The wfCPUNK Branch
Prediction of protein structure with the UNRES force field aided by secondary-structure and contact prediction
A.G. Lipska1, P. Krupa2, M.A. Mozolewska2, A.K. Sieradzan1, C. Kieslich3, M. Onel3, U. Shah3, Y. He2, Y. Yin2, Ł. Golon1, R. Ślusarz1, M. Ślusarz1, A. Liwo1, C.A. Floudas3, H.A. Scheraga2 and S.N. Crivelli4*
1 - Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland, 2 -Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, 14853-1301, U.S.A., 3 – Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3372; USA, 4 – Department of Computer Science, UC Davis, One Shields Ave., Davis, CA 95616, USA
Physics-based methods for protein-structure prediction are attractive because of their independence of structural databases; however, the accuracy of the existing force fields is not yet good enough for these methods to be used in a standalone mode. As part of the WeFold project, in CASP12 we explored the use of contact-prediction and secondary-structure-prediction information together with the coarse-grained physics-based united-residue (UNRES) model for polypeptide chains1 in simulations. Owing to the simplicity of UNRES, it can perform de novo simulations of the folding of large proteins and, at the same time, it is closely connected to the physics of interactions.
In the UNRES model , a polypeptide chain is represented by a sequence of alpha-carbon atoms connected by virtual bonds with attached united side chains and united peptide groups positioned halfway between the consecutive C(alpha) atoms. The UNRES force field used in the current CASP experiment was optimized by using the maximum-likelihood approach to force field calibration that we developed recently .
In the first step of the prediction, coarse-grained simulations with the UNRES force field, with dihedral-angle and distance restraints determined as described below were employed to carry out Multiplexed Replica Exchange Molecular Dynamics (MREMD) . The conformational ensemble was determined with the aid of the weighted histogram analysis method (WHAM) and dissected into 5 clusters whose average conformations constituted the candidate predictions in the coarse-grained representation . The obtained coarse-grained structures were converted to all-atom structures and refined by doing short MD runs with the AMBER12 force field.
Secondary structure prediction was performed using conSSert , a consensus SVM model that utilizes as features the probabilities for coil, helix, and strand as predicted by PSSPRED , PSIPRED , RAPTORX , and SPINE-X . The secondary structure predictions from conSSert were used to search a structural template library, using a modified implementation of HHsuite . The prediction of tertiary contacts was based on tertiary contacts extracted from the structural templates identified by conSSert/HHsuite, and two types of contact predictions were generated: general tertiary contacts and beta-sheet topology contacts. For the prediction of general tertiary C(beta) contacts, Delaunay triangulation was applied to identify C(beta) contacts in each identified structural template, and a consensus score based on the template probability from HHsuite was used to rank observed contacts. The beta-sheet topology method takes as input secondary structure and template-based tertiary contact predictions and utilizes two MILP models: (i) a MILP model for strand pair alignment, and (ii) a MILP model for beta-sheet topology that utilizes constraints to provide physically relevant topologies.
We postpone the assessment of the approach until the official release of CASP12 results.
The UNRES package is available at www.unres.pl.
1. Liwo,A., Czaplewski,C., Ołdziej,S., Rojas,A.V., Kaźmierkiewicz,R., Makowski,M., Murarka, R.K. & Scheraga,H.A. (2008) Simulation of protein structure and dynamics with the coarse-grained UNRES force field. ed. G. Voth, Taylor & Francis, Chapter 8, pp. 107-122.
2. Zaborowski,B., Jagieła,D., Czaplewski,C., Hałabis,A., Lewandowska,A., Żmudzińska,W., Ołdziej,S., Karczyńska,A., Omieczynski,C., Wirecki,T. & Liwo.A. (2015) A maximum-likelihood approach to force-field calibration. J. Chem. Inf. Model. 55, 2050-2070.
3. Czaplewski,C., Kalinowski,S. & Liwo,A., Scheraga,H.A. (2009) Application of multiplexed replica exchange molecular dynamics to the UNRES force field: Tests with α and α+β proteins. J Chem. Theory Comput. 5, 627-640.
4. Liwo,A., Khalili,M., Czaplewski,C., Kalinowski,S., Ołdziej,S., Wachucik,K. & Scheraga,H.A. (2007) Modification and optimization of the united-residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins. J. Pys. Chem. B 111, 260-285.
5. Kieslich,C.A., Smadbeck,J., Khoury,G.A. & Floudas,C.A. (2015) conSSert: Consensus SVM models for accurate prediction of ordered secondary structure. J. Chem. Inf. Modeling, 56, 455-461.
6. Zhang, Y. http://zhanglab.ccmb.med.umich.edu/PSSpred.
7. McGuffin, L.J., Bryson, K. & Jones, D.T. (2000) The PSIPRED protein structure prediction server. Bioinformatics, 16, 404-405.
8. Wang,Z., Zhao,F., Peng,J. & Xu,J. (2011) Protein 8-class secondary structure prediction using conditional neural fields. Proteomics, 11, 3786-3792.
9. Faraggi,E., Zhang,T., Yang,Y., Kurgan,L. & Zhou,Y. (2012) SPINE X: Improving protein secondary structure prediction by multi-step learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comp Chem, 33, 259-67.
10. Söding,J. Protein homology detection by HMM-HMM comparison. Bioinformatics 2005, 21: 951-960.