Configurational‐bias sampling technique for predicting side‐chain conformations in proteins

Prediction of side‐chain conformations is an important component of several biological modeling applications. In this work, we have developed and tested an advanced Monte Carlo sampling strategy for predicting side‐chain conformations. Our method is based on a cooperative rearrangement of atoms that belong to a group of neighboring side‐chains. This rearrangement is accomplished by deleting groups of atoms from the side‐chains in a particular region, and regrowing them with the generation of trial positions that depends on both a rotamer library and a molecular mechanics potential function. This method allows us to incorporate flexibility about the rotamers in the library and explore phase space in a continuous fashion about the primary rotamers. We have tested our algorithm on a set of 76 proteins using the all‐atom AMBER99 force field and electrostatics that are governed by a distance‐dependent dielectric function. When the tolerance for correct prediction of the dihedral angles is a <20° deviation from the native state, our prediction accuracies for χ1 are 83.3% and for χ1 and χ2 are 65.4%. The accuracies of our predictions are comparable to the best results in the literature that often used Hamiltonians that have been specifically optimized for side‐chain packing. We believe that the continuous exploration of phase space enables our method to overcome limitations inherent with using discrete rotamers as trials.

[1]  Thomas Lengauer,et al.  FlexE: efficient molecular docking considering protein structure variations. , 2001, Journal of molecular biology.

[2]  Z. Xiang,et al.  On the role of the crystal environment in determining protein side-chain conformations. , 2002, Journal of molecular biology.

[3]  D. Benjamin Gordon,et al.  Exact rotamer optimization for protein design , 2003, J. Comput. Chem..

[4]  N. Grishin,et al.  Side‐chain modeling with an optimized scoring function , 2002, Protein science : a publication of the Protein Society.

[5]  Fernando A. Escobedo,et al.  A configurational-bias approach for the simulation of inner sections of linear and cyclic molecules , 2000 .

[6]  Jeanmarie Guenot,et al.  Variability of conformations at crystal contacts in BPTI represent true low‐energy structures: Correspondence among lattice packing and molecular dynamics structures , 1992, Proteins.

[7]  D. Case,et al.  Exploring protein native states and large‐scale conformational changes with a modified generalized born model , 2004, Proteins.

[8]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[9]  Adrian A Canutescu,et al.  Access the most recent version at doi: 10.1110/ps.03154503 References , 2003 .

[10]  I Lasters,et al.  All in one: a highly detailed rotamer library improves both accuracy and speed in the modelling of sidechains by dead-end elimination. , 1997, Folding & design.

[11]  Roland L. Dunbrack,et al.  Backbone-dependent rotamer library for proteins. Application to side-chain prediction. , 1993, Journal of molecular biology.

[12]  J. Ilja Siepmann,et al.  Self-Adapting Fixed-End-Point Configurational-Bias Monte Carlo Method for the Regrowth of Interior Segments of Chain Molecules with Strong Intramolecular Interactions , 2000 .

[13]  D. Benjamin Gordon,et al.  Radical performance enhancements for combinatorial optimization algorithms based on the dead-end elimination theorem , 1998, Journal of Computational Chemistry.

[14]  S. L. Mayo,et al.  De novo protein design: fully automated sequence selection. , 1997, Science.

[15]  M. Levitt,et al.  Accuracy of side‐chain prediction upon near‐native protein backbones generated by ab initio folding methods , 1998, Proteins.

[16]  Z. Xiang,et al.  Extending the accuracy limits of prediction for side-chain conformations. , 2001, Journal of molecular biology.

[17]  A Joshua Wand,et al.  Improved side‐chain prediction accuracy using an ab initio potential energy function and a very large rotamer library , 2004, Protein science : a publication of the Protein Society.

[18]  Juan J. de Pablo,et al.  Extended continuum configurational bias Monte Carlo methods for simulation of flexible molecules , 1995 .

[19]  Kumar,et al.  Determination of the chemical potentials of polymeric systems from Monte Carlo simulations. , 1991, Physical review letters.

[20]  J. Richardson,et al.  Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. , 1999, Journal of molecular biology.

[21]  L. Looger,et al.  Computational design of receptor and sensor proteins with novel functions , 2003, Nature.

[22]  O. Schueler‐Furman,et al.  Improved side‐chain modeling for protein–protein docking , 2005, Protein science : a publication of the Protein Society.

[23]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[24]  Roland L. Dunbrack,et al.  Bayesian statistical analysis of protein side‐chain rotamer preferences , 1997, Protein science : a publication of the Protein Society.

[25]  D. van der Spoel,et al.  GROMACS: A message-passing parallel molecular dynamics implementation , 1995 .

[26]  I Lasters,et al.  Enhanced dead-end elimination in the search for the global minimum energy conformation of a collection of protein side chains. , 1995, Protein engineering.

[27]  Gregory D. Hawkins,et al.  Parametrized Models of Aqueous Free Energies of Solvation Based on Pairwise Descreening of Solute Atomic Charges from a Dielectric Medium , 1996 .

[28]  George A. Kaminski,et al.  Force Field Validation Using Protein Side Chain Prediction , 2002 .

[29]  J R Desjarlais,et al.  De novo design of the hydrophobic cores of proteins , 1995, Protein science : a publication of the Protein Society.

[30]  John R Desjarlais,et al.  A de novo redesign of the WW domain , 2003, Protein science : a publication of the Protein Society.

[31]  Peter A. Kollman,et al.  AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules , 1995 .

[32]  M. Vásquez,et al.  Modeling side-chain conformation. , 1996, Current opinion in structural biology.

[33]  P. Harbury,et al.  Automated design of specificity in molecular recognition , 2003, Nature Structural Biology.

[34]  M Karplus,et al.  Side-chain torsional potentials: effect of dipeptide, protein, and solvent environment. , 1979, Biochemistry.

[35]  Viktor Hornak,et al.  Using PC clusters to evaluate the transferability of molecular mechanics force fields for proteins , 2003, J. Comput. Chem..

[36]  J. Pablo,et al.  Monte Carlo Simulation of Free-Standing Polymer Films near the Glass Transition Temperature , 2002 .

[37]  D B Gordon,et al.  Branch-and-terminate: a combinatorial optimization algorithm for protein design. , 1999, Structure.

[38]  J. K. Hwang,et al.  Side-chain prediction by neural networks and simulated annealing optimization. , 1995, Protein engineering.

[39]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[40]  R. Cracknell,et al.  Rotational insertion bias: a novel method for simulating dense phases of structured particles, with particular application to water , 1990 .

[41]  M. Deem,et al.  Analytical rebridging Monte Carlo: Application to cis/trans isomerization in proline-containing, cyclic peptides , 1999, physics/9904057.

[42]  C. Sander,et al.  Fast and simple monte carlo algorithm for side chain optimization in proteins: Application to model building by homology , 1992, Proteins.

[43]  M Karplus,et al.  Protein sidechain conformer prediction: a test of the energy function. , 1998, Folding & design.

[44]  Martin Zacharias,et al.  Protein–protein docking with a reduced protein model accounting for side‐chain flexibility , 2003, Protein science : a publication of the Protein Society.

[45]  Juan J. de Pablo,et al.  A biased Monte Carlo technique for calculation of the density of states of polymer films , 2002 .

[46]  Daan Frenkel,et al.  Configurational bias Monte Carlo: a new sampling scheme for flexible chains , 1992 .

[47]  Ronald M. Levy,et al.  The SGB/NP hydration free energy model based on the surface generalized born solvent reaction field and novel nonpolar hydration free energy estimators , 2002, J. Comput. Chem..

[48]  R. Abagyan,et al.  Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. , 1994, Journal of molecular biology.

[49]  Jeffrey J. Gray,et al.  Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. , 2003, Journal of molecular biology.

[50]  D. Baker,et al.  Design of a Novel Globular Protein Fold with Atomic-Level Accuracy , 2003, Science.

[51]  Johan Desmet,et al.  The dead-end elimination theorem and its use in protein side-chain positioning , 1992, Nature.

[52]  L L Looger,et al.  Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics. , 2001, Journal of molecular biology.

[53]  R. Lavery,et al.  A new approach to the rapid determination of protein side chain conformations. , 1991, Journal of biomolecular structure & dynamics.

[54]  R. Abagyan,et al.  Soft protein–protein docking in internal coordinates , 2002, Protein science : a publication of the Protein Society.

[55]  W F van Gunsteren,et al.  Computer simulation as a tool for tracing the conformational differences between proteins in solution and in the crystalline state. , 1984, Journal of molecular biology.

[56]  J. Mendes,et al.  Improved modeling of side‐chains in proteins with rotamer‐based methods: A flexible rotamer model , 1999, Proteins.

[57]  Roland L. Dunbrack,et al.  Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool. , 1997, Journal of molecular biology.