scholarly journals Pfam: The protein families database in 2021

2020 ◽  
Vol 49 (D1) ◽  
pp. D412-D419 ◽  
Author(s):  
Jaina Mistry ◽  
Sara Chuguransky ◽  
Lowri Williams ◽  
Matloob Qureshi ◽  
Gustavo A Salazar ◽  
...  
Keyword(s):  
Protein Sequences ◽  
Pfam Family ◽  
Protein Families ◽  
Pfam Database

Abstract The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/.

scholarly journals Density Peak clustering of protein sequences associated to a Pfam clan reveals clear similarities and interesting differences with respect to manual family annotation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Elena Tea Russo ◽  
Alessandro Laio ◽  
Marco Punta
Keyword(s):  
Practical Importance ◽  
Protein Sequences ◽  
Protein Family ◽  
Pfam Family ◽  
Large Set ◽  
Protein Families ◽  
Density Peak ◽  
Domain Experts ◽  
Pseudouridine Synthase ◽  
Density Peak Clustering

Abstract Background The identification of protein families is of outstanding practical importance for in silico protein annotation and is at the basis of several bioinformatic resources. Pfam is possibly the most well known protein family database, built in many years of work by domain experts with extensive use of manual curation. This approach is generally very accurate, but it is quite time consuming and it may suffer from a bias generated from the hand-curation itself, which is often guided by the available experimental evidence. Results We introduce a procedure that aims to identify automatically putative protein families. The procedure is based on Density Peak Clustering and uses as input only local pairwise alignments between protein sequences. In the experiment we present here, we ran the algorithm on about 4000 full-length proteins with at least one domain classified by Pfam as belonging to the Pseudouridine synthase and Archaeosine transglycosylase (PUA) clan. We obtained 71 automatically-generated sequence clusters with at least 100 members. While our clusters were largely consistent with the Pfam classification, showing good overlap with either single or multi-domain Pfam family architectures, we also observed some inconsistencies. The latter were inspected using structural and sequence based evidence, which suggested that the automatic classification captured evolutionary signals reflecting non-trivial features of protein family architectures. Based on this analysis we identified a putative novel pre-PUA domain as well as alternative boundaries for a few PUA or PUA-associated families. As a first indication that our approach was unlikely to be clan-specific, we performed the same analysis on the P53 clan, obtaining comparable results. Conclusions The clustering procedure described in this work takes advantage of the information contained in a large set of pairwise alignments and successfully identifies a set of putative families and family architectures in an unsupervised manner. Comparison with the Pfam classification highlights significant overlap and points to interesting differences, suggesting that our new algorithm could have potential in applications related to automatic protein classification. Testing this hypothesis, however, will require further experiments on large and diverse sequence datasets.

scholarly journals In Silico Analysis: Annotations about Structural and Functional Features of DUF 2726 Family Member Proteins

2016 ◽  
pp. 53-58
Author(s):  
SM Sabbir Alam ◽  
M Ruhul Amin ◽  
M Anwar Hossain
Keyword(s):  
Nucleic Acid ◽  
Nuclear Localization ◽  
Nuclear Localization Signal ◽  
Bacterial Species ◽  
Nucleic Acid Binding ◽  
Protein Families ◽  
Binding Properties ◽  
Pfam Database ◽  
Localization Signal ◽  
Functional Features

Domains of unknown functions (DUFs) are a big set of protein families within the Pfam database that includes proteins of unknown function. In the absence of functional information, proteins are classified into different families based on conserved amino acid sequences and are potentially functionally important. In Pfam database, the numbers of families of DUFs are rapidly increasing and in current the fraction of DUF families had increased to about twenty two percent of all protein families. In this study we targeted DUF2726 member proteins which are mainly present in different bacterial species of Gamma-proteobacteria and have a particular domain organization. We analyzed the protein sequences of domain DUF2726 using different computational tools and databases. We found that this domain contains a nuclear localization signal peptide, which is conserved in Escherichia spp. and Shigella spp. It were also predicted that it has nucleic acid binding properties. Analyzing protein-protein interactions functional partners associated with DUF 2726 were revealed. Protein secondary structure, transmembrane helices structure were predicted. We have found that it has gene neighbourhood and co-occurrences with protein RepA and RepB. RepA and RepB are functionally associated with replication. RepA is a replication protein and RepB is a replication regulatory protein. Presence of a nucleic acid binding properties, a nuclear localization signal (NLS) signalling peptide, and possible interaction pattern with replication proteins, conjectures its possible role as a NLS like signalling peptide.Bangladesh J Microbiol, Volume 31, Number 1-2,June-Dec 2014, pp 53-58

scholarly journals Phylogenetic correlations have limited effect on coevolution-based contact prediction in proteins

2020 ◽  
Author(s):  
Edwin Rodriguez Horta ◽  
Martin Weigt
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Sequences ◽  
Protein Families ◽  
Coupling Analysis ◽  
Contact Prediction ◽  
Phylogenetic Relations ◽  
Direct Coupling Analysis

AbstractCoevolution-based contact prediction, either directly by coevolutionary couplings resulting from global statistical sequence models or using structural supervision and deep learning, has found widespread application in protein-structure prediction from sequence. However, one of the basic assumptions in global statistical modeling is that sequences form an at least approximately independent sample of an unknown probability distribution, which is to be learned from data. In the case of protein families, this assumption is obviously violated by phylogenetic relations between protein sequences. It has turned out to be notoriously difficult to take phylogenetic correlations into account in coevolutionary model learning. Here, we propose a complementary approach: we develop two strategies to randomize or resample sequence data, such that conservation patterns and phylogenetic relations are preserved, while intrinsic (i.e. structure- or function-based) coevolutionary couplings are removed. An analysis of these data shows that the strongest coevolutionary couplings, i.e. those used by Direct Coupling Analysis to predict contacts, are only weakly influenced by phylogeny. However, phylogeny-induced spurious couplings are of similar size to the bulk of coevolutionary couplings, and dissecting functional from phylogeny-induced couplings might lead to more accurate contact predictions in the range of intermediate-size couplings.The code is available at https://github.com/ed-rodh/Null_models_I_and_II.Author summaryMany homologous protein families contain thousands of highly diverged amino-acid sequences, which fold in close-to-identical three-dimensional structures and fulfill almost identical biological tasks. Global coevolutionary models, like those inferred by the Direct Coupling Analysis (DCA), assume that families can be considered as samples of some unknown statistical model, and that the parameters of these models represent evolutionary constraints acting on protein sequences. To learn these models from data, DCA and related approaches have to also assume that the distinct sequences in a protein family are close to independent, while in reality they are characterized by involved hierarchical phylogenetic relationships. Here we propose Null models for sequence alignments, which maintain patterns of amino-acid conservation and phylogeny contained in the data, but destroy any coevolutionary couplings, frequently used in protein structure prediction. We find that phylogeny actually induces spurious non-zero couplings. These are, however, significantly smaller that the largest couplings derived from natural sequences, and therefore have only little influence on the first predicted contacts. However, in the range of intermediate couplings, they may lead to statistically significant effects. Dissecting phylogenetic from functional couplings might therefore extend the range of accurately predicted structural contacts down to smaller coupling strengths than those currently used.

scholarly journals Learning protein constitutive motifs from sequence data

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jérôme Tubiana ◽  
Simona Cocco ◽  
Rémi Monasson
Keyword(s):  
Sequence Data ◽  
Protein Sequences ◽  
Sequence Information ◽  
Ligand Specificity ◽  
Protein Families ◽  
Restricted Boltzmann Machines ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Model Protein ◽  
Lattice Proteins

Statistical analysis of evolutionary-related protein sequences provides information about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families from sequence information. We here apply RBM to 20 protein families, and present detailed results for two short protein domains (Kunitz and WW), one long chaperone protein (Hsp70), and synthetic lattice proteins for benchmarking. The features inferred by the RBM are biologically interpretable: they are related to structure (residue-residue tertiary contacts, extended secondary motifs (α-helixes and β-sheets) and intrinsically disordered regions), to function (activity and ligand specificity), or to phylogenetic identity. In addition, we use RBM to design new protein sequences with putative properties by composing and 'turning up' or 'turning down' the different modes at will. Our work therefore shows that RBM are versatile and practical tools that can be used to unveil and exploit the genotype–phenotype relationship for protein families.

scholarly journals Reducing Dimensionality in Remote Homology Detection

2021 ◽  
Vol 9 (12) ◽  
pp. 1052-1054
Author(s):  
S. Dinesh
Keyword(s):  
Protein Sequence ◽  
Protein Sequences ◽  
High Dimensional ◽  
Homology Detection ◽  
Protein Families ◽  
Classification Techniques ◽  
Remote Homology ◽  
High Dimensional Datasets ◽  
Remote Homology Detection

Abstract: Homology detection plays a major role in bioinformatics. Different type of methods is used for Homology detection. Here we extract the information from protein sequences and then uses the various algorithm to predict the similarity between protein families. SVM most commonly used the algorithm in homology detection. Classification techniques are not suitable for homology detection because theyare not suitable for high dimensional datasets. Soreducing the higher dimensionality is very important than easily can predict the similarity of protein families. Keywords: Homology detection, Protein, Sequence, Reducing dimensionality, BLAST, SCOP.

scholarly journals Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

2017 ◽  
Vol 114 (13) ◽  
pp. E2662-E2671 ◽  
Author(s):  
Guido Uguzzoni ◽  
Shalini John Lovis ◽  
Francesco Oteri ◽  
Alexander Schug ◽  
Hendrik Szurmant ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Large Scale ◽  
Structural Models ◽  
Direct Coupling ◽  
Pfam Family ◽  
Sequence Information ◽  
Structural Description ◽  
Protein Families ◽  
Coupling Analysis ◽  
Direct Coupling Analysis

Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.

scholarly journals Deep neural language modeling enables functional protein generation across families

2021 ◽  
Author(s):  
Ali Madani ◽  
Ben Krause ◽  
Eric R Greene ◽  
Subu Subramanian ◽  
Benjamin P Mohr ◽  
...  
Keyword(s):  
Amino Acid ◽  
De Novo ◽  
Language Model ◽  
Protein Sequences ◽  
Chorismate Mutase ◽  
Language Models ◽  
Protein Families ◽  
Evolutionary Trajectory ◽  
Artificial Proteins ◽  
Artificial Protein

Bypassing nature's evolutionary trajectory, de novo protein generation - defined as creating artificial protein sequences from scratch - could enable breakthrough solutions for biomedical and environmental challenges. Viewing amino acid sequences as a language, we demonstrate that a deep learning-based language model can generate functional artificial protein sequences across families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. Our protein language model is trained by simply learning to predict the next amino acid for over 280 million protein sequences from thousands of protein families, without biophysical or coevolutionary modeling. We experimentally evaluate model-generated artificial proteins on five distinct antibacterial lysozyme families. Artificial proteins show similar activities and catalytic efficiencies as representative natural lysozymes, including hen egg white lysozyme, while reaching as low as 44% identity to any known naturally-evolved protein. The X-ray crystal structure of an enzymatically active artificial protein recapitulates the conserved fold and positioning of active site residues found in natural proteins. We demonstrate our language model's ability to be adapted to different protein families by accurately predicting the functionality of artificial chorismate mutase and malate dehydrogenase proteins. These results indicate that neural language models successfully perform de novo protein generation across protein families and may prove to be a tool to shortcut evolution.

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