Parallel evolution strategy for protein threading.

A protein-sequence folds into a specific shape in order to function in its aqueous state. If the primary sequence of a protein is given, what is its three dimensional structure? This is a long-standing problem in the field of molecular biology and it has large implication to drug design and cure. Among several proposed approaches, protein threading represents one of the most promising technique. The protein threading problem (PTP) is the problem of determining the three-dimensional structure of a given but arbitrary protein sequence from a set of known structures of other proteins. This problem is known to be NP-hard and current computational approaches to threading are time-consuming and data-intensive. In this thesis, we proposed an evolution strategy (ES) based approach for protein threading (EST). We also developed two parallel approaches for the PTP problem and both are parallelizations of our novel EST. The first method, we call SQST-PEST (Single Query Single Template Parallel EST) threads a single query against a single template. We use ES to find the best alignment between the query and the template, and ES is parallelized. The second method, we call SQMT-PEST (Single Query Multiple Templates Parallel EST) to allow for threading a single query against multiple templates within reasonable time. We obtained better results than current comparable approaches, as well as significant reduction in execution time.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .I85. Source: Masters Abstracts International, Volume: 44-03, page: 1403. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Empirical Potential Function for Simplified Protein Models: Combining Contact and Local Sequence-Structure Descriptors

An effective potential function is critical for protein structure prediction and folding simulation. Simplified protein models such as those requiring only $C_\alpha$ or backbone atoms are attractive because they enable efficient search of the conformational space. We show residue specific reduced discrete state models can represent the backbone conformations of proteins with small RMSD values. However, no potential functions exist that are designed for such simplified protein models. In this study, we develop optimal potential functions by combining contact interaction descriptors and local sequence-structure descriptors. The form of the potential function is a weighted linear sum of all descriptors, and the optimal weight coefficients are obtained through optimization using both native and decoy structures. The performance of the potential function in test of discriminating native protein structures from decoys is evaluated using several benchmark decoy sets. Our potential function requiring only backbone atoms or $C_\alpha$ atoms have comparable or better performance than several residue-based potential functions that require additional coordinates of side chain centers or coordinates of all side chain atoms. By reducing the residue alphabets down to size 5 for local structure-sequence relationship, the performance of the potential function can be further improved. Our results also suggest that local sequence-structure correlation may play important role in reducing the entropic cost of protein folding.Comment: 20 pages, 5 figures, 4 tables. In press, Protein

Experiment Planning for Protein Structure Elucidation and Site-Directed Protein Recombination

In order to most effectively investigate protein structure and improve protein function, it is necessary to carefully plan appropriate experiments. The combinatorial number of possible experiment plans demands effective criteria and efficient algorithms to choose the one that is in some sense optimal. This thesis addresses experiment planning challenges in two significant applications. The first part of this thesis develops an integrated computational-experimental approach for rapid discrimination of predicted protein structure models by quantifying their consistency with relatively cheap and easy experiments (cross-linking and site-directed mutagenesis followed by stability measurement). In order to obtain the most information from noisy and sparse experimental data, rigorous Bayesian frameworks have been developed to analyze the information content. Efficient algorithms have been developed to choose the most informative, least expensive, and most robust experiments. The effectiveness of this approach has been demonstrated using existing experimental data as well as simulations, and it has been applied to discriminate predicted structure models of the pTfa chaperone protein from bacteriophage lambda. The second part of this thesis seeks to choose optimal breakpoint locations for protein engineering by site-directed recombination. In order to increase the possibility of obtaining folded and functional hybrids in protein recombination, it is necessary to retain the evolutionary relationships among amino acids that determine protein stability and functionality. A probabilistic hypergraph model has been developed to model these relationships, with edge weights representing their statistical significance derived from database and a protein family. The effectiveness of this model has been validated by showing its ability to distinguish functional hybrids from non-functional ones in existing experimental data. It has been proved to be NP-hard in general to choose the optimal breakpoint locations for recombination that minimize the total perturbation to these relationships, but exact and approximate algorithms have been developed for a number of important cases

Protein Threading for Genome-Scale Structural Analysis

Protein structure prediction is a necessary tool in the field of bioinformatic analysis. It is a non-trivial process that can add a great deal of information to a genome annotation. This dissertation deals with protein structure prediction through the technique of protein fold recognition and outlines several strategies for the improvement of protein threading techniques. In order to improve protein threading performance, this dissertation begins with an outline of sequence/structure alignment energy functions. A technique called Violated Inequality Minimization is used to quickly adapt to the changing energy landscape as new energy functions are added. To continue the improvement of alignment accuracy and fold recognition, new formulations of energy functions are used for the creation of the sequence/structure alignment. These energies include a formulation of a gap penalty which is dependent on sequence characteristics different from the traditional constant penalty. Another proposed energy is dependent on conserved structural patterns found during threading. These structural patterns have been employed to refine the sequence/structure alignment in my research. The section on Linear Programming Algorithm for protein structure alignment deals with the optimization of an alignment using additional residue-pair energy functions. In the original version of the model, all cores had to be aligned to the target sequence. Our research outlines an expansion of the original threading model which allows for a more flexible alignment by allowing core deletions. Aside from improvements in fold recognition and alignment accuracy, there is also a need to ensure that these techniques can scale for the computational demands of genome level structure prediction. A heuristic decision making processes has been designed to automate the classification and preparation of proteins for prediction. A graph analysis has been applied to the integration of different tools involved in the pipeline. Analysis of the data dependency graph allows for automatic parallelization of genome structure prediction. These different contributions help to improve the overall performance of protein threading and help distribute computations across a large set of computers to help make genome scale protein structure prediction practically feasible

Graph algorithms for NMR resonance assignment and cross-link experiment planning

The study of three-dimensional protein structures produces insights into protein function at the molecular level. Graphs provide a natural representation of protein structures and associated experimental data, and enable the development of graph algorithms to analyze the structures and data. This thesis develops such graph representations and algorithms for two novel applications: structure-based NMR resonance assignment and disulfide cross-link experiment planning for protein fold determination. The first application seeks to identify correspondences between spectral peaks in NMR data and backbone atoms in a structure (from x-ray crystallography or homology modeling), by computing correspondences between a contact graph representing the structure and an analogous but very noisy and ambiguous graph representing the data. The assignment then supports further NMR studies of protein dynamics and protein-ligand interactions. A hierarchical grow-and-match algorithm was developed for smaller assignment problems, ensuring completeness of assignment, while a random graph approach was developed for larger problems, provably determining unique matches in polynomial time with high probability. Test results show that our algorithms are robust to typical levels of structural variation, noise, and missings, and achieve very good overall assignment accuracy. The second application aims to rapidly determine the overall organization of secondary structure elements of a target protein by probing it with a set of planned disulfide cross-links. A set of informative pairs of secondary structure elements is selected from graphs representing topologies of predicted structure models. For each pair in this ``fingerprint\u27\u27, a set of informative disulfide probes is selected from graphs representing residue proximity in the models. Information-theoretic planning algorithms were developed to maximize information gain while minimizing experimental complexity, and Bayes error plan assessment frameworks were developed to characterize the probability of making correct decisions given experimental data. Evaluation of the approach on a number of structure prediction case studies shows that the optimized plans have low risk of error while testing only a very small portion of the quadratic number of possible cross-link candidates

Interior-point methods for minimization of potential energy functions of polypeptides

Master'sMASTER OF ENGINEERIN

Binding site matching in rational drug design: Algorithms and applications

© 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved. Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning

Graphics Processing Unit Accelerated Coarse-Grained Protein-Protein Docking

Graphics processing unit (GPU) architectures are increasingly used for general purpose computing, providing the means to migrate algorithms from the SISD paradigm, synonymous with CPU architectures, to the SIMD paradigm. Generally programmable commodity multi-core hardware can result in significant speed-ups for migrated codes. Because of their computational complexity, molecular simulations in particular stand to benefit from GPU acceleration. Coarse-grained molecular models provide reduced complexity when compared to the traditional, computationally expensive, all-atom models. However, while coarse-grained models are much less computationally expensive than the all-atom approach, the pairwise energy calculations required at each iteration of the algorithm continue to cause a computational bottleneck for a serial implementation. In this work, we describe a GPU implementation of the Kim-Hummer coarse-grained model for protein docking simulations, using a Replica Exchange Monte-Carlo (REMC) method. Our highly parallel implementation vastly increases the size- and time scales accessible to molecular simulation. We describe in detail the complex process of migrating the algorithm to a GPU as well as the effect of various GPU approaches and optimisations on algorithm speed-up. Our benchmarking and profiling shows that the GPU implementation scales very favourably compared to a CPU implementation. Small reference simulations benefit from a modest speedup of between 4 to 10 times. However, large simulations, containing many thousands of residues, benefit from asynchronous GPU acceleration to a far greater degree and exhibit speed-ups of up to 1400 times. We demonstrate the utility of our system on some model problems. We investigate the effects of macromolecular crowding, using a repulsive crowder model, finding our results to agree with those predicted by scaled particle theory. We also perform initial studies into the simulation of viral capsids assembly, demonstrating the crude assembly of capsid pieces into a small fragment. This is the first implementation of REMC docking on a GPU, and the effectuate speed-ups alter the tractability of large scale simulations: simulations that otherwise require months or years can be performed in days or weeks using a GPU