iFC²: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content

qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids

Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/. This new tool should be particularly useful to predict details of protein-NA interactions for large protein families and proteomes.

An Enhanced Protein Fold Recognition for Low Similarity Datasets Using Convolutional and Skip-Gram Features With Deep Neural Network

The protein fold recognition is one of the important tasks of structural biology, which helps in addressing further challenges like predicting the protein tertiary structures and its functions. Many machine learning works are published to identify the protein folds effectively. However, very few works have reported the fold recognition accuracy above 80% on benchmark datasets. In this study, an effective set of global and local features are extracted from the proposed Convolutional (Conv) and SkipXGram bi-gram (SXGbg) techniques, and the fold recognition is performed using the proposed deep neural network. The performance of the proposed model reported 91.4% fold accuracy on one of the derived low similarity (< 25%) datasets of latest extended version of SCOPe_2.07. The proposed model is further evaluated on three popular and publicly available benchmark datasets such as DD, EDD, and TG and obtained 85.9%, 95.8%, and 88.8% fold accuracies, respectively. This work is first to report fold recognition accuracy above 85% on all the benchmark datasets. The performance of the proposed model has outperformed the best state-of-the-art models by 5% to 23% on DD, 2% to 19% on EDD, and 3% to 30% on TG dataset.

A Segmentation-Based Method to Extract Structural and Evolutionary Features for Protein Fold Recognition

Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. Despite all the efforts that have been made so far, finding an accurate and fast computational approach to solve the PFR still remains a challenging problem for bioinformatics and computational biology. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in position specific scoring matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of occurrence feature to extract global discriminatory information from PSSM and SPINE-X. By applying a support vector machine (SVM) to our extracted features, we enhance the protein fold prediction accuracy for 7.4 percent over the best results reported in the literature. We also report 73.8 percent prediction accuracy for a data set consisting of proteins with less than 25 percent sequence similarity rates and 80.7 percent prediction accuracy for a data set with proteins belonging to 110 folds with less than 40 percent sequence similarity rates. We also investigate the relation between the number of folds and the number of features being used and show that the number of features should be increased to get better protein fold prediction results when the number of folds is relatively large.

Comprehensively designed consensus of standalone secondary structure predictors improves Q3 by over 3%

Protein fold is defined by a spatial arrangement of three types of secondary structures (SSs) including helices, sheets, and coils/loops. Current methods that predict SS from sequences rely on complex machine learning-derived models and provide the three-state accuracy (Q3) at about 82%. Further improvements in predictive quality could be obtained with a consensus-based approach, which so far received limited attention. We perform first-of-its-kind comprehensive design of a SS consensus predictor (SScon), in which we consider 12 modern standalone SS predictors and utilize Support Vector Machine (SVM) to combine their predictions. Using a large benchmark data-set with 10 random training-test splits, we show that a simple, voting-based consensus of carefully selected base methods improves Q3 by 1.9% when compared to the best single predictor. Use of SVM provides additional 1.4% improvement with the overall Q3 at 85.6% and segment overlap (SOV3) at 83.7%, when compared to 82.3 and 80.9%, respectively, obtained by the best individual methods. We also show strong improvements when the consensus is based on ab-initio methods, with Q3 = 82.3% and SOV3 = 80.7% that match the results from the best template-based approaches. Our consensus reduces the number of significant errors where helix is confused with a strand, provides particularly good results for short helices and strands, and gives the most accurate estimates of the content of individual SSs in the chain. Case studies are used to visualize the improvements offered by the consensus at the residue level. A web-server and a standalone implementation of SScon are available at http://biomine.ece.ualberta.ca/SSCon/ .

TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the C_α-N bond (Phi) and the C_α-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/.

A series of PDB related databases for everyday needs

The Protein Data Bank (PDB) is the world-wide repository of macromolecular structure information. We present a series of databases that run parallel to the PDB. Each database holds one entry, if possible, for each PDB entry. DSSP holds the secondary structure of the proteins. PDBREPORT holds reports on the structure quality and lists errors. HSSP holds a multiple sequence alignment for all proteins. The PDBFINDER holds easy to parse summaries of the PDB file content, augmented with essentials from the other systems. PDB_REDO holds re-refined, and often improved, copies of all structures solved by X-ray. WHY_NOT summarizes why certain files could not be produced. All these systems are updated weekly. The data sets can be used for the analysis of properties of protein structures in areas ranging from structural genomics, to cancer biology and protein design.