Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold

DeepSF: deep convolutional neural network for mapping protein sequences to folds

Motivation Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein se quence into one of 1195 known folds, which is useful for both fold recognition and the study of se quence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and map it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 77.0%. We compare our method with a top profile profile alignment method - HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 14.5%-29.1% higher than HHSearch on template-free modeling targets and 4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.Comment: 28 pages, 13 figure

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.Comment: JGG and SMK contributed equally to the wor

Deep and self-taught learning for protein accessible surface area prediction

ASA captures the degree of burial or surface accessibility of a protein residue. It is a very important indicator of the behavior of amino acids within a protein as well. It can be used to find protein interactions, interfaces, folding states, etc. Calculation of the ASA requires the presence of the structure of the protein. However, structure determination for proteins is expensive and requires significant technical effort. As a consequence, the prediction of ASA is a very important and fundamental problem in Bioinformatics and Proteomics. In this work, we have investigated self-taught machine learning methods along with deep neural network to predict the residue level accessible surface area (ASA) of a protein. We have found that deep learning neural networks can predict the ASA of the residues in a protein accurately. Furthermore, the proposed deep learning based method does not require the use of computationally demanding features such as the position specific scoring matrix (PSSM) which have been used in previous works. A simple Blosum62 matrix based position dependent representation of amino acids in a sequence window gives comparable performance. This is particularly attractive for proteome wide prediction of ASA. We have used various self-taught learning schemes for obtaining an optimal feature representation from unlabeled data. These include a sparse and regularized autoencoder neural network and a dictionary based learning scheme. We have used unlabeled data from the protein universe in an attempt to improve the feature representation. We have also evaluated the performance of a stochastic gradient based predictor of accessible surface area for different feature representations

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

DeepREx-WS: A web server for characterising protein–solvent interaction starting from sequence

Protein–solvent interaction provides important features for protein surface engineering when the structure is absent or partially solved. Presently, we can integrate the notion of solvent exposed/buried residues with that of their flexibility and intrinsic disorder to highlight regions where mutations may increase or decrease protein stability in order to modify proteins for biotechnological reasons, while preserving their functional integrity. Here we describe a web server, which provides the unique possibility of integrating knowledge of solvent and non-solvent exposure with that of residue conservation, flexibility and disorder of a protein sequence, for a better understanding of which regions are relevant for protein integrity. The core of the webserver is DeepREx, a novel deep learning-based tool that classifies each residue in the sequence as buried or exposed. DeepREx is trained on a high-quality, non-redundant dataset derived from the Protein Data Bank comprising 2332 monomeric protein chains and benchmarked on a blind test set including 200 protein sequences unrelated with the training set. Results show that DeepREx performs at the state-of-the-art in the field. In turn, the Web Server, DeepREx-WS, supplements the predictions of DeepREx with features that allow a better characterisation of exposed and buried regions: i) residue conservation derived from multiple sequence alignment; ii) local sequence hydrophobicity; iii) residue flexibility computed with MEDUSA; iv) a predictor of secondary structure; v) the presence of disordered regions as derived from MobiDB-Lite3.0. The web server allows browsing, selecting and intersecting the different features. We demonstrate a possible application of the DeepREx-WS for assisting the identification of residues to be variated in protein surface engineering processes

New methods for protein structure prediction using machine learning and deep learning

Computational protein structure prediction is one of the most challenging problems in bioinformatics area. Due to the widespread use of sampling-and-selection strategy, protein model quality assessment became important. In this dissertation, new machine learning and deep learning methods have been proposed for protein model quality assessment, protein contact prediction, protein model refinement, and loop modeling. The goal of model quality assessment (QA) is to estimate the quality of predicted protein models. First, two new single-model QA methods based on Residual Neural Networks, called PDRN and VDRN, were proposed to achieve state-of-the-art performance. They used a comprehensive set of structure features to predict a quality score in the range of [0, 1]. Next, three single-model QA methods, MMQA-1 MMQA-2 and MMQA-HE, were proposed based on ideas of two-stage learning and hierarchical ensembles. MMQA-1 and MMQA-2 divided the entire feature set into two different sets and used different feature sets and training data in each stage of learning. In addition, MMQA-HE created ensembles of models in the first stage of learning for improved performance. In CASP14, MMQA-1 ranked NO. 2 in terms of average GDT-TS difference. MMQA-2 and MMQA-HE outperformed MMQA-1 consistently across different QA performance metrics in our experiments. Furthermore, a quasi-single-model QA method called INC-QA was proposed using a new method that trained a deep neural network as a QA predictor for each protein target based on template structure information generated from the target sequence. Experimental results using CASP data showed that INC-QA achieved state-of-the-art results, outperforming existing methods on CASP QA stage 2 category on CASP 13 targets. With the release of groundbreaking protein structure prediction software AlphaFold2 and RosettaFold, many research teams start using them to generate highly accurate protein models. We evaluated the performance of different QA methods on models generated by them with random modification by 3DRobot and found that multi-model QA methods were still better than single-model QA methods on these kind of high-performance model pools. Finally, in terms of the prediction of overall folding accuracy and overall interface accuracy for protein complexes in CASP15, we found a strong correlation between the predicted folding accuracy and predicted interface accuracy of protein models. Loop modeling tries to predict the conformation of a relatively short stretch of protein backbone and sidechain. It is a difficult problem due to conformational variability. AlphaFold2 achieved outstanding results in 3-D protein structure prediction and was expected to perform well on loop modeling. We investigated the performances of AlphaFold2 variants on loop modeling benchmark datasets and proposed an efficient constant-time method of using AlphaFold2 for loop modeling, called IAFLoop. To predict the structure of a loop region, IAFLoop ran a fast version of AlphaFold2 with a reduced database without ensembling on an extended segment of the target loop region, and used RMSD based consensus scores to select the top models. Our experimental results showed that IAFLoop generated highly accurate loop models, outperforming basic AlphaFold2 by up to 17 percent in RMSD error, while using less than half of the time. Compared to the previous best method, IAFLoop reduces the RMSD error by more than half. Contact map prediction is to predict whether the Euclidean distance between two C[beta] atoms (C[alpha] for Glycine) in a protein structure is less than 8 angstroms. Contacts information can act as a powerful constraint for determining the overall structural and assist the protein 3D structure prediction process. Based on MUFold-Contact, a new two-stage multi-branch deep neural network based on Residual Network and Inception V3 Network was proposed to improve the performance of MUFold-Contact. In the first stage, distance maps of shortrange, medium-range and long-range residue pairs were predicted, respectively, and the predicted distance along with other features were used as input to predict a binary contact map in the second stage. The role of protein structure refinement is to take models generated by protein structure prediction process and bring them closer to the true native structure. Inspired by AlphaFold in CASP13, a new protein structure refinement process MUFOLD-REFINE based on distance distribution of template pool was developed and achieve improved performance over the MUFOLD refinement method used in CASP13Includes bibliographical references