Advances in structure and small molecule docking predictions for crystallized G-Protein coupled receptors

This dissertation discusses two main aspects of protein-ligand interaction for G-Protein coupled receptors: structure predictions of the flexible loop domains and docking into these receptors. The prediction of loop structure has been long worked on in the context of native, globular proteins. In this work it is extended to transmembrane proteins, which requires an explicit integration of the lipid bilayer into the loop prediction calculation. In the initial work, this new approach to loop prediction yields highly accurate 3-dimensional structures of the intra and intercellular loops of four G-protein coupled receptors--the A2A adenosine, bovine rhodopsin, β1 and β2 adronergic receptors. For these cases, the loops were predicted in the context of a completely native crystal structure. In subsequent work the approach was extended to work on perturbed cases, where all loops and tails were removed, and side chains near the loop being predicted were in nonnative conformations. Lastly, a full homology model of the β2 adronergic receptor was successfully built from the β1 adronegric receptor as its template. Work on docking into these receptors focuses on the kappa opioid receptor. Known antagonist binders are discriminated from a set of decoy nonbinders via docking calculations. Two new terms were added to the scoring function, WScore to achieve this, based on a detailed molecular understanding of how the receptor works

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Orientation-dependent backbone-only residue pair scoring functions for fixed backbone protein design

<p>Abstract</p> <p>Background</p> <p>Empirical scoring functions have proven useful in protein structure modeling. Most such scoring functions depend on protein side chain conformations. However, backbone-only scoring functions do not require computationally intensive structure optimization and so are well suited to protein design, which requires fast score evaluation. Furthermore, scoring functions that account for the distinctive relative position and orientation preferences of residue pairs are expected to be more accurate than those that depend only on the separation distance.</p> <p>Results</p> <p>Residue pair scoring functions for fixed backbone protein design were derived using only backbone geometry. Unlike previous studies that used spherical harmonics to fit 2D angular distributions, Gaussian Mixture Models were used to fit the full 3D (position only) and 6D (position and orientation) distributions of residue pairs. The performance of the 1D (residue separation only), 3D, and 6D scoring functions were compared by their ability to identify correct threading solutions for a non-redundant benchmark set of protein backbone structures. The threading accuracy was found to steadily increase with increasing dimension, with the 6D scoring function achieving the highest accuracy. Furthermore, the 3D and 6D scoring functions were shown to outperform side chain-dependent empirical potentials from three other studies. Next, two computational methods that take advantage of the speed and pairwise form of these new backbone-only scoring functions were investigated. The first is a procedure that exploits available sequence data by averaging scores over threading solutions for homologs. This was evaluated by applying it to the challenging problem of identifying interacting transmembrane alpha-helices and found to further improve prediction accuracy. The second is a protein design method for determining the optimal sequence for a backbone structure by applying Belief Propagation optimization using the 6D scoring functions. The sensitivity of this method to backbone structure perturbations was compared with that of fixed-backbone all-atom modeling by determining the similarities between optimal sequences for two different backbone structures within the same protein family. The results showed that the design method using 6D scoring functions was more robust to small variations in backbone structure than the all-atom design method.</p> <p>Conclusions</p> <p>Backbone-only residue pair scoring functions that account for all six relative degrees of freedom are the most accurate and including the scores of homologs further improves the accuracy in threading applications. The 6D scoring function outperformed several side chain-dependent potentials while avoiding time-consuming and error prone side chain structure prediction. These scoring functions are particularly useful as an initial filter in protein design problems before applying all-atom modeling.</p

A threading approach to protein structure prediction: studies on TNF-like molecules, Rev proteins, and protein kinases

The main focus of this dissertation is the application of the threading approach to specific biological problems. The threading scheme developed in our group targets incorporating important structural features necessary for detecting structural similarity between the target sequence and the template structure. This enables us to use our threading method to solve problems for which sequence-based methods are not very much useful. We applied our threading method to predict the three-dimensional structures of lentivirus (EIAV, HIV-1, FIV, SIV) Rev proteins. Predicted structures of Rev proteins suggest that they share a structural similarity among themselves (four-helix bundle). Also, the threading approach has been utilized for screening for potential TNF-like molecules in Arabidopsis. The threading approach identified 35 potential TNF-like proteins in Arabidopsis, six of which are particularly interesting to be tested for the receptor kinase ligand activity. Threading method has also been used to identify potentially new protein kinases, which are not included in the protein kinase data base of C. elegans and Arabidopis. We identified eleven potentially new protein kinases and an additional protein worth investigating for protein kinase activity in C. elegans. Further, we identified ten potentially new protein kinases and additional four proteins worth investigating for the protein kinase activity in Arabidopsis

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

Protein 3D Structure Computed from Evolutionary Sequence Variation

The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing