Sequence context-specific profiles for homology searching.
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ABSTRACT:
SUBMITTER: Biegert A
PROVIDER: S-EPMC2645910 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:CPHmodels-3.0 is a web server predicting protein 3D structure by use of single template homology modeling. The server employs a hybrid of the scoring functions of CPHmodels-2.0 and a novel remote homology-modeling algorithm. A query sequence is first attempted modeled using the fast CPHmodels-2.0 profile-profile scoring function suitable for close homology modeling. The new computational costly remote homology-modeling algorithm is only engaged provided that no suitable PDB template is identified in the initial search. CPHmodels-3.0 was benchmarked in the CASP8 competition and produced models for 94% of the targets (117 out of 128), 74% were predicted as high reliability models (87 out of 117). These achieved an average RMSD of 4.6 A when superimposed to the 3D structure. The remaining 26% low reliably models (30 out of 117) could superimpose to the true 3D structure with an average RMSD of 9.3 A. These performance values place the CPHmodels-3.0 method in the group of high performing 3D prediction tools. Beside its accuracy, one of the important features of the method is its speed. For most queries, the response time of the server is <20 min. The web server is available at http://www.cbs.dtu.dk/services/CPHmodels/.
| S-EPMC2896139 | biostudies-literature
Project description:The effectiveness of sequence alignment in detecting structural homology among protein sequences decreases markedly when pairwise sequence identity is low (the so-called "twilight zone" problem of sequence alignment). Alternative sequence comparison strategies able to detect structural kinship among highly divergent sequences are necessary to address this need. Among them are alignment-free methods, which use global sequence properties (such as amino acid composition) to identify structural homology in a rapid and straightforward way. We explore the viability of using tetramer sequence fragment composition profiles in finding structural relationships that lie undetected by traditional alignment. We establish a strategy to recast any given protein sequence into a tetramer sequence fragment composition profile, using a series of amino acid clustering steps that have been optimized for mutual information. Our method has the effect of compressing the set of 160,000 unique tetramers (if using the 20-letter amino acid alphabet) into a more tractable number of reduced tetramers (approximately 15-30), so that a meaningful tetramer composition profile can be constructed. We test remote homology detection at the topology and fold superfamily levels using a comprehensive set of fold homologs, culled from the CATH database that share low pairwise sequence similarity. Using the receiver-operating characteristic measure, we demonstrate potentially significant improvement in using information-optimized reduced tetramer composition, over methods relying only on the raw amino acid composition or on traditional sequence alignment, in homology detection at or below the "twilight zone".
| S-EPMC2933786 | biostudies-literature
Project description:Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns database-free, de novo sequenced peptide sequences generated through PEAKS software with neuropeptide database sequences to identify neuropeptides based on an alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various C. sapidus neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides.
2023-02-02 | PXD037058 | Pride
Project description:The mechanism by which pre-synaptic RecA nucleoprotein filaments efficiently locate sequence homology across genomic DNA remains unclear. Here, using atomic force microscopy, we directly investigate the intermediates of the RecA-mediated homologous recombination process and find it to be highly cooperative, involving multiple phases. Initially, the process is dominated by a rapid 'association' phase, where multiple filaments interact on the same dsDNA simultaneously. This cooperative nature is reconciled by the observation of localized dense clusters of pre-synaptic filaments interacting with the observed dsDNA molecules. This confinement of reactive species within the vicinity of the dsDNA, is likely to play an important role in ensuring that a high interaction rate between the nucleoprotein filaments and the dsDNA can be achieved. This is followed by a slower 'resolution' phase, where the synaptic joints either locate sequence homology and progress to a post-synaptic joint, or dissociate from the dsDNA. Surprisingly, the number of simultaneous synaptic joints decreases rapidly after saturation of the dsDNA population, suggesting a reduction in interaction activity of the RecA filaments. We find that the time-scale of this decay is in line with the time-scale of the dispersion of the RecA filament clusters, further emphasising the important role this cooperative phenomena may play in the RecA-facilitated homology search.
| S-EPMC5714135 | biostudies-literature
Project description:Natural products have a long history of providing potent probes into protein biosynthesis, with numerous molecules serving as valuable therapeutics. The marine natural product girolline has been described as an inhibitor of protein synthesis. Here, we demonstrate that it is not a general translation inhibitor but represents a sequence-specific modulator of translation factor eIF5A. Girolline interferes with ribosome-eIF5A interaction and induces ribosome stalling, primarily on AAA-encoded lysine. Our data furthermore indicate that eIF5A plays a physiological role in ribosome-associated quality control (RQC) and is important in maintaining the efficiency of translational progress. Girolline, therefore, provides a potent tool compound for understanding the interplay between protein production and quality control in a physiological setting and offers a new and selective means of modulating gene expression.
2024-06-10 | GSE233886 | GEO
Project description:Natural products have a long history of providing potent probes into protein biosynthesis, with numerous molecules serving as valuable therapeutics. The marine natural product girolline has been described as an inhibitor of protein synthesis. Here, we demonstrate that it is not a general translation inhibitor but represents a sequence-specific modulator of translation factor eIF5A. Girolline interferes with ribosome-eIF5A interaction and induces ribosome stalling, primarily on AAA-encoded lysine. Our data furthermore indicate that eIF5A plays a physiological role in ribosome-associated quality control (RQC) and is important in maintaining the efficiency of translational progress. Girolline, therefore, provides a potent tool compound for understanding the interplay between protein production and quality control in a physiological setting and offers a new and selective means of modulating gene expression. numerous molecules serving as valuable therapeutics. The marine natural product girolline has been described as an inhibitor of protein synthesis. Here, we demonstrate that it is not a general translation inhibitor but represents a sequence-specific modulator of translation factor eIF5A. Girolline interferes with ribosome-eIF5A interaction and induces ribosome stalling, primarily on30 AAA-encoded lysine. Our data furthermore indicate that eIF5A plays a physiological role in ribosome-associated quality control (RQC) and is important in maintaining the efficiency of translational progress. Girolline, therefore, provides a potent tool compound for understanding the interplay between protein production and quality control in a physiological setting and offers a new and selective means of modulating gene expression.
2024-10-17 | GSE264204 | GEO
Project description:Selecting near-native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner-specific sequence homology-based protein-protein interface predictor (PS-HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state-of-the-art docking scoring functions using Success Rate (the percentage of cases that have at least one near-native conformation among the top m conformations) and Hit Rate (the percentage of near-native conformations that are included among the top m conformations). In cases where it is possible to obtain partner-specific (PS) interface predictions from PS-HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state-of-the-art energy-based scoring functions (improving Success Rate by up to 4-fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39-fold). The latter result underscores the importance of using partner-specific interface residues in scoring docked conformations. We show that DockRank, when used to re-rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/.
| S-EPMC4417613 | biostudies-literature
Project description:miRNAs form a class of noncoding RNAs, involved in post-transcriptional regulation of gene expression, broadly studied for their involvement in physiological and pathological context. Inhibition of mature miRNA transcripts, commonly used in miRNA loss-of-function experiments, may not be specific in case of miRNAs with high sequence homology, e.g. miRNAs from the same seed family. Phenotypic effects of miRNA repression might be biased by the repression of highly similar miRNAs. Another challenge is simultaneous inhibition of multiple miRNAs encoded within policistronic clusters, potentially co-regulating common biological processes. To elucidate roles of miRNA clusters and miRNAs with high sequence homology, it is of key importance to selectively repress only the miRNAs of interest. Targeting miRNAs on genomic level with CRISPR/dCas9-based methods is an attractive alternative to blocking mature miRNAs. Yet, so far no clear guidelines on the design of CRISPR inhibition (CRISPRi) experiments, specifically for miRNA repression, have been proposed. To address this need, here we propose a strategy for effective inhibition of miRNAs and miRNA clusters using CRISPRi. We provide clues on how to approach the challenges in using CRISPR/dCas in miRNA studies, which include prediction of miRNA transcription start sites (TSSs) and the design of single guide RNAs (sgRNAs). The strategy implements three TSS prediction online tools, dedicated specifically for miRNAs: miRStart, FANTOM 5 miRNA atlas, DIANA-miRGen, and CRISPOR tool for sgRNAs design; it includes testing and selection of optimal sgRNAs. We demonstrate that compared to siRNA/shRNA-based miRNA silencing, CRISPRi improves the repression specificity for miRNAs with highly similar sequence and contribute to higher uniformity of the effects of silencing the whole miRNA clusters. This strategy may be adapted for CRISPR-mediated activation (CRISPRa) of miRNA expression.
| S-EPMC9012752 | biostudies-literature
Project description:MotivationOwing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles.ResultsHere, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well.Availability and implementationFor users' convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote/
| S-EPMC7537947 | biostudies-literature
Project description:UnlabelledRemote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request.Contactsarkar@labri.fr.
| S-EPMC3574063 | biostudies-literature