Identification Of Protein Families Research Articles

Fusion/fission protein family identification in Archaea.

The majority of newly discovered archaeal lineages remain without a cultivated representative, but scarce experimental data from the cultivated organisms show that they harbor distinct functional repertoires. To unveil the ecological as well as evolutionary impact of Archaea from metagenomics, new computational methods need to be developed, followed by in-depth analysis. Among them is the genome-wide protein fusion screening performed here. Natural fusions and fissions of genes not only contribute to microbial evolution but also complicate the correct identification and functional annotation of sequences. The products of these processes can be defined as fusion (or composite) proteins, the ones consisting of two or more domains originally encoded by different genes and split proteins, and the ones originating from the separation of a gene in two (fission). Fusion identifications are required for proper phylogenetic reconstructions and metabolic pathway completeness assessments, while mappings between fused and unfused proteins can fill some of the existing gaps in metabolic models. In the archaeal genome-wide screening, more than 1,900 fusion/fission protein clusters were identified, belonging to both newly sequenced and well-studied lineages. These protein families are mainly associated with different types of metabolism, genetic, and cellular processes. Moreover, 162 of the identified fusion/fission protein families are archaeal specific, having no identified fused homolog within the bacterial domain. Our approach was validated by the identification of experimentally characterized fusion/fission cases. However, around 25% of the identified fusion/fission families lack functional annotations for both composite and split states, showing the need for experimental characterization in Archaea.IMPORTANCEGenome-wide fusion screening has never been performed in Archaea on a broad taxonomic scale. The overlay of multiple computational techniques allows the detection of a fine-grained set of predicted fusion/fission families, instead of rough estimations based on conserved domain annotations only. The exhaustive mapping of fused proteins to bacterial organisms allows us to capture fusion/fission families that are specific to archaeal biology, as well as to identify links between bacterial and archaeal lineages based on cooccurrence of taxonomically restricted proteins and their sequence features. Furthermore, the identification of poorly characterized lineage-specific fusion proteins opens up possibilities for future experimental and computational investigations. This approach enhances our understanding of Archaea in general and provides potential candidates for in-depth studies in the future.

A comprehensive framework for advanced protein classification and function prediction using synergistic approaches: Integrating bispectral analysis, machine learning, and deep learning

Proteins are fundamental components of diverse cellular systems and play crucial roles in a variety of disease processes. Consequently, it is crucial to comprehend their structure, function, and intricate interconnections. Classifying proteins into families or groups with comparable structural and functional characteristics is a crucial aspect of this comprehension. This classification is crucial for evolutionary research, predicting protein function, and identifying potential therapeutic targets. Sequence alignment and structure-based alignment are frequently ineffective techniques for identifying protein families.This study addresses the need for a more efficient and accurate technique for feature extraction and protein classification. The research proposes a novel method that integrates bispectrum characteristics, deep learning techniques, and machine learning algorithms to overcome the limitations of conventional methods. The proposed method uses numbers to represent protein sequences, utilizes bispectrum analysis, uses different topologies for convolutional neural networks to pull out features, and chooses robust features to classify protein families. The goal is to outperform existing methods for identifying protein families, thereby enhancing classification metrics. The materials consist of numerous protein datasets, whereas the methods incorporate bispectrum characteristics and deep learning strategies. The results of this study demonstrate that the proposed method for identifying protein families is superior to conventional approaches. Significantly enhanced quality metrics demonstrated the efficacy of the combined bispectrum and deep learning approaches. These findings have the potential to advance the field of protein biology and facilitate pharmaceutical innovation. In conclusion, this study presents a novel method that employs bispectrum characteristics and deep learning techniques to improve the precision and efficiency of protein family identification. The demonstrated advancements in classification metrics demonstrate this method’s applicability to numerous scientific disciplines. This furthers our understanding of protein function and its implications for disease and treatment.

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

BackgroundThe 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.ResultsWe 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.ConclusionsThe 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.

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Major SCP/TAPS protein expansion in Lucilia cuprina is associated with novel tandem array organisation and domain architecture

BackgroundLarvae of the Australian sheep blowfly, Lucilia cuprina, parasitise sheep by feeding on skin excretions, dermal tissue and blood, causing severe damage known as flystrike or myiasis. Recent advances in -omic technologies and bioinformatic data analyses have led to a greater understanding of blowfly biology and should allow the identification of protein families involved in host-parasite interactions and disease. Current literature suggests that proteins of the SCP (Sperm-Coating Protein)/TAPS (Tpx-1/Ag5/PR-1/Sc7) (SCP/TAPS) superfamily play key roles in immune modulation, cross-talk between parasite and host as well as developmental and reproductive processes in parasites.MethodsHere, we employed a bioinformatics workflow to curate the SCP/TAPS protein gene family in L. cuprina. Protein sequence, the presence and number of conserved CAP-domains and phylogeny were used to group identified SCP/TAPS proteins; these were compared to those found in Drosophila melanogaster to make functional predictions. In addition, transcription levels of SCP/TAPS protein-encoding genes were explored in different developmental stages.ResultsA total of 27 genes were identified as belonging to the SCP/TAPS gene family: encoding 26 single-domain proteins each with a single CAP domain and a solitary double-domain protein containing two conserved cysteine-rich secretory protein/antigen 5/pathogenesis related-1 (CAP) domains. Surprisingly, 16 SCP/TAPS predicted proteins formed an extended tandem array spanning a 53 kb region of one genomic region, which was confirmed by MinION long-read sequencing. RNA-seq data indicated that these 16 genes are highly transcribed in all developmental stages (excluding the embryo).ConclusionsFuture work should assess the potential of selected SCP/TAPS proteins as novel targets for the control of L. cuprina and related parasitic flies of major socioeconomic importance.Graphical

HIPPI: highly accurate protein family classification with ensembles of HMMs.

BackgroundGiven a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics.ResultsWe present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy.ConclusionHIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3097-0) contains supplementary material, which is available to authorized users.

Molecular diagnosis and the Italian Board for ISAC

The Component Resolved Diagnostic (CRD) approach has been developed when highly purified or recombinant allergen molecules have become available. These molecules are the allergenic proteins toward which the specific and clinically relevant IgE immune response is directed. So, the identification of protein families and cross-reactivity patterns of importance in allergy have been possible. The Italian advisory BOARD for ISAC was born: to evaluate the advantages, disadvantages and placement in diagnosis of CRD studying its application in allergic patients; to facilitate the interpretation of molecular diagnostics for clinical allergists; to evaluate the effectiveness of CRD in improving diagnostic risk assessment and early preventive treatment of allergic diseases. In the last years, its fields of interest have been: the evaluation of the performance of CRD on multi-sensitized allergic patients with respiratory symptoms and on poly-sensitized athletes; the evolution of IgE repertoire directed to single allergenic components by evaluating allergic patients with different age at a molecular level; the relevance of results obtained using allergen microarray technique for describing the IgE repertoire in allergic patients by reviewing the main articles focused on CRD published in the last 2 years; the need for an educational program focused on this new diagnostic tool also through the creation of an exhaustive and interactive explanation of the laboratory report molecular allergy; the investigation of the performance and potential additional diagnostic values of the ISAC microarray in a real-life clinical setting, taking into account also the economic values.

Sifting through genomes with iterative-sequence clustering produces a large, phylogenetically diverse protein-family resource

BackgroundNew computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences.ResultsWe implemented fully automated and high-throughput procedures to de novo cluster proteins into families based upon global alignment similarity. Our approach employs an iterative clustering strategy in which homologs of known families are sifted out of the search for new families. The resulting reduction in computational complexity enables us to rapidly identify novel protein families found in new genomes and to perform efficient, automated updates that keep pace with genome sequencing. We refer to protein families identified through this approach as “Sifting Families,” or SFams. Our analysis of ~10.5 million protein sequences from 2,928 genomes identified 436,360 SFams, many of which are not represented in other protein family databases. We validated the quality of SFam clustering through statistical as well as network topology–based analyses.ConclusionsWe describe the rapid identification of SFams and demonstrate how they can be used to annotate genomes and metagenomes. The SFam database catalogs protein-family quality metrics, multiple sequence alignments, hidden Markov models, and phylogenetic trees. Our source code and database are publicly available and will be subject to frequent updates (http://edhar.genomecenter.ucdavis.edu/sifting_families/).

Family relationships: should consensus reign?—consensus clustering for protein families

Reliable identification of protein families is key to phylogenetic analysis, functional annotation and the exploration of protein function diversity in a given phylogenetic branch. As more and more complete genomes are sequenced, there is a need for powerful and reliable algorithms facilitating protein families construction. We have formulated the problem of protein families construction as an instance of consensus clustering, for which we designed a novel algorithm that is computationally efficient in practice and produces high quality results. Our algorithm uses an election method to construct consensus families from competing clustering computations. Our consensus clustering algorithm is tailored to serve the specific needs of comparative genomics projects. First, it provides a robust means to incorporate results from different and complementary clustering methods, thus avoiding the need for an a priori choice that may introduce computational bias in the results. Second, it is suited to large-scale projects due to the practical efficiency. And third, it produces high quality results where families tend to represent groupings by biological function. This method has been used for Génolevures project to compute protein families of Hemiascomycetous yeasts. The data are available online at http://cbi.labri.fr/Genolevures/fam/

Identification and distribution of protein families in 120 completed genomes using Gene3D

Using a new protocol, PFscape, we undertake a systematic identification of protein families and domain architectures in 120 complete genomes. PFscape clusters sequences into protein families using a Markov clustering algorithm (Enright et al., Nucleic Acids Res 2002;30:1575-1584) followed by complete linkage clustering according to sequence identity. Within each protein family, domains are recognized using a library of hidden Markov models comprising CATH structural and Pfam functional domains. Domain architectures are then determined using DomainFinder (Pearl et al., Protein Sci 2002;11:233-244) and the protein family and domain architecture data are amalgamated in the Gene3D database (Buchan et al., Genome Res 2002;12:503-514). Using Gene3D, we have investigated protein sequence space, the extent of structural annotation, and the distribution of different domain architectures in completed genomes from all kingdoms of life. As with earlier studies by other researchers, the distribution of domain families shows power-law behavior such that the largest 2,000 domain families can be mapped to approximately 70% of nonsingleton genome sequences; the remaining sequences are assigned to much smaller families. While approximately 50% of domain annotations within a genome are assigned to 219 universal domain families, a much smaller proportion (< 10%) of protein sequences are assigned to universal protein families. This supports the mosaic theory of evolution whereby domain duplication followed by domain shuffling gives rise to novel domain architectures that can expand the protein functional repertoire of an organism. Functional data (e.g. COG/KEGG/GO) integrated within Gene3D result in a comprehensive resource that is currently being used in structure genomics initiatives and can be accessed via http://www.biochem.ucl.ac.uk/bsm/cath/Gene3D/.

The PRINTS database: a resource for identification of protein families.

The PRINTS database houses a collection of protein fingerprints, which may be used to assign family and functional attributes to uncharacterised sequences, such as those currently emanating from the various genome-sequencing projects. The April 2002 release includes 1,700 family fingerprints, encoding approximately 10,500 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Fingerprints are groups of conserved motifs that, taken together, provide diagnostic protein family signatures. They derive much of their potency from the biological context afforded by matching motif neighbours; this makes them at once more flexible and powerful than single-motif approaches. The technique further departs from other pattern-matching methods by readily allowing the creation of fingerprints at superfamily-, family- and subfamily-specific levels, thereby allowing more fine-grained diagnoses. Here, we provide an overview of the method of protein fingerprinting and how the results of fingerprint analyses are used to build PRINTS and its relational cousin, PRINTS-S.

PROSITE: a documented database using patterns and profiles as motif descriptors.

Among the various databases dedicated to the identification of protein families and domains, PROSITE is the first one created and has continuously evolved since. PROSITE currently consists of a large collection of biologically meaningful motifs that are described as patterns or profiles, and linked to documentation briefly describing the protein family or domain they are designed to detect. The close relationship of PROSITE with the SWISS-PROT protein database allows the evaluation of the sensitivity and specificity of the PROSITE motifs and their periodic reviewing. In return, PROSITE is used to help annotate SWISS-PROT entries. The main characteristics and the techniques of family and domain identification used by PROSITE are reviewed in this paper.

Functional classification of cNMP-binding proteins and nucleotide cyclases with implications for novel regulatory pathways in Mycobacterium tuberculosis.

We have analyzed the cyclic nucleotide (cNMP)-binding protein and nucleotide cyclase superfamilies using Bayesian computational methods of protein family identification and classification. In addition to the known cNMP-binding proteins (cNMP-dependent kinases, cNMP-gated channels, cAMP-guanine nucleotide exchange factors, and bacterial cAMP-dependent transcription factors), new functional groups of cNMP-binding proteins were identified, including putative ABC-transporter subunits, translocases, and esterases. Classification of the nucleotide cyclases revealed subtle differences in sequence conservation of the active site that distinguish the five classes of cyclases: the multicellular eukaryotic adenylyl cyclases, the eukaryotic receptor-type guanylyl cyclases, the eukaryotic soluble guanylyl cyclases, the unicellular eukaryotic and prokaryotic adenylyl cyclases, and the putative prokaryotic guanylyl cyclases. Phylogenetic distribution of the cNMP-binding proteins and cyclases was analyzed, with particular attention to the 22 complete archaeal and eubacterial genome sequences. Mycobacterium tuberculosis H37Rv and Synechocystis PCC6803 were each found to encode several more putative cNMP-binding proteins than other prokaryotes; many of these proteins are of unknown function. M. tuberculosis also encodes several more putative nucleotide cyclases than other prokaryotic species.

GENE FAMILY IDENTIFICATION NETWORK DESIGN FOR PROTEIN SEQUENCE ANALYSIS

With the exponential accumulation of sequence data, continued progress in the Human Genome Project will depend increasingly on advanced computational tools to manage and analyze the data. Utilizing information embedded within families of homologous sequences, a gene family identification approach may facilitate the understanding of gene functions. We have developed a GeneFIND (Gene Family Identification Network Design) system for database searching against gene families. It provides rapid and accurate protein family identification by combining global and motif sequence similarities and incorporating ProClass family information. Multi-level filters are used, starting with the MOTIFIND neural networks and BLAST search, followed by SSEARCH alignment, motif pattern match, hidden Markov modeling of motifs and ClustalW motif alignment. GeneFIND has been implemented as a full-scale system for the classification of more than 1200 ProSite and 6000 PIR families. It has been used to identify thousands of new family members and is well suited for genomic sequence analysis. The system is available for on-line family identification from our WWW server ().

GeneFIND web server for protein family identification and information retrieval.

An integrated database and search system has been developed for protein family identification and information retrieval, as an approach to undertake the highly complex, genomic-scale problem of molecular sequence database search and organization. http://diana.uthct.edu wu@uthct.edu

Classification and function prediction of proteins using diagnostic amino acid patterns.

Protein sequence similarities offer a convenient means for the classification and identification of protein families and superfamilies. Frequently, proteins descended from a common ancestor preserve their basic three-dimensional conformations even when they have accumulated large numbers of amino acid substitutions and short insertions or deletions. These may prohibit establishment of homology or evolutionary relationships by traditional global sequence alignment means. Limited regions of sequence similarity can also be the result of evolutionary convergence driven by a need for a common function. Regardless of whether divergent or convergent evolution played a role in the appearance of local sequence similarities, these confined regions of similarity can provide insight into structural and functional relationships of proteins that otherwise fail to show significant similarity by global alignment methods.

A new ATP-binding fold in actin, hexokinase and Hsc70

One of a cell biologist's favourite occupations is to discover the proteins that perform newly described functions in the cell. Very often lately, this has resulted in the identification of protein families whose related amino acid sequences reflect similar functions, but can proteins with totally unrelated sequences have similar structures and functions? In this review, Ken Holmes, Chris Sander and Alfonso Valencia describe the structural similarities between three well-known proteins that have no readily detectable primary sequence similarities but for which X-ray crystallography has revealed very similar structures. A comparison of their structures provides insights into their common mechanisms of action and into protein evolution, and has been used to detect related proteins in sequence data bases.