted in changes in the conformational dynamics of the induced fit step (FIGS.13A-13C). Therefore, a small subset of residues located only in the N-terminal lobe were responsible for the majority of the change in the E.I E*.I equilibrium, which is the most important step in the Gleevec binding mechanism.

With the importance of these 15 residues clearly established, rationalization of the change in the energy landscape at an atomistic level can be attempted using the ANC-AS x-ray structures. Most of these 15 amino acids were distant from the drug-binding pocket and were part of a hydrogen-bonding network in both of the AMPPCP- and Gleevec-bound conformations in ANC-AS and Src. In contrast, amino acid changes of these residues in AS(+15) and Abl prohibited such hydrogen bonding networks (FIG.4F;FIG.4G). Without being bound by theory, it is hypothesized that the lack of these hydrogen bonds allowed the P-loop to close over Gleevec in a kinked conformation, while in Src and ANC-AS the identified hydrogen bonds prohibited such a conformational change (FIG.4E). A stabilizing role of the N-lobe hydrogen bond networks for the P-loop is consistent with the clear P-loop electron density in Src and ANC-AS bound to nucleotide, in contrast to the high B-factors or missing P-loop electron density in the corresponding Abl structures (FIGS.14A-14C).

It is noted that the difference in P-loop conformation for kinase/gleevec structures has been discussed previously as the potential basis for differential affinity (21). However, a sequence swap of the two P-loop residue differences placing Abl residues into Src, F278Y and Q275G, failed to increase Src's affinity towards Gleevec (3). The data described herein suggest that the kinked P-loop seen in the Gleevec-bound Abl structure is stabilized by a hydrogen bond between Y272 in the P-loop and N341 in the D-helix (FIG.4H), in addition to other interactions with the drug. However, this energetically favorable interaction is only possible in the absence of the restricting hydrogen bonds in the N-lobe identified above (FIG.4G).

A long-standing problem in molecular biology is how to establish the sequence determinants for specificity within protein families. As a modern anthropogenic creation, Gleevec could not have provided evolutionary pressure for the divergence of the Src and Abl kinase families. However, the ancestral kinases delivered a deeper understanding of the molecular mechanism underlying the impressive selectivity of a modern cancer drug. Surprisingly, Gleevec takes full advantage of the evolution of “incidental” differences in the Src and Abl energy landscapes, even though the structure-based design of Gleevec did not have this in mind. In addition, Gleevec binding served as an experimental readout for the natural evolution of the DFG in/out equilibrium, which is widely considered to be a key element for differential regulation in the protein kinase kingdom, although the corresponding mechanism has been elusive (3, 4, 7, 17, 18). It was found that a gradual evolution of the DFG in/out equilibrium was governed by residues far removed from the catalytic site.

There is of course a natural evolutionary pressure in the development of Gleevec resistance. During the therapeutic use of Gleevec in chronic myelogenous leukemia patients, a number of clinically relevant resistance mutations have evolved, including the most common Abl(T315I) mutation (22) (FIGS.5A-5G). This single amino acid mutation drastically decreased the affinity for Gleevec (Kdof 12±5 μM at 25° C.). This mutation has been called the “gatekeeper” mutation because of the hypothesis that the Ile residue obstructs binding due to steric hindrance (23) (24). Surprisingly, it was found that the binding step is in fact unaltered by the T315I mutation, but that the subsequent induced fit step is severely hampered (FIGS.5A-5G). As described before this latter step of conformational dynamics after drug binding is the key for high affinity in the wild type protein, and it is the very same step that is altered under the evolutionary pressure in cancer cells treated with Gleevec.

Previous ancestral reconstruction studies fall into two types: reconstruction of highly conserved protein families that remain relatively unchanged in function and sequence over a vast period of time (up to 4 billion years) (25-28), and reconstructions within metazoan lineages (within the last 600 million years) characterized by large functional divergence caused by a small number of amino acids changes (29, 30). The system differs from both categories with respect to the time period (ANC-AS is ˜1 billion years old) and the number of residues involved. In addition, the implications of the ancestral reconstruction performed here are mainly focused on revealing the atomistic mechanism of a modern cancer drug for modern kinases. The results described herein on the gradual change in energy landscape from the common ancestor to modern kinases, and the data described herein for the resistance mutant that evolved under natural pressure, advocate that altering conformational dynamics—hence energy landscapes—may be a crucial driving force in evolution.

The results described herein were obtained using the following methods and materials.

Methods and Materials

Ancestral Protein Sequence Reconstruction.

Seventy-six sequences were selected from the NCBI non-redundant protein sequence database spanning the Tec, Src and Abl kinase subfamilies. Both phylogeny and alignment were co-estimated using the Bayesian BAli-Phy software package (FIGS.6A-6B) (1). The analysis was performed using the RS07 insertion/deletion model, LG amino acid substitution matrix, estimating equilibrium amino acid frequencies, with gamma distributed rates across sites (four categories). Two independent chains were run until the ASDSF and PSRF-80% CI criteria fell below 0.01 and 1.01 respectively. Ancestral sequences were inferred using the marginal likelihood method implemented in PAML (2), with the maximum a posteriori phylogeny and expected parameters (normalized equilibrium frequencies, gamma shape parameter) from the BAli-Phy run.

It is noted that although ancestral reconstruction is a well-established method (3, 4) it is still a developing field, and the underlying assumptions should be considered (5). The reconstructed proteins are probabilistic inferences. The estimated probability of reconstructing the exact actual ancestral sequence is the product of the probabilities for each site in the protein, and hence the overall probability is vanishingly small. However, the histograms of the posterior probabilities associated with each inferred position in the ancestral proteins (FIGS.6A-6B) show that the estimated confidence is high (PP>95%) for the great majority of ancestral residues. In fast evolving regions of the protein the majority of the ambiguous residues are expected to be selectively neutral or nearly neutral, and the sequence alternatives involve chemically conservative substitutions. These mathematical considerations also reflect the fact that, like modern proteins, the ancestral proteins existed in large populations of organisms (in this case single-celled eukaryotes), comprising a polymorphic ensemble of similar proteins that changed over time. From a practical perspective, reconstructed sequences can be viewed as representatives of groups of proteins that are likely similar to ancestral sequences in biophysically relevant ways.

Expression and Purification

Ancestral sequence cDNAs were constructed by Genscript. Ancestral and extant inserts were sub-cloned into pET-41M vector containing a His-tag and MBP-tag on the N-terminus. Vector was co-transformed with the YOPH phosphatase (6) to ensure de-phosphorylated protein and to lower toxicity of the insert into GROEL competent BL-21 cells (GROEL under Tetracycline induction). Cells were grown in TB media to an OD of 0.8 at 37° C. then switched to 18° C. for 1 hour before induction with 100 uM of IPTG. Cells were allowed to grow for 16 hours at 18° C. Cells were lysed in the presence of Benzonase by sonication. After purification via a Talon and MBP column the tags were cleaved with His-tagged TEV-protease overnight at 4° C. while dialyzing against storage buffer (25 mM Tris-HCl pH 8, 500 mM NaCl, 5% Glycerol). Cleaved sample was collected and run over Ni-NTA column to remove His-tagged TEV, cleaved MBP and uncleaved His-MBP-Kinase contaminants. Flow-through was collected, concentrated to 5 ml and passed over a 16/60 S-100 gel filtration column. All columns were run at 4° C. Samples where confirmed to be unphosphorylated by western blot using a standard phosphorylated-Tyr antibody.

Protein activity was assayed using the Antibody Beacon™ Tyrosine Kinase Assay Kit (Molecular Probes). In addition to kit components the reaction mixture contained 10-50 nM of protein, 500 uM of standard peptide EAIYAAPFAKKK (SEQ ID NO: 1), and 1 mM Mg ATP. Phosphorylated peptides of known concentration were used for fluorescence level calibration. All reactions were performed at 25° C. Ki's for Gleevec were calculated from IC50's using the standard equation:

Ki=IC50/(1+([ATP]Km))
where we used a Km for ATP of 70 uM. For several samples, the resulting rates were validated by HPLC analysis of the reaction products using Agilent Infinity 1260 and C18-AR columns from ACE. Phosphorylated and unphosphorylated peptides were separated using a linear gradient between 0 and 40% of acetonitrile with 0.1% TFA as a mobile phase. The results were within experimental error with the fluorescence assays.

For dissociation constant (Kd) measurements of Gleevec to the ancestors, 10 nM of kinase was mixed with 2-1000 nM of Gleevec. Binding was monitored via changes in Trp fluorescence. Measurements were done using the FluoroMax-4 (Jobin-Yvon) fluorimeter. Tryptophanes were excited at 295 nm, and fluorescence was detected at 350 nm. Extracted intensities were fitted to a generalized binding equation:

F=F0+A·[I]+[Et]+Kd-([I]+[Et]+Kd)2-4·[Et]·[I]))2·[Et],
where [Et] is total enzyme concentration, [I] concentration of Gleevec, F0and A are background fluorescent and a scaling factor respectively. The dissociation constant (Kd) of Gleevec Abl (T315I) could not be determined by Trp fluorescence because of too weak binding and severe inner filter effects of the drug at the high concentrations. Only ITC at 25° C. gave a reliable data for the Gleevec affinity to Abl (T315I). Titrations were carried out on a Nano ITC (TA instruments) and analyzed with the NanoAnalyze software. Injectant was added in 1 L volume, every 180 s. The concentrations used were 25 M Abl (T315I) and 340 M Gleevec.
X-Ray Crystallography.

Hexagonal crystals of ancestor ANC-AS with bound AMPPCP (with dimensions h=50-100 μm, a=20 μm) were grown for three days and were flash frozen in liquid nitrogen. 6.3 mg/ml of lysine modified (ethylated) protein was crystallized at 18° C. using the hanging drop method in 50 mM TRIS, pH 8.0, 500 mM NaCl, 5% Glycerol, 20 mM MgCl2, 2 mM Imidazole, 1 mM AMPPCP, mixed 1:1 with 2.2 M Ammonium Sulfate. The data were indexed, integrated and scaled using programs from the CCP4 suite (XIA2) (7). Molecular replacement was performed with CCP4 MOLREP (8) using a human ABL kinase structure (pdb code 2HYY) as an initial search model. Model refinement was performed using PHENIX (9) and CCP4 REFMAC (10). Models were built using COOT and WINCOOT (11). Molecular replacement and the first refinement cycles were done without the nucleotide and the magnesium ion in the model. Later, AMPPCP was placed into the positive peak of the difference electron density map. No density could be confidently determined for the magnesium ion. In an effort to minimize model bias, simulated annealing (both Cartesian and torsion angles) was performed with PHENIX using default parameters for several rounds. Table 1 and 2 summarize the data collection/processing statistics and the refinement statistics. Model validation was done with MOLPROBITY (12).

Two-dimensional plates of ANC-AS with bound Gleevec (with dimensions 300 μm×300 μm) grew within one week on dust particles using the sitting drop method. These crystals were later used for microseeding using the hanging drop method. Smaller but 3-dimensional plates (100 μm×100 μm×15 μm) where flash frozen in liquid nitrogen. For both steps, 10 mg/ml of lysine modified (ethylated) protein was used in 30 mM TRIS pH 8.0, 500 mM NaCl, 1 mM Gleevec, mixed 1:1 with 200 mM Ammonium Acetate, 100 mM Sodium Acetate Trihydrate pH 4.6 and 30% PEG 4000, at 18° C. XDS (13) was used for indexing and integration while scaling was done with AIMLESS (14) (CCP4). Further processing and model building was done as described for protein in the presence of AMPPCP.

Solutions of 10 ul of 225X Sypro Orange, 15 ul of storage buffer (50 mM HEPES pH 8, 500 mM NaCl, 5% Glycerol and 10 mM TCEP) and 5 ul of 100 uM protein was added to a 96-well PCR plate. A control containing the storage buffer+Sypro Orange was added. The plates were sealed with optical sealing tap and heated in a Applied Biosystems 9600 real-time PCR machine from 20 to 100 degrees with increments of 0.2 degrees Celsius. Fluorescence of the Sypro orange dye was measured by exciting at 490 nm and measuring at 575 nm.

Stopped-Flow Kinetics Experiments and Data Analysis.

All stopped-flow experiments were performed with the Applied Photophysics SX-20 instrument at 5° C. or 25° C. as specified in the text. Binding was monitored via changes in tryptophan fluorescence, samples were excited at 295 nm (9 nm bandwidth) and emission was detected using a long-pass 320 nm cut-off filter. After mixing the concentration of kinase was 0.1 M, and the concentration of Gleevec was varied. To study dissociation kinetics, protein (at 0.1-1 M) was pre-incubated with 0.1-100 M of Gleevec (depending on the Kdof the kinase) for 10 minutes, placed into the 0.5 mL syringe and then diluted 11-fold. All experiments were performed in a buffer containing 50 mM TRIS, 500 mM NaCl, 1 mM MgCl2, 1 mM TCEP and 5% DMSO (pH 8.0). Data were analyzed using Applied Photophysics software. Kinetic fluorescence traces were fitted to a single or multi-exponential function. To account for photobleaching, an additional exponential term was included into the fitting function. This rate was fixed to the value determined in control experiments where protein was mixed with buffer in the absence of Gleevec.

Analysis of Kinetic Data.

The following naming convention is used throughout the text. Different states of the enzyme without or with bound inhibitor are called E, E.I and E*I, respectively. The conformation of the DFG-loop is specified with “DFG-in” or “DFG-out” subscripts. Rates describing the time dependence of experimentally observed changes in fluorescence are called observed rates. F denotes the amplitude of the observed fluorescent signal and is generated by combined fluorescence from all enzyme species. kon, koff, kconf+and kconf−are rate constants and correspond to individual microscopic steps in the reaction schemes.

In this scheme the first step (conformational selection), EDFG-inEDFG-out, is fast and not directly observed in the kinetic experiment. However the equilibrium between these two states affects the population of the binding competent state (EDFG-out) and hence is reflected in the amplitude of the next step in the scheme (the binding step). This phenomenon allowed qualitative tracking of the evolutionary change in the DFG-in/DFG-out equilibrium along the phylogenetic tree (FIG.3A).

In all of the binding kinetic experiments, the concentration of the inhibitor was much greater than concentration of the enzyme ([I]>>[E]). Under such conditions the binding is a pseudo-first-order reaction (EDFG-out+I EDFG-out.I) and thus characterized by a linear dependence of the observed binding rate (kfast) on inhibitor concentration (FIG.2F;FIG.5D;FIG.13A). This linear dependence is the feature that allows clear identification of the phase corresponding to binding in the multi-exponential kinetic traces. In contrast, the observed rate that characterizes the conformational change after binding (the induced fit step, kslow) has a non-linear dependence on inhibitor concentration, since the transient concentration of the EDFG-out.I depends on inhibitor concentration (FIG.2G;FIG.5E;FIG.13B) (15). These plots of kfast, kslowas a function of inhibitor concentration can be used to extract the microscopic rate constants for different steps of the binding scheme. From the linear plot of kslowvs. [I] one can extract the konobs(which is equal to the slope of the line) and the koff(which is equal to the intercept). It is noted that konobsis not a microscopic rate constant kon, but rather is a product of konand the fractional population of the kinase in the binding capable state PDFG-out: konobs=kon×PDFG-out. As a consequence, konobsreflects both the EDFG-inEDFG-outequilibrium and the rate of the physical binding step simultaneously.

The Gleevec dissociation experiment was used to determine the kconf−rate constant. Since the fluorescent change observed in this experiment was mono-exponential and much slower than the koff(determined as described above), the rate constant characterizing the dissociation must be attributed to the conformational change kconf−. In addition, the value of the plateau on the kslowvs [I] graph (FIG.2G;FIG.5E;FIG.13B) determines the sum kconf++kconf−, which allows calculating the value of kconf+. Thereby the system is fully determined (15).

Knowledge of the individual microscopic constants enables calculation of the overall Kdcalc:

Kdcalc=Kbindobs·KIF(1+KIF)
Where Kdcalcis the overall dissociation constant, Kbindobsand KIFcorrespond to the observed dissociation constant for binding and equilibrium constant for the induced fit step respectively. This calculated Kdcalccan be compared with the value of Kdmeasured(FIG.9), which was determined in an independent thermodynamic experiment (FIG.2B;FIG.9). Such a comparison serves as an independent verification of the model and the determined parameters.

The structures of the catalytic domain of ANC-AS in its active and inhibited state were solved at 2.91 Å and 2.05 Å, respectively. One monomeric kinase molecule can be found in the asymmetric unit cell of both crystal structures. The activation loop and the C-terminal residues in both models, and the residues 97-100 (inhibited state model only) could not be traced into the electron density map. The P-loop (residues 17-24) and the loop between the D- and E-helix (residues 95-100) of the active state model have high B-factors (two times of the average B-factor). However, there is enough main chain density to model these amino acids.

Mutational Screen to Pinpoint Essential Residues for Gleevec Selectivity.

There is a large difference in Gleevec affinity between ANC-AS and ANC-A2, and 70 mutations separate ANC-AS from ANC-A2. Many of these mutations are likely unnecessary to shift Gleevec affinity and may simply be neutral substitutions. Identifying the functional residues is challenging, as there are still quite a few differences between the two nodes. A conquer-and-divide strategy was used, whereby the mutational set between ANC-AS and ANC-A2 into the N-lobe set and the C-lobe sets were partitioned (see red/medium grey and blue/dark grey dotsFIGS.12A-12B). These two sets where further split into solvent-exposed residues and core residues (light and dark dots inFIGS.12A-12B). Constructs were made containing combinations of these sets of mutations. Proteins were expressed using the same protocol as ancestral proteins (see methods described herein). Constructs containing only N-lobe mutations expressed normally (seeFIGS.12A-12B). Surprisingly, constructs that contained the C-lobe mutations did not express, with the exception of C-lobe mutations of only the core residues. All of the N-lobe mutations showed reduced activity relative to the extant or ancestral constructs (FIGS.12A-12B), ranging from 6-fold to 1200-fold less activity when compared to Abl. These results highlight the remarkable ability of ancestral sequence reconstruction, as opposed to rational design, to produce enzymes with high levels of activity that are comparable to modern day enzymes.

References—Example 1—Methods And Materials

Dynamics of Human Protein Kinases Linked to Drug Selectivity

Protein kinases are promising cancer drug targets due to their overexpression and deregulation in cancer. Both Aurora A, a Serine/Threonine kinase, and Abl, a Tyrosine kinase have become attractive targets for the development of new anticancer therapies. In particular, the Asp-Phe-Gly (DFG) motif, in the activation loop of kinases has been intensely explored in the past decade as a hot-spot for designing compounds capable of keeping the kinase in an inactive conformation. Using a combination of fast fluorescence kinetics, X-ray crystallography and fluorine NMR experiments, a universal drug binding mechanism that rationalizes selectivity, affinity and drug resistance in Ser/Thr and Tyr kinases is proposed.

Both the Ser/Thr kinase Aurora A, and the Tyr kinase Abl are important targets for the development of new anticancer therapies. A longstanding question is how to inhibit specifically and effectively those kinases. For this aim, understanding of the inhibition mechanism of Aurora A and Abl by different drugs is essential. The binding kinetics of two distinct kinase drugs, Danusertib and Gleevec, to Aurora A, Abl, and the Gleevec resistant mutant T315I Abl were characterized. Results herein show that inhibitors affinities do not rely exclusively on the recognition of a specific conformation of the Asp-Phe-Gly loop of the kinase. Quantitative binding kinetics described herein put forward an opposing mechanism in which a slow conformational change after drug binding (i.e., induced fit) dictates drug affinity.

Introduction

Due to its central role in cellular processes and involvement in various types of cancers (1-3), protein kinases have become the number one drug target of the 21thcentury (4; 5). Despite their large therapeutic relevance, the development of specific kinase inhibitors proved to be extremely challenging because they must discriminate between the very similar structures of a large number of kinases in human cells. One of the biggest success stories is the specific Abl kinase inhibitor Gleevec for the treatment of chronic myelogenous leukemia (CML) (6) highlighting the therapeutic benefit of a drug that targets specifically one kinase in terms of cancer treatment efficiency and minimizes side effects. While being a multi-billion cancer drug, the mechanism responsible for the impressive specificity has been elusive until recently. For Gleevec and other kinase inhibitors it has been proposed that the conformational state of the fully conserved DFG (for Asp-Phe-Gly) loop (7) dictates drug specificity (8). Strikingly, recent quantitative binding kinetics put forward an opposing mechanism in which an induced fit step after drug binding is responsible for Gleevec specificity.

Here the question of whether such a fundamentally distinct mechanism might be a more general principle for drug efficiency and specificity not only for Tyr kinases such as Abl, but also for Ser/Thr kinases, is explored. To this end, the binding kinetics of two distinct kinase drugs, Danusertib and Gleevec, to the Ser/Thr kinase Aurora A and the Tyr kinase Abl were compared. Aurora A kinase is one of the key regulators of mitotic events, including mitotic entry, centrosome maturation and spindle formation (9-11), and neuronal migration (12). Aurora A has attracted significant attention in recent years because it is overexpressed in many tumors ranging from breast and colon, to ovary, skin, and other tissues. For these reasons, Aurora A is a popular target for the development of targeted agents for cancer (1-3; 13; 14). So far, the clinical significance of Aurora A inhibition by drugs has been established, but very little is known about the binding kinetic of drugs to the kinase. High-resolution X-ray structures of Aurora A kinase bound to different inhibitors (15-18) have been solved, but the selectivity profile of the kinase inhibitors remain very difficult to explain.

Danusertib or PHA739358 (Nerviano Medical Sciences, Italy) is a small ATP competitor of all Aurora kinase members (IC50=13, 79 and 61 nM for Aurora A, B and C respectively (19; 20). Danusertib was one of the first Aurora kinase inhibitors to enter in phase I and II clinical trials (21; 22). An X-ray structure of Aurora A kinase with Danusertib bound shows the DFG loop in the -out conformation (17) (PDB code 2J50). Interestingly, Danusertib also inhibits several receptor tyrosine kinases such as Abl (IC50=25 nM) (23; 24). Notably, in CML, Danusertib binds with high affinity to the Abl kinase domain, including the Gleevec resistant T315I Abl mutant (25). The mutation of Thr315 to Ile is responsible for up to 25% of all clinically observed resistances in CML patients undergoing Gleevec and second-generation tyrosine kinase inhibitors therapies (26) (such as Dasatinib, Nilotinib and Bosutinib). This mutation is called “gatekeeper residue mutation” due to the hypothesis put forward that Gleevec cannot bind due to the steric hindrance imposed by the substitution of threonine by isoleucine (27).

Here it is shown that this proposed mechanism is not correct and that the resistance for Gleevec is rather caused by a severe impairment of the induced fit step. Importantly, Danusertib can efficiently bind to wild-type and T315I Abl kinase because of the preservation of the induced fit step that is of different nature for this drug. Consequently, Danusertib promises to be an attractive candidate for anti-tumor therapy for patients with this mutation (Clinical trial number NCT number=NTC00766324).

Combining X-ray crystallography, NMR spectroscopy and fast kinetics, a novel view of the underlying mechanism for kinase inhibitor affinity and selectivity including insight into drug resistance mechanism is proposed. Differential drug binding is rooted in the dynamic personality of each individual kinase that evolved for its natural substrates.

The results described herein were obtained using the following methods and materials.

Aurora A, Abl and Abl T315I were expressed inE. Coli.Protein purifications and subsequent analyses were carried out as described herein.

Cloning and Purification of Aurora A and Abl/Abl T315I

All the proteins were produced and purified as described. All the proteins used have been analyzed by mass spectrometry.

Crystals of dephosphorylated A (122-403) in complex with AMPPCP were grown at 18° C. by vapor diffusion and the hanging drop method. A 2:1 ratio of protein mixture:mother liquor was obtained by combining 300 M (10 mg/ml) dephosphorylated A(122-403)+1.5 mM AMPPCP with 0.2 M lithium sulfate monohydrate, 0.1 M bisTris pH5.5, 25% PEG3350. Similarly, crystals of dephosphorylated A(122-403) apo were grown at 18° C. by vapor diffusion and the sitting drop method. A 1:1 ratio of protein mixture:mother liquor was obtained by combining 300 M (10 mg/ml) dephosphorylated A(122-403) with 0.15 M ammonium acetate, 0.1 M TrisHCl pH 7.5, 35% PEG3350 using 20% PEG400, 20% Ethylene glycol, 10% water, 50% mother liquor as a cryo solution. Diffraction data were collected at 100K at Advanced Light Source (Lawrence Berkeley National Laboratory) beamlines (8.2.1 and 8.2.2). Data were processed, scaled, phased, and refined in sequence by using iMOSFLM, Scala, Phase, and REFMAC5 in CCP4. The initial molecular replacement models were used as a search model from Aurora kinase A structure (PDB code 1MQ4).

Aurora A bound to AMPPCP have the PDB code 4UTD and the PDB code is 4UTE for the apo form. First refinement was carried out, followed by manual rebuilding in Coot, and iterative further refinements were carried out using PHENIX (FIGS.20A-20C).

Wild type and W277L Aurora A labeled selectively on tryptophans were produced using classical M9 minimum media complemented with all the amino acids (0.5 g/L) except tryptophan or with15NH4Cl (for uniform15N labeling). For tryptophans specific labeling samples, 1 hr prior induction, 30 mg of 5-19F-L-tryptophan or15N L-tryptophan were added to the media (43). A final buffer exchange step using a buffer that contains 50 mM HEPES (pH=7.3), 50 mM NaCl, 20 mM MgCl2, 5 mM TCEP, 2 M TMAO was done prior to analysis. The samples were concentrated to 200-300 μM using a 10 KDa cut-off membrane.

All NMR experiments were performed on an Agilent/Varian Unity Inova 500 MHz spectrometer, equipped with a 1H/19F switchable probe tuned to fluorine (470.23 MHz). All 1D19F spectra were recorded with a sweep width of ˜60 ppm, a 0.5 s acquisition time, 10000 transients, a 1.5 s relaxation delay time, and a 12 μs 90° pulse width, giving rise to a total acquisition time of 2.5 h per spectrum. To remove background signal from the probe and avoid baseline distortions, data acquisition was started after an ˜100 μs delay (using the “delacq” macro) and appropriate shifting of the data followed by backward linear prediction was performed using NMRPipe. The data were apodized with an exponential filter (2.5 Hz line broadening) and zero-filled before Fourier transform, where applicable data sets were added together to improve the signal-to-noise ratio.19F chemical shifts were referenced externally to trifluoroacetic acid (TFA) at −76.55 ppm.

Fluorescence Experiments

All fluorescence measurements were done at 25° C. except the Gleevec kinetics that were measured at 10° C. and 5° C. for Aurora A and Abl/Abl T315I respectively because the binding of the drug (Konobserved) is too fast at higher temperature. 100 mM/50 mM stock solutions of Danusertib/Gleevec (purchased from selleckchem.com) dissolved in 100% DMSO were used and stored at −20° C. The stopped-flow instrument is a SX20 series from AppliedPhotophysics. The spectrofluorimeter Fluorimax-4 from Horiba Scientific is temperature controlled and equipped with an autotitrator.

Aurora A Wild-Type and W277L Mutant Binding to Danusertib or Gleevec

Tryptophan fluorescence spectroscopy is used to monitor drugs binding kinetics to Aurora A using W277 as a fluorescence probe. W277 contribution to the protein fluorescence is shown inFIGS.21B-21C. In the binding experiment or Kon, increasing concentration of Danusertib/Gleevec were quickly mixed to 0.5 μM Aurora A (ratio 1:10). A significant increase (for Danusertib) and decrease (for Gleevec) in the fluorescence intensity of Aurora A (excitation at 295 nm, emission cut-off at 320 nm) can be seen due to the drug binding. Based on this signal the characteristic kinetic constant (kobs) values were fitted using a mono-, double or a triple exponential equation. In the release of the drug experiment or koff, 0.3 μM/0.3 μM Aurora A/Danusertib complex were diluted with buffer (ratio 1:30). A significant decrease in the fluorescence intensity of Aurora A (excitation at 295 nm, emission at 340 nm) can be seen due to the Danusertib release. The fluorescence signal was recorded every min for 1 s during 2 or 6 hrs using the Horiba fluorimeter using photobleaching minimization option. For Aurora A/Gleevec complex, the release of the drug was recorded after a 10 times dilution of the complex using the stopped-flow instrument during 1 s (excitation at 295 nm, emission cut-off at 320 nm). Based on this signal the characteristic kinetic constant (kobs) values were fitted using a monoexponential equation. The same procedure was used for Abl and Abl T315I gatekeeper binding to Gleevec or Danusertib.

Dissociation Constant Parameter Calculated from the Kinetics

FRET using intrinsic tryptophan fluorescence is used to monitor MantATP binding kinetics to Aurora A at 10° C. In the binding experiment or Kon, increasing concentration of MantATP were quickly mixed to 0.5 μM Aurora A (ratio 1:10, excitation at 295 nm, emission cut-off at 395 nm). In the release of MantATP experiment or koff, 10 μM/10 μM Aurora A/MantATP complex were diluted with buffer (ratio 1:10). A significant decrease in the fluorescence intensity of Aurora A (excitation at 295 nm, emission cut-off at 395 nm) can be seen due to the MantATP release.

Macroscopic Dissociation Constant Experiments

Fluorescence titration experiments were measured using Horiba fluorimeter. Increasing quantities of kinase-drug complex (0.2-0.5 nM kinase and 20 nM drug) or kinase-MantATP (1 μM kinase and 2 mM MantATP) were injected into the kinase solution (1 μM kinase). The excitation wavelength used is 295 nm (bandwidth=5 nm) and the emission is 340 nm (bandwidth=20 nm). In all experiments, 5 mM equilibration time was used between two injections. The dissociation constant (KD) derived from the fit of the equation:

The results of the experiments herein are now described.

Dephosphorylated Aurora A Samples Both an Inactive and Active Structure

A large wealth of X-ray structures and functional assays led to the general notion that unphosphorylated Aurora A and, more universal, Ser/Thr kinases are in an inactive structure and that phosphorylation or activator binding induces the active structure. A comparison of many X-ray structures of “inactive” and “active” forms of Ser/Thr kinases resulted in an elegant universal proposal of the structural hallmarks for the active state by Taylor and collaborators (28): the completion of both the regulatory and catalytic spines spanning the N- and C-terminal domains in the active state.

It was surprisingly found that two crystals from the same crystallization well captured the active and inactive conformation of unphosphorylated Aurora A (FIG.15A;FIG.15B;FIG.15E;FIG.20A). The inactive structure (PDB code: 4C3R) perfectly superimposes with the well-known inactive unphosphorylated Aurora A structures (PDB code: 1MUO) (29) and the activation loop is not visible as commonly observed for kinases lacking phosphorylation of the activation loop. The active structure superimposes extremely well with the previously published phosphorylated active structure (PDB code: 1OL7) (30) (FIG.15C;FIG.15F) and the activation loop is visible without Thr288 being phosphorylated although the B-factors are high. Every hallmark of the active state including the DFG flip into the DFG-in position essential for completing the regulatory spine is seen for the unphosphorylated protein. In contrast, the DFG loop is in the -out position for the inactive form (FIG.15D). As a side note, in the active structure, electron density is seen for a Mg2+ion in the tighter Mg2+-binding site coordinated to the -and -phosphates of AMPPCP and to D274. In the inactive structure, no electron density for Mg2+can be identified possibly due to the fact that D274 is rotated out (DFG-out) and therefore lost as coordination partner to the Mg2+.

It is pointed out that in Aurora kinases a Trp, Trp277, is immediately following the DFG motif and displays drastically different orientation whether Aurora A is in an active (DFG-in) or inactive (DFG-out) conformation (FIG.15D). This Trp is unique for the Aurora family in the Ser/Thr kinome and the nature of this residue has been suggested for tuning the substrate specificity (31). Importantly, this Trp was used as probe to monitor the DFG flip and drug binding in real time described below.

The fact that the inactive and active state is seen in the crystal implies that both are sampled, however, it does not deliver information about the relative populations or interconversion rates. Therefore, an experimental approach was next set out to attempt to monitor the conformational exchange of the DFG in/out flip in solution. Owing to the importance of the DFG flip for activity, regulation and drug design, there have been extensive efforts to characterize this conformational equilibrium in solution. NMR is a possible method for such characterization, however efforts on several Ser/Thr and Tyr kinases led to the general conclusion that the activation loop including the DFG motif and most of the active site cannot be detected due to exchange broadening, and they can only been seen after binding of drugs that stabilize conformations.

1H-15N HSQC Experiments on Fully Labeled Samples and Tryptophans

15N specific labeling of Aurora A proved to be no exception as many peaks are missing (FIG.20C). Therefore, a strategy to overcome this general problem of exchange broadening, that hampers the detection of the DFG equilibrium, was sought. Aurora A was produced containing four F19-labeled tryptophans (FIGS.15G-15I) for one-dimensional spectra to deal with the exchange broadening while providing sensitivity close to proton NMR (32). For apo and AMPPCP-bound Aurora A, indeed four peaks were observed. One peak is very broad and is therefore a prime candidate for Trp277 adjacent to the DFG loop (FIG.15H). A W277L mutation confirmed this assignment (FIG.15I). This mutant is still active (FIGS.21A-21C), most likely because this Trp is not conserved in Ser/Thr kinases with a Leu at the position for several family members. Mutating each of the other three Trp that are much more conserved resulted in insoluble proteins. From the broad lineshape for the Trp277 peak it was estimated that the DFG loop interconverted on an intermediate timescale. Determination of the relative populations of the two states and exact rate constants of interconversion was not possible with these physical constraints of the system, however this missing piece was obtained by stopped-flow kinetics of drug binding as described in the next paragraph.

Kinetics of Danusertib Binding to Aurora A: Three-Step Kinetics That Couples a Conformational Selection and an Induced Fit Mechanism

Through groundbreaking experiments on the Tyr kinases Abl and Src, the concept of drug selectivity based on the DFG conformation has received considerable attention in kinase drug discovery (27; 33). A recent report provides kinetic evidence for such conformational selection, but identifies an induced fit step after drug binding as the overwhelming contribution for Gleevec selectivity towards Abl compared to Src. The question of whether this mechanism of Gleevec binding to Abl might exemplify a more general mechanism for kinase inhibitors was explored. First, the kinetics of Danusertib binding to Aurora A directly by a series of rapid mixing experiments using intrinsic tryptophan fluorescence was measured. For inhibitor binding to Aurora A, fluorescence kinetics at all Danusertib concentrations were triple exponential at 25° C. (FIG.16A). The dependence of the three observed rates constants on drug concentration is linear for one of these rates (FIG.16B) and non linear for the other two with apparent plateaus reached at approximately 0.13 s−1and 6 s−1(FIG.16C-16D).

A three-step mechanism was deciphered as follows. The kinetic step with linear inhibitor concentration dependence is typical of the second-order binding step while non-linear concentration dependence rate hints at protein conformational transitions. As an important additional experiment, the dissociation kinetics for Danusertib was measured and is slow, taking hours to be released (FIG.16E). Rationalization of such complex binding kinetics cannot be done by visual inspection and kinetic intuition any more, which can, in fact, be misleading. In order to elucidate the correct binding mechanism, all kinetic traces were globally fit assuming all possible three-step binding schemes (FIG.22;FIGS.23A-23B;FIGS.24A-24D). The result was unambiguous with a conformational interconversion in the free protein as the faster of the two conformational transitions and a far-shifted induced fit step after Danusertib binding as the slower step (FIG.16G). All “true” microscopic rate constants were obtained from the global fit (FIG.22andFIGS.23A-23B) demonstrating sampling of two conformations in the free protein with an equilibrium constant of 0.23, a fast binding step that accounts for an affinity of 0.83 μM for this step, and a very far-shifted induced fit step with a Keqof 5×10−4.

A powerful independent validation of the selected binding scheme can be obtained by comparing the macroscopically measured overall KDof for Danusertib with the calculated macroscopic KDfrom the kinetic scheme (FIG.16F-16GandFIGS.28A-28B) according to Equation 2 (described in the materials and methods of Example 2), which indeed delivered values that were within experimental error.

It was hypothesize that the first step in our scheme reflects the interconversion between the inactive and active structures that are correlated with the DFG-in and -out position (FIG.15A-15I;FIGS.25A-25C) because (i) the two X-ray structures sampled for the apo-protein show Trp277 in very different environments (FIG.20A-20B), (ii) Danusertib has been proposed to selectively bind to the DFG-out conformation based on a co-crystal structure, (iii) the dissociation constant of Danusertib for the phosphorylated form of Aurora A (in DFG-in active state) was 104weaker than for the unphosphorylated form (FIG.25A) and (iiii) the amount of exchange broadening for W277 in the NMR experiment was in agreement with the kinetics of interconversion in the free enzyme measured by fluorescence (FIG.15H).

The results herein illuminate trivial but profound principles of binding affinities and lifetimes of drug/target complexes: any conformational selection step weakens the overall inhibitor affinity, while an induced fit tightens the affinity in relation to the amount of equilibrium shift in the enzyme/drug complex (Equations 1, 2 and 3). For Danusertib, the DFG-in/-out equilibrium weakens the overall affinity by only 20%, however, the conformational change after drug binding results in a three orders of magnitude tighter binding.

Gleevec Binding to Aurora A Demonstrates Role of the Induced Fit Step

In order to assess which kinetic step(s) controls the drug affinity and selectivity, the binding kinetics for Gleevec, to Aurora A were analyzed. At 25° C., the binding of Gleevec to Aurora A was too fast to be monitored. At 10° C., the binding kinetics at Gleevec concentrations above 5 μM was monoexponential with a linear dependence on the ligand concentration providing a konvalue of 1 μM−1s−1(FIG.16H-16I). It was puzzling that by using a different drug, the binding kinetics changed from a triple exponential binding kinetics with two conformational exchange steps to the simplest pseudo-first order binding kinetics (seeFIGS.29A-29Cfor further description of orders of binding kinetics). Particularly concerning is the apparent lack of the kinetic phase previously assigned to the DFG-in to -out flip since (i) Gleevec is considered to be a DFG-out specific inhibitor and (ii) this conformational exchange happens before binding hence is independent from drug binding. It was noticed, however, that Gleevec binding to Aurora A caused a decrease in fluorescence while all three phases for Danusertib binding show fluorescence increases. The suspicion that the DFG flip (with a corresponding increase in fluorescence) was masked by the large amplitude of fluorescence decrease from the Gleevec binding step was confirmed by repeating Gleevec binding kinetics at very low drug concentrations showing the expected fluorescence increase due to the DFG-out selection (FIGS.26A-26C). The latter result strongly supported the DFG-in/out equilibrium in Aurora A and the selective binding of both drugs to the DFG-out state. What happened to the induced fit step was then investigated.

The Danusertib binding kinetics data suggest that the conformational transition after drug binding (i.e., induced fit) dramatically enhances drug affinity. If this hypothesis is correct, the absence of this additional induced fit step for Gleevec in the fluorescence kinetics should be reflected in a higher KDand a faster overall dissociation of the drug. Indeed, Gleevec bound to Aurora A with a KDof 55 μM (FIG.26D) and dissociated with an apparent rate constant of 50 s−1(FIG.16J). Two pieces of independent evidence establishes that there is indeed not an induced fit step for Gleevec binding to Aurora A: (i) the calculated KDfrom the kinetic scheme is in agreement with the macroscopic KD(FIG.28B), and (ii) the observed off rate (FIG.16J) now coincided with the physical dissociation rate (intercept of the konobserved,FIG.16I) consequently being 106-107faster than the Danusertib -off rate (FIG.16E). In summary, the lack of an induced fit step for Gleevec binding to Aurora A was the major reason for the weak binding and not the DFG loop conformation (FIG.16K).

Revealing the Mechanism of the Gleevec Resistant Gatekeeper Mutant T315I Abl and Mechanism of Inhibition Rescue

Despite the enormous success of Gleevec as a highly selective drug for Bcr-Abl, a growing number of resistant mutations demand the development of second and third generation inhibitors. An understanding of the underlying mechanism responsible for the resistance may guide this mission (34) (FIGS.17A-17D;FIGS.27A-27C). One of the major Gleevec resistant mutations in Abl developed in patients is T315I, labeled as Gatekeeper mutation because of the proposed steric hindrance for Gleevec binding (35; 36). Surprisingly it was found that T315I “binds” Gleevec similarly to the wild-type, meaning that the physical binding step was almost identical (FIG.17C). Strikingly, the induced fit step was severely affected resulting in a much weaker overall affinity (KD=12 μM for T315I (FIG.27A) compared to 4 nM for wild type, (FIG.17D). It is emphasized that the Gleevec resistance by this mutation (i.e. meaning weak affinity) was solely due to alterations in the conformational change step after binding, and not due to the binding/unbinding of the drug. A second observation is the fact that this mutation also affected the conformational exchange between the binding competent and incompetent state of the free protein to the point that this DFG flip is now detectable in the stopped-flow fluorescence binding kinetics (FIG.17A;FIG.17C). However this DFG in/out equilibrium change has a negligible effect on the Gleevec affinity.

The T315I resistant mutation represents a serious therapeutic problem since second-generation tyrosine kinase inhibitors are ineffective (26). However, Danusertib, the drug used against Aurora A, and for which the kinetics of binding to Aurora was already described herein, has been shown to be effective against T315I Abl (FIG.27A). In an effort to elucidate the underlying atomistic mechanism, a co-crystal structure of T315I Abl kinase bound to Danusertib (PDB code: 2V7A) was solved showing the inhibitor bound to an active conformation with a DFG-in loop conformation (25). However, results from the binding kinetics of Danusertib to wild-type and T315I Abl underscores the importance of these experiments in elucidating the underlying mechanism and illustrates the generality of an induced fit step for a tight affinity for drugs (FIGS.18A-18I). The differences in affinity were not rooted in the DFG loop conformation as one might conclude from these X-ray structures, but always in the induced fit step (FIG.18E;FIG.18H). For both proteins, Danusertib binding was followed by a very slow induced fit step (FIG.18C) that is far-shifted, thereby increasing the overall affinity by this coupled equilibrium (FIG.19E;). Clearly, the nature of the induced fit step with Gleevec and Danusertib was different resulting in the ability for Danusertib to maintain high affinity for the gatekeeper mutant. Differences in the conformational changes after binding of the different drugs can be rationalized from the fact that these drugs extended to different parts of the protein upon binding.

Inhibitors take advantage of built-in dynamics for ATP binding. The binding kinetics of the ATP-competitive inhibitors with the natural substrate ATP were compared (FIGS.19A-19F). In order to measure stopped-flow kinetics for ATP binding FRET was measured by exciting Trp in Aurora A and detecting fluorescence transfer on Mant-ATP. It was found that ATP could bind to either the DFG -in or -out conformation and that nucleotide binding (FIG.19B) was also followed by an induced fit step (FIG.19C). Importantly, the latter conformational change was much faster and not as far-shifted compared to the inhibitor-bound states (FIG.19F). Faster conformational changes are of course a prerequisite for efficient turnover; whereas very slow conformational changes particularly the reverse induced fit reaction is at the heart of action for an efficient drug because it results in tight binding and a long lifetime of the drug on the target. In summary, binding of different ligands to the ATP binding site, such as nucleotides or ATP-competitive inhibitors, is comprised by the physical binding step followed by an induced fit step. The nature of the induced fit step varies by definition for the different ligands since it happens as a result of ligand binding.

Additional Characterization of Danusertib and AT9283 Binding to Aurora A

FIG.38,FIG.39,FIG.40,FIG.41,FIG.42,FIGS.43A-43C,FIGS.44A-44B, andFIG.45provide further characterization and elucidation of the binding mechanism and kinetics of binding of AT9283 and Danusertib to Aurora A. AT9283 binds to the “DFG_in” conformation whereas Danusertib binds to the “DFG_out” conformation. Results herein provide a finer characterization of the binding mechanism, kinetics, and energy landscape of the conformational selection step and induced fit step of binding to the active site of Aurora A.

Discussion

A central issue for drug design is to understand in detail the target/inhibitor interactions. This difficult task has primarily been tackled by comparing X-ray structures of the apo and inhibitor-bound targets, and by docking methods. Here it is revealed why the oversimplification in such a “two-state static view” cannot explain inhibitor affinity and specificity because the energy landscape of ligand binding is more complex even on the level of kinetically distinct states. In other words, both the apo enzyme and the ligand bound states were comprised of two conformations that could be experimentally distinguished because their interconversion was slower than microseconds. Crucially, the relative energies of these interconverting conformations dictated the overall affinity for the inhibitors. It was found that for kinase inhibitors, conformational selection and induced fit (37; 38) are at play. Strikingly, a far-shifted induced fit step was found to be the key step for all tight binders, and not the previously favored conformational selection of the DFG-in and -out structures. This mechanism seems to be general for different kinases and its inhibitors therefore providing a platform for future computational and experimental efforts in rational drug design. The “use” of a far-shifted induced fit step for a good drug is logical for three reasons: (i) it increases the affinity for the drug by this coupled equilibrium, (ii) it increases the residence time of the drug on the target via the slow reverse rate of the induced fit, and (iii) it is specific for each drug because it happens after the drug binding. The increased drug residence time has significant pharmacological advantages because it leads to a longer biological effect, a decrease of side effects and a lower risk of metabolic drug modification. Such inhibitors have long been described as slow tight-binding inhibitors.

Data described herein further deliver direct experimental information about the extensively discussed DFG-flip in kinases (33; 39-41). Dephosphorylated Aurora A, previously proposed to be exclusively in the inactive state, adopted both the DFG-out inactive and DFG-in active conformations in the same mother liquor. The existence of this equilibrium in solution was further substantiated by NMR and finally quantified using stopped-flow kinetics of drug binding. These new findings unambiguously establish the nature of this DFG flip both structurally and kinetically and resolve the longstanding question of its role for drug affinity.

The platform developed herein to monitor the detailed steps for drug binding delivered unexpected insight into the mechanism of drug resistance for the clinically common Abl gatekeeper mutation. It was found that the binding of Gleevec was not sterically hindered by the T315I mutation as previously described, but that this mutation severely affected the crucial induced fit step. Ponatinib (Ariad Pharmaceuticals) was approved in 2013 as second-line CML treatment, and was the only licensed tyrosine kinase inhibitor that binds to the T315I mutated kinase successfully (42). However, recently, the Food and Drug Administration (FDA) suspended Ponatinib distribution due to patients safety concern. Danusertib, originally used as Aurora inhibitor, has been proposed as a potential novel second-line inhibitor against this resistance mutation and indeed it was found that for this drug the induced fit step stayed intact, consequently preserving tight binding. Without intending to be bound by theory, it is believed that this is due the fact that the nature of the induced fit step is different for Danusertib and Gleevec because they are able to interact with different parts of the protein.

Results described herein exemplify why rational drug design is so challenging. The characterization of the complete free energy landscape of drug binding is needed, which will require more sophisticated computational approaches guided by experimental data such as provided in our study. A good illustration of this point are the computational reports focusing on the DFG flip as the key determinant responsible for Gleevec selectivity that now have been ruled out by kinetic measurements. The data herein suggest that future energy calculation should be focusing on the induced fit step. Clearly more experimental data for a series of inhibitors are essential to guide energy difference calculations. There is a large conformational space available for specific inhibitors even for kinases with very similar folds since the action does not happen on a single structure but on a complex energy landscape that is different for each kinase. It has been shown here that the inhibitors take advantage of the inherent plasticity of the enzymes that evolved for its activity and regulation.

Evolution of an Allosteric Activation Mechanism Enables Fine-Tuning of Aurora Kinase Activity

Despite a myriad of cellular events being governed by allostery, evolution of this process is yet a fairly unexplored territory. The main difficulty relies in finding the right model system that would span a large evolutionary window for unbiasedly assessing meaningful interactions. In the present study, Ancestral Sequence Reconstruction was used to resurrect ancestors of two co-localizing proteins, Aurora kinase and its allosteric activator, TPX2. Isothermal Titration calorimetry (ITC) and High Performance Liquid Chromatography (HPLC)-based assays were used to assess the degree of interaction and allosteric activation of Aurora kinase by TPX2 from different evolutionary periods. It was observed that a binding event was necessary and sufficient for driving interaction of these proteins and that Aurora kinase evolved to feel the effects of TPX2. This showed a regulation mechanism whereby phosphorylation of Aurora kinase preceded allosteric activation by TPX2, and proved to be a more-elegant, higher-order fine-tuning of Aurora kinase in higher, complex organisms.

Introduction

Allostery is the process by which a subset of spatially clustered amino acids can cooperatively influence the behavior of a different subset of amino acids, remote from the interaction site. This phenomenon governs many crucial cellular signaling processes ranging from oxygen transport [1], to synaptic transmission [2], to modulation of catalytic rates of enzymes [3, 4].

Despite the importance of allostery in living organisms, a mechanistic understanding of the evolution of this process has proven extremely challenging to obtain. The first technical difficulty arises in uncoupling meaningful interactions given the rugged energy landscape of coevolution. The second challenge pertains to choosing the right model system that could span a large evolutionary window. Most recently, this latter point was addressed by Coyle et al. [5] where proteins spanning 1 billion years of evolution (fromS. pombetoS. cerevisiae) were studied. However, works similar to the one mentioned above are few and far in between.

The question of how allosteric modulators and their partners coevolved is explored herein. Kuriyan and Eisenberg put forth an elegant theory to coevolution: colocalization of proteins, either through recombination or compartmentalization, gives the opportunity for nonspecific surface residue contacts to evolve into productive interactions [6]. A question of why is it that some surface residues provide productive coevolution basins while others don't was put forth. Ranganathan's lab addressed this question through SCA (Statistical Coupling Analysis), a tool that infers evolution based on sequence alignments of multiple proteins from varying organisms [7-9]. Through SCA, they showed that (a) overall, evolution of amino acids in a protein was a weakly coupled process (most aminoacids evolve independently of each-other) but that (b) several hotspots, typically accounting for 10-30% of aminoacids, were most responsible for coevolution [8]. Thus, the hotspots became the productive basins that Kuriyan and Eisenberg were referring to while discussing coevolution of allostery. Although very elegant in nature, most of the evolution of allostery remains theoretical given the technical challenges mentioned above.

In the current work, the hypothesis set forth by Kuriyan, Eisenberg and Ranganathan was experimentally addressed through a novel approach: the study of coevolution of allostery based on Ancestral Sequence Reconstruction (ASR). Not only did this method allow a look at a significantly larger evolutionary window (4+ billion years to present), but it also resolved the problem of finding the optimal model organism since our work is done using anE. coli-based expression system. Having addressed these two technical challenges, an in-depth mechanistic study of a coevolving set of proteins whose interaction is governed by allostery was conducted: that of Aurora A, an oncogenic Ser/Thr kinase, and its allosteric activator, Targeting Protein for Xklp2, TPX2. Aberrant levels of Aurora A lead to improper centrosome maturation, abnormal spindle formation, problems signaling mitotic entry and cancerous growth [10-27]. TPX2 targets Aurora A to the spindle microtubules and allosterically activates the protein by inducing an active conformation of the dephosphorylated, lowly-active form of the kinase [28, 29] and causes a conformational rearrangement of the phosphorylated, active Aurora A which leads to protection of the phosphate group on T288 from dephosphorylation [30]. Therefore, a mechanistic understanding of the coevolution of allostery in Aurora A-TPX2 could help identify hotspots in both of these proteins that could later be explored for much sought-after Aurora A inhibitors.

To this goal, Aurora and TPX2 ancestors were resurrected from different evolutionary periods. Two of the four Aurora ancestors (AurANC1and AurANC2) belonged to a period in time where TPX2 was not present. This was not surprising given that Aurora is a significantly older protein that TPX2, first appearing in protists (single-cell eukaryotes) while the oldest annotated sequence for TPX2 that is available, belongs to the plants and animals split. ITC was used to biophysically characterize the interaction between ancestral and modern day Aurora kinases and TPX2. Having assessed the degree of binding, activity assays were then performed to evaluate the potential allosteric effect of TPX2 on Aurora kinases.

The following observations were made. First, all resurrected Aurora ancestors were active on their own, with the phosphorylated form of these kinases being exceedingly more active than the dephosphorylated form, as expected. Second, AurANC1and AurANC2from the pre-canonical-TPX2 era could bind weakly but could not sense the allosteric effect of either the ancestral or the modern TPX2. Third, AurANC3and AurANC4could bind to ancestral and modern TPX2s with similar affinity, however, their response to the allosteric activation by TPX2 was incremental. Walking along the evolutionary timescale from younger to older canocical-TPX2-era Auroras (AurANC3→AurANC4→AurAmodern), the fold increase in kinase activity due to the presence of TPX2 went from 2→6→16 fold.

Remarkably, this suggested an adaptation on the energy landscape of Aurora whereby binding to TPX2 preceded the ability of Aurora kinase to “feel” the allosteric effects of TPX2. To further test this hypothesis, two novel mutant Aurora kinases were generated where binding to TPX2 was either diminished (Y199H/T288V AurAmodern) or enhanced (H199Y/T288V AurANC3), but the response to TPX2 did not change. In other words, once saturated with TPX2, Y199H/T288V AurAmodernfelt the same increase in allosteric activation by TPX2. Analogously, H199Y/T288V AurANC3, despite its increased binding affinity to TPX2, did not experience an increase in allosteric activation.

Through these experiments, Aurora ancestors were used to guide in the discovery of Y199 as a hotspot in the Aurora A-TPX2 interaction: Y199 contributed significantly to the heat of interaction between these partners, but it did not affect activation by allostery. Data herein thus provide a novel approach to studying coevolution of allostery. They also show that allosteric regulation by TPX2 followed phosphorylation as an additional mechanism of fine-tuning Aurora kinase activity. The data herein is also in line with the Kuriyan-Eisenberg-Ranagathan model whereby a small subset of amino acids contribute to the overall binding between partner proteins.

The results described herein were obtained using the following methods and materials.

Cloning and Purification of Aurora A Kinase

TEV-cleavable, His6-tagged Aurora A kinase, either modern (residues 122-403) or ancestral constructs (residues 133-403 in equivalent Aurora A numbering), were cloned into pET28a and expressed in Rosetta 2 (DE3)E. colicells (Stratagene) for 13-15 h at 21° C. Cells were centrifuged at 5000 rpm for 15 min, resuspended in Buffer A, and sonicated in the presence of EDTA-free protease inhibitor cocktail and DNAse for 4 min (20 s on, 20 s off, 3.0 V). Lysates thus obtained were filtered using a 0.22 m filtering unit and passed through a NiNTA column. The protein was eluted at 20% Buffer B and Aurora A kinase fractions were pooled and TEV-cleaved overnight at 4° C. in a 5 kDa dialysis cassette that was exchanged against buffer C. Cleaved Aurora A was passed through another nickel column to remove any uncleaved reactants and His6-TEV-protease, and then purified to homogeneity through a 26/60 S200 size exclusion column. Protein thus produced was aliquoted and flash-frozen before being stored at −80° C. and used for kinase assays. Mutant modern Aurora A122-403T288V, ancestral Aurora A133-403T288V, modern Aurora A122-403Y199H/T288V and ancestral Aurora A133-403H199Y/T288V were also purified the same way.

Thrombin-cleavable, His6-tagged, GB1-fused TPX2, either modern or ancestral constructs (residues 1-45), were cloned into pET28a and expressed in Rosetta 2 (DE3)E. colicells (Stratagene) for 5 h at 37° C. Cells were pelleted, resuspended, sonicated, centrifuged, and then passed through a first Ni2+ column as discussed above. The protein was eluted at 20% Buffer B and TPX2 fractions were pooled and thrombin-cleaved overnight at 4° C. in a 2 kDa dialysis cassette that was exchanged against buffer C. Cleaved TPX2 was passed through a tandem benzamidine-nickel column so as to remove any uncleaved reactants as well as thrombin, and then purified to homogeneity through a 26/60 S200 size exclusion column. TPX2 thus produced was aliquoted and flash-frozen before being stored at −80° C. and used for kinase assays. Typical yields were 50-60 mg of TPX2 per liter ofE. coliculture.

In Vitro Kinase Assays

Aurora A, either modern or ancestral, either phosphorylated or T288V mutant protein was mixed with Kemptide (LRRASLG) in the absence or presence of TPX2 in kinase buffer (20 mM TrisHCl, 200 mM NaCl, 3% (v/v) glycerol, 20 mM MgCl2, 1 mM TCEP, pH 7.50). TPX2 concentrations varied depending on the experiment. Please refer to the figure legends for more detail. The Kemptide substrate comprises the consensus sequence for Aurora A ([R/K/N]-R-X-[S/T]-B where B is any hydrophobic residue with the exception of Pro [34-36]. Kemptide was ordered through Genscript. The reaction was initiated with the addition of 5 mM ATP. 5 L timepoints were collected, resuspended in 10 L 6% (v/v) trichloroacetic acid (in water) to quench the reaction and neutralized with 50 L 100 mM KH2PO4, pH 8.0 to provide the appropriate pH for nucleotide separation. The mixture was then passed through a 0.22 m SpinX column to remove any protein precipitation. Reverse Phase High Performance Liquid Chromatography (RP-HPLC) and an ACE 5 C18-AR, 100 Å pore size column, was used to separate nucleotides (data not shown) as well as peptides. For peptide runs the optimal injection volume for analysis was 20 L. Nucleotide runs were routinely performed to ensure no unproductive hydrolysis was occurring during the experiment. An isocratic elution run in 100 mM KH2PO4, pH 6.0, was performed for this purpose. For the peptide runs, a gradient of 0-30% of elution buffer lasting 10 min at 0.4 ml/min was sufficient to separate phosphorylated from non-phosphorylated species. The running buffer was 0.1% TFA (v/v) in water whereas the elution buffer was 100% acetonitrile.

All titrations were carried out using Nano ITC (TA instruments) and analyzed via the NanoAnalyze software using the independent fit model. Injectant was added in 1 L volume, every 180 s, with a constant stirring speed at 350 rpm and at 25° C. Prior to ITC titration, both protein and peptide were dialyzed/resuspended in 20 mM TrisHCl, 200 mM NaCl, 10% (v/v) glycerol, 1 mM TCEP, pH 7.50. The concentrations used for each of the runs are shown inFIGS.35A-35C.

The results of the experiments herein are now described.

Results & Discussion

Aurora ancestors are active and precede TPX2 ancestors in the evolution timescale. Two modes of activation of Aurora A kinase are currently accepted: phosphorylation of a conserved activation loop residue (T288) and allosteric activation through binding of TPX2. Previously, it was shown that allosteric activation was preferentially skewed for the lowly-active, dephosphorylated form of Aurora A [31]. It is hypothesized that activation by TPX2 was a recent evolutionary adaptation in the regulation of Aurora A, one that followed phosphorylation. In the study herein, the evolution of allostery between these two proteins was investigated.

A closer look at the Manning tree (FIG.30A), showed that Aurora kinases (red branch) were an old branch of the kinome and similar to the AGC family of proteins. Modern day Aurora sequences from various organisms were aligned and that alignment was used to generate a Bayesian-based phylogenetic tree (FIG.30B). AGC and CAMK families were also used to increase the robustness of our alignment and XX was used as the outgroup. Subsequently, this tree was the input for PAML [32] to generate ancestral proteins. Similarly, to generate the TPX2 ancestors TPX2 from various organisms were aligned and WVD2 was used as the outgroup (FIG.34).

Four ancestors of Aurora kinase (AurANC1-4) were resurrected. Reverse Phase High Performance Liquid Chromatography (RP-HPLC) was used to test the activity of these ancestors towards a model substrate peptide Kemptide (LRRASLG, where the phosphorylated residue is bolded). These proteins had activities comparable to that of modern Aurora A, both in their phosphorylated forms (FIG.30C, left) and their dephosphorylated-like form (T288V mutant Aurora kinases,FIG.30C, right).

Aurora kinases are older and were present long before a canonical TPX2 protein existed (FIG.31). In fact, there are currently no annotated TPX2 sequences in the database that pre-date the split between plants and animals. On the other hand, Aurora kinase sequences go back in time as far asEncephalitozoon cuniculi,a microsporidian—an intracellular parasite—and a single-cell Eukaryote informally known as a protist [33]. Thus, two of the four resurrected Aurora kinases (AurANC1and AurANC2), belong to an evolutionary period prior to the appearance of a canonical TPX2 motif.

Aurora Ancestors That Postdate the Appearance of TPX2, Bind Tightly and are Allosterically Activated by TPX2

To test whether modern or ancestral Aurora or TPX2 proteins interact with each-other, Isothermal Titration calorimetry was used to quantify the extent of binding of these proteins (FIG.32AandFIGS.35A-35C). It was found that a weak, non-quantifiable interaction signal between Aurora ancestors that predated TPX2 (AurANC1and AurANC2) and TPX2 from either ancestral sequence reconstruction or modern day protein. Curiously, younger Aurora ancestors (AurANC3and AurANC4) that coincided in evolutionary time with the presence of TPX2, could bind tightly and rather indiscriminately to TPX2 with Kd's comparable to that of the modern protein. This seemed to suggest that the Aurora-TPX2 binding event did not significantly evolve past the plant-animal evolutionary split, which was when TPX2 first appeared.

Having established the existence of the Aurora-TPX2 interaction, next allosteric activation by TPX2, or perhaps lack thereof, was sought be quantified. Since the dynamic range of TPX2 on Aurora A is larger for its dephosphorylated-like form, T288V mutants of Aurora were used to observe the fold increase in activity of these mutants in the presence of TPX2. It was not expected that TPX2 ancestors (TPX2ANC3-4) would allosterically increase the activity of Aurora ancestors of the pre-canonical-TPX2 era (AurANC1and AurANC2). Conversely, it was expected that younger Aurora ancestors (AurANC3and AurANC4) would respond to the allosteric effect of TPX2. This is in fact what was observed. AurANC1and AurANC2did not experience an increase in the rate of Kemptide phosphorylation in the presence of TPX2ANC3, the TPX2 ancestor that was closest to them. On the other hand, younger Aurora ancestors experienced an incremental increase (2×→6×→16×) in allosteric activation by TPX2 the closer the move towards modern-day Aurora was (FIG.32B).

Given that Aurora kinases that postdate the appearance of TPX2 (AurANC3and AurANC4) can bind with similar affinity to TPX2 from different evolutionary periods, next the allosteric increase in rate by mismatched pairs of Aurora and TPX2 was investigated (FIG.32CandFIG.36). Even with the mismatched pairs, the effect of allosteric activation by TPX2 gradually increased moving from older to younger Auroras of the evolutionary timescale.

Initiation of an Aurora-TPX2 Binding Event Preceded Evolution of Aurora to Respond to the Allosteric Effect of TPX2.

The data thus far hinted at a model whereby a productive Aurora-TPX2 binding event needed to first be established for Aurora to later evolve to “feel” the allosteric activation effect of TPX2. To test this hypothesis a residue was looked for, a potential binding “hotspot” in Aurora that, once mutated, could either increase or decrease binding to TPX2 without effecting modulation in allosteric activation.

The structure of dephosphorylated Aurora A bound to TPX2 was used to identify key residues in Aurora A that made extensive contacts with TPX2. The evolution of these residues in time was looked at and it was determined Y199 was a potential TPX2-binding hotspot since this residue was a His in Aurora ancestors that predated the appearance of TPX2 (FIG.33A;FIG.33B;FIG.33C). In fact, Y199H weakened the binding of modern Aurora A to modern TPX2 by approximately 20 fold, clearly suggesting the significant implication of the residue in the overall Aurora A-TPX2 heat of interaction (FIG.33B). As hypothesized, once saturated with TPX2 however, this Y199H mutant was still capable of fully responding to the allosteric activation by modern TPX2 (FIG.33B).

Analogously, the opposite mutation (H199Y) in AurANC2significantly increased the binding of a pre-TPX2 era Aurora ancestor to modern TPX2. However, although binding was more tightly established, AurANC2could not be allosterically activated by TPX2.

Conclusions

Exploitation of allostery in regulating protein kinase activity is particularly fascinating given that kinases share remarkable structural similarity, yet they are affected by allosteric modulators with astounding selectivity. This raises the question of how allosteric modulators and their target protein kinases coevolved.

In the present study, Ancestral Sequence Reconstruction and anE. coli-based expression system were used to resurrect ancestral Aurora kinase and TPX2 and study their coevolution. It was observed that Aurora ancestors that existed before the canonical TPX2 came around, bound very weakly to and were not allosterically activated by, TPX2. Aurora ancestors that existed around the same time that TPX2 existed, bound with similar affinity to TPX2 and were differentially regulated with the younger ANCs being more responsive to the effect of TPX2. These findings suggest a model whereby a binding event needed to occur prior to Aurora kinase evolving to respond to the allosteric effect of TPX2. This model suggests that allosteric regulation by TPX2 postdated phosphorylation as an additional mechanism in fine-tuning Aurora kinase activity.

Other Embodiments