pinecone/core-2020-05-10-deduplication huggingface dataset:
Structure-based tailoring of compound libraries for high-throughput screening: discovery of novel EphB4 kinase inhibitors.

High-throughput docking is a computational tool frequently used to discover small-molecule inhibitors of enzymes or receptors of known three-dimensional structure. Because of the large number of molecules in chemical libraries, automatic procedures to prune multimillion compound collections are useful for high-throughput docking and necessary for in vitro screening. Here, we propose an anchor-based library tailoring approach (termed ALTA) to focus a chemical library by docking and prioritizing molecular fragments according to their binding energy which includes continuum electrostatics solvation. In principle, ALTA does not require prior knowledge of known inhibitors, but receptor-based pharmacophore information (hydrogen bonds with the hinge region) is additionally used here to identify molecules with optimal anchor fragments for the ATP-binding site of the EphB4 receptor tyrosine kinase. The 21,418 molecules of the focused library (from an initial collection of about 730,000) are docked into EphB4 and ranked by force-field-based energy including electrostatic solvation. Among the 43 compounds tested in vitro, eight molecules originating from two different anchors show low-micromolar activity in a fluorescence-based enzymatic assay. Four of them are active in a cell-based assay and are potential anti-angiogenic compounds
Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network

This article attempts to increase the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins through improved learning. Most methods developed for improving the back propagation algorithm of artificial neural networks are limited to small neural networks. Here, we introduce a guided-learning method suitable for networks of any size. The method employs a part of the weights for guiding and the other part for training and optimization. We demonstrate this technique by predicting residue solvent accessibility and real-value backbone torsion angles of proteins. In this application, the guiding factor is designed to satisfy the intuitive condition that for most residues, the contribution of a residue to the structural properties of another residue is smaller for greater separation in the protein-sequence distance between the two residues. We show that the guided-learning method makes a 2-4% reduction in 10-fold cross-validated mean absolute errors (MAE) for predicting residue solvent accessibility and backbone torsion angles, regardless of the size of database, the number of hidden layers and the size of input windows. This together with introduction of two-layer neural network with a bipolar activation function leads to a new method that has a MAE of 0.11 for residue solvent accessibility, 368 for w, and 228 for /.Full Tex
Consensus scoring for enriching near-native structures from protein–protein docking decoys

The identification of near native protein-protein complexes among a set of decoys remains highly challenging. A strategy for improving the success rate of near native detection is to enrich near native docking decoys in a small number of top ranked decoys. Recently, we found that a combination of three scoring functions (energy, conservation, and interface propensity) can predict the location of binding interface regions with reasonable accuracy. Here, these three scoring functions are modified and combined into a consensus scoring function called ENDES for enriching near native docking decoys. We found that all individual scores result in enrichment for the majority of 28 targets in ZDOCK2.3 decoy set and the 22 targets in Benchmark 2.0. Among the three scores, the interface propensity score yields the highest enrichment in both sets of protein complexes. When these scores are combined into the ENDES consensus score, a significant increase in enrichment of near-native structures is found. For example, when 2000 dock decoys are reduced to 200 decoys by ENDES, the fraction of near-native structures in docking decoys increases by a factor of about six in average.Full Tex
Predicting residue–residue contact maps by a two-layer, integrated neural-network method

A neural network method (SPINE-2D) is introduced to provide a sequence-based prediction of residue-residue contact maps. This method is built on the success of SPINE in predicting secondary structure, residue solvent accessibility, and backbone torsion angles via large-scale training with overfit protection and a twolayer  neural network. SPINE- 2D achieved a 10-fold crossvalidated accuracy of 47% (62%) for top L/5 predicted contacts between two residues with sequence separation of six  or more and an accuracy of 24 6 1% for nonlocal contacts with sequence separation of 24 residues or more. The accuracies of 23% and 26% for nonlocal contact predictions are achieved for two independent datasets of 500 proteins and 82 CASP 7 targets,  respectively. A comparison with other methods indicates that SPINE-2D is among the  most accurate methods for contact- map prediction.Full Tex
Refining near-native protein–protein docking decoys by local resampling and energy minimization

How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 A ࠯r more and only 7% increased by 0.5 A ࠯r more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed.Full Tex
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Protein Long Local Structure Prediction

International audienceA relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterizing recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11-residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a Calpha RMSD less than 2.5 A from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared with the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65% mean gain. 85.33% of proteins have better prediction results with a 9.43% average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions. Proteins 2009. (c) 2009 Wiley-Liss, Inc
An all-atom knowledge-based energy function for protein-DNA threading, docking decoy discrimination, and prediction of transcription-factor binding profiles

How to make an accurate representation of protein-DNA interaction by an energy function is a long-standing unsolved problem in structural biology. Here, we modified a statistical potential based on the distancescaled, finite ideal-gas reference state so that it is optimized for protein-DNA interactions. The changes include a volume-fraction correction to account for unmixable atom types in proteins and DNA in addition to the usage of a low-count correction, residue/base-specific atom types, and a shorter cutoff distance for protein-DNA interactions. The new statistical energy functions are tested in threading and docking decoy discriminations and prediction of protein-DNA binding affinities and transcriptionfactor binding profiles. The results indicate that new proposed energy functions are among the best in existing energy functions for protein-DNA interactions. The new energy functions are available as a web-server called DDNA 2.0 at http://sparks. informatics.iupui.edu. The server version was trained by the entire 212 protein-DNA complexes.Full Tex
Structural study of Carcinus maenas hemocyanin by native ESI-MS: Interaction with L-lactate and divalent cations

International audienceThe interaction Of L-lactate and divalent cations with Carcinus maenas hemocyanin has been probed by electrospray ionization mass spectrometry under conditions preserving noncovalent interactions (native ESI-MS). C. maenas native hemocyanin in the hemolymph occurs mainly as dodecamers and to a lesser extent as hexamers. A progressive acidification with formic acid after alkaline dissociation resulted in the preferential recruitment of the two lightest subunits into light dodecamers, a molecular complex absent from native hemolymph, in addition to regular dodecamers and hexamers. Addition of L-lactic acid also induced the recruitment of these subunits, even at alkaline pH. A dodecamer-specific subunit is needed to enable aggregation over the hexameric state. Experiments with EDTA suggested the existence of different binding sites and association constants for divalent cations within hexameric structures and at the interface between two hexamerS. L-lactic acid specific interaction with the lightest subunits was not inhibited by removal of the divalent cations
Comparison of DNA binding across protein superfamilies

Specific protein-DNA interactions are central to a wide group of processes in the cell and have been studied both experimentally and computational\ud
ly over the years. Despite the increasing collection of protein-DNA complexes, so far only a few studies have aimed at dissecting the structural characteristics of DNA binding among evolutionarily related proteins. Some questions that remain to be answered are: a) what is the contribution of the different readout mechanisms in members of a given structural superfamily, b) what is the degree of interface similarity among superfamily members and how this affects binding specificity, c) how DNA-binding protein superfamilies distribute across taxa, and d) is there a general or family-specific code for the recognition of DNA. We have recently developed a straightforward method to dissect the interface of protein-DNA complexes at the atomic level\ud
 and here we apply it to study 175 proteins belonging to 9 representative superfamilies. Our results indicate that evolutionarily unrelated DNA-binding domains broadly conserve specificity statistics, such as the ratio of indirect/direct readout and the frequency of atomic interactions, therefore supporting the existence of a set of recognition rules. It is also found that interface conservation follows trends that are superfamily-specific. Finally, this paper identifies tendencies in the phylogenetic distribution of transcription factors, which might be related to the evolution of regulatory \ud
networks, and postulates that the modular nature of zinc finger proteins can explain its role in large genomes, as it allows for larger binding interfaces in a single protein molecule.Peer reviewe
Quantitative modeling of peptide binding to TAP using support vector machine

The transport of peptides to the endoplasmic reticulum by the transporter associated with antigen processing (TAP) is a necessary step towards determining CD8 T cell epitopes. In this work, we have studied the predictive performance of support vector machine models trained on single residue positions and residue combinations drawn from a large dataset consisting of 613 nonamer peptides of known affinity to TAP. Predictive performance of these TAP affinity models was evaluated under 10-fold cross-validation experiments and measured using Pearson's correlation coefficients (R(p)). Our results show that every peptide position (P1-P9) contributes to TAP binding (minimum R(p) of 0.26 +/- 0.11 was achieved by a model trained on the P6 residue), although the largest contributions to binding correspond to the C-terminal end (R(p) = 0.68 +/- 0.06) and the P1 (R(p) = 0.51 +/- 0.09) and P2 (0.57 +/- 0.08) residues of the peptide. Training the models on additional peptide residues generally improved their predictive performance and a maximum correlation (R(p) = 0.89 +/- 0.03) was achieved by a model trained on the full-length sequences or a residue selection consisting of the first 5 N- and last 3 C-terminal residues of the peptides included in the training set. A system for predicting the binding affinity of peptides to TAP using the methods described here is readily available for free public use at http://imed.med.ucm.es/Tools/tapreg/
Characterization of intrinsically disordered proteins with electrospray ionization mass spectrometry: conformational heterogeneity of alpha-synuclein

Conformational heterogeneity of alpha-synuclein was studied with electrospray ionization mass spectrometry by analyzing protein ion charge state distributions, where the extent of multiple charging reflects compactness of the protein conformations in solution. Although alpha-synuclein lacks a single well-defined structure under physiological conditions, it was found to sample four distinct conformational states, ranging from a highly structured one to a random coil. The compact highly structured state of alpha-synuclein is present across the entire range of conditions tested (pH ranging from 2.5 to 10, alcohol content from 0% to 60%), but is particularly abundant in acidic solutions. The only other protein state populated in acidic solutions is a partially folded intermediate state lacking stable tertiary structure. Another, more compact intermediate state is induced by significant amounts of ethanol used as a co-solvent and appears to represent a partially folded conformation with high beta-sheet content. Protein dimerization is observed throughout the entire range of conditions tested, although only acidic solutions favor formation of highly structured dimers of alpha-synuclein. These dimers are likely to present the earliest stages in protein aggregation leading to globular oligomers and, subsequently, protofibril
p-Coumaric acid decarboxylase from Lactobacillus plantarum: Structural insights into the active site and decarboxylation catalytic mechanism

p-Coumaric acid decarboxylases (PDCs)\ud
catalyze the nonoxidative decarboxylation\ud
of hydroxycinnamic acids to generate the\ud
corresponding vinyl derivatives. Despite\ud
the biotechnological relevance of PDCs in\ud
food industry, their catalytic mechanism\ud
remains largely unknown. Here, we\ud
report insights into the structural basis of\ud
catalysis for the homodimeric PDC from\ud
Lactobacillus plantarum (LpPDC). The\ud
global fold of LpPDC is based on a flattened\ud
b-barrel surrounding an internal\ud
cavity. Crystallographic and functional\ud
analyses of single-point mutants of residues\ud
located within this cavity have permitted\ud
identifying a potential substratebinding\ud
pocket and also to provide structural\ud
evidences for rearrangements of surface\ud
loops so that they can modulate the\ud
accessibility to the active site. Finally,\ud
combination of the structural and functional\ud
data with in silico results enables\ud
us to propose a two-step catalytic mechanism\ud
for decarboxylation of p-coumaric\ud
acid by PDCs where Glu71 is involved in\ud
proton transfer, and Tyr18 and Tyr20 are\ud
involved in the proper substrate orientation\ud
and in the release of the CO2\ud
productThe authors acknowledge access to ESRF beamlines\ud
ID23-1, ID29, and BM16, and local support by A.M.\ud
Gonc¸alves (ID23-1), X. Thibault (ID29), and G.C. Fox\ud
(BM16). H. Rodrı´guez is a recipient of an I3P predoctoral\ud
fellowship from the CSICPeer reviewe
Improving computational protein design by using structure-derived sequence profile

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main-chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that  are structurally matched to the five-residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino-acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign-SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild-type sequences. Meanwhile, it reduces  8% (from 22% to 14%) to the number of designed sequences that are not homologous to  any known protein sequences according to psiblast.  More importantly, the sequences designed by RosettaDesign-SR have 2-3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild-type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign-SR should be less likely to aggregate and more likely to have unique structures due to more specific polar  interactions.Full Tex
Fluctuations of backbone torsion angles obtained from NMR-determined structures and their prediction

Protein molecules exhibit varying degrees of flexibility throughout their three-dimensional structures. Protein structural flexibility is often characterized by fluctuations in the Cartesian coordinate space. On the other hand, the protein backbone can be mostly defined by two torsion angles / and w only. We introduce a new flexibility descriptor, backbone torsion-angle fluctuation derived from the variation of backbone torsion angles from different NMR models. The torsion-angle fluctuations correlate with mean-squared spatial fluctuations derived from the same collection of NMR models. We developed a neural-network based real-value predictor based on sequence information only. The predictor achieved ten-fold cross-validated correlation coefficients of 0.59 and 0.60, and mean absolute errors of 22.78 and 24.38 for the angle fluctuation of / and w, respectively. This predictor is expected to be useful for function prediction and protein structure prediction when predicted torsion angles are used as restraints.Full Tex
Mechanism of reactant and product dissociation from the anthrax edema factor: a locally enhanced sampling and steered molecular dynamics study

The anthrax edema factor is a toxin overproducing damaging levels of cyclic adenosine mono-phosphate (cAMP) and pyrophosphate (PPi) from ATP. Here, mechanisms of dissociation of ATP and products (cAMP, PPi) from the active site are studied using locally enhanced sampling (LES) and steered molecular dynamics simulations. Various substrate conformations and ionic binding modes found in crystallographic structures are considered. LES simulations show that PPi and cAMP dissociate through different solvent accessible channels, while ATP dissociation requires significant active site exposure to solvent. The ionic content of the active site directly affects the dissociation of ATP and products. Only one ion dissociates along with ATP in the two-Mg2+ binding site, suggesting that the other ion binds EF prior to ATP association. Dissociation of reaction products cAMP and PPi is impaired by direct electrostatic interactions between products and Mg2+ ions. This provides an explanation for the inhibitory effect of high Mg2+ concentrations on EF enzymatic activity. Breaking of electrostatic interactions is dependent on a competitive binding of water molecules to the ions, and thus on the solvent accessibility of the active site. Consequently, product dissociation seems to be a two-step process. First, ligands are progressively solvated while preserving the most important electrostatic interactions, in a process that is dependent on the flexibility of the active site. Second, breakage of the electrostatic bonds follows, and ligands diffuse into solvent. In agreement with this mecanism, product protonation facilitates dissociation
Free-energy function for discriminating the native fold of a protein from misfolded decoys.

In this study, free-energy function (FEF) for discriminating the native fold of a protein from misfolded decoys was investigated. It is a physics-based function using an all-atom model, which comprises the hydration entropy (HE) and the total dehydration penalty (TDP). The HE is calculated using a hybrid of a statistical-mechanical theory applied to a molecular model for water and the morphometric approach. The energetic component is suitably taken into account in a simple manner as the TDP. On the basis of the results from a careful test of the FEF, which have been performed for 118 proteins in representative decoy sets, we show that its performance is distinctly superior to that of any other function. The FEF varies largely from model to model for the candidate models for the native structure (NS) obtained from nuclear magnetic resonance experiments, but we can find models or a model for which the FEF becomes lower than for any of the decoy structures. A decoy set is not suited to the test of a free-energy or potential function in cases where a protein isolated from a protein complex is considered and the structure in the complex is used as the model NS of the isolated protein without any change or where portions of the terminus sides of a protein are removed and the percentage of the secondary structures lost due to the removal is significantly high. As these findings are made possible, we can assume that our FEF precisely captures the features of the true NS