A Web Service for Protein Refinement and Refinement of Membrane Proteins

The structures obtained from homology modeling methods are of intermediate resolution 1-3Ã… from true structure. Energy minimization methods allow us to refine the proteins and obtain native like structures. Previous work shows that some of these methods performed well on soluble proteins. So we extended this work on membrane proteins. Prediction of membrane protein structures is a particularly important, since they are important biological drug targets, and since their number is vanishingly small, as a result of the inherent difficulties in working with these molecules experimentally. Hence there is a pressing need for alternative computational protein structure prediction methods. This work tests the ability of common molecular mechanics potential functions (AMBER99/03) and a hybrid knowledge-based potential function (KB_0.1) to refine near-native structures of membrane proteins in vacuo. A web based utility for protein refinement has been developed and deployed based on the KB_0.1 potential to refine proteins

A Web Service for Protein Refinement and Refinement of Membrane Proteins

The structures obtained from homology modeling methods are of intermediate resolution 1-3Ã… from true structure. Energy minimization methods allow us to refine the proteins and obtain native like structures. Previous work shows that some of these methods performed well on soluble proteins. So we extended this work on membrane proteins. Prediction of membrane protein structures is a particularly important, since they are important biological drug targets, and since their number is vanishingly small, as a result of the inherent difficulties in working with these molecules experimentally. Hence there is a pressing need for alternative computational protein structure prediction methods. This work tests the ability of common molecular mechanics potential functions (AMBER99/03) and a hybrid knowledge-based potential function (KB_0.1) to refine near-native structures of membrane proteins in vacuo. A web based utility for protein refinement has been developed and deployed based on the KB_0.1 potential to refine proteins

The Application of Hybridized Genetic Algorithms to the Protein Folding Problem

The protein folding problem consists of attempting to determine the native conformation of a protein given its primary structure. This study examines various methods of hybridizing a genetic algorithm implementation in order to minimize an energy function and predict the conformation (structure) of Met-enkephalin. Genetic Algorithms are semi-optimal algorithms designed to explore and exploit a search space. The genetic algorithm uses selection, recombination, and mutation operators on populations of strings which represent possible solutions to the given problem. One step in solving the protein folding problem is the design of efficient energy minimization techniques. A conjugate gradient minimization technique is described and tested with different replacement frequencies. Baidwinian, Lamarckian, and probabilistic Lamarckian evolution are all tested. Another extension of simple genetic algorithms can be accomplished with niching. Niching works by de-emphasizing solutions based on their proximity to other solutions in the space. Several variations of niching are tested. Experiments are conducted to determine the benefits of each hybridization technique versus each other and versus the genetic algorithm by itself. The experiments are geared toward trying to find the lowest possible energy and hence the minimum conformation of Met-enkephalin. In the experiments, probabilistic Lamarckian strategies were successful in achieving energies below that of the published minimum in QUANTA

Formulation of Hybrid Knowledge-Based/Molecular Mechanics Potentials for Protein Structure Refinement and a Novel Graph Theoretical Protein Structure Comparison and Analysis Technique

Proteins are the fundamental machinery that enables the functions of life. It is critical to understand them not just for basic biology, but also to enable medical advances. The field of protein structure prediction is concerned with developing computational techniques to predict protein structure and function from a protein’s amino acid sequence, encoded for directly in DNA, alone. Despite much progress since the first computational models in the late 1960’s, techniques for the prediction of protein structure still cannot reliably produce structures of high enough accuracy to enable desired applications such as rational drug design. Protein structure refinement is the process of modifying a predicted model of a protein to bring it closer to its native state. In this dissertation a protein structure refinement technique, that of potential energy minimization using hybrid molecular mechanics/knowledge based potential energy functions is examined in detail. The generation of the knowledge-based component is critically analyzed, and in the end, a potential that is a modest improvement over the original is presented. This dissertation also examines the task of protein structure comparison. In evaluating various protein structure prediction techniques, it is crucial to be able to compare produced models against known structures to understand how well the technique performs. A novel technique is proposed that allows an in-depth yet intuitive evaluation of the local similarities between protein structures. Based on a graph analysis of pairwise atomic distance similarities, multiple regions of structural similarity can be identified between structures independently of relative orientation. Multidomain structures can be evaluated and this technique can be combined with global measures of similarity such as the global distance test. This method of comparison is expected to have broad applications in rational drug design, the evolutionary study of protein structures, and in the analysis of the protein structure prediction effort