Computational studies of proteins and nucleic acids: On pKa calculations in RNA and the use of structure to improve sequence alignments

Computational Studies of Proteins and Nucleic Acids: On pKa Calculations in RNA and the Use of Structure to Improve Sequence Alignments Christopher Lung Kong Tang Proteins with similar sequences fold into similar structures. This observation has been used extensively in homology modeling, a method used to predict the structure of a protein target by aligning its amino acid sequence to that of a related protein template whose structure is known. A homology model is constructed by transferring the coordinates of the structure from the template to the target using the alignment of their sequences as a guide. However, even proteins with very little sequence similarity may have similar structures, or share common substructures. Recognizing these relationships (a process called fold recognition), and providing accurate alignments once they are found, are two important problems that continue to be a challenge. Structural alignments can reveal similarities in proteins even where no significant sequence similarity exists. Prior work has shown that structural alignments can also capture important sequence signals even between very distantly related proteins. Exactly how this is applied to fold recognition and the sequence alignment problem has been the subject of much active research. Often, some of the most accurate methods for fold recognition and sequence alignment combine several different types of sequence and structural information. These include information such as actual or predicted secondary structure, amino acid preferences for different structural environments, and the lengths and distributions of gaps. Sequence profiles describe positions in a protein by the frequency they can be replaced by each of the twenty amino acids. This thesis describes the creation of sequence profiles based on structural alignments called HMAP (hybrid multi-dimensional alignment of profiles). In particular, they are called "hybrid" because they combine and use several kinds of information, including amino acid preferences, and actual or predicted secondary structure. Profiles can be merged based on alignments derived from the structural superposition of related templates. In this thesis, these profiles are tested for their ability to detect structurally-related proteins in comprehensive benchmarks based on: (1) the protein classification database: SCOP, and (2) a quantitative measure of protein structural distance (PSD) to define true structural relationships. We find that the use of actual and predicted secondary structure greatly improves fold recognition and alignment quality, and the level of alignment accuracy achieved compares favorably to some of the top-performing methods in blind structure prediction experiments such as CASP. Profiles merged based on structural alignments also perform well, suggesting that weak sequence signals may be captured by these profiles. Structural relationships, such as those detected by using HMAP in fold recognition, can be used to make functional inferences for proteins with unknown biological function. Indeed, it is sometimes possible to transfer functional information such as the ligand binding sites or active site residues once an alignment has been made between two proteins. This is made possible in part by the growing number of protein structures being contributed to the RCSB. There is now also an increasing number of functional RNA molecules whose structures have been solved. These RNA structures can similarly provide answers to questions about the functions of different kinds of RNA molecules, ranging from riboswitches to ribozymes. Ribozymes are RNA molecules that catalyze specific chemical reactions. The mechanism by which RNA can do this has been the subject of much scientific study as well as human curiosity. Recently, it has been found that hepatitis delta virus (HDV) and other ribozymes do not require chemical groups other than those normally found in RNA for catalysis, and in fact, some nucleobase (base) groups need to be protonated in order to function effectively. This has implied that RNA is capable of stabilizing protonated nucleotides at physiological pH, and in so doing, shift the pKas of one or more of its own bases. Protonated nucleotides are found in the structures of a wide variety of RNAs, and can be shown to play structural as well as functional roles. Methods based on the Poisson-Boltzmann equation have been quite successful in calculating amino acid pKa shifts in proteins. Solutions of the Poisson-Boltzmann equation describe the electrostatic potential around charged and polar molecules immersed in an aqueous solvent with mobile counterions. The focus of the research presented here was to establish whether methods based on this equation could be successfully applied to RNA molecules. In particular, we started with MCCE, a method for calculating pKa shifts in proteins, and added the capability to apply it to RNA molecules. This work has required addressing several issues: (1) the nonlinear behavior of electrostatic potentials in the presence of highly charged molecules, for example in RNA, has not usually been treated in protein pKa calculations, (2) the lack of atomic charge parameters for protonated nucleotides has required their development and testing, and (3) a set of experimental results from the literature was needed to test the validity of the calculations. The work presented in this thesis now shows that the method: (1) can reproduce pKa shirts that have been measured experimentally, though some appear to be overestimated, (2) reproduces changes in the pKa shifts due to changes in salt concentration, (3) predicts the locations of nucleotides that may be functionally important due to their unusual pKa shifts, and (4) can help explain how structural features contribute to the pKa shifts of nucleotides in ribozymes and other RNAs. Applied to several RNAs including the HDV and hairpin ribozymes, the results support the idea that RNA structure can stabilize protonated nucleotides where they may be critical for structure or function. The structural features that help stabilize these shifts are also discussed. With the development of our method, the structural information obtained from RNA molecules can now be used for calculating the pKa shifts of nucleotides in RNA. Calculations of pKa shifts in RNA can also be used as they are in proteins. Like in proteins, the identification of chemical groups that have abnormally shifted pKas or titration curves can lead to the identification of active site residues in RNA. With the development of a pKa calculation for RNA molecules, this kind of analysis, when used in a database-wide search, may also provide much needed information about the functions of many more RNA molecules. As the number of RNA structures continues to grow, such information can become even more valuable when methods for aligning RNA sequences to their homologs are applied, and the information from one RNA structure is leveraged by applying the information to RNA molecules of similar structures.