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A structural study for the optimisation of functional motifs encoded in protein sequences.

ABSTRACT: BACKGROUND: A large number of PROSITE patterns select false positives and/or miss known true positives. It is possible that--at least in some cases--the weak specificity and/or sensitivity of a pattern is due to the fact that one, or maybe more, functional and/or structural key residues are not represented in the pattern. Multiple sequence alignments are commonly used to build functional sequence patterns. If residues structurally conserved in proteins sharing a function cannot be aligned in a multiple sequence alignment, they are likely to be missed in a standard pattern construction procedure. RESULTS: Here we present a new procedure aimed at improving the sensitivity and/ or specificity of poorly-performing patterns. The procedure can be summarised as follows: 1. residues structurally conserved in different proteins, that are true positives for a pattern, are identified by means of a computational technique and by visual inspection. 2. the sequence positions of the structurally conserved residues falling outside the pattern are used to build extended sequence patterns. 3. the extended patterns are optimised on the SWISS-PROT database for their sensitivity and specificity. The method was applied to eight PROSITE patterns. Whenever structurally conserved residues are found in the surface region close to the pattern (seven out of eight cases), the addition of information inferred from structural analysis is shown to improve pattern selectivity and in some cases selectivity and sensitivity as well. In some of the cases considered the procedure allowed the identification of functionally interesting residues, whose biological role is also discussed. CONCLUSION: Our method can be applied to any type of functional motif or pattern (not only PROSITE ones) which is not able to select all and only the true positive hits and for which at least two true positive structures are available. The computational technique for the identification of structurally conserved residues is already available on request and will be soon accessible on our web server. The procedure is intended for the use of pattern database curators and of scientists interested in a specific protein family for which no specific or selective patterns are yet available.

SUBMITTER: Via A

PROVIDER: S-EPMC420233 | biostudies-literature | 2004 Apr

REPOSITORIES: biostudies-literature

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A structural study for the optimisation of functional motifs encoded in protein sequences.

Via Allegra A Helmer-Citterich Manuela M

BMC bioinformatics 20040430

<h4>Background</h4>A large number of PROSITE patterns select false positives and/or miss known true positives. It is possible that--at least in some cases--the weak specificity and/or sensitivity of a pattern is due to the fact that one, or maybe more, functional and/or structural key residues are not represented in the pattern. Multiple sequence alignments are commonly used to build functional sequence patterns. If residues structurally conserved in proteins sharing a function cannot be aligned ...[more]

PMID: 15119965

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Project description:BackgroundAutomatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost.ResultsWildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm.ConclusionsThe mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (http://biominer.csie.cyu.edu.tw/wildspan).

Project description:Influenza A viruses (IAV) are responsible for recurrent influenza epidemics and occasional devastating pandemics in humans and animals. They belong to the Orthomyxoviridae family and their genome consists of eight (-) sense viral RNA (vRNA) segments of different lengths coding for at least 11 viral proteins. A heterotrimeric polymerase complex is bound to the promoter consisting of the 13 5'-terminal and 12 3'-terminal nucleotides of each vRNA, while internal parts of the vRNAs are associated with multiple copies of the viral nucleoprotein (NP), thus forming ribonucleoproteins (vRNP). Transcription and replication of vRNAs result in viral mRNAs (vmRNAs) and complementary RNAs (cRNAs), respectively. Complementary RNAs are the exact positive copies of vRNAs; they also form ribonucleoproteins (cRNPs) and are intermediate templates in the vRNA amplification process. On the contrary, vmRNAs have a 5' cap snatched from cellular mRNAs and a 3' polyA tail, both gained by the viral polymerase complex. Hence, unlike vRNAs and cRNAs, vmRNAs do not have a terminal promoter able to recruit the viral polymerase. Furthermore, synthesis of at least two viral proteins requires vmRNA splicing. Except for extensive analysis of the viral promoter structure and function and a few, mostly bioinformatics, studies addressing the vRNA and vmRNA structure, structural studies of the influenza A vRNAs, cRNAs, and vmRNAs are still in their infancy. The recent crystal structures of the influenza polymerase heterotrimeric complex drastically improved our understanding of the replication and transcription processes. The vRNA structure has been mainly studied in vitro using RNA probing, but its structure has been very recently studied within native vRNPs using crosslinking and RNA probing coupled to next generation RNA sequencing. Concerning vmRNAs, most studies focused on the segment M and NS splice sites and several structures initially predicted by bioinformatics analysis have now been validated experimentally and their role in the viral life cycle demonstrated. This review aims to compile the structural motifs found in the different RNA classes (vRNA, cRNA, and vmRNA) of influenza viruses and their function in the viral replication cycle.

Project description:BACKGROUND: Bacterial populations are highly successful at colonizing new habitats and adapting to changing environmental conditions, partly due to their capacity to evolve novel virulence and metabolic pathways in response to stress conditions and to shuffle them by horizontal gene transfer (HGT). A common theme in the evolution of new functions consists of gene duplication followed by functional divergence. UlaG, a unique manganese-dependent metallo-?-lactamase (MBL) enzyme involved in L-ascorbate metabolism by commensal and symbiotic enterobacteria, provides a model for the study of the emergence of new catalytic activities from the modification of an ancient fold. Furthermore, UlaG is the founding member of the so-called UlaG-like (UlaGL) protein family, a recently established and poorly characterized family comprising divalent (and perhaps trivalent) metal-binding MBLs that catalyze transformations on phosphorylated sugars and nucleotides. RESULTS: Here we combined protein structure-guided and sequence-only molecular phylogenetic analyses to dissect the molecular evolution of UlaG and to study its phylogenomic distribution, its relatedness with present-day UlaGL protein sequences and functional conservation. Phylogenetic analyses indicate that UlaGL sequences are present in Bacteria and Archaea, with bona fide orthologs found mainly in mammalian and plant-associated Gram-negative and Gram-positive bacteria. The incongruence between the UlaGL tree and known species trees indicates exchange by HGT and suggests that the UlaGL-encoding genes provided a growth advantage under changing conditions. Our search for more distantly related protein sequences aided by structural homology has uncovered that UlaGL sequences have a common evolutionary origin with present-day RNA processing and metabolizing MBL enzymes widespread in Bacteria, Archaea, and Eukarya. This observation suggests an ancient origin for the UlaGL family within the broader trunk of the MBL superfamily by duplication, neofunctionalization and fixation. CONCLUSIONS: Our results suggest that the forerunner of UlaG was present as an RNA metabolizing enzyme in the last common ancestor, and that the modern descendants of that ancestral gene have a wide phylogenetic distribution and functional roles. We propose that the UlaGL family evolved new metabolic roles among bacterial and possibly archeal phyla in the setting of a close association with metazoans, such as in the mammalian gastrointestinal tract or in animal and plant pathogens, as well as in environmental settings. Accordingly, the major evolutionary forces shaping the UlaGL family include vertical inheritance and lineage-specific duplication and acquisition of novel metabolic functions, followed by HGT and numerous lineage-specific gene loss events.

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A structural study for the optimisation of functional motifs encoded in protein sequences.

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A structural study for the optimisation of functional motifs encoded in protein sequences.

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