We have characterized a novel type of PSI-BLAST error, homologous over-extension (HOE), using embedded PFAM domain queries on searches against a reference library containing Pfam-annotated UniProt sequences and random synthetic sequences. PSI-BLAST makes two types of errors: alignments to non-homologous regions and HOE alignments that begin in a homologous region, but extend beyond the homology into neighboring sequence regions. When the neighboring sequence region contains a non-homologous domain, PSI-BLAST can incorporate the unrelated sequence into its position specific scoring matrix, which then finds non-homologous proteins with significant expectation values. HOE accounts for the largest fraction of the initial false positive (FP) errors, and the largest fraction of FPs at iteration 5. In searches against complete protein sequences, 5–9% of alignments at iteration 5 are non-homologous. HOE frequently begins in a partial protein domain; when partial domains are removed from the library, HOE errors decrease from 16 to 3% of weighted coverage (hard queries; 35–5% for sampled queries) and no-error searches increase from 2 to 58% weighed coverage (hard; 16–78% sampled). When HOE is reduced by not extending previously found sequences, PSI-BLAST specificity improves 4–8-fold, with little loss in sensitivity.
BLAST is a commonly-used software package for comparing a query sequence to a database of known sequences; in this study, we focus on protein sequences. Position-specific-iterated BLAST (PSI-BLAST) iteratively searches a protein sequence database, using the matches in round i to construct a position-specific score matrix (PSSM) for searching the database in round i + 1. Biegert and Söding developed Context-sensitive BLAST (CS-BLAST), which combines information from searching the sequence database with information derived from a library of short protein profiles to achieve better homology detection than PSI-BLAST, which builds its PSSMs from scratch.
We describe a new method, called domain enhanced lookup time accelerated BLAST (DELTA-BLAST), which searches a database of pre-constructed PSSMs before searching a protein-sequence database, to yield better homology detection. For its PSSMs, DELTA-BLAST employs a subset of NCBI’s Conserved Domain Database (CDD). On a test set derived from ASTRAL, with one round of searching, DELTA-BLAST achieves a ROC5000 of 0.270 vs. 0.116 for CS-BLAST. The performance advantage diminishes in iterated searches, but DELTA-BLAST continues to achieve better ROC scores than CS-BLAST.
DELTA-BLAST is a useful program for the detection of remote protein homologs. It is available under the “Protein BLAST” link at http://blast.ncbi.nlm.nih.gov.
This article was reviewed by Arcady Mushegian, Nick V. Grishin, and Frank Eisenhaber.
A typical PSI-BLAST search consists of iterative scanning and alignment of a large sequence database during which a scoring profile is progressively built and refined. Such a profile can also be stored and used to search against a different database of sequences. Using it to search against a database of consensus rather than native sequences is a simple add-on that boosts performance surprisingly well. The improvement comes at a price: we hypothesized that random alignment score statistics would differ between native and consensus sequences. Thus PSI-BLAST-based profile searches against consensus sequences might incorrectly estimate statistical significance of alignment scores. In addition, iterative searches against consensus databases may fail. Here, we addressed these challenges in an attempt to harness the full power of the combination of PSI-BLAST and consensus sequences.
We studied alignment score statistics for various types of consensus sequences. In general, the score distribution parameters of profile-based consensus sequence alignments differed significantly from those derived for the native sequences. PSI-BLAST partially compensated for the parameter variation. We have identified a protocol for building specialized consensus sequences that significantly improved search sensitivity and preserved score distribution parameters. As a result, PSI-BLAST profiles can be used to search specialized consensus sequences without sacrificing estimates of statistical significance. We also provided results indicating that iterative PSI-BLAST searches against consensus sequences could work very well. Overall, we showed how a widely popular and effective method could be used to identify significantly more relevant similarities among protein sequences.
Motivation: A typical PSI-BLAST search consists of iterative scanning and alignment of a large sequence database during which a scoring profile is progressively built and refined. Such a profile can also be stored and used to search against a different database of sequences. Using it to search against a database of consensus rather than native sequences is a simple add-on that boosts performance surprisingly well. The improvement comes at a price: we hypothesized that random alignment score statistics would differ between native and consensus sequences. Thus PSI-BLAST-based profile searches against consensus sequences might incorrectly estimate statistical significance of alignment scores. In addition, iterative searches against consensus databases may fail. Here, we addressed these challenges in an attempt to harness the full power of the combination of PSI-BLAST and consensus sequences.
Results: We studied alignment score statistics for various types of consensus sequences. In general, the score distribution parameters of profile-based consensus sequence alignments differed significantly from those derived for the native sequences. PSI-BLAST partially compensated for the parameter variation. We have identified a protocol for building specialized consensus sequences that significantly improved search sensitivity and preserved score distribution parameters. As a result, PSI-BLAST profiles can be used to search specialized consensus sequences without sacrificing estimates of statistical significance. We also provided results indicating that iterative PSI-BLAST searches against consensus sequences could work very well. Overall, we showed how a very popular and effective method could be used to identify significantly more relevant similarities among protein sequences.
Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases.
We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K.
Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.
Protein families could be related to each other at broad levels that group them as superfamilies. These relationships are harder to detect at the sequence level due to high evolutionary divergence. Sequence searches are strongly directed and influenced by the best representatives of families that are viewed as starting points. PSSMs are useful approximations and mathematical representations of protein alignments, with wide array of applications in bioinformatics approaches like remote homology detection, protein family analysis, detection of new members and evolutionary modelling. Computational intensive searches have been performed using the neural network based sensitive sequence search method called FASSM to identify the Best Representative PSSMs for families reported in Pfam database version 22.
We designed a novel data mining approach for the assessment of individual sequences from a protein family to identify a single Best Representative PSSM profile (BRP) per protein family. Using the approach, a database of protein family-specific best representative PSSM profiles called 3PFDB has been developed. PSSM profiles in 3PFDB are curated using performance of individual sequence as a reference in a rigorous scoring and coverage analysis approach using FASSM. We have assessed the suitability of 10, 85,588 sequences derived from seed or full alignments reported in Pfam database (Version 22). Coverage analysis using FASSM method is used as the filtering step to identify the best representative sequence, starting from full length or domain sequences to generate the final profile for a given family. 3PFDB is a collection of best representative PSSM profiles of 8,524 protein families from Pfam database.
Availability of an approach to identify BRPs and a curated database of best representative PSI-BLAST derived PSSMs for 91.4% of current Pfam family will be a useful resource for the community to perform detailed and specific analysis using family-specific, best-representative PSSM profiles. 3PFDB can be accessed using the URL: http://caps.ncbs.res.in/3pfdb
Profile hidden Markov models (HMMs) are amongst the most successful procedures for detecting remote homology between proteins. There are two popular profile HMM programs, HMMER and SAM. Little is known about their performance relative to each other and to the recently improved version of PSI-BLAST. Here we compare the two programs to each other and to non-HMM methods, to determine their relative performance and the features that are important for their success. The quality of the multiple sequence alignments used to build models was the most important factor affecting the overall performance of profile HMMs. The SAM T99 procedure is needed to produce high quality alignments automatically, and the lack of an equivalent component in HMMER makes it less complete as a package. Using the default options and parameters as would be expected of an inexpert user, it was found that from identical alignments SAM consistently produces better models than HMMER and that the relative performance of the model-scoring components varies. On average, HMMER was found to be between one and three times faster than SAM when searching databases larger than 2000 sequences, SAM being faster on smaller ones. Both methods were shown to have effective low complexity and repeat sequence masking using their null models, and the accuracy of their E-values was comparable. It was found that the SAM T99 iterative database search procedure performs better than the most recent version of PSI-BLAST, but that scoring of PSI-BLAST profiles is more than 30 times faster than scoring of SAM models.
The EMBL-European Bioinformatics Institute (EMBL-EBI) offers public access to patent sequence data, providing a valuable service to the intellectual property and scientific communities. The non-redundant (NR) patent sequence databases comprise two-level nucleotide and protein sequence clusters (NRNL1, NRNL2, NRPL1 and NRPL2) based on sequence identity (level-1) and patent family (level-2). Annotation from the source entries in these databases is merged and enhanced with additional information from the patent literature and biological context. Corrections in patent publication numbers, kind-codes and patent equivalents significantly improve the data quality. Data are available through various user interfaces including web browser, downloads via FTP, SRS, Dbfetch and EBI-Search. Sequence similarity/homology searches against the databases are available using BLAST, FASTA and PSI-Search. In this article, we describe the data collection and annotation and also outline major changes and improvements introduced since 2009. Apart from data growth, these changes include additional annotation for singleton clusters, the identifier versioning for tracking entry change and the entry mappings between the two-level databases.
A SIB-BLAST web server (http://sib-blast.osc.edu) has been established for investigators to use the SimpleIsBeautiful (SIB) algorithm for sequence-based homology detection. SIB was developed to overcome the model corruption frequently observed in the later iterations of PSI-BLAST searches. The algorithm compares resultant hits from the second iteration to the final iteration of a PSI-BLAST search, calculates the figure of merit for each ‘overlapped’ hit and re-ranks the hits according to their figure of merit. By validating hits generated from the last profile against hits from the first profile when the model is least corrupted, the true and false positives are better delineated, which in turn, improves the accuracy of iterative PSI-BLAST searches. Notably, this improvement to PSI-BLAST comes at minimal computational cost as SIB-BLAST utilizes existing results already produced in a PSI-BLAST search.
In past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.
The models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM (Support Vector Machine) model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy (mean of class-wise accuracy) of 68.47%. In order to utilize evolutionary information, a SVM model was developed using PSSM (Position-Specific Scoring Matrix) profiles obtained from PSI-BLAST (Position-Specific Iterated BLAST) and overall accuracy achieved was of 86.62% with average accuracy of 73.71%. In addition, HMM (Hidden Markov Model), MEME/MAST (Multiple Em for Motif Elicitation/Motif Alignment and Search Tool) and hybrid model that combined two or more models were also developed. We achieved maximum overall accuracy of 86.8% with average accuracy of 89.00% using combination of PSSM based SVM model and MEME/MAST. Performance of our method was compared with that of the existing methods developed for predicting subcellular locations of Gram-positive bacterial proteins.
A highly accurate method has been developed for predicting subcellular location of mycobacterial proteins. This method also predicts very important class of proteins that is membrane-attached proteins. This method will be useful in annotating newly sequenced or hypothetical mycobacterial proteins. Based on above study, a freely accessible web server TBpred http://www.imtech.res.in/raghava/tbpred/ has been developed.
Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.
An average of sensitivity and specificity using PSSMs is up to 8.7% better than the prediction with sequence information only. Much smaller data sets could be used to generate PSSM with minimal loss of prediction accuracy.
One problem in using PSSM-derived prediction is obtaining lengthy and time-consuming alignments against large sequence databases. In order to speed up the process of generating PSSMs, we tried to use different reference data sets (sequence space) against which a target protein is scanned for PSI-BLAST iterations. We find that a very small set of proteins can actually be used as such a reference data without losing much of the prediction value. This makes the process of generating PSSMs very rapid and even amenable to be used at a genome level. A web server has been developed to provide these predictions of DNA-binding sites for any new protein from its amino acid sequence.
Online predictions based on this method are available at
Since 2004 the European Bioinformatics Institute (EMBL-EBI) has provided access to a wide range of databases and analysis tools via Web Services interfaces. This comprises services to search across the databases available from the EMBL-EBI and to explore the network of cross-references present in the data (e.g. EB-eye), services to retrieve entry data in various data formats and to access the data in specific fields (e.g. dbfetch), and analysis tool services, for example, sequence similarity search (e.g. FASTA and NCBI BLAST), multiple sequence alignment (e.g. Clustal Omega and MUSCLE), pairwise sequence alignment and protein functional analysis (e.g. InterProScan and Phobius). The REST/SOAP Web Services (http://www.ebi.ac.uk/Tools/webservices/) interfaces to these databases and tools allow their integration into other tools, applications, web sites, pipeline processes and analytical workflows. To get users started using the Web Services, sample clients are provided covering a range of programming languages and popular Web Service tool kits, and a brief guide to Web Services technologies, including a set of tutorials, is available for those wishing to learn more and develop their own clients. Users of the Web Services are informed of improvements and updates via a range of methods.
Sequence alignments may be the most fundamental computational resource for molecular biology. The best methods that identify sequence relatedness through profile–profile comparisons are much slower and more complex than sequence–sequence and sequence–profile comparisons such as, respectively, BLAST and PSI-BLAST. Families of related genes and gene products (proteins) can be represented by consensus sequences that list the nucleic/amino acid most frequent at each sequence position in that family. Here, we propose a novel approach for consensus-sequence-based comparisons. This approach improved searches and alignments as a standard add-on to PSI-BLAST without any changes of code. Improvements were particularly significant for more difficult tasks such as the identification of distant structural relations between proteins and their corresponding alignments. Despite the fact that the improvements were higher for more divergent relations, they were consistent even at high accuracy/low error rates for non-trivially related proteins. The improvements were very easy to achieve; no parameter used by PSI-BLAST was altered and no single line of code changed. Furthermore, the consensus sequence add-on required relatively little additional CPU time. We discuss how advanced users of PSI-BLAST can immediately benefit from using consensus sequences on their local computers. We have also made the method available through the Internet (http://www.rostlab.org/services/consensus/).
SCANPS performs iterative profile searching similar to PSI-BLAST but with full dynamic programing on each cycle and on-the-fly estimation of significance. This combination gives good sensitivity and selectivity that outperforms PSI-BLAST in domain-searching benchmarks. Although computationally expensive, SCANPS exploits onchip parallelism (MMX and SSE2 instructions on Intel chips) as well as MPI parallelism to give acceptable turnround times even for large databases. A web server developed to run SCANPS searches is now available at http://www.compbio.dundee.ac.uk/www-scanps. The server interface allows a range of different protein sequence databases to be searched including the SCOP database of protein domains. The server provides the user with regularly updated versions of the main protein sequence databases and is backed up by significant computing resources which ensure that searches are performed rapidly. For SCOP searches, the results may be viewed in a new tree-based representation that reflects the structure of the SCOP hierarchy; this aids the user in placing each hit in the context of its SCOP classification and understanding its relationship to other domains in SCOP.
Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens.
In the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of ith and i+1th amino acid residues), higher order dipeptide composition (pairs of ith and i+2nd residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features.
VirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server – VirulentPred, at http://bioinfo.icgeb.res.in/virulent/.
The Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC) specification was
created by the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to
enable computational access to molecular-interaction data resources by means of a standard
Web Service and query language. Currently providing >150 million binary interaction
evidences from 28 servers globally, the PSICQUIC interface allows the concurrent search of
multiple molecular-interaction information resources using a single query. Here, we
present an extension of the PSICQUIC specification (version 1.3), which has been released
to be compliant with the enhanced standards in molecular interactions. The new release
also includes a new reference implementation of the PSICQUIC server available to the data
providers. It offers augmented web service capabilities and improves the user experience.
PSICQUIC has been running for almost 5 years, with a user base growing from only 4 data
providers to 28 (April 2013) allowing access to 151 310 109 binary interactions. The power
of this web service is shown in PSICQUIC View web application, an example of how to
simultaneously query, browse and download results from the different PSICQUIC servers.
This application is free and open to all users with no login requirement (http://www.ebi.ac.uk/Tools/webservices/psicquic/view/main.xhtml).
All-versus-all BLAST, which searches for homologous pairs of sequences in a database of proteins, is used to identify potential orthologs, to find new protein families, and to provide rapid access to these homology relationships. As DNA sequencing accelerates and data sets grow, all-versus-all BLAST has become computationally demanding.
We present FastBLAST, a heuristic replacement for all-versus-all BLAST that relies on alignments of proteins to known families, obtained from tools such as PSI-BLAST and HMMer. FastBLAST avoids most of the work of all-versus-all BLAST by taking advantage of these alignments and by clustering similar sequences. FastBLAST runs in two stages: the first stage identifies additional families and aligns them, and the second stage quickly identifies the homologs of a query sequence, based on the alignments of the families, before generating pairwise alignments. On 6.53 million proteins from the non-redundant Genbank database (“NR”), FastBLAST identifies new families 25 times faster than all-versus-all BLAST. Once the first stage is completed, FastBLAST identifies homologs for the average query in less than 5 seconds (8.6 times faster than BLAST) and gives nearly identical results. For hits above 70 bits, FastBLAST identifies 98% of the top 3,250 hits per query.
FastBLAST enables research groups that do not have supercomputers to analyze large protein sequence data sets. FastBLAST is open source software and is available at http://microbesonline.org/fastblast.
Motivation: The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments.
Results: We found that position-specific gap penalties have an advantage over uniform gap costs. We did not explore optimizing distinct uniform gap costs for each query. For Pfam, PSSMs iteratively constructed from seeds based on HMM consensus sequences perform equivalently to HMMs that were adjusted to have constant gap transition probabilities, albeit with much greater variance. We observed no effect of composition-specific gap costs on retrieval performance. These results suggest possible improvements to the PSI-BLAST protein database search program.
Availability: The scripts for performing evaluations are available upon request from the authors.
The EMBL-EBI provides access to various mainstream sequence analysis applications. These include sequence similarity search services such as BLAST, FASTA, InterProScan and multiple sequence alignment tools such as ClustalW, T-Coffee and MUSCLE. Through the sequence similarity search services, the users can search mainstream sequence databases such as EMBL-Bank and UniProt, and more than 2000 completed genomes and proteomes. We present here a new framework aimed at both novice as well as expert users that exposes novel methods of obtaining annotations and visualizing sequence analysis results through one uniform and consistent interface. These services are available over the web and via Web Services interfaces for users who require systematic access or want to interface with customized pipe-lines and workflows using common programming languages. The framework features novel result visualizations and integration of domain and functional predictions for protein database searches. It is available at http://www.ebi.ac.uk/Tools/sss for sequence similarity searches and at http://www.ebi.ac.uk/Tools/msa for multiple sequence alignments.
The EMBL Nucleotide Sequence Database (http://www.ebi.ac.uk/embl/), maintained at the European Bioinformatics Institute (EBI), incorporates, organizes and distributes nucleotide sequences from public sources. The database is a part of an international collaboration with DDBJ (Japan) and GenBank (USA). Data are exchanged between the collaborating databases on a daily basis to achieve optimal synchrony. The web-based tool, Webin, is the preferred system for individual submission of nucleotide sequences, including Third Party Annotation (TPA) and alignment data. Automatic submission procedures are used for submission of data from large-scale genome sequencing centres and from the European Patent Office. Database releases are produced quarterly. The latest data collection can be accessed via FTP, email and WWW interfaces. The EBI’s Sequence Retrieval System (SRS) integrates and links the main nucleotide and protein databases as well as many other specialist molecular biology databases. For sequence similarity searching, a variety of tools (e.g. FASTA and BLAST) are available that allow external users to compare their own sequences against the data in the EMBL Nucleotide Sequence Database, the complete genomic component subsection of the database, the WGS data sets and other databases. All available resources can be accessed via the EBI home page at http://www.ebi.ac.uk.
Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the “multiple segment Viterbi” (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call “sparse rescaling”. These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.
Searching sequence databases is one of the most important applications in computational molecular biology. The main workhorse in the field is the BLAST suite of programs. Since the introduction of BLAST in the 1990's, important theoretical advances in homology search methodology have been made using probabilistic inference methods and hidden Markov models (HMMs). However, previous software implementations of these newer probabilistic methods were slower than BLAST by about 100-fold. This hindered their utility, because computation speed is so critical with the rapidly increasing size of modern sequence databases. Here I describe the acceleration methods I implemented in a new, freely available profile HMM software package, HMMER3. HMMER3 makes profile HMM searches about as fast as BLAST, while retaining the power of using probabilistic inference technology.
Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server.
In seeking more powerful descriptors, the profile-profile alignment score generated from the COMPASS algorithm was first considered as a new descriptor (i.e., PPA). When considering a profile-profile alignment between two proteins in the context of fold recognition, one protein is regarded as a template (i.e., its 3D structure is known). Instead of a sequence profile derived from a Psi-blast search, a structure-seeded profile for the template protein was generated by searching its structural neighbors with the assistance of the TM-align structural alignment algorithm. Moreover, the COMPASS algorithm was used again to derive a profile-structural-profile-alignment-based descriptor (i.e., PSPA). We trained and tested the new DescFold in a total of 1,835 highly diverse proteins extracted from the SCOP 1.73 version. When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset.
The new DescFold method was intensively benchmarked to have very competitive performance compared with some well-established fold recognition methods, suggesting that it can serve as a useful tool to assist in template-based protein structure prediction. The DescFold server is freely accessible at http://126.96.36.199/DescFold/index.html.
Identification of RNA homologs within genomic stretches is difficult when pairwise sequence identity is low or unalignable flanking residues are present. In both cases structure-sequence or profile/family-sequence alignment programs become difficult to apply because of unreliable RNA structures or family alignments. As such, local sequence-sequence alignment programs are frequently used instead. We have recently demonstrated that maximal expected accuracy alignments using partition function match probabilities (implemented in Probalign) are significantly better than contemporary methods on heterogeneous length protein sequence datasets, thus suggesting an affinity for local alignment.
We create a pairwise RNA-genome alignment benchmark from RFAM families with average pairwise sequence identity up to 60%. Each dataset contains a query RNA aligned to a target RNA (of the same family) embedded in a genomic sequence at least 5K nucleotides long. To simulate common conditions when exact ends of an ncRNA are unknown, each query RNA has 5' and 3' genomic flanks of size 50, 100, and 150 nucleotides. We subsequently compare the error of the Probalign program (adjusted for local alignment) to the commonly used local alignment programs HMMER, SSEARCH, and BLAST, and the popular ClustalW program with zero end-gap penalties. Parameters were optimized for each program on a small subset of the benchmark. Probalign has overall highest accuracies on the full benchmark. It leads by 10% accuracy over SSEARCH (the next best method) on 5 out of 22 families. On datasets restricted to maximum of 30% sequence identity, Probalign's overall median error is 71.2% vs. 83.4% for SSEARCH (P-value < 0.05). Furthermore, on these datasets Probalign leads SSEARCH by at least 10% on five families; SSEARCH leads Probalign by the same margin on two of the fourteen families. We also demonstrate that the Probalign mean posterior probability, compared to the normalized SSEARCH Z-score, is a better discriminator of alignment quality. All datasets and software are available online.
We demonstrate, for the first time, that partition function match probabilities used for expected accuracy alignment, as done in Probalign, provide statistically significant improvement over current approaches for identifying distantly related RNA sequences in larger genomic segments.
The Catalytic Site Atlas (CSA) provides catalytic residue annotation for enzymes in the Protein Data Bank. It is available online at http://www.ebi.ac.uk/thornton-srv/databases/CSA. The database consists of two types of annotated site: an original hand-annotated set containing information extracted from the primary literature, using defined criteria to assign catalytic residues, and an additional homologous set, containing annotations inferred by PSI-BLAST and sequence alignment to one of the original set. The CSA can be queried via Swiss-Prot identifier and EC number, as well as by PDB code. CSA Version 1.0 contains 177 original hand- annotated entries and 2608 homologous entries, and covers ∼30% of all EC numbers found in PDB. The CSA will be updated on a monthly basis to include homologous sites found in new PDBs, and new hand-annotated enzymes as and when their annotatation is completed.
Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins.
All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring Matrix (PSSM), generated by PSI-BLAST, was developed and achieved an MCC value of 0.36 with an accuracy of 68.34%. Finally, we have integrated the PROSITE domain information with PSSM and developed an SVM model that has achieved an MCC value of 0.38 with 69.09% accuracy.
BLAST has been found inefficient to distinguish between cancer and non-cancerlectins. We analyzed the protein sequences of cancer and non-cancerlectins and identified interesting patterns. We have been able to identify PROSITE domains that are preferred in cancer and non-cancerlectins and thus provided interesting insights into the two types of proteins. The method developed in this study will be useful for researchers studying cancerlectins, lectins and cancer biology. The web-server based on the above study, is available at http://www.imtech.res.in/raghava/cancer_pred/