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Enhancing prediction of short linear protein motifs with Wregex 3.0.

ABSTRACT: Short linear motifs (SLiMs) play an important role in protein-protein interactions. However, SLiM patterns are intrinsically permissive and result into many matches that occur just by chance, specially when targeting large datasets. To prioritize these matches as candidates for functional testing, we developed Wregex (Weighted regular expression), which uses a position-specific scoring matrix (PSSM) to order a list of regular expression matches according to a PSSM-derived score. Here we present Wregex 3.0, an improved version with new functionalities such as the support for a second auxiliary motif to help refining prediction of a primary SLiM, and post-translational modifications (PTMs) enrichment taking into account that many regulatory SLiM-mediated interactions are modulated by one or more PTMs. This version also incorporates a number of new features such as a convenient use of subproteomes, showing UniProt annotations such as disordered regions, searching for all known motifs and generating decoy databases for enrichment analysis. We provide case studies to illustrate how these new Wregex functionalities enhance prediction of short linear protein motifs. The Wregex 3.0 server is freely accessible at https://ehubio.ehu.eus/wregex3/.

SUBMITTER: Prieto G

PROVIDER: S-EPMC11318550 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature

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Enhancing prediction of short linear protein motifs with Wregex 3.0.

Prieto Gorka G Rodríguez Jose A JA Fullaondo Asier A

Computational and structural biotechnology journal 20240717

Short linear motifs (SLiMs) play an important role in protein-protein interactions. However, SLiM patterns are intrinsically permissive and result into many matches that occur just by chance, specially when targeting large datasets. To prioritize these matches as candidates for functional testing, we developed Wregex (Weighted regular expression), which uses a position-specific scoring matrix (PSSM) to order a list of regular expression matches according to a PSSM-derived score. Here we present ...[more]

PMID: 39135888

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Project description:BackgroundShort linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1).ResultsWe implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions.ConclusionWe have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.

Project description:BackgroundAn important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches.ResultsWe propose a novel approach of finding correlated short sequence motifs from protein-protein interaction data to effectively circumvent the above-mentioned limitations. Correlated motifs are those motifs that consistently co-occur only in pairs of interacting protein sequences, and could possibly interact with each other directly or indirectly to mediate interactions. We adopted the (l, d)-motif model and formulate finding the correlated motifs as an (l, d)-motif pair finding problem. We present both an exact algorithm, D-MOTIF, as well as its approximation algorithm, D-STAR to solve this problem. Evaluation on extensive simulated data showed that our approach not only eliminated the need for any prior protein grouping, but is also more robust in extracting motifs from noisy interaction data. Application on two biological datasets (SH3 interaction network and TGFbeta signaling network) demonstrates that the approach can extract correlated motifs that correspond to actual interacting subsequences.ConclusionThe correlated motif approach outlined in this paper is able to find correlated linear motifs from sparse and noisy interaction data. This, in turn, will expedite the discovery of novel linear binding motifs, and facilitate the studies of biological pathways mediated by them.

Project description:BACKGROUND: Linear motifs are short modules of protein sequences that play a crucial role in mediating and regulating many protein-protein interactions. The function of linear motifs strongly depends on the context, e.g. functional instances mainly occur inside flexible regions that are accessible for interaction. Sometimes linear motifs appear as isolated islands of conservation in multiple sequence alignments. However, they also occur in larger blocks of sequence conservation, suggesting an active role for the neighbouring amino acids. RESULTS: The evolution of regions flanking 116 functional linear motif instances was studied. The conservation of the amino acid sequence and order/disorder tendency of those regions was related to presence/absence of the instance. For the majority of the analysed instances, the pairs of sequences conserving the linear motif were also observed to maintain a similar local structural tendency and/or to have higher local sequence conservation when compared to pairs of sequences where one is missing the linear motif. Furthermore, those instances have a higher chance to co-evolve with the neighbouring residues in comparison to the distant ones. Those findings are supported by examples where the regulation of the linear motif-mediated interaction has been shown to depend on the modifications (e.g. phosphorylation) at neighbouring positions or is thought to benefit from the binding versatility of disordered regions. CONCLUSION: The results suggest that flanking regions are relevant for linear motif-mediated interactions, both at the structural and sequence level. More interestingly, they indicate that the prediction of linear motif instances can be enriched with contextual information by performing a sequence analysis similar to the one presented here. This can facilitate the understanding of the role of these predicted instances in determining the protein function inside the broader context of the cellular network where they arise.

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Enhancing prediction of short linear protein motifs with Wregex 3.0.

Publications

Enhancing prediction of short linear protein motifs with Wregex 3.0.

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