This dissertation explores the self-consistent mean field (SCMF) algorithm for protein side-chain prediction: it's improvement with a protein-dependent optimized rotamer library and it's application as a key step in protein docking. Given the conformation of the protein backbone, side-chain prediction algorithms pack the side-chains. Here side-chain prediction is presented as an optimization problem containing three major components: (1) the degrees of freedom that define the conformations that protein side-chains may assume, (2) a sampling procedure for generating possible side-chain packing and (3) a scoring function to rank these packings. We examine each of these components for ways to improve side-chain prediction.
We present an improvement to side-chain prediction that focuses on the first of the above components. Most side-chain packing algorithms constrain side-chain conformation to torsion angles found in rotamer libraries that are statistically compiled using databases of know protein structures. The resulting side-chain packings do not reflect the diversity of torsion angles found in nature. We introduce an alternative method that is free of statistics. We begin with a rotamer library that is based only upon stereochemical considerations. This rotamer library is then optimized independently for each protein under study. We show that this optimization step restores the diversity of conformations observed in native proteins. We combine this protein-specific rotamer library method with the SCMF sampling approach and a physics-based scoring function into a new side-chain prediction method, SCMF-PDRL. Using a large test set of 831 proteins, we show that this new method compares favorably with competing methods such as SCAP, OPUS-Rota, and SCWRL4.
Protein docking is a major research area in which side-chain prediction is a step. We explore the use of the SCMF algorithm in docking, proposing a docking strategy that includes three steps: (i) generate ensembles of conformations for multiple patches that span the surfaces of the two proteins considered, (ii) develop a fast method for generating candidate docked conformations of the two proteins based on these ensembles, and (iii), develop an energy function that ranks the candidate conformations such that the actual native docked structures can be identified. Focusing on the first step, we seek to generate candidates for docking that approximate the bound state well, even in cases where there is backbone and/or side-chain difference from unbound to bound states. Using a database of protein dimers for which the bound and unbound structures of the monomers are known, we show that from the unbound patch we are able to generate candidates for docking that approximate the bound structure.
|Advisor:||Koehl, Patrice A.|
|Commitee:||Amenta, Nina, Holbrook, Stephen R.|
|School:||University of California, Davis|
|School Location:||United States -- California|
|Source:||DAI-B 73/03, Dissertation Abstracts International|
|Subjects:||Molecular biology, Bioinformatics, Computer science|
|Keywords:||Computer modeling, Protein docking, Rotamer library, Self consistent mean field, Side-chain prediction|
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