Computational methods for protein secondary structure prediction using multiple sequence alignments.

Most of the protein structure prediction methods use a multi-step process, which often includes secondary structure prediction, contact prediction, fragment generation, clustering, etc. For many years, secondary structure prediction has been the workhorse for numerous methods aimed at predicting protein structure and function. This paper presents a new mixed integer linear optimization (MILP)-based consensus method: a Consensus scheme based On a mixed integer liNear optimization method for seCOndary stRucture preDiction (CONCORD). Based on seven secondary structure prediction methods, SSpro, DSC, PROF, PROFphd, PSIPRED, Predator and GorIV, the MILP-based consensus method combines the strengths of different methods, maximizes the number of correctly predicted amino acids and achieves a better prediction accuracy. The method is shown to perform well compared with the seven individual methods when tested on the PDBselect25 training protein set using sixfold cross validation. It also performs well compared with another set of 10 online secondary structure prediction servers (including several recent ones) when tested on the CASP9 targets ( http://predictioncenter.org/casp9/ ). The average Q3 prediction accuracy is 83.04 per cent for the sixfold cross validation of the PDBselect25 set and 82.3 per cent for the CASP9 targets. We have developed a MILP-based consensus method for protein secondary structure prediction. A web server, CONCORD, is available to the scientific community at http://helios.princeton.edu/CONCORD .

BackgroundCatalase (EC 1.11.1.6) is a heme-containing tetrameric enzyme that plays a critical role in signaling and hydrogen peroxide metabolism. It was the first enzyme to be crystallized and isolated. Catalase is a well-known industrial enzyme used in diagnostic and analytical methods in the form of biomarkers and biosensors, as well as in the textile, paper, food, and pharmaceutical industries. In silico analysis of CAT genes and proteins has gained increased interest, emphasizing the development of biomarkers and drug designs. The present work aims to understand the catalase evolutionary relationship of plant species and analyze its physicochemical characteristics, homology, phylogenetic tree construction, secondary structure prediction, and 3D modeling of protein sequences and its validation using a variety of conventional computational methods to assist researchers in better understanding the structure of proteins. ResultsAround 65 plant catalase sequences were computationally evaluated and subjected to bioinformatics assessment for physicochemical characterization, multiple sequence alignment, phylogenetic construction, motif and domain identification, and secondary and tertiary structure prediction. The phylogenetic tree revealed six unique clusters where diversity of plant catalases was found to be the largest for Oryza sativa. The thermostability and hydrophilic nature of these proteins were primarily observed, as evidenced by a relatively high aliphatic index and negative GRAVY value. The distribution of 5 sequence motifs was uniformly distributed with a width length of 50 with the best possible amino residue sequences that resemble the plant catalase PLN02609 superfamily. Using SOPMA, the predicted secondary structure of the protein sequences revealed the predominance of the random coil. The predicted 3D CAT model from Arabidopsis thaliana was a homotetramer, thermostable protein with 59-KDa weight, and its structural validation was confirmed by PROCHECK, ERRAT, Verify3D, and Ramachandran plot. The functional relationships of our query sequence revealed the glutathione reductase as the closest interacting protein of query protein. ConclusionsThis theoretical plant catalases in silico analysis provide insight into its physiochemical characteristics and functional and structural understanding and its evolutionary behavior and exploring protein structure-function relationships when crystal structures are unavailable.

Nowadays, the best methods for protein secondary structure prediction are based on neural network and support vector machine, and both of them incorporate the information from multiple sequences alignment. However the two methods were executed on different training and testing data sets. A comparison between the two methods has been carried on here. We use the most stringent cross validation test procedure to assess the two methods on CB513, which is one of the most popular used data sets. Neural network achieved a Q/sub 3/ accuracy of 74.2%, while support vector machine got Q/sub 3/ of 76.6%, which was slightly better than NN.

The Protein Data Bank (PDB), the ultimate source for data in structural biology, is inherently imbalanced. To alleviate biases, virtually all structural biology studies use nonredundant (NR) subsets of the PDB, which include only a fraction of the available data. An alternative approach, dubbed redundancy-weighting (RW), down-weights redundant entries rather than discarding them. This approach may be particularly helpful for machine-learning (ML) methods that use the PDB as their source for data. Methods for secondary structure prediction (SSP) have greatly improved over the years with recent studies achieving above 70% accuracy for eight-class (DSSP) prediction. As these methods typically incorporate ML techniques, training on RW datasets might improve accuracy, as well as pave the way toward larger and more informative secondary structure classes. This study compares the SSP performances of deep-learning models trained on either RW or NR datasets. We show that training on RW sets consistently results in better prediction of 3- (HCE), 8- (DSSP) and 13-class (STR2) secondary structures. The ML models, the datasets used for their derivation and testing, and a stand-alone SSP program for DSSP and STR2 predictions, are freely available under LGPL license in http://meshi1.cs.bgu.ac.il/rw. Supplementary data are available at Bioinformatics online.

Multiple sequence alignments are essential tools for establishing the homology relations between proteins. Essential amino acids for the function and/or the structure are generally conserved, thus providing key arguments to help in protein characterization. However for distant proteins, it is more difficult to establish, in a reliable way, the homology relations that may exist between them. In this article, we show that secondary structure prediction is a valuable way to validate protein families at low identity rate. We show that the analysis of the secondary structures compatibility is a reliable way to discard non-related proteins in low identity multiple alignment. This validation is possible through our NPS@ server (http://npsa-pbil.ibcp.fr)

Protein secondary structure predictions and amino acid long range contact map predictions from primary sequence of proteins have been explored to aid in modelling protein tertiary structures. In order to evaluate the usefulness of secondary structure and 3D-residue contact prediction methods to model protein structures we have used the known Q3 (alpha-helix,beta-strands and irregular turns/loops) secondary structure information, along with residue-residue contact information as restraints for MODELLER. We present here results of our modelling studies on 30 best resolved single domain protein structures of varied lengths. The results shows that it is very difficult to obtain useful models even with 100% accurate secondary structure predictions and accurate residue contact predictions for up to 30% of residues in a sequence. The best models that we obtained for proteins of lengths 37, 70, 118, 136 and 193 amino acid residues are of RMSDs 4.17, 5.27, 9.12, 7.89 and 9.69,respectively. The results show that one can obtain better models for the proteins which have high percent of alpha-helix content. This analysis further shows that MODELLER restrain optimization program can be useful only if we have truly homologous structure(s) as a template where it derives numerous restraints, almost identical to the templates used. This analysis also clearly indicates that even if we satisfy several true residue-residue contact distances, up to 30%of their sequence length with fully known secondary structural information, we end up predicting model structures much distant from their corresponding native structures.

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query's location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences.

Predicting protein secondary structure using a mixed-modal SVM method in a compound pyramid model

The predictive limits of the amino acid composition for the secondary structural content (percentage of residues in the secondary structural states helix, sheet, and coil) in proteins are assessed quantitatively. For the first time, techniques for prediction of secondary structural content are presented which rely on the amino acid composition as the only information on the query protein. In our first method, the amino acid composition of an unknown protein is represented by the best (in a least square sense) linear combination of the characteristic amino acid compositions of the three secondary structural types computed from a learning set of tertiary structures. The second technique is a generalization of the first one and takes into account also possible compositional couplings between any two sorts of amino acids. Its mathematical formulation results in an eigenvalue/eigenvector problem of the second moment matrix describing the amino acid compositional fluctuations of secondary structural types in various proteins of a learning set. Possible correlations of the principal directions of the eigenspaces with physical properties of the amino acids were also checked. For example, the first two eigenvectors of the helical eigenspace correlate with the size and hydrophobicity of the residue types respectively. As learning and test sets of tertiary structures, we utilized representative, automatically generated subsets of Protein Data Bank (PDB) consisting of non-homologous protein structures at the resolution thresholds < or = 1.8A, < or = 2.0A, < or = 2.5A, and < or = 3.0 A. We show that the consideration of compositional couplings improves prediction accuracy, albeit not dramatically. Whereas in the self-consistency test (learning with the protein to be predicted), a clear decrease of prediction accuracy with worsening resolution is observed, the jackknife test (leave the predicted protein out) yielded best results for the largest dataset (< or = 3.0A, almost no difference to the self-consistency test!), i.e., only this set, with more than 400 proteins, is sufficient for stable computation of the parameters in the prediction function of the second method. The average absolute error in predicting the fraction of helix, sheet, and coil from amino acid composition of the query protein are 13.7, 12.6, and 11.4%, respectively with r.m.s. deviations in the range of 8.6 divided by 11.8% for the 3.0 A dataset in a jackknife test. The absolute precision of the average absolute errors is in the range of 1 divided by 3% as measured for other representative subsets of the PDB. Secondary structural content prediction methods found in the literature have been clustered in accordance with their prediction accuracies. To our surprise, much more complex secondary structure prediction methods utilized for the same purpose of secondary structural content prediction achieve prediction accuracies very similar to those of the present analytic techniques, implying that all the information beyond the amino acid composition is, in fact, mainly utilized for positioning the secondary structural state in the sequence but not for determination of the overall number of residues in a secondary structural type. This result implies that higher prediction accuracies cannot be achieved relying solely on the amino acid composition of an unknown query protein as prediction input. Our prediction program SSCP has been made available as a World Wide Web and E-mail service.

BackgroundAlignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.ResultsTwo predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets.ConclusionsPredicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0749-z) contains supplementary material, which is available to authorized users.

Prediction of protein secondary structure provides information that is useful for other prediction methods like fold recognition and ab initio 3D prediction. A consensus prediction constructed from the output of several methods should yield more reliable results than each of the individual methods. We present an approach that reveals subtle but systematic differences in the output of different secondary structure prediction methods allowing the derivation of coherent consensus predictions. The method uses a machine learning technique that builds decision trees from existing data. The first results of our analysis show that consensus prediction of protein secondary structure may be improved both quantitatively and qualitatively.

Background: Due to the growing amount of biological data, it is often necessary to select the most optimal estimation method for DNA sequence alignment across livestock species. One of the most important benches of genomics is to modelling homology between considered DNA sequences. A multiple sequence alignment is a potent tool for molecular and evolutionary biology, and there are several programs and algorithms applicable for this purpose. The purpose of this paper was to study the most commonly used DNA alignment algorithms to select the optimal tool dedicated for short sequences.Methods: Four steps of bioinformatics pipelines were considered to benchmark the algorithms for multiple DNA sequence alignment across livestock species: 1) selection of reference genome sequences of ARS1.2 for cattle, EquCab3.0 for horse and vicPac2 for alpaca with a low E-value using TBLASTn 2) removing gaps for these sequences 3) alignment of obtained sequences using examined algorithms 4) matching the quality of aligned sequences with sequences of reference genomes by more software. The time of computation was archived for the whole analysis. The seven programs were utilized, each based on different alignment algorithms, namely: ClustalO, ClustalW, Kalign, MAFFT, MUSCLE, Probcons and T-Coffee.Results: The result obtained in this study showed that the fastest is progressive algorithms such as Kalign or MUSCLE-FAST. Moreover, the iterative algorithms like MAFFT and MUSCLE revealed a higher quality of the alignment. The T-Coffee and Probcons programs were computational cost-effective; simultaneously, they were generating a medium-quality calculation in a relatively long time. The best quality of alignment was shown by iterative variants of the MAFFT program; however, the speed of the calculations was relatively low. The fastest algorithm was Kalign, making alignment much faster than the competitors, but achieving average results in the quality of the alignment. The average speed ratio concerning the quality of the analyzed algorithms was obtained by the progressive version of MAFFT, NS1.Conclusions: We conclude that the results of this study can be used to re-alignment of variant primers in new livestock genome releases.

Multiple sequence alignment (MSA) methods infer the homologous regions within DNA and protein sequences. Among the other algorithms for MSA, the center star method (CSM) is useful when processing a large number of sequences. In this method, the partially conserved regions among the non-center sequences tend to hide in the alignment. Therefore, existence of subsets of similar sequences in the input set leads to a significant accuracy loss. As a solution to the above problem, this research introduces an algorithm for MSA based on a solution proposed by D. Gusfield in 1991. In this research, the sequences are first grouped into subsets, aligned separately using CSM and finally these alignments are merged by applying progressive alignment procedure. The detailed solution for this problem is presented in the following section.

Ribonucleic acid is vital in numerous stages of protein synthesis; it also possesses important functional and structural roles within the cell. The function of an RNA molecule within a particular organic system is principally determined by its structure. The current physical methods available for structure determination are time-consuming and expensive. Hence, computational methods for structure prediction are sought after. The energies involved by the formation of secondary structure elements are significantly greater than those of tertiary elements. Therefore, RNA structure prediction focuses on secondary structure. We present P-RnaPredict, a parallel evolutionary algorithm for RNA secondary structure prediction. The speedup provided by parallelization is investigated with five sequences, and a dramatic improvement in speedup is demonstrated, especially with longer sequences. An evaluation of the performance of P-RnaPredict in terms of prediction accuracy is made through comparison with 10 individual known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA and 16S rRNA) and the mfold dynamic programming algorithm. P-RnaPredict is able to predict structures with higher true positive base pair counts and lower false positives than mfold on certain sequences. P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).

Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the β- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.