A multi-task deep-learning system for predicting membrane associations and secondary structures of proteins

Accurate prediction of secondary structures and transmembrane segments is often the first step towards modeling the tertiary structure of a protein. Existing methods are either specialized in one class of proteins or developed to predict one type of 1D structural attributes (secondary structure, topology, or transmembrane segment). In this work, we develop a new method for simultaneous prediction of secondary structure, transmembrane segment, and transmembrane topology with no a priori assumption on the class of the input protein sequence. The new method, Membrane Association and Secondary Structures of Proteins (MASSP) predictor, uses multi-tiered neural networks that incorporate recent innovations in machine learning. The first tier is a multi-task multi-layer convolutional neural network (CNN) that learns patterns in image-like input position-specific-scoring matrices (PSSMs) and predicts residue-level 1D structural attributes. The second tier is a long short-term memory (LSTM) neural network that treats the predictions of the first tier from the perspective of natural language processing and predicts the class of the input protein sequence. We curated a non-redundant data set consisting of 54 bitopic, 241 multi-spanning TM-alpha, 77 TM-beta, and 372 soluble proteins, respectively for training and testing MASSP. For secondary structure prediction, the mean three-state accuracy (Q3) of MASSP is 0.830, better than the Q3 of PSIPRED (0.829) and that of SPINE-X (0.813) and substantially better than that of Jufo9D (0.762) and RaptorX-Property (0.741). The mean segment overlap score (SOV) of MASSP is 0.752, gaining at least 7.7% improvement over all the other four methods. For transmembrane topology prediction, MASSP has a performance comparable to OCTOPUS and substantially better than MEMSAT3 and TMHMM2 on TM-alpha proteins, and on TM-beta proteins, MASSP is significantly better than both BOCTOPUS2 and PRED-TMBB2. By integrating prediction of secondary structure and transmembrane segments in a deep-learning framework, MASSP improves performance over previous methods, has broader applicability, and enables proteome scale predictions.

Predicción computacional de estructura terciaria de las proteínas humanas Hsp27, αB-cristalina y HspB8

<strong></strong> <p><strong></strong><strong>Objective:</strong> To make computational predictions of the structure of the human proteins Hsp27, αB-crystalline and HspB8. <strong>Materials and methods</strong>. The prediction of the secondary structure was obtained by a consensus of the programs for secondary prediction GOR 4, nnPred, Sspro, APSSP2, JPredict, Porter, Prof, SOPMA, HNN and Psi-Pred. The models of tertiary structure were built from fragments homologous to proteins with tertiary known structure that were obtained by multiple alignments. Using the primary sequence we obtained the antigenicity profiles of native proteins and we analyzed the profiles of hydrophobicity, polarity, flexibility and accessibility of both native and mutant proteins. <strong>Results</strong>. Predictions of the secondary and tertiary structures of the studied proteins show that in the three cases, more than 65% are coil regions, 20-25 % are folded sheet and less than 10% are alpha helix. Analyses of the primary structure show that at least one of the studied profiles in every mutation is altered. <strong>Conclusions</strong>. The comparative analyses of structure suggest that mutations affect the solubility of the mutated proteins and hence affect their function as molecular chaperones</p> <p><strong>Key words</strong>: Hsp27, αB-cristalline, HspB8, prediction of secondary structure, computational model of tertiary structure</p><br />