Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

Predictive and experimental approaches for elucidating protein–protein interactions and quaternary structures

The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

© 2015 Maheshwari and Brylinski. Background: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. Results: To address this problem, we developed eRankPPI, an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRankPPI employs multiple features including interface probability estimates calculated by eFindSitePPI and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRankPPI consistently outperforms state-of-the-art algorithms improving the success rate by ∼10 %. Conclusions: eRankPPI was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/eRankPPI

Sequence homology based protein-protein interacting residue predictions and the applications in ranking docked conformations

Protein-protein interactions play a central role in the formation of protein complexes and the biological pathways that orchestrate virtually all cellular processes. Three dimensional structures of a complex formed by a protein with one or more of its interaction partners provide useful information regarding the specific amino acid residues that make up the interface between proteins. The emergence of high throughput techniques such as Yeast 2 Hybrid (Y2H) assays has made it possible to identify putative interactions between thousands of proteins (but not the interfaces that form the structural basis of interactions or the structures of protein complexes that result from such interactions). Reliable identification of the specific amino acid residues that form the interface of a protein with one or more other proteins is critical for understanding the structural and physico-chemical basis of protein interactions and their role in key cellular processes, for predicting protein complexes, for validating protein interactions predicted by high throughput methods, for ranking conformations of protein complexes generated by docking, and for identifying and prioritizing drug targets in computational drug design. However, given the high cost of experimental determination of the structures of protein complexes, there is an urgent need for reliable and fast computational methods for identifying interface residues and/or predicting the structure of a complex formed by a protein of interest with its interaction partners. Given the large and growing gap between the number of known protein sequences and the number of experimentally determined structures, sequence-based methods for predicting protein-protein interfaces are of particular interest. Against this background, we develop HomPPI ( http://homppi.cs.iastate.edu/), a class of sequence homology based approaches to protein interface prediction. We present two variants of HomPPI: (i) NPS-HomPPI (non-partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner. NPS-HomPPI is based on the results of a systematic analysis of the conditions under which interface residues of a query protein are conserved among its sequence homologs (and hence can be inferred from the known interface residues in proteins that are sequence homologs of the query protein). Our experiments suggest that when sequence homologs of the query protein can be reliably identified, NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. (ii) PS-HomPPI (partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein. PS-HomPPI is based on a systematic analysis of the conditions under which the interface residues that make up the interface between a query protein and its interaction partner are preserved among their homo-interologs, i.e., complexes formed by their respective sequence homologs. To the best of our knowledge, with the exception of protein-protein docking (which is computationally much more expensive than PS-HomPPI), PS-HomPPI is one of the first partner-specific protein-protein interface predictors. Our experiments with PS-HomPPI show that when homo-interologs of a query protein and its putative interaction partner can be reliably identified, the interface predictions generated by PS-HomPPI are significantly more reliable than those generated by NPS-HomPPI. Protein-Protein Docking offers a powerful approach to computational determination of the 3-dimensional conformation of protein complexes and protein-protein interfaces. However, the reliability of conformations produced by docking is limited by the efficacy of the scoring functions used to select a few near-native conformations from among tens of thousands of possible conformations, generated by docking programs. Against this background, we introduce DockRank, a novel approach to rank docked conformations based on the degree to which the interface residues inferred from the docked conformation match the interface residues predicted by a partner-specific sequence homology based interface predictor PS-HomPPI. We compare, on a data set of 69 docked cases with 54,000 decoys per case, the ranking of conformations produced using DockRank\u27s interface similarity scoring function applied to predicted interface residues obtained from four protein interface predictors: PS-HomPPI, and three NPS interface predictors NPS-HomPPI, PRISE, and meta-PPISP, with the rankings produced by two state-of-the-art energy-based scoring functions ZRank and IRAD. Our results show that DockRank significantly outperforms these ranking methods. Our results that NPS interface predictors (homology based and machine learning-based methods) failed to select near-native conformations that are superior to those selected by DockRank (partner-specific interface prediction based), highlight the importance of the knowledge of the binding partners in using predicted interfaces to rank docked models. The application of DockRank, as a third-party scoring function without access to all the original docked models, for improving ClusPro results on two benchmark data sets of 32 and 56 test cases shows the viability of combining our scoring function with existing docking software. An online implementation of DockRank is available at http://einstein.cs.iastate.edu/DockRank/

Structure-based Prediction of Protein-protein Interaction Networks across Proteomes

Protein-protein interactions (PPIs) orchestrate virtually all cellular processes, therefore, their exhaustive exploration is essential for the comprehensive understanding of cellular networks. Significant efforts have been devoted to expand the coverage of the proteome-wide interaction space at molecular level. A number of experimental techniques have been developed to discover PPIs, however these approaches have some limitations such as the high costs and long times of experiments, noisy data sets, and often high false positive rate and inter-study discrepancies. Given experimental limitations, computational methods are increasingly becoming important for detection and structural characterization of PPIs. In that regard, we have developed a novel pipeline for high-throughput PPI prediction based on all-to-all rigid body docking of protein structures. We focus on two questions, ‘how do proteins interact?’ and ‘which proteins interact?’. The method combines molecular modeling, structural bioinformatics, machine learning, and functional annotation data to answer these questions and it can be used for genome-wide molecular reconstruction of protein-protein interaction networks. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Further, we validated our method against a few human pathways. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques

Scheduling and Tuning Kernels for High-performance on Heterogeneous Processor Systems

Accelerated parallel computing techniques using devices such as GPUs and Xeon Phis (along with CPUs) have proposed promising solutions of extending the cutting edge of high-performance computer systems. A significant performance improvement can be achieved when suitable workloads are handled by the accelerator. Traditional CPUs can handle those workloads not well suited for accelerators. Combination of multiple types of processors in a single computer system is referred to as a heterogeneous system. This dissertation addresses tuning and scheduling issues in heterogeneous systems. The first section presents work on tuning scientific workloads on three different types of processors: multi-core CPU, Xeon Phi massively parallel processor, and NVIDIA GPU; common tuning methods and platform-specific tuning techniques are presented. Then, analysis is done to demonstrate the performance characteristics of the heterogeneous system on different input data. This section of the dissertation is part of the GeauxDock project, which prototyped a few state-of-art bioinformatics algorithms, and delivered a fast molecular docking program. The second section of this work studies the performance model of the GeauxDock computing kernel. Specifically, the work presents an extraction of features from the input data set and the target systems, and then uses various regression models to calculate the perspective computation time. This helps understand why a certain processor is faster for certain sets of tasks. It also provides the essential information for scheduling on heterogeneous systems. In addition, this dissertation investigates a high-level task scheduling framework for heterogeneous processor systems in which, the pros and cons of using different heterogeneous processors can complement each other. Thus a higher performance can be achieve on heterogeneous computing systems. A new scheduling algorithm with four innovations is presented: Ranked Opportunistic Balancing (ROB), Multi-subject Ranking (MR), Multi-subject Relative Ranking (MRR), and Automatic Small Tasks Rearranging (ASTR). The new algorithm consistently outperforms previously proposed algorithms with better scheduling results, lower computational complexity, and more consistent results over a range of performance prediction errors. Finally, this work extends the heterogeneous task scheduling algorithm to handle power capping feature. It demonstrates that a power-aware scheduler significantly improves the power efficiencies and saves the energy consumption. This suggests that, in addition to performance benefits, heterogeneous systems may have certain advantages on overall power efficiency

PKD1 3D Structure Model and Docking Studies for New PKD Inhibitors

Protein kinase Ds (PKDs) are diacylglycerol (DAG)-regulated serine/threonine protein kinases. In intact cells, PKDs are key mediators in cellular processes pertaining to multiple diseases, including cancer, heart diseases, angiogenesis and immune dysfunctions. A number of the novel, potent, and structurally diverse ATP-competitive PKD inhibitors have been reported to selectively modulate the PKD activity and thus, to achieve a potential therapeutic effect on related diseases. Due to a lack of the crystal structure, we have constructed a 3D structure of the human PKD1 protein by using homology modeling. Then, by using our established protein docking protocol, we docked novel PKD inhibitory small molecules and found the hit compounds exhibiting higher binding scores with reasonable binding mode in comparison with the reported active PKD1 inhibitors. Also, we calculated both 2D and 3D molecular similarity between our identified compounds and previously reported PKD1 inhibitors. Moreover, we predicted the possible off-targets of our compounds and our prediction has been validated through a topomer similarity study. In this study, we demonstrated that computational tools, i.e., docking and molecular similarity calculation can be applied to explore the PKD1/inhibitor interactions. In addition, the docking studies and the detailed docking poses provide insight for better understanding of the possible mechanism of a bioactive PKD1 inhibitor in order to guide future optimization for new drug design and discovery

The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement

Exhaustive exploration of molecular interactions at the level of complete proteomes requires efficient and reliable computational approaches to protein function inference. Ligand docking and ranking techniques show considerable promise in their ability to quantify the interactions between proteins and small molecules. Despite the advances in the development of docking approaches and scoring functions, the genome-wide application of many ligand docking/screening algorithms is limited by the quality of the binding sites in theoretical receptor models constructed by protein structure prediction. In this study, we describe a new template-based method for the local refinement of ligand-binding regions in protein models using remotely related templates identified by threading. We designed a Support Vector Regression (SVR) model that selects correct binding site geometries in a large ensemble of multiple receptor conformations. The SVR model employs several scoring functions that impose geometrical restraints on the Cα positions, account for the specific chemical environment within a binding site and optimize the interactions with putative ligands. The SVR score is well correlated with the RMSD from the native structure; in 47% (70%) of the cases, the Pearson\u27s correlation coefficient is \u3e0.5 (\u3e0.3). When applied to weakly homologous models, the average heavy atom, local RMSD from the native structure of the top-ranked (best of top five) binding site geometries is 3.1. Å (2.9. Å) for roughly half of the targets; this represents a 0.1 (0.3). Å average improvement over the original predicted structure. Focusing on the subset of strongly conserved residues, the average heavy atom RMSD is 2.6. Å (2.3. Å). Furthermore, we estimate the upper bound of template-based binding site refinement using only weakly related proteins to be ∼2.6. Å RMSD. This value also corresponds to the plasticity of the ligand-binding regions in distant homologues. The Binding Site Refinement (BSR) approach is available to the scientific community as a web server that can be accessed at http://cssb.biology.gatech.edu/bsr/. © 2010 Elsevier Inc