The problem of understanding the relationship between protein structure and sequence is central to the areas of protein design, homologous modeling, mutation analysis and the understanding and prediction of protein folding. We have developed a computer algorithm which is able to determine the conformation of short pieces of polypeptide chain in the larger protein environment. The algorithm systematically generates all possible conformations for a piece of structure and uses discriminatory functions based on electrostatics, including solvation, and exposed non- polar area to select a correct conformation. The method has been used to determine the conformation of a set of loops in known protein structures and to choose conformations most compatible with crystallographic data. A ligand docking procedure has been developed, utilizing the electrostatic discriminatory functions. A model based on the dominance of the burial of nonpolar area in stabilizing structure has allowed the identification of small independent folding units in proteins . Analyses of steric strain and electrostatics in proteins have been carried out to aid in the development of discriminatory functions. The scope of the algorithm will be enhanced, allowing the treatment of longer sequences. Discrimination based on packing, atom overlap and residue conformational preference will be added. A conditional probability formalism, measuring the information supporting the hypothesis that a structure is correct, will be developed to allow proper integration of the different discriminatory functions. Two additional conformational search techniques, torsion space Monte Carlo and genetic algorithms will introduced. Preliminary results show that the Monte Carlo method is able to determine the conformation of independent folding units, and that genetic algorithms should be able to greatly extent the effectiveness of these conformational search methods.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Molecular and Cellular Biophysics Study Section (BBCA)
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University of MD Biotechnology Institute
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DeWeese-Scott, Carol; Moult, John (2004) Molecular modeling of protein function regions. Proteins 55:942-61
Oliva, M T; Moult, J (1999) Local electrostatic optimization in proteins. Protein Eng 12:727-35
Samudrala, R; Moult, J (1998) An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. J Mol Biol 275:895-916
Samudrala, R; Moult, J (1998) Determinants of side chain conformational preferences in protein structures. Protein Eng 11:991-7
Samudrala, R; Moult, J (1998) A graph-theoretic algorithm for comparative modeling of protein structure. J Mol Biol 279:287-302
Samudrala, R; Moult, J (1997) Handling context-sensitivity in protein structures using graph theory: bona fide prediction. Proteins Suppl 1:43-9
Pedersen, J T; Moult, J (1997) Ab initio protein folding simulations with genetic algorithms: simulations on the complete sequence of small proteins. Proteins Suppl 1:179-84
Braxenthaler, M; Unger, R; Auerbach, D et al. (1997) Chaos in protein dynamics. Proteins 29:417-25
Pedersen, J T; Moult, J (1995) Ab initio structure prediction for small polypeptides and protein fragments using genetic algorithms. Proteins 23:454-60
Braxenthaler, M; Avbelj, F; Moult, J (1995) Structure, dynamics and energetics of initiation sites in protein folding: I. Analysis of a 1 ns molecular dynamics trajectory of an early folding unit in water: the helix I/loop I-fragment of barnase. J Mol Biol 250:239-57

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