HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons

Prytuliak, Roman; Volkmer, Michael; Meier, Markus; Habermann, Bianca

doi:10.1093/nar/gkx810

Cited by 10 publications

(7 citation statements)

References 28 publications

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“…Although we observed evidence for the presence of functional novel motifs and the recovery of known instances, computational de novo SLiM prediction remains a difficult challenge despite the plethora of available SLiM prediction methods, and thus the number of false positive predictions is expected to be high (Prytuliak et al, 2017). Our approach is vulnerable to additional uncertainties, which can be attributed to multiple confounders throughout our workflow in addition to the inherent false positive rate of the motif prediction method.…”

Section: Discussionmentioning

confidence: 95%

Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs

Kleshchevnikov

Sandaltzopoulou

Benz

et al. 2021

Preprint

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Linear motifs have an integral role in dynamic cell functions including cell signalling, the cell cycle and others. However, due to their small size, low complexity, degenerate nature, and frequent mutations, identifying novel functional motifs is a challenging task. Viral proteins rely extensively on the molecular mimicry of cellular linear motifs for modifying cell signalling and other processes in ways that favour viral infection. This study aims to discover human linear motifs convergently evolved also in disordered regions of viral proteins, under the hypothesis that these will result in enrichment in functional motif instances. We systematically apply computational motif prediction, combined with implementation of several functional and structural filters to the most recent publicly available human-viral and human-human protein interaction network. By limiting the search space to the sequences of viral proteins, we observed an increase in the sensitivity of motif prediction, as well as improved enrichment in known instances compared to the same analysis using only human protein interactions. We identified > 8,400 motif instances at various confidence levels, 105 of which were supported by all functional and structural filters applied. Overall, we provide a pipeline to improve the identification of functional linear motifs from interactomics datasets and a comprehensive catalogue of putative human motifs that can contribute to our understanding of the human domain-linear motif code and the mechanisms of viral interference with this.

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Section: Discussionmentioning

confidence: 95%

Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs

Kleshchevnikov

Sandaltzopoulou

Benz

et al. 2021

Preprint

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“…Truncation of the Euc1 CC domain abolished the interaction with Slx5 ( Fig EV3B) as well as Euc1 dimerization in Y2H (Appendix Fig S5C and D); however, Slx5 interaction was also lost by truncation of the region between aa 140 and 183, indicating a potential Slx5binding site in this region (compare Fig EV3B and Appendix Fig S5C, Euc1 1-140). To guide our search, we used HH-MOTiF for de novo motif prediction (Prytuliak et al, 2017) using Euc1 aa 81-183 and a set of putative Slx5 substrates as query. We introduced mutations in predicted binding sites downstream of the CC domain: Two of these strongly diminished binding to Slx5 while leaving dimerization intact (Slx5-binding mutant 1 and 2, SBM1: aa 139-143 ENQKK>ANAAA, SBM2: aa 162-165 KEVF>AAAA, Fig 5D and Appendix Fig S6A and B).…”

Section: Of 19mentioning

confidence: 99%

Slx5/Slx8‐dependent ubiquitin hotspots on chromatin contribute to stress tolerance

et al. 2019

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Chromatin is a highly regulated environment, and protein association with chromatin is often controlled by post‐translational modifications and the corresponding enzymatic machinery. Specifically, SUMO ‐targeted ubiquitin ligases ( STU bLs) have emerged as key players in nuclear quality control, genome maintenance, and transcription. However, how STU bLs select specific substrates among myriads of SUMO ylated proteins on chromatin remains unclear. Here, we reveal a remarkable co‐localization of the budding yeast STU bL Slx5/Slx8 and ubiquitin at seven genomic loci that we term “ubiquitin hotspots”. Ubiquitylation at these sites depends on Slx5/Slx8 and protein turnover on the Cdc48 segregase. We identify the transcription factor‐like Ymr111c/Euc1 to associate with these sites and to be a critical determinant of ubiquitylation. Euc1 specifically targets Slx5/Slx8 to ubiquitin hotspots via bipartite binding of Slx5 that involves the Slx5 SUMO ‐interacting motifs and an additional, novel substrate recognition domain. Interestingly, the Euc1‐ubiquitin hotspot pathway acts redundantly with chromatin modifiers of the H2A.Z and Rpd3L pathways in specific stress responses. Thus, our data suggest that STU bL‐dependent ubiquitin hotspots shape chromatin during stress adaptation.

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“…Since we are looking for sequence motifs related to the positive class, we exclude motifs related to the negative class. Evaluation on ELM dataset: We compare the DiMotif performance with two recent motif discovery tools: (i) HH-Motif [26] as an instance of non-discriminative methods and (ii) DLocalMotif [30] as an instance of discriminative approaches. We evaluate the performances over the 20 problem settings related to 5 types of motifs in the ELM database.…”

Section: Dimotif Protein Sequence Motif Discoverymentioning

confidence: 99%

“…finding overrepresented protein sub-sequences in a set of sequences of similar phenotype (positive sequences). Examples of non-discriminative methods are SLiMFinder [23] (regular expression based approach), GLAM2 [24] (simulated annealing algorithm for alignments of SLiMs), MEME [25] (mixture model fitting by expectation-maximization), HH-MOTiF [26] (Hidden Markov Model (HMM) model based approach on multiple sequence alignment). Since other randomly conserved patterns may also exist in the positive sequences, reducing the false positive rate is a challenge for motif discovery [27].…”

Section: Introductionmentioning

confidence: 99%

“…Evaluation of mined motifs can be also subjective. Since the extracted motifs do not always exactly match the experimental motifs, residue-level or site-level evaluations have been proposed [26,32]. Despite great efforts in this area, computational motif mining has remained a challenging task and the state-of-the-art de novo approaches have reported relatively low precision and recall scores, even at the residue level [26].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)

Asgari

McHardy

Mofrad

2018

Preprint

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In this paper, we present peptide-pair encoding (PPE), a general-purpose probabilistic segmentation of protein sequences into commonly occurring variable-length sub-sequences. The idea of PPE segmentation is inspired by the byte-pair encoding (BPE) text compression algorithm, which has recently gained popularity in subword neural machine translation. We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence. PPE can be inferred over a large set of protein sequences (Swiss-Prot) and then applied to a set of unseen sequences. This representation can be widely used as the input to any downstream machine learning tasks in protein bioinformatics. In particular, here, we introduce this representation through protein motif mining and protein sequence embedding. (i) DiMotif: we present DiMotif as an alignment-free discriminative motif miner and evaluate the method for finding protein motifs in different settings. The significant motifs extracted could reliably detect the integrins, integrin-binding, and biofilm formation-related proteins on a reserved set of sequences with high F1 scores. In addition, DiMotif could detect experimentally verified motifs related to nuclear localization signals.(ii) ProtVecX: we extend k-mer based protein vector (ProtVec) embedding to variable-length protein embedding using PPE sub-sequences. We show that the new method of embedding can marginally outperform ProtVec in enzyme prediction as well as toxin prediction tasks. Availability: Implementations of our method will be available under the Apache 2 licence at

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HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons

Cited by 10 publications

References 28 publications

Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs

Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs

Slx5/Slx8‐dependent ubiquitin hotspots on chromatin contribute to stress tolerance

Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)

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