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Motivation. Protein contact map describes the pairwise spatial and functionalrelationship of residues in a protein and contains key information for protein3D structure prediction. Although studied extensively, it remains verychallenging to predict contact map using only sequence information. Mostexisting methods predict the contact map matrix element-by-element, ignoringcorrelation among contacts and physical feasibility of the whole contact map. Acouple of recent methods predict contact map based upon residue co-evolution,taking into consideration contact correlation and enforcing a sparsityrestraint, but these methods require a very large number of sequence homologsfor the protein under consideration and the resultant contact map may be stillphysically unfavorable.  Results. This paper presents a novel method PhyCMAP for contact mapprediction, integrating both evolutionary and physical restraints by machinelearning and integer linear programming (ILP). The evolutionary restraintsinclude sequence profile, residue co-evolution and context-specific statisticalpotential. The physical restraints specify more concrete relationship amongcontacts than the sparsity restraint. As such, our method greatly reduces thesolution space of the contact map matrix and thus, significantly improvesprediction accuracy. Experimental results confirm that PhyCMAP outperformscurrently popular methods no matter how many sequence homologs are availablefor the protein under consideration. PhyCMAP can predict contacts withinminutes after PSIBLAST search for sequence homologs is done, much faster thanthe two recent methods PSICOV and EvFold.  See http://raptorx.uchicago.edu for the web server.

Predicting protein contact map using evolutionary and physical constraints by integer programming (extended version)