MOTIVATION: Accurately predicting protein side-chain conformations is an important subproblem of the broader protein structure prediction problem. Several methods exist for generating fairly accurate models for moderate-size proteins in seconds or less. However, a major limitation of these methods is their inability to model post-translational modifications (PTMs) and unnatural amino acids. In natural living systems, the chemical groups added following translation are often critical for the function of the protein. In engineered systems, unnatural amino acids are incorporated into proteins to explore structure-function relationships and create novel proteins. RESULTS: We present a new version of SIDEpro to predict the side chains of proteins containing non-standard amino acids, including 15 of the most frequently observed PTMs in the Protein Data Bank and all types of phosphorylation. SIDEpro uses energy functions that are parameterized by neural networks trained from available data. For PTMs, the [Formula: see text] and [Formula: see text] accuracies are comparable with those obtained for the precursor amino acid, and so are the RMSD values for the atoms shared with the precursor amino acid. In addition, SIDEpro can accommodate any PTM or unnatural amino acid, thus providing a flexible prediction system for high-throughput modeling of proteins beyond the standard amino acids. AVAILABILITY AND IMPLEMENTATION: SIDEpro programs and Web server, rotamer libraries and data are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu/

Residue-residue contact prediction is a fundamental problem in protein structure prediction. Hower, despite considerable research efforts, contact prediction methods are still largely unreliable. Here we introduce a novel deep machine-learning architecture which consists of a multidimensional stack of learning modules. For contact prediction, the idea is implemented as a three-dimensional stack of Neural Networks NN^k_{ij}, where i and j index the spatial coordinates of the contact map and k indexes ''time''. The temporal dimension is introduced to capture the fact that protein folding is not an instantaneous process, but rather a progressive refinement. Networks at level k in the stack can be trained in supervised fashion to refine the predictions produced by the previous level, hence addressing the problem of vanishing gradients, typical of deep architectures. Increased accuracy and generalization capabilities of this approach are established by rigorous comparison with other classical machine learning approaches for contact prediction. The deep approach leads to an accuracy for difficult long-range contacts of about 30%, roughly 10% above the state-of-the-art. Many variations in the architectures and the training algorithms are possible, leaving room for further improvements. Furthermore, the approach is applicable to other problems with strong underlying spatial and temporal components.

MOTIVATION: Residue-residue contact prediction is important for protein structure prediction and other applications. However, the accuracy of current contact predictors often barely exceeds 20% on long-range contacts, falling short of the level required for ab initio structure prediction. RESULTS: Here, we develop a novel machine learning approach for contact map prediction using three steps of increasing resolution. First, we use 2D recursive neural networks to predict coarse contacts and orientations between secondary structure elements. Second, we use an energy-based method to align secondary structure elements and predict contact probabilities between residues in contacting alpha-helices or strands. Third, we use a deep neural network architecture to organize and progressively refine the prediction of contacts, integrating information over both space and time. We train the architecture on a large set of non-redundant proteins and test it on a large set of non-homologous domains, as well as on the set of protein domains used for contact prediction in the two most recent CASP8 and CASP9 experiments. For long-range contacts, the accuracy of the new CMAPpro predictor is close to 30%, a significant increase over existing approaches. AVAILABILITY: CMAPpro is available as part of the SCRATCH suite at http://scratch.proteomics.ics.uci.edu/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.