AlignmentViewer: Sequence Analysis of Large Protein Families.
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ABSTRACT:
SUBMITTER: Reguant R
PROVIDER: S-EPMC7570326 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCurrent volumes of sequence data can lead to large numbers of hits identified on a search, typically in the range of 10s to 100s of thousands. It is often quite difficult to tell from these raw results whether the search has been a success or has picked-up sequences with little or no relationship to the query. The best approach to this problem is to cluster and align the resulting families, however, existing methods concentrate on fast clustering and either do not align the sequences or only perform a limited alignment.ResultsA method (MULSEL) is presented that combines fast peptide-based pre-sorting with a following cascade of mini-alignments, each of which are generated with a robust profile/profile method. From these mini-alignments, a representative sequence is selected, based on a variety of intrinsic and user-specified criteria that are combined to produce the sequence collection for the next cycle of alignment. For moderate sized sequence collections (10s of thousands) the method executes on a laptop computer within seconds or minutes.ConclusionsMULSEL bridges a gap between fast clustering methods and slower multiple sequence alignment methods and provides a seamless transition from one to the other. Furthermore, it presents the resulting reduced family in a graphical manner that makes it clear if family members have been misaligned or if there are sequences present that appear inconsistent.
| S-EPMC4971687 | biostudies-literature
Project description:BackgroundThe alignment of large numbers of protein sequences is a challenging task and its importance grows rapidly along with the size of biological datasets. State-of-the-art algorithms have a tendency to produce less accurate alignments with an increasing number of sequences. This is a fundamental problem since many downstream tasks rely on accurate alignments.ResultsWe present learnMSA, a novel statistical learning approach of profile hidden Markov models (pHMMs) based on batch gradient descent. Fundamentally different from popular aligners, we fit a custom recurrent neural network architecture for (p)HMMs to potentially millions of sequences with respect to a maximum a posteriori objective and decode an alignment. We rely on automatic differentiation of the log-likelihood, and thus, our approach is different from existing HMM training algorithms like Baum-Welch. Our method does not involve progressive, regressive, or divide-and-conquer heuristics. We use uniform batch sampling to adapt to large datasets in linear time without the requirement of a tree. When tested on ultra-large protein families with up to 3.5 million sequences, learnMSA is both more accurate and faster than state-of-the-art tools. On the established benchmarks HomFam and BaliFam with smaller sequence sets, it matches state-of-the-art performance. All experiments were done on a standard workstation with a GPU.ConclusionsOur results show that learnMSA does not share the counterintuitive drawback of many popular heuristic aligners, which can substantially lose accuracy when many additional homologs are input. LearnMSA is a future-proof framework for large alignments with many opportunities for further improvements.
| S-EPMC9673500 | biostudies-literature
Project description:BackgroundPhylogenetic study of protein sequences provides unique and valuable insights into the molecular and genetic basis of important medical and epidemiological problems as well as insights about the origins and development of physiological features in present day organisms. Consensus phylogenies based on the bootstrap and other resampling methods play a crucial part in analyzing the robustness of the trees produced for these analyses.MethodologyOur focus was to increase the number of bootstrap replications that can be performed on large protein datasets using the maximum parsimony, distance matrix, and maximum likelihood methods. We have modified the PHYLIP package using MPI to enable large-scale phylogenetic study of protein sequences, using a statistically robust number of bootstrapped datasets, to be performed in a moderate amount of time. This paper discusses the methodology used to parallelize the PHYLIP programs and reports the performance of the parallel PHYLIP programs that are relevant to the study of protein evolution on several protein datasets.ConclusionsCalculations that currently take a few days on a state of the art desktop workstation are reduced to calculations that can be performed over lunchtime on a modern parallel computer. Of the three protein methods tested, the maximum likelihood method scales the best, followed by the distance method, and then the maximum parsimony method. However, the maximum likelihood method requires significant memory resources, which limits its application to more moderately sized protein datasets.
| S-EPMC2981553 | biostudies-literature
Project description:In protein structure space, protein structures cluster into four elongated regions when mapped based solely on similarity among the 3D structures. These four regions correspond to the four major classes of present-day proteins defined by the contents of secondary structure types and their topological arrangement. Evolution of and restriction to these four classes suggest that, in most cases, the evolution of genes may have been constrained or selected to those genetic changes that results in structurally stable proteins occupying one of the four "allowed" regions of the protein structure space, "structural selection," an important component of natural selection in gene evolution. Our studies on tracing the "common structural ancestor" for each protein sequence family of known structure suggest that: (i) recently emerged proteins belong mostly to three classes; (ii) the proteins that emerged earlier evolved to gain a new class; and (iii) the proteins that emerged earliest evolved to become the present-day proteins in the four major classes, with the fourth-class proteins becoming the most dominant population. Furthermore, our studies also show that not all present-day proteins evolved from one single set of proteins in the last common ancestral organism, but new common ancestral proteins were "born" at different evolutionary times, not traceable to one or two ancestral proteins: "the multiple birth model" for the evolution of protein sequence families.
| S-EPMC1560931 | biostudies-literature
Project description:Accurate detection of protein families allows assignment of protein function and the analysis of functional diversity in complete genomes. Recently, we presented a novel algorithm called TribeMCL for the detection of protein families that is both accurate and efficient. This method allows family analysis to be carried out on a very large scale. Using TribeMCL, we have generated a resource called TRIBES that contains protein family information, comprising annotations, protein sequence alignments and phylogenetic distributions describing 311 257 proteins from 83 completely sequenced genomes. The analysis of at least 60 934 detected protein families reveals that, with the essential families excluded, paralogy levels are similar between prokaryotes, irrespective of genome size. The number of essential families is estimated to be between 366 and 426. We also show that the currently known space of protein families is scale free and discuss the implications of this distribution. In addition, we show that smaller families are often formed by shorter proteins and discuss the reasons for this intriguing pattern. Finally, we analyse the functional diversity of protein families in entire genome sequences. The TRIBES protein family resource is accessible at http://www.ebi.ac.uk/research/cgg/tribes/.
| S-EPMC169885 | biostudies-literature
Project description:Allosteric interactions between residues that are spatially apart and well separated in sequence are important in the function of multimeric proteins as well as single-domain proteins. This observation suggests that, among the residues that are involved in long-range communications, mutation at one site should affect interactions at a distant site. By adopting a sequence-based approach, we present an automated approach that uses a generalization of the familiar sequence entropy in conjunction with a coupled two-way clustering algorithm, to predict the network of interactions that trigger allosteric interactions in proteins. We use the method to identify the subset of dynamically important residues in three families, namely, the small PDZ family, G protein-coupled receptors (GPCR), and the Lectins, which are cell-adhesion receptors that mediate the tethering and rolling of leukocytes on inflamed endothelium. For the PDZ and GPCR families, our procedure predicts, in agreement with previous studies, a network containing a small number of residues that are involved in their function. Application to the Lectin family reveals a network of residues interspersed throughout the C-terminal end of the structure that are responsible for binding to ligands. Based on our results and previous studies, we propose that functional robustness requires that only a small subset of distantly connected residues be involved in transmitting allosteric signals in proteins.
| S-EPMC2242462 | biostudies-literature
Project description:Detection of protein families in large databases is one of the principal research objectives in structural and functional genomics. Protein family classification can significantly contribute to the delineation of functional diversity of homologous proteins, the prediction of function based on domain architecture or the presence of sequence motifs as well as comparative genomics, providing valuable evolutionary insights. We present a novel approach called TRIBE-MCL for rapid and accurate clustering of protein sequences into families. The method relies on the Markov cluster (MCL) algorithm for the assignment of proteins into families based on precomputed sequence similarity information. This novel approach does not suffer from the problems that normally hinder other protein sequence clustering algorithms, such as the presence of multi-domain proteins, promiscuous domains and fragmented proteins. The method has been rigorously tested and validated on a number of very large databases, including SwissProt, InterPro, SCOP and the draft human genome. Our results indicate that the method is ideally suited to the rapid and accurate detection of protein families on a large scale. The method has been used to detect and categorise protein families within the draft human genome and the resulting families have been used to annotate a large proportion of human proteins.
| S-EPMC101833 | biostudies-literature
Project description:Dali and HSSP are derived databases organizing protein space in the structurally known regions. We use an automatic structure alignment program (Dali) for the classification of all known 3D structures based on all-against-all comparison of 3D structures in the Protein Data Bank. The HSSP database associates 1D sequences with known 3D structures using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). As a result, the HSSP database not only provides aligned sequence families, but also implies secondary and tertiary structures covering 36% of all sequences in Swiss-Prot. The structure classification by Dali and the sequence families in HSSP can be browsed jointly from a web interface providing a rich network of links between neighbours in fold space, between domains and proteins, and between structures and sequences. In particular, this results in a database of explicit multiple alignments of protein families in the twilight zone of sequence similarity. The organization of protein structures and families provides a map of the currently known regions of the protein universe that is useful for the analysis of folding principles, for the evolutionary unification of protein families and for maximizing the information return from experimental structure determination. The databases are available from http://www.embl-ebi.ac.uk/dali/
| S-EPMC148146 | biostudies-other
Project description:PurposeTo identify causal gene mutations in 14 families with autosomal dominant (AD) high myopia using exome sequencing.MethodsSelect individuals from 14 large Caucasian families with high myopia were exome sequenced. Gene variants were filtered to identify potential pathogenic changes. Sanger sequencing was used to confirm variants in original DNA, and to test for disease cosegregation in additional family members. Candidate genes and chromosomal loci previously associated with myopic refractive error and its endophenotypes were comprehensively screened.ResultsIn 14 high myopia families, we identified 73 rare and 31 novel gene variants as candidates for pathogenicity. In seven of these families, two of the novel and eight of the rare variants were within known myopia loci. A total of 104 heterozygous nonsynonymous rare variants in 104 genes were identified in 10 out of 14 probands. Each variant cosegregated with affection status. No rare variants were identified in genes known to cause myopia or in genes closest to published genome-wide association study association signals for refractive error or its endophenotypes.ConclusionsWhole exome sequencing was performed to determine gene variants implicated in the pathogenesis of AD high myopia. This study provides new genes for consideration in the pathogenesis of high myopia, and may aid in the development of genetic profiling of those at greatest risk for attendant ocular morbidities of this disorder.
| S-EPMC5382835 | biostudies-literature
Project description:A fundamental problem with applying Consensus, Weight-Matrix or hidden Markov models as search tools for biosequences is that there is no way to know, from the model, if the modeled sequences display any dependencies between positional identities. In some instances, these dependencies are crucial in correctly accepting or rejecting other sequences as members of the family. MAVL (multiple alignment variation linker) and StickWRLD provide a web-based method to visually survey the model-training sequences to discover and characterize possible dependencies. Initially introduced for nucleic acid sequences, with MAVL/StickWRLD, it is easy to distinguish typical DNA or RNA structural dependencies in input families, identify mixed populations of distinct subfamilies, or discover novel dependencies that result from binding interactions or other selective pressures [W. Ray (2004) Nucleic Acids Res., 32, W59-W63]. Since the announcement of MAVL/StickWRLD for nucleic acids, one of the most requested new features has been the extension of this visualization method to support protein alignments. We are pleased to report that this extension has been successful, that the basic visualization has been augmented in several ways to enhance protein viewing, and that the results with protein alignments are even more dramatic than with NA alignments. MAVL/StickWRLD can be accessed at http://www.microbial-pathogenesis.org/stickwrld/.
| S-EPMC1160135 | biostudies-literature