Machine Learning approaches for Contact Maps prediction in CASP 9 experiment

Residue contact maps are bi-dimensional data structures that encode the three-dimensional structure of a protein by storing the presence of contacts among protein backbone residues. Contact maps have a key role in most state-of-the-art protein structure prediction pipelines, i.e. the prediction of three dimensional space conformation of aminoacids composing the proteins. We have designed a system (XXStoutbeta) for the prediction of residue contact maps from the sequence of amino acids composing the protein. The system is based on Recursive Neural Networks, which are capable of learning an input-output mapping from sets of examples. Moreover data structures and loading/unloading algorithms have been designed for efficiently managing contact maps in primary and secondary memory. XXStout-beta has performed well at the latest CASP9 world-wide protein structure prediction competition, and is integrated in the public, high-throughput structure prediction server Distill. In this paper we present informally results of XXStout-beta in CASP9 competition. 1 Contact Map prediction In Nature, the process of protein folding is observed when a protein is synthesized in the cell. Although many factors may facilitate the folding process, the shape of a protein, with only limited exceptions is directly determined by its amino acid sequence, i.e. one can assume that the map between the sequence and the structure is a function. Finding this function is the so called Protein Folding Problem (PFP). PFP has been an hot topic for decades among a vast community of scientists. This has been tackled in various ways, ranging from physical simulations to knowledge-based methods such as machine learning. In the latter case one needs to observe a (sufficiently large) number of known protein structures and try to understand/learn the laws behind the folding process, or more simply the map between sequences and structure, either in part (e.g. sequence to secondary structure) or as a whole. Solving the PFP computationally 3 http://distill.ucd. ie/distill/ is appealing, as experimental determination of the structure is a complex, timeconsuming and expensive process, and as a consequence as of 2011 we only know the structures of approximately 70,000 proteins, or approximately one every 200 proteins for which we have revealed the sequence. Every two years the CASP experiment challenges computational folding methods to predict the (then unknown) structure of several tens of proteins. Among other categories, CASP assesses the prediction of protein residue contact maps, i.e. the set of mutual distances between residues in a protein, quantised into two states (contact, for distances smaller than a threshold, and non-contact otherwise). Correct contact maps have been shown to be lead to reasonably good 3D structures [5, 6], and predicted contact maps have been used for driving protein folding in the ab initio case (that is, when a protein is folded without relying on homology to another protein of known structure), for selecting and ranking folded protein models, for predicting folding times, protein domain boundaries, secondary structure, etc. We have designed a novel predictor of protein residue contact maps. The predictor exploits a diverse, complex set of inputs, including the residue sequence, evolutionary information in the form of a profile of residue frequencies extracted from a multiple sequence alignment of homologous proteins (of unknown structure), predicted secondary structure, solvent accessibility and contact density and, most importantly, near and remote structural templates (when available) obtained by various methods. The predictor has two types of outputs: a simple contact/non-contact binary classification (as per CASP rules); a 4-class distance map. The latter output is used as a constraint to reconstruct 3-dimensional protein structures. 2 Neural Networks for Prediction