Refinement of protein termini in template‐based modeling using conformational space annealing

The rapid increase in the number of experimentally determined protein structures in recent years enables us to obtain more reliable protein tertiary structure models than ever by template‐based modeling. However, refinement of template‐based models beyond the limit available from the best templates is still needed for understanding protein function in atomic detail. In this work, we develop a new method for protein terminus modeling that can be applied to refinement of models with unreliable terminus structures. The energy function for terminus modeling consists of both physics‐based and knowledge‐based potential terms with carefully optimized relative weights. Effective sampling of both the framework and terminus is performed using the conformational space annealing technique. This method has been tested on a set of termini derived from a nonredundant structure database and two sets of termini from the CASP8 targets. The performance of the terminus modeling method is significantly improved over our previous method that does not employ terminus refinement. It is also comparable or superior to the best server methods tested in CASP8. The success of the current approach suggests that similar strategy may be applied to other types of refinement problems such as loop modeling or secondary structure rearrangement. Proteins 2011; © 2011 Wiley‐Liss, Inc.

[1]  Keehyoung Joo,et al.  All‐atom chain‐building by optimizing MODELLER energy function using conformational space annealing , 2009, Proteins.

[2]  Keehyoung Joo,et al.  High accuracy template based modeling by global optimization , 2007, Proteins.

[3]  David Baker,et al.  Protein Structure Prediction Using Rosetta , 2004, Numerical Computer Methods, Part D.

[4]  Michael Levitt,et al.  Near-native structure refinement using in vacuo energy minimization , 2007, Proceedings of the National Academy of Sciences.

[5]  Arne Elofsson,et al.  Identification of correct regions in protein models using structural, alignment, and consensus information , 2006, Protein science : a publication of the Protein Society.

[6]  Torsten Schwede,et al.  Assessment of CASP7 predictions for template‐based modeling targets , 2007, Proteins.

[7]  Roland L. Dunbrack,et al.  proteins STRUCTURE O FUNCTION O BIOINFORMATICS Improved prediction of protein side-chain conformations with SCWRL4 , 2022 .

[8]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[9]  Yang Zhang,et al.  Scoring function for automated assessment of protein structure template quality , 2004, Proteins.

[10]  F. Sherman,et al.  N-terminal acetyltransferases and sequence requirements for N-terminal acetylation of eukaryotic proteins. , 2003, Journal of molecular biology.

[11]  Guoli Wang,et al.  PISCES: a protein sequence culling server , 2003, Bioinform..

[12]  Jianlin Cheng,et al.  MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8 , 2010, Bioinform..

[13]  D. Baker,et al.  Modeling structurally variable regions in homologous proteins with rosetta , 2004, Proteins.

[14]  Masaki Sasai,et al.  A coarse-grained Langevin molecular dynamics approach to de novo protein structure prediction. , 2008, Biochemical and biophysical research communications.

[15]  Li Xie,et al.  Structural refinement of protein segments containing secondary structure elements: Local sampling, knowledge‐based potentials, and clustering , 2006, Proteins.

[16]  A. Liwo,et al.  Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Oliver F. Lange,et al.  Structure prediction for CASP8 with all‐atom refinement using Rosetta , 2009, Proteins.

[18]  Hongyi Zhou,et al.  Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction , 2002, Protein science : a publication of the Protein Society.

[19]  G Vriend,et al.  Completion and refinement of 3‐D homology models with restricted molecular dynamics: Application to targets 47, 58, and 111 in the CASP modeling competition and posterior analysis , 2002, Proteins.

[20]  Barry Honig,et al.  Loop modeling: Sampling, filtering, and scoring , 2007, Proteins.

[21]  J. Skolnick,et al.  TOUCHSTONE II: a new approach to ab initio protein structure prediction. , 2003, Biophysical journal.

[22]  Christopher J. Williams,et al.  The other 90% of the protein: Assessment beyond the Cαs for CASP8 template‐based and high‐accuracy models , 2009, Proteins.

[23]  Yang Zhang,et al.  I-TASSER server for protein 3D structure prediction , 2008, BMC Bioinformatics.

[24]  Yang Zhang,et al.  I‐TASSER: Fully automated protein structure prediction in CASP8 , 2009, Proteins.

[25]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[26]  B. Honig,et al.  A hierarchical approach to all‐atom protein loop prediction , 2004, Proteins.

[27]  A. Sali,et al.  Statistical potential for assessment and prediction of protein structures , 2006, Protein science : a publication of the Protein Society.

[28]  Y. Hanyu,et al.  Functional diversity of protein C-termini: more than zipcoding? , 2002, Trends in cell biology.

[29]  Yaoqi Zhou,et al.  Specific interactions for ab initio folding of protein terminal regions with secondary structures , 2008, Proteins.

[30]  Burkhard Rost,et al.  Evaluation of template‐based models in CASP8 with standard measures , 2009, Proteins.

[31]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[32]  Hao Fan,et al.  Refinement of homology‐based protein structures by molecular dynamics simulation techniques , 2004, Protein science : a publication of the Protein Society.

[33]  Michael Levitt,et al.  Consistent refinement of submitted models at CASP using a knowledge‐based potential , 2010, Proteins.

[34]  T. Südhof,et al.  Three-Dimensional Structure of the Synaptotagmin 1 C2B-Domain Synaptotagmin 1 as a Phospholipid Binding Machine , 2001, Neuron.

[35]  William H. Press,et al.  Numerical recipes , 1990 .

[36]  K. Dill,et al.  Assessment of the protein‐structure refinement category in CASP8 , 2009, Proteins.

[37]  P. Bradley,et al.  High-resolution structure prediction and the crystallographic phase problem , 2007, Nature.

[38]  Hongyi Zhou,et al.  What is a desirable statistical energy functions for proteins and how can it be obtained? , 2007, Cell Biochemistry and Biophysics.

[39]  Chaok Seok,et al.  Protein loop modeling by using fragment assembly and analytical loop closure , 2010, Proteins.

[40]  Keehyoung Joo,et al.  Multiple sequence alignment by conformational space annealing. , 2008, Biophysical journal.

[41]  Yaoqi Zhou,et al.  Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all‐atom statistical energy functions , 2008, Protein science : a publication of the Protein Society.

[42]  M Feig,et al.  Accurate reconstruction of all‐atom protein representations from side‐chain‐based low‐resolution models , 2000, Proteins.