Analysis and prediction of loop segments in protein structures

The accurate modeling of loop segments in proteins is an important component of the overall protein folding problem. The challenge of the protein folding problem is to understand and predict the formation of the native three-dimensional structure of a protein given its primary amino acid sequence. In this paper, two methods are introduced to determine the structure of loop segments within the context of ASTRO-FOLD, an overall approach for the structure prediction of proteins. These approaches address a more difficult problem than that of traditional loop prediction in the sense that the separation distances between the loop stem regions are not assumed to be known a priori. When considering these additional degrees of freedom, the proposed methods perform extremely well, which is a result of both new modeling and algorithmic developments. In particular, the methods are validated on a testbed of benchmark protein systems, as well as a number of blind predictions from the recent CASP5 experiment.

[1]  Christodoulos A. Floudas,et al.  Rigorous convex underestimators for general twice-differentiable problems , 1996, J. Glob. Optim..

[2]  C. Adjiman,et al.  A global optimization method, αBB, for general twice-differentiable constrained NLPs—II. Implementation and computational results , 1998 .

[3]  C. Floudas,et al.  ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. , 2003, Biophysical journal.

[4]  G L Gilliland,et al.  Two crystal structures of the B1 immunoglobulin-binding domain of streptococcal protein G and comparison with NMR. , 1994, Biochemistry.

[5]  J. Greer,et al.  Model for haptoglobin heavy chain based upon structural homology. , 1980, Proceedings of the National Academy of Sciences of the United States of America.

[6]  John L. Klepeis,et al.  A new class of hybrid global optimization algorithms for peptide structure prediction: integrated hybrids , 2003 .

[7]  J. Deisenhofer,et al.  Crystallographic refinement of the structure of bovine pancreatic trypsin inhibitor at l.5 Å resolution , 1975 .

[8]  John L. Klepeis,et al.  Ab initio Tertiary Structure Prediction of Proteins , 2003, J. Glob. Optim..

[9]  A. Lesk,et al.  Common features of the conformations of antigen‐binding loops in immunoglobulins and application to modeling loop conformations , 1992, Proteins.

[10]  J. L. Klepeis,et al.  Predicting peptide structures using NMR data and deterministic global optimization , 1999 .

[11]  C. Floudas,et al.  A deterministic global optimization approach for molecular structure determination , 1994 .

[12]  R A Friesner,et al.  Prediction of loop geometries using a generalized born model of solvation effects , 1999, Proteins.

[13]  John L. Klepeis,et al.  Ab initio prediction of helical segments in polypeptides , 2002, J. Comput. Chem..

[14]  A. Neumaier,et al.  A global optimization method, αBB, for general twice-differentiable constrained NLPs — I. Theoretical advances , 1998 .

[15]  Christodoulos A. Floudas,et al.  Two results on bounding the roots of interval polynomials , 1999 .

[16]  T. Blundell,et al.  Conformational analysis and clustering of short and medium size loops connecting regular secondary structures: A database for modeling and prediction , 1996, Protein science : a publication of the Protein Society.

[17]  H. Scheraga,et al.  Exact analytical loop closure in proteins using polynomial equations , 1999 .

[18]  John L. Klepeis,et al.  Predicting solvated peptide conformations via global minimization of energetic atom-to-atom interactions , 1998 .

[19]  A. Sali,et al.  Modeling of loops in protein structures , 2000, Protein science : a publication of the Protein Society.

[20]  Christodoulos A. Floudas,et al.  Deterministic global optimization - theory, methods and applications , 2010, Nonconvex optimization and its applications.

[21]  J L Klepeis,et al.  Hybrid global optimization algorithms for protein structure prediction: alternating hybrids. , 2003, Biophysical journal.

[22]  C. Levinthal,et al.  Predicting antibody hypervariable loop conformation. I. Ensembles of random conformations for ringlike structures , 1987, Biopolymers.

[23]  John L. Klepeis,et al.  Free energy calculations for peptides via deterministic global optimization , 1999 .

[24]  A M Gronenborn,et al.  Solution structure, backbone dynamics and chitin binding of the anti-fungal protein from Streptomyces tendae TU901. , 2001, Journal of molecular biology.

[25]  Robert Huber,et al.  Structure of bovine pancreatic trypsin inhibitor , 1984 .

[26]  H. Scheraga,et al.  Conformational analysis of the 20 naturally occurring amino acid residues using ECEPP. , 1977, Macromolecules.

[27]  John L. Klepeis,et al.  Deterministic Global Optimization and Ab Initio Approaches for the Structure Prediction of Polypeptides, Dynamics of Protein Folding, and Protein‐Protein Interactions , 2002 .

[28]  A. Gronenborn,et al.  A novel, highly stable fold of the immunoglobulin binding domain of streptococcal protein G. , 1993, Science.

[29]  Christodoulos A. Floudas,et al.  Deterministic Global Optimization in Design, Control, and Computational Chemistry , 1997 .

[30]  Abhinandan Jain,et al.  A fast recursive algorithm for molecular dynamics simulation , 1993 .

[31]  Cinque S. Soto,et al.  Evaluating conformational free energies: The colony energy and its application to the problem of loop prediction , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Nils Tönshoff,et al.  Implementation and Computational Results , 1997 .

[33]  H. Scheraga,et al.  Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides , 1994 .