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Zebra2 is a highly automated web-tool to search for subfamily-specific and conserved positions (i.e. the determinants of functional diversity as well as the key catalytic and structural residues) in protein superfamilies. The bioinformatic analysis is facilitated by Mustguseal—a companion web-server to automatically collect and superimpose a large representative set of functionally diverse homologs with high structure similarity but low sequence identity to the selected query protein. The results are automatically prioritized and provided at four information levels to facilitate the knowledge-driven expert selection of the most promising positions on-line: as a sequence similarity network; interfaces to sequence-based and 3D-structure-based analysis of conservation and variability; and accompanied by the detailed annotation of proteins accumulated from the integrated databases with links to the external resources. The integration of Zebra2 and Mustguseal web-tools provides the first of its kind out-of-the-box open-access solution to conduct a systematic analysis of evolutionarily related proteins implementing different functions within a shared 3D-structure of the superfamily, determine common and specific patterns of function-associated local structural elements, assist to select hot-spots for rational design and to prepare focused libraries for directed evolution. The web-servers are free and open to all users at https://biokinet.belozersky.msu.ru/zebra2, no login required.

Motivation Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the conventional UniProt database. Here we aim to solve this issue by exploring the rich sequence data from the metagenome sequencing projects. Results Based on the improved MSA constructed from the metagenome sequence data, we developed MapPred, a new deep learning-based contact prediction method. MapPred consists of two component methods, DeepMSA and DeepMeta, both trained with the residual neural networks. DeepMSA was inspired by the recent method DeepCov, which was trained on 441 matrices of covariance features. By considering the symmetry of contact map, we reduced the number of matrices to 231, which makes the training more efficient in DeepMSA. Experiments show that DeepMSA outperforms DeepCov by 10–13% in precision. DeepMeta works by combining predicted contacts and other sequence profile features. Experiments on three benchmark datasets suggest that the contribution from the metagenome sequence data is significant with P-values less than 4.04E-17. MapPred is shown to be complementary and comparable the state-of-the-art methods. The success of MapPred is attributed to three factors: the deeper MSA from the metagenome sequence data, improved feature design in DeepMSA and optimized training by the residual neural networks. Availability and implementation http://yanglab.nankai.edu.cn/mappred/. Supplementary information Supplementary data are available at Bioinformatics online.

The selection of proteotypic peptides, that is, detectable unique representatives of proteins of interest, is a key step in targeted proteomics. To date, much effort has been made to understand the mechanisms underlying peptide detection in liquid chromatography-tandem mass spectrometry (LC-MS/MS) based shotgun proteomics and to predict proteotypic peptides in the absence of experimental LC-MS/MS data. However, the prediction accuracy of existing tools is still unsatisfactory. We find that one crucial reason is their neglect of the significant influence of protein proteolytic digestion on peptide detectability in shotgun proteomics. Here, we present an Advanced Proteotypic Peptide Predictor (AP3), which explicitly takes peptide digestibility into account for the prediction of proteotypic peptides. Specifically, peptide digestibility is first predicted for each peptide and then incorporated as a feature into the peptide detectability prediction model. Our results demonstrated that peptide digestibility is the most important feature for the accurate prediction of proteotypic peptides in our model. Compared with the existing available algorithms, AP3 showed 10.3-34.7% higher prediction accuracy. On a targeted proteomics data set, AP3 accurately predicted the proteotypic peptides for proteins of interest, showing great potential for assisting the design of targeted proteomics experiments.

Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distinguish one set of antibody sequences from antibody sequences in a reference set. The pipeline extracts feature fingerprints from sequences. The fingerprints represent germline, CDR canonical structure, isoelectric point and frequent positional motifs. Machine learning and statistical significance testing techniques are applied to antibody sequences and extracted feature fingerprints to identify distinguishing feature values and combinations thereof. To demonstrate how it works, we applied the pipeline on sets of antibody sequences known to bind or inhibit the activities of matrix metalloproteinases (MMPs), a family of zinc-dependent enzymes that promote cancer progression and undesired inflammation under pathological conditions, against reference datasets that do not bind or inhibit MMPs. ASAP-SML identifies features and combinations of feature values found in the MMP-targeting sets that are distinct from those in the reference sets., Author summary The availability of machine learning techniques and the exponential growth of sequencing data presents new opportunities to identify features that endow antibodies with the ability to disrupt the functions of biological targets. We have created a pipeline that uses statistical testing and machine learning techniques to determine features that are overrepresented in a specified set of antibody sequences in comparison to a reference set. The pipeline is referred to as Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML). We demonstrate the use of ASAP-SML by analyzing sets of antibodies that inhibit matrix metalloproteinases (MMPs) against reference sets. ASAP-SML performs within and across set similarity analysis. As in prior studies, our analysis of these datasets shows that features associated with the antibody heavy chain are more likely to differentiate MMP-targeting antibody sequences from reference antibody sequences. Further, ASAP-SML identifies several features in the MMP-targeting set that are distinct from the reference sets. Using design recommendation trees, ASAP-SML suggests combinations of features that can be included or excluded to augment the targeting set with additional candidate MMP-targeting antibody sequences.

RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow for that it needs to generate PSSM matrix as its feature. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we develop a deep learning model called Deep-RBPPred. With the balance and imbalance training set, we obtain Deep-RBPPred-balance and Deep-RBPPred-imbalance models. Deep-RBPPred has three advantages comparing to previous methods. (1) Deep-RBPPred only needs few physicochemical properties based on protein sequences. (2) Deep-RBPPred runs much faster. (3) Deep-RBPPred has a good generalization ability. In the meantime, Deep-RBPPred is still as good as the state-of-the-art method. Testing in A. thaliana, S. cerevisiae and H. sapiens proteomes, MCC values are 0.82 (0.82), 0.65 (0.69) and 0.85 (0.80) for balance model (imbalance model) when the score cutoff is set to 0.5, respectively. In the same testing dataset, different machine learning algorithms (CNN and SVM) are also compared. The results show that CNN-based model can identify more RBPs than SVM-based. In comparing the balance and imbalance model, both CNN-base and SVM-based tend to favor the majority class in the imbalance set. Deep-RBPPred forecasts 280 (balance model) and 265 (imbalance model) of 299 new RBP. The sensitivity of balance model is about 7% higher than the state-of-the-art method. We also apply deep-RBPPred to 30 eukaryotes and 109 bacteria proteomes downloaded from Uniprot to estimate all possible RBPs. The estimating result shows that rates of RBPs in eukaryote proteomes are much higher than bacteria proteomes.

Motivation Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually. Results We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein–ligand (peptides, nucleic acids and small substrates) and protein–protein interaction databases. Availability and implementation A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git. Supplementary information Supplementary data are available at Bioinformatics online.

While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.

Summary We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences. Availability and implementation This feature is available in MAFFT versions 7.355 or later at https://mafft.cbrc.jp/alignment/software/mpi.html. Supplementary information Supplementary data are available at Bioinformatics online.

Background HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins. Results We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite. Conclusion The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.

Proteases are multifunctional, promiscuous enzymes that degrade proteins as well as peptides and drive important processes in health and disease. Current technology has enabled the construction of libraries of peptide substrates that detect protease activity, which provides valuable biological information. An ideal library would be orthogonal, such that each protease only hydrolyzes one unique substrate, however this is impractical due to off-target promiscuity (i.e., one protease targets multiple different substrates). Therefore, when a library of probes is exposed to a cocktail of proteases, each protease activates multiple probes, producing a convoluted signature. Computational methods for parsing these signatures to estimate individual protease activities primarily use an extensive collection of all possible protease-substrate combinations, which require impractical amounts of training data when expanding to search for more candidate substrates. Here we provide a computational method for estimating protease activities efficiently by reducing the number of substrates and clustering proteases with similar cleavage activities into families. We envision that this method will be used to extract meaningful diagnostic information from biological samples., Author summary The activity of enzymatic proteins, which are called proteases, drives numerous important processes in health and disease: including cancer, immunity, and infectious disease. Many labs have developed useful diagnostics by designing sensors that measure the activity of these proteases. However, if we want to detect multiple proteases at the same time, it becomes impractical to design sensors that only detect one protease. This is due to a phenomenon called protease promiscuity, which means that proteases will activate multiple different sensors. Computational methods have been created to solve this problem, but the challenge is that these often require large amounts of training data. Further, completely different proteases may be detected by the same subset of sensors. In this work, we design a computational method to overcome this problem by clustering similar proteases into "subfamilies", which increases estimation accuracy. Further, our method tests multiple combinations of sensors to maintain accuracy while minimizing the number of sensors used. Together, we envision that this work will increase the amount of useful information we can extract from biological samples, which may lead to better clinical diagnostics.

Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A, predicting four epitopes recognized by antibodies present in 87% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Additionally, we predicted 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. Experimentally validating these candidate epitopes could help identify diagnostic biomarkers, vaccine components, and therapeutic targets. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires.

Transferring knowledge between species is key for many biological applications, but is complicated by divergent and convergent evolution. Many current approaches for this problem leverage sequence and interaction network data to transfer knowledge across species, exemplified by network alignment methods. While these techniques do well, they are limited in scope, creating metrics to address one specific problem or task. We take a different approach by creating an environment where multiple knowledge transfer tasks can be performed using the same protein representations. Specifically, our kernel-based method, MUNK, integrates sequence and network structure to create functional protein representations, embedding proteins from different species in the same vector space. First we show proteins in different species that are close in MUNK-space are functionally similar. Next, we use these representations to share knowledge of synthetic lethal interactions between species. Importantly, we find that the results using MUNK-representations are at least as accurate as existing algorithms for these tasks. Finally, we generalize the notion of a phenolog (‘orthologous phenotype’) to use functionally similar proteins (i.e. those with similar representations). We demonstrate the utility of this broadened notion by using it to identify known phenologs and novel non-obvious ones supported by current research.

Motivation Multiple sequence alignment (MSA) is commonly used to analyze sets of homologous protein or DNA sequences. This has lead to the development of many methods and packages for MSA over the past 30 years. Being able to compare different methods has been problematic and has relied on gold standard benchmark datasets of ‘true’ alignments or on MSA simulations. A number of protein benchmark datasets have been produced which rely on a combination of manual alignment and/or automated superposition of protein structures. These are either restricted to very small MSAs with few sequences or require manual alignment which can be subjective. In both cases, it remains very difficult to properly test MSAs of more than a few dozen sequences. PREFAB and HomFam both rely on using a small subset of sequences of known structure and do not fairly test the quality of a full MSA. Results In this paper we describe QuanTest, a fully automated and highly scalable test system for protein MSAs which is based on using secondary structure prediction accuracy (SSPA) to measure alignment quality. This is based on the assumption that better MSAs will give more accurate secondary structure predictions when we include sequences of known structure. SSPA measures the quality of an entire alignment however, not just the accuracy on a handful of selected sequences. It can be scaled to alignments of any size but here we demonstrate its use on alignments of either 200 or 1000 sequences. This allows the testing of slow accurate programs as well as faster, less accurate ones. We show that the scores from QuanTest are highly correlated with existing benchmark scores. We also validate the method by comparing a wide range of MSA alignment options and by including different levels of mis-alignment into MSA, and examining the effects on the scores. Availability and Implementation QuanTest is available from http://www.bioinf.ucd.ie/download/QuanTest.tgz Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: Tandem Repeats (TRs) are abundant in proteins, having a variety of fundamental functions. In many cases, evolution has blurred their repetitive patterns. This leads to the problem of distinguishing between sequences that contain highly imperfect TRs, and the sequences without TRs. The 3D structure of proteins can be used as a benchmarking criterion for TR detection in sequences, because the vast majority of proteins having TRs in sequences are built of repetitive 3D structural blocks. According to our benchmark, none of the existing scoring methods are able to clearly distinguish, based on the sequence analysis, between structures with and without 3D TRs. Results: We developed a scoring tool called Tally, which is based on a machine learning approach. Tally is able to achieve a better separation between sequences with structural TRs and sequences of aperiodic structures, than existing scoring procedures. It performs at a level of 81% sensitivity, while achieving a high specificity of 74% and an Area Under the Receiver Operating Characteristic Curve of 86%. Tally can be used to select a set of structurally and functionally meaningful TRs from all TRs detected in proteomes. The generated dataset is available for benchmarking purposes. Availability and implementation: Source code is available upon request. Tool and dataset can be accessed through our website: http://bioinfo.montp.cnrs.fr/?r=Tally. Contact: andrey.kajava@crbm.cnrs.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategies, and computational machine learning algorithms. In some cases, these tools may yield inconsistent and conflicting prediction results. It is important to consider such conflicting or contradictory predictions from multiple prediction programs during protein annotation, especially in the case of a multiclass classification problem such as subcellular localization. Hence, to address this issue, this work proposes the use of the particle swarm optimization (PSO) algorithm to combine the prediction outputs from multiple different subcellular localization predictors with the aim of integrating diverse prediction models to enhance the final predictions. Herein, we present PSO-LocBact, a consensus classifier based on PSO that can be used to combine the strengths of several preexisting protein localization predictors specially designed for bacteria. Our experimental results indicate that the proposed method can resolve inconsistency problems in subcellular localization prediction for both Gram-negative and Gram-positive bacterial proteins. The average accuracy achieved on each test dataset is over 98%, higher than that achieved with any individual predictor.

Protein Direct Coupling Analysis (DCA), which predicts residue-residue contacts based on covarying positions within a multiple sequence alignment, has been remarkably effective. This suggests that there is more to learn from sequence correlations than is generally assumed, and calls for deeper investigations into DCA and perhaps into other types of correlations. Here we describe an approach that enables such investigations by measuring, as an estimated p-value, the statistical significance of the association between residue-residue covariance and structural interactions, either internal or homodimeric. Its application to thirty protein superfamilies confirms that direct coupling (DC) scores correlate with 3D pairwise contacts with very high significance. This method also permits quantitative assessment of the relative performance of alternative DCA methods, and of the degree to which they detect direct versus indirect couplings. We illustrate its use to assess, for a given protein, the biological relevance of alternative conformational states, to investigate the possible mechanistic implications of differences between these states, and to characterize subtle aspects of direct couplings. Our analysis indicates that direct pairwise correlations may be largely distinct from correlated patterns associated with functional specialization, and that the joint analysis of both types of correlations can yield greater power. Data, programs, and source code are freely available at http://evaldca.igs.umaryland.edu., Author summary The success of Direct Coupling Analysis (DCA) for protein structure prediction suggests that multiple sequence alignments implicitly contain more structural information than had previously been realized, and prompts deeper investigations of the sequence correlations uncovered by either DCA or other approaches. To aid such investigations and thereby broaden the utility of and improve DCA, we describe an approach that measures the statistical significance of the association between DCA and either 3D structure or correlated patterns associated with functional specialization. This approach can be used to evaluate the relative performance of DCA methods, their ability to distinguish direct from indirect couplings, and the potential biological relevance and mechanistic implications of alternative conformations and homodimeric interactions.

Nature Methods, 13 (9), ISSN:1548-7105, ISSN:1548-7091

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein–protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable.This article is part of a Special Issue entitled: Computational Proteomics.

Protein-protein interactions exist ubiquitously and play important roles in the life cycles of living cells. The interaction sites (residues) are essential to understanding the underlying mechanisms of protein-protein interactions. Previous research has demonstrated that the accurate identification of protein-protein interaction sites (PPIs) is helpful for developing new therapeutic drugs because many drugs will interact directly with those residues. Because of its significant potential in biological research and drug development, the prediction of PPIs has become an important topic in computational biology. However, a severe data imbalance exists in the PPIs prediction problem, where the number of the majority class samples (non-interacting residues) is far larger than that of the minority class samples (interacting residues). Thus, we developed a novel cascade random forests algorithm (CRF) to address the serious data imbalance that exists in the PPIs prediction problem. The proposed CRF resolves the negative effect of data imbalance by connecting multiple random forests in a cascade-like manner, each of which is trained with a balanced training subset that includes all minority samples and a subset of majority samples using an effective ensemble protocol. Based on the proposed CRF, we implemented a new sequence-based PPIs predictor, called CRF-PPI, which takes the combined features of position-specific scoring matrices, averaged cumulative hydropathy, and predicted relative solvent accessibility as model inputs. Benchmark experiments on both the cross validation and independent validation datasets demonstrated that the proposed CRF-PPI outperformed the state-of-the-art sequence-based PPIs predictors. The source code for CRF-PPI and the benchmark datasets are available online at http://csbio.njust.edu.cn/bioinf/CRF-PPI for free academic use.

Motivation: The sensitivity of de novo short linear motif (SLiM) prediction is limited by the number of patterns (the motif space) being assessed for enrichment. QSLiMFinder uses specific query protein information to restrict the motif space and thereby increase the sensitivity and specificity of predictions. Results: QSLiMFinder was extensively benchmarked using known SLiM-containing proteins and simulated protein interaction datasets of real human proteins. Exploiting prior knowledge of a query protein likely to be involved in a SLiM-mediated interaction increased the proportion of true positives correctly returned and reduced the proportion of datasets returning a false positive prediction. The biggest improvement was seen if a short region of the query protein flanking the interaction site was known. Availability and implementation: All the tools and data used in this study, including QSLiMFinder and the SLiMBench benchmarking software, are freely available under a GNU license as part of SLiMSuite, at: http://bioware.soton.ac.uk. Contact: richard.edwards@unsw.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

One of the key tasks related to proteins is the similarity comparison of protein sequences in the area of bioinformatics and molecular biology, which helps the prediction and classification of protein structure and function. It is a significant and open issue to find similar proteins from a large scale of protein database efficiently. This paper presents a new distance based protein similarity analysis using a new encoding method of protein sequence which is based on fractal dimension. The protein sequences are first represented into the 1-dimensional feature vectors by their biochemical quantities. A series of Hybrid method involving discrete Wavelet transform, Fractal dimension calculation (HWF) with sliding window are then applied to form the feature vector. At last, through the similarity calculation, we can obtain the distance matrix, by which, the phylogenic tree can be constructed. We apply this approach by analyzing the ND5 (NADH dehydrogenase subunit 5) protein cluster data set. The experimental results show that the proposed model is more accurate than the existing ones such as Su's model, Zhang's model, Yao's model and MEGA software, and it is consistent with some known biological facts.

The fact that amino acid sequences dictate the tertiary structures of proteins has been known for more than five decades. While the molecular pathways to tertiary structure are still being worked out, with the axiom that similar sequences adopt similar structures, computational methods are being developed continually in parallel, utilizing the Protein Data Bank structural repository and homologue detection strategies to predict structures of sequences of interest. The success of this approach is limited by the ability to unravel the hidden similarities among amino acid sequences. We consider here the 20 amino acids as a complete set of chemical templates in the physicochemical space of proteins and propose a new structural and chemical classification of amino acids. An integration of this perspective into the conventional evolutionary methods of similarity detection leads to an unprecedented increase in the accuracy in homologue detection, resulting in improved protein structure prediction. The performance is validated on a large data set of 11716 unique proteins, and the results are benchmarked against conventional methods. The availability of good quality protein structures helps in structure-based drug design endeavors and in establishing protein structure-function correlations.

Sequencing costs have dropped much faster than Moore's law in the past decade, and sensitive sequence searching has become the main bottleneck in the analysis of large (meta)genomic datasets. While previous methods sacrificed sensitivity for speed gains, the parallelized, open-source software MMseqs2 overcomes this trade-off: In three-iteration profile searches it reaches 50% higher sensitivity than BLAST at 83-fold speed and the same sensitivity as PSI-BLAST at 270 times its speed. MMseqs2 therefore offers great potential to increase the fraction of annotatable (meta)genomic sequences.

Motivation: Homology models of proteins are of great interest for planning and analysing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all required tools, programs and databases into a single web-based workspace facilitates access to homology modelling from a computer with web connection without the need of downloading and installing large program packages and databases. Results: SWISS-MODEL workspace is a web-based integrated service dedicated to protein structure homology modelling. It assists and guides the user in building protein homology models at different levels of complexity. A personal working environment is provided for each user where several modelling projects can be carried out in parallel. Protein sequence and structure databases necessary for modelling are accessible from the workspace and are updated in regular intervals. Tools for template selection, model building and structure quality evaluation can be invoked from within the workspace. Workflow and usage of the workspace are illustrated by modelling human Cyclin A1 and human Transmembrane Protease 3. Availability: The SWISS-MODEL workspace can be accessed freely at Contact: Torsten.Schwede@unibas.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development. Results: For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding. Supplementary information: Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf

Motivation Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought. Results In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthew’s correlation coefficient, with an overall accuracy of 74% and Matthew’s correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability. Availability and implementation PaRSnIP software is available for download under GitHub (https://github.com/RedaRawi/PaRSnIP). Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in protein–protein interactions. Such predictions are typically based on machine learning methods that take advantage of the properties and sequence positions of amino acids that are known to be involved in interaction. In this paper, we evaluate the importance of various features using Random Forest (RF), and include as a novel feature backbone flexibility predicted from sequences to further optimise protein interface prediction. Results We observe that there is no single sequence feature that enables pinpointing interacting sites in our Random Forest models. However, combining different properties does increase the performance of interface prediction. Our homomeric-trained RF interface predictor is able to distinguish interface from non-interface residues with an area under the ROC curve of 0.72 in a homomeric test-set. The heteromeric-trained RF interface predictor performs better than existing predictors on a independent heteromeric test-set. We trained a more general predictor on the combined homomeric and heteromeric dataset, and show that in addition to predicting homomeric interfaces, it is also able to pinpoint interface residues in heterodimers. This suggests that our random forest model and the features included capture common properties of both homodimer and heterodimer interfaces. Availability and Implementation The predictors and test datasets used in our analyses are freely available (http://www.ibi.vu.nl/downloads/RF_PPI/). Supplementary information Supplementary data are available at Bioinformatics online.

Mixture spectra occur quite frequently in a typical wet-lab mass spectrometry experiment, which result from the concurrent fragmentation of multiple precursors. The ability to efficiently and confidently identify mixture spectra is essential to alleviate the existent bottleneck of low mass spectra identification rate. However, most of the traditional computational methods are not suitable for interpreting mixture spectra, because they still take the assumption that the acquired spectra come from the fragmentation of a single precursor. In this manuscript, we formulate the mixture spectra de novo sequencing problem mathematically, and propose a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra data sets to verify the merits of the proposed algorithm.

Protein methylation, an important post-translational modification, plays crucial roles in many cellular processes. The accurate prediction of protein methylation sites is fundamentally important for revealing the molecular mechanisms undergoing methylation. In recent years, computational prediction based on machine learning algorithms has emerged as a powerful and robust approach for identifying methylation sites, and much progress has been made in predictive performance improvement. However, the predictive performance of existing methods is not satisfactory in terms of overall accuracy. Motivated by this, we propose a novel random-forest-based predictor called MePred-RF, integrating several discriminative sequence-based feature descriptors and improving feature representation capability using a powerful feature selection technique. Importantly, unlike other methods based on multiple, complex information inputs, our proposed MePred-RF is based on sequence information alone. Comparative studies on benchmark datasets via vigorous jackknife tests indicate that our proposed MePred-RF method remarkably outperforms other state-of-the-art predictors, leading by a 4.5 percent average in terms of overall accuracy. A user-friendly webserver that implements the proposed method has been established for researchers' convenience, and is now freely available for public use through http://server.malab.cn/MePred-RF. We anticipate our research tool to be useful for the large-scale prediction and analysis of protein methylation sites.

One of the challenging problems in bioinformatics is the prediction of protein function. Protein function is the main key that can be used to classify different proteins. Protein function can be inferred experimentally with very small throughput or computationally with very high throughput. Computational methods are sequence based or structure based. Structure-based methods produce more accurate protein function prediction. In this article, we propose a new protein structure representation for efficient protein function prediction. The representation is based on three-dimensional patterns of protein residues. In the analysis, we used protein function based on enzyme activity through six mechanistically diverse enzyme superfamilies: amidohydrolase, crotonase, haloacid dehalogenase, isoprenoid synthase type I, and vicinal oxygen chelate. We applied three different classification methods, naïve Bayes, k-nearest neighbors, and random forest, to predict the enzyme superfamily of a given protein. The prediction accuracy using the proposed representation outperforms a recently introduced representation method that is based only on the distance patterns. The results show that the proposed representation achieved prediction accuracy up to 98%, with improvement of about 10% on average.

Motivation: Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Results: Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts—around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. Availability and implementation: MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Contact: d.t.jones@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Distinguishing between amyloid fibril-forming and amorphous β-aggregating aggregation-prone regions (APRs) in proteins and peptides is crucial for designing novel biomaterials and improved aggregation inhibitors for biotechnological and therapeutic purposes. Results: Adjacent and alternate position residue pairs in hexapeptides show distinct preferences for occurrence in amyloid fibrils and amorphous β-aggregates. These observations were converted into energy potentials that were, in turn, machine learned. The resulting tool, called Generalized Aggregation Proneness (GAP), could successfully distinguish between amyloid fibril-forming and amorphous β-aggregating hexapeptides with almost 100 percent accuracies in validation tests performed using non-redundant datasets. Conclusion: Accuracies of the predictions made by GAP are significantly improved compared with other methods capable of predicting either general β-aggregation or amyloid fibril-forming APRs. This work demonstrates that amino acid side chains play important roles in determining the morphological fate of β-mediated aggregates formed by short peptides. Availability and implementation: http://www.iitm.ac.in/bioinfo/GAP/ . Contact: gromiha@iitm.ac.in Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: The protein structure prediction approaches can be categorized into template-based modeling (including homology modeling and threading) and free modeling. However, the existing threading tools perform poorly on remote homologous proteins. Thus, improving fold recognition for remote homologous proteins remains a challenge. Besides, the proteome-wide structure prediction poses another challenge of increasing prediction throughput. In this study, we presented FALCON@home as a protein structure prediction server focusing on remote homologue identification. The design of FALCON@home is based on the observation that a structural template, especially for remote homologous proteins, consists of conserved regions interweaved with highly variable regions. The highly variable regions lead to vague alignments in threading approaches. Thus, FALCON@home first extracts conserved regions from each template and then aligns a query protein with conserved regions only rather than the full-length template directly. This helps avoid the vague alignments rooted in highly variable regions, improving remote homologue identification. We implemented FALCON@home using the Berkeley Open Infrastructure of Network Computing (BOINC) volunteer computing protocol. With computation power donated from over 20 000 volunteer CPUs, FALCON@home shows a throughput as high as processing of over 1000 proteins per day. In the Critical Assessment of protein Structure Prediction (CASP11), the FALCON@home-based prediction was ranked the 12th in the template-based modeling category. As an application, the structures of 880 mouse mitochondria proteins were predicted, which revealed the significant correlation between protein half-lives and protein structural factors. Availability and implementation: FALCON@home is freely available at http://protein.ict.ac.cn/FALCON/. Contact: shuaicli@cityu.edu.hk, dbu@ict.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Homology search methods are dominated by the central paradigm that sequence similarity is a proxy for common ancestry and, by extension, functional similarity. For determining sequence similarity in proteins, most widely used methods use models of sequence evolution and compare amino-acid strings in search for conserved linear stretches. Probabilistic models or sequence profiles capture the position-specific variation in an alignment of homologous sequences and can identify conserved motifs or domains. While profile-based search methods are generally more accurate than simple sequence comparison methods, they tend to be computationally more demanding. In recent years, several methods have emerged that perform protein similarity searches based on domain composition. However, few methods have considered the linear arrangements of domains when conducting similarity searches, despite strong evidence that domain order can harbour considerable functional and evolutionary signal. Results: Here, we introduce an alignment scheme that uses a classical dynamic programming approach to the global alignment of domains. We illustrate that representing proteins as strings of domains (domain arrangements) and comparing these strings globally allows for a both fast and sensitive homology search. Further, we demonstrate that the presented methods complement existing methods by finding similar proteins missed by popular amino-acid–based comparison methods. Availability: An implementation of the presented algorithms, a web-based interface as well as a command-line program for batch searching against the UniProt database can be found at http://rads.uni-muenster.de. Furthermore, we provide a JAVA API for programmatic access to domain-string–based search methods. Contact: terrapon.nicolas@gmail.com or ebb@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Peptides play important roles in signalling, regulation and immunity within an organism. Many have successfully been used as therapeutic products often mimicking naturally occurring peptides. Here we present PeptideLocator for the automated prediction of functional peptides in a protein sequence. Results: We have trained a machine learning algorithm to predict bioactive peptides within protein sequences. PeptideLocator performs well on training data achieving an area under the curve of 0.92 when tested in 5-fold cross-validation on a set of 2202 redundancy reduced peptide containing protein sequences. It has predictive power when applied to antimicrobial peptides, cytokines, growth factors, peptide hormones, toxins, venoms and other peptides. It can be applied to refine the choice of experimental investigations in functional studies of proteins. Availability and implementation: PeptideLocator is freely available for academic users at http://bioware.ucd.ie/. Contact: denis.shields@ucd.ie Supplementary information: Supplementary data are available at Bioinformatics online.

Based on all kinds of adjacent amino acids (AAA), we map each protein primary sequence into a 400 by ($(L-1)$) matrix $({\schmi M})$. In addition, we further derive a normalized 400-tuple mathematical descriptors $({\schmi D})$, which is extracted from the primary protein sequences via singular values decomposition (SVD) of the matrix. The obtained 400-D normalized feature vectors (NFVs) further facilitate our quantitative analysis of protein sequences. Using the normalized representation of the primary protein sequences, we analyze the similarity for different sequences upon two data sets: 1) ND5 sequences from nine species and 2) transferrin sequences of 24 vertebrates. We also compared the results in this study with those from other related works. These two experiments illustrate that our proposed NFV-AAA approach does perform well in the field of similarity analysis of sequence.

Computing similarity or dissimilarity between protein structures is an important task in structural biology. A conventional method to compute protein structure dissimilarity requires structural alignment of the proteins. However, defining one best alignment is difficult, especially when the structures are very different. In this paper, we propose a new similarity measure for protein structure comparisons using a set of multi-view 2D images of 3D protein structures. In this approach, each protein structure is represented by a subspace from the image set. The similarity between two protein structures is then characterized by the canonical angles between the two subspaces. The primary advantage of our method is that precise alignment is not needed. We employed Grassmann Discriminant Analysis (GDA) as the subspace-based learning in the classification framework. We applied our method for the classification problem of seven SCOP structural classes of protein 3D structures. The proposed method outperformed the k-nearest neighbor method (k-NN) based on conventional alignment-based methods CE, FATCAT, and TM-align. Our method was also applied to the classification of SCOP folds of membrane proteins, where the proposed method could recognize the fold HEM-binding four-helical bundle (f.21) much better than TM-Align.

Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method 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 neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one 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 but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. Availability http://raptorx.uchicago.edu/ContactMap/, Author Summary Protein contact prediction and contact-assisted folding has made good progress due to direct evolutionary coupling analysis (DCA). However, DCA is effective on only some proteins with a very large number of sequence homologs. To further improve contact prediction, we borrow ideas from deep learning, which has recently revolutionized object recognition, speech recognition and the GO game. Our deep learning method can model complex sequence-structure relationship and high-order correlation (i.e., contact occurrence patterns) and thus, improve contact prediction accuracy greatly. Our test results show that our method greatly outperforms the state-of-the-art methods regardless how many sequence homologs are available for a protein in question. Ab initio folding guided by our predicted contacts may fold many more test proteins than the other contact predictors. Our contact-assisted 3D models also have much better quality than homology models built from the training proteins, especially for membrane proteins. One interesting finding is that even trained mostly with soluble proteins, our method performs very well on membrane proteins. Recent blind CAMEO test confirms that our method can fold large proteins with a new fold and only a small number of sequence homologs.

Protein remote homology detection is an important task in computational proteomics. Some computational methods have been proposed, which detect remote homology proteins based on different features and algorithms. As noted in previous studies, their predictive results are complementary to each other. Therefore, it is intriguing to explore whether these methods can be combined into one package so as to further enhance the performance power and application convenience. In view of this, we introduced a protein representation called profile-based pseudo protein sequence to extract the evolutionary information from the relevant profiles. Based on the concept of pseudo proteins, a new predictor, called “dRHP-PseRA”, was developed by combining four state-of-the-art predictors (PSI-BLAST, HHblits, Hmmer, and Coma) via the rank aggregation approach. Cross-validation tests on a SCOP benchmark dataset have demonstrated that the new predictor has remarkably outperformed any of the existing methods for the same purpose on ROC50 scores. Accordingly, it is anticipated that dRHP-PseRA holds very high potential to become a useful high throughput tool for detecting remote homology proteins. For the convenience of most experimental scientists, a web-server for dRHP-PseRA has been established at http://bioinformatics.hitsz.edu.cn/dRHP-PseRA/.

none 10 no The MATLAB implementation of the method described in the article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/. MOTIVATION: Independent component analysis (ICA) is a signal processing technique that can be utilized to recover independent signals from a set of their linear mixtures. We propose ICA for the analysis of signals obtained from large proteomics investigations such as clinical multi-subject studies based on MALDI-TOF MS profiling. The method is validated on simulated and experimental data for demonstrating its capability of correctly extracting protein profiles from MALDI-TOF mass spectra. RESULTS: The comparison on peak detection with an open-source and two commercial methods shows its superior reliability in reducing the false discovery rate of protein peak masses. Moreover, the integration of ICA and statistical tests for detecting the differences in peak intensities between experimental groups allows to identify protein peaks that could be indicators of a diseased state. This data-driven approach demonstrates to be a promising tool for biomarker-discovery studies based on MALDI-TOF MS technology. none Mantini D; Petrucci F; Del Boccio P; Pieragostino D; Di Nicola M; Lugaresi A; Federici G; Sacchetta P; Di Ilio C; Urbani A Mantini D; Petrucci F; Del Boccio P; Pieragostino D; Di Nicola M; Lugaresi A; Federici G; Sacchetta P; Di Ilio C; Urbani A

Summary The reduced amino acids perform powerful ability for both simplifying protein complexity and identifying functional conserved regions. However, dealing with different protein problems may need different kinds of cluster methods. Encouraged by the success of pseudo-amino acid composition algorithm, we developed a freely available web server, called PseKRAAC (the pseudo K-tuple reduced amino acids composition). By implementing reduced amino acid alphabets, the protein complexity can be significantly simplified, which leads to decrease chance of overfitting, lower computational handicap and reduce information redundancy. PseKRAAC delivers more capability for protein research by incorporating three crucial parameters that describes protein composition. Users can easily generate many different modes of PseKRAAC tailored to their needs by selecting various reduced amino acids alphabets and other characteristic parameters. It is anticipated that the PseKRAAC web server will become a very useful tool in computational proteomics and protein sequence analysis. Availability and Implementation Freely available on the web at http://bigdata.imu.edu.cn/psekraac Supplementary information Supplementary data are available at Bioinformatics online.

{\it De novo} protein sequencing is essential for understanding cellular processes that govern the function of living organisms and all post-translational events and other sequence modifications that occur after a protein has been constructed from its corresponding DNA code. By obtaining the order of the amino acids that composes a given protein one can then determine both its secondary and tertiary structures through structure prediction, which is used to create models for protein aggregation diseases such as Alzheimer's Disease. Mass spectrometry is the current technique of choice for {\it de novo} sequencing. However, because some amino acids have the same mass the sequence cannot be completely determined in many cases. Here, we propose a new technique for {\it de novo} protein sequencing that involves translocating a polypeptide through a synthetic nanochannel and measuring the ionic current of each amino acid through an intersecting {\it perpendicular} nanochannel. To calculate the transverse ionic current blockaded by a given amino acid we use a Monte Carlo method along with Ramachandran plots to determine the available flow area, modified by the local density of ions obtained from molecular dynamics and the local flow velocity ratio derived from the Stokes equation. We find that the distribution of ionic currents for each of the 20 proteinogenic amino acids encoded by eukaryotic genes is statistically distinct, showing this technique's potential for {\it de novo} protein sequencing., 12 pages (9 of main text, 3 of supporting information), 4 figures, 1 table in supporting information

Protein complex identification is an important goal of protein-protein interaction analysis. To date, development of computational methods for detecting protein complexes has been largely motivated by genome-scale interaction data sets from high-throughput assays such as yeast two-hybrid or tandem affinity purification coupled with mass spectrometry (TAP-MS). However, due to the popularity of small to intermediate-scale affinity purification-mass spectrometry (AP-MS) experiments, protein complex detection is increasingly discussed in local network analysis. In such data sets, protein complexes cannot be detected using binary interaction data alone because the data contain interactions with tagged proteins only and, as a result, interactions between all other proteins remain unobserved, limiting the scope of existing algorithms. In this article, we provide a pragmatic review of network graph-based computational algorithms for protein complex analysis in global interactome data, without requiring any computational background. We discuss the practical gap in applying these algorithms to recently surging small to intermediate-scale AP-MS data sets, and review alternative clustering algorithms using quantitative proteomics data and their limitations.

Motivation: Analysis of protein–protein interaction networks (PPINs) at the system level has become increasingly important in understanding biological processes. Comparison of the interactomes of different species not only provides a better understanding of species evolution but also helps with detecting conserved functional components and in function prediction. Method and Results: Here we report a PPIN alignment method, called PINALOG, which combines information from protein sequence, function and network topology. Alignment of human and yeast PPINs reveals several conserved subnetworks between them that participate in similar biological processes, notably the proteasome and transcription related processes. PINALOG has been tested for its power in protein complex prediction as well as function prediction. Comparison with PSI-BLAST in predicting protein function in the twilight zone also shows that PINALOG is valuable in predicting protein function. Availability and implementation: The PINALOG web-server is freely available from http://www.sbg.bio.ic.ac.uk/~pinalog. The PINALOG program and associated data are available from the Download section of the web-server. Contact: m.sternberg@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Protein engineering by combinatorial site-directed mutagenesis evaluates a portion of the sequence space near a target protein, seeking variants with improved properties (e.g., stability, activity, immunogenicity). In order to improve the hit-rate of beneficial variants in such mutagenesis libraries, we develop methods to select optimal positions and corresponding sets of the mutations that will be used, in all combinations, in constructing a library for experimental evaluation. Our approach, OCoM (Optimization of Combinatorial Mutagenesis), encompasses both degenerate oligonucleotides and specified point mutations, and can be directed accordingly by requirements of experimental cost and library size. It evaluates the quality of the resulting library by one- and two-body sequence potentials, averaged over the variants. To ensure that it is not simply recapitulating extant sequences, it balances the quality of a library with an explicit evaluation of the novelty of its members. We show that, despite dealing with a combinatorial set of variants, in our approach the resulting library optimization problem is actually isomorphic to single-variant optimization. By the same token, this means that the two-body sequence potential results in an NP-hard optimization problem. We present an efficient dynamic programming algorithm for the one-body case and a practically-efficient integer programming approach for the general two-body case. We demonstrate the effectiveness of our approach in designing libraries for three different case study proteins targeted by previous combinatorial libraries--a green fluorescent protein, a cytochrome P450, and a beta lactamase. We found that OCoM worked quite efficiently in practice, requiring only 1 hour even for the massive design problem of selecting 18 mutations to generate 10⁷ variants of a 443-residue P450. We demonstrate the general ability of OCoM in enabling the protein engineer to explore and evaluate trade-offs between quality and novelty as well as library construction technique, and identify optimal libraries for experimental evaluation.

Many biologically important protein-protein interactions (PPIs) have been found to be mediated by short linear motifs (SLiMs). These interactions are mediated by the binding of a protein domain, often with a nonlinear interaction interface, to a SLiM. We propose a method called D-SLIMMER to mine for SLiMs in PPI data on the basis of the interaction density between a nonlinear motif (i.e., a protein domain) in one protein and a SLiM in the other protein. Our results on a benchmark of 113 experimentally verified reference SLiMs showed that D-SLIMMER outperformed existing methods notably for discovering domain-SLiMs interaction motifs. To illustrate the significance of the SLiMs detected, we highlighted two SLiMs discovered from the PPI data by D-SLIMMER that are variants of the known ELM SLiM, as well as a literature-backed SLiM that is yet to be listed in the reference databases. We also presented a novel SLiM predicted by D-SLIMMER that was strongly supported by existing biological literatures. These examples showed that D-SLIMMER is able to find SLiMs that are biologically relevant.

Proliferation of drug-resistant diseases raises the challenge of searching for new, more efficient antibiotics. Currently, some of the most effective antibiotics (i.e., Vancomycin and Daptomycin) are cyclic peptides produced by non-ribosomal biosynthetic pathways. The isolation and sequencing of cyclic peptide antibiotics, unlike the same activity with linear peptides, is time-consuming and error-prone. The dominant technique for sequencing cyclic peptides is nuclear magnetic resonance (NMR)-based and requires large amounts (milligrams) of purified materials that, for most compounds, are not possible to obtain. Given these facts, there is a need for new tools to sequence cyclic non-ribosomal peptides (NRPs) using picograms of material. Since nearly all cyclic NRPs are produced along with related analogs, we develop a mass spectrometry approach for sequencing all related peptides at once (in contrast to the existing approach that analyzes individual peptides). Our results suggest that instead of attempting to isolate and NMR-sequence the most abundant compound, one should acquire spectra of many related compounds and sequence all of them simultaneously using tandem mass spectrometry. We illustrate applications of this approach by sequencing new variants of cyclic peptide antibiotics from Bacillus brevis, as well as sequencing a previously unknown family of cyclic NRPs produced by marine bacteria. Supplementary Material is available online at www.liebertonline.com/cmb.

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.

Protein-protein interactions (PPIs) are crucial to most biochemical processes in human beings. Although many human PPIs have been identified by experiments, the number is still limited compared to the available protein sequences of human organisms. Recently, many computational methods have been proposed to facilitate the recognition of novel human PPIs. However the existing methods only concentrated on the information of individual PPI, while the systematic characteristic of protein-protein interaction networks (PINs) was ignored. In this study, a new method was proposed by combining the global information of PINs and protein sequence information. Random forest (RF) algorithm was implemented to develop the prediction model, and a high accuracy of 91.88% was obtained. Furthermore, the RF model was tested using three independent datasets with good performances, suggesting that our method is a useful tool for identification of PPIs and investigation into PINs as well.

Protein remote homology detection is a critical step toward annotating its structure and function. Supervised learning algorithms such as support vector machine are currently the most accurate methods. The position-specific score matrices (PSSMs) contain wealthy information about the evolutionary relationship of proteins. However, the PSSMs often have different lengths, which are difficult to be used by machine-learning methods. In this study, a simple, fast and powerful method is presented for protein remote homology detection, which combines support vector machine with auto-cross covariance transformation. The PSSMs are converted into a series of fixed-length vectors by auto-cross covariance transformation and these vectors are then input to a support vector machine classifier for remote homology detection. The sequence-order effects can be effectively captured by this scheme. Experiments are performed on well-established datasets, and the remote homology is simulated at the superfamily and the fold level, respectively. The results show that the proposed method, referred to as ACCRe, is comparable or even better than the state-of-the-art methods in terms of detection performance, and its time complexity is superior to those of other profile-based SVM methods. The auto-cross covariance transformation provides a novel way for the usage of evolutionary information, which can be widely used for protein-level studies.