Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space

Kaushik, Sushmita; Nair, Archana Padmanabhan; Mutt, Eshita; Subramanian, Hari Prasanna; Sowdhamini, Ramanathan

doi:10.1093/bioinformatics/btv538

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…Fragstatt identifies local sequence similarities by means of HHblits, 32 which is a state‐of‐the‐art method for the comparison of HMMs and the detection of remote homology 33 . In order to increase the sensitivity beyond previous studies, 25,34 Fragstatt cascades several HMM searches, because this approach has proven enhanced sensitivity in remote homology detection 35 …”

Section: Resultsmentioning

confidence: 99%

Evidence for the preferential reuse of sub‐domain motifs in primordial protein folds

Heizinger

Merkl

2021

Proteins

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A comparison of protein backbones makes clear that not more than approximately 1400 different folds exist, each specifying the three‐dimensional topology of a protein domain. Large proteins are composed of specific domain combinations and many domains can accommodate different functions. These findings confirm that the reuse of domains is key for the evolution of multi‐domain proteins. If reuse was also the driving force for domain evolution, ancestral fragments of sub‐domain size exist that are shared between domains possessing significantly different topologies. For the fully automated detection of putatively ancestral motifs, we developed the algorithm Fragstatt that compares proteins pairwise to identify fragments, that is, instantiations of the same motif. To reach maximal sensitivity, Fragstatt compares sequences by means of cascaded alignments of profile Hidden Markov Models. If the fragment sequences are sufficiently similar, the program determines and scores the structural concordance of the fragments. By analyzing a comprehensive set of proteins from the CATH database, Fragstatt identified 12 532 partially overlapping and structurally similar motifs that clustered to 134 unique motifs. The dissemination of these motifs is limited: We found only two domain topologies that contain two different motifs and generally, these motifs occur in not more than 18% of the CATH topologies. Interestingly, motifs are enriched in topologies that are considered ancestral. Thus, our findings suggest that the reuse of sub‐domain sized fragments was relevant in early phases of protein evolution and became less important later on.

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Section: Resultsmentioning

confidence: 99%

Evidence for the preferential reuse of sub‐domain motifs in primordial protein folds

Heizinger

Merkl

2021

Proteins

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“…Homology tools used for collecting gene sequences from databases and metagenomes, such as BLAST [21], HMMER [38], MMseqs2 [39], or PSI-BLAST [40], are sensitive and have the ability to detect remote homology between sequences. While detecting distant homologs is useful, especially when analyzing envi-ronmental metagenomic data, such sensitivity often comes with a price: increased levels of false positive sequences [27]. In the context of viral and microbial ecology, false positives can include non-functional versions of the protein of interest, correctly annotated proteins that do not span a predetermined region of interest, and proteins that share a conserved region or domain with the protein of interest, but are not the desired protein.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, highly sensitive homology search tools used for annotating and identifying marker genes within metagenomes often have high false positive rates [27]. Identifying false positives in functional annotations is an active area of research and many techniques are available.…”

Section: Introductionmentioning

confidence: 99%

“…Often, such environmental sequence annotations are several steps away from a confident, biochemical annotation, which can quickly lead to inaccuracies resulting from "error percolation" [24]. Furthermore, highly sensitive homology search tools used for annotating and identifying marker genes within metagenomes often have high false positive rates [27]. Identifying false positives in functional annotations is an active area of research and many techniques are available.…”

Section: Introductionmentioning

confidence: 99%

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PASV: Automatic protein partitioning and validation using conserved residues

Harrison

et al. 2021

Preprint

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Background: Increasingly, researchers use protein-coding genes from targeted PCR amplification or direct metagenomic sequencing in community and population ecology. Analysis of protein-coding genes presents different challenges from those encountered in traditional SSU rRNA studies. Most protein-coding sequences are annotated based on homology to other computationally-annotated sequences, which can lead to inaccurate annotations. Therefore, the results of sensitive homology searches must be validated to remove false-positives and assess functionality. Multiple lines of in silico evidence can be gathered by examining conserved domains and residues identified through biochemical investigations. However, manually validating sequences in this way can be time consuming and error prone, especially in large environmental studies. Results: An automated pipeline for protein active site validation (PASV) was developed to improve validation and partitioning accuracy for protein-coding sequences, combining multiple sequence alignment with expert domain knowledge. PASV was tested using commonly misannotated proteins: ribonucleotide reductase (RNR), alternative oxidase (AOX), and plastid terminal oxidase (PTOX). PASV partitioned 9,906 putative Class I alpha and Class II RNR sequences from bycatch in a global viral metagenomic investigation with >99% true positive and true negative rates. PASV predicted the class of 2,579 RNR sequences in >98% agreement with manual annotations. PASV correctly partitioned all 336 tested AOX and PTOX sequences. Conclusions: PASV provides an automated and accurate way to address post-homology search validation and partitioning of protein-coding marker genes. Source code is released under the MIT license and is found with documentation and usage examples on GitHub at https://github.com/mooreryan/pasv.

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“…The ensemble of models optimized using average precision (APR) ranks close homologs well and pays less attention to distant or remote homologs. But it is often interesting to detect remote homologs as it can facilitate the assignment of putative function to uncharacterized proteins improving genomic function annotations (Söding, 2005;Eddy, 1998Eddy, , 2011Kaushik et al, 2016). Optimization of rank last (RKL) to build an ensemble of models helps us achieving this latter goal.…”

Section: Introductionmentioning

confidence: 99%

Ensembles of phalanxes across assessment metrics for robust ranking of homologous proteins

Tomal,

Welch,

Zamar

2017

Preprint

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An ensemble of models, where each model is constructed on a diverse subset of feature variables, is proposed to rank rare class items ahead of majority class items in a highly unbalanced two class problem. The proposed ensemble relies on an algorithm to group the feature variables into subsets where the variables in a subset work better together in a model and the variables in different subsets work better in separate models. The strength of the ensemble of models depends on the algorithm's ability to identify strong and diverse subsets of feature variables. A second phase of ensembling is achieved by aggregating several ensembles of models each optimized on a diverse evaluation metric.The resulting ensemble is called ensemble of models and metrics. Here, the diverse/complementary evaluation metrics ensure increased diversity among ensembles of models to aggregate. The ensembles are applied to the protein homology data, downloaded from the 2004 KDD cup competition website, to rank proteins in such a way that the rare homologous proteins are found ahead of the majority non-homologous proteins. The ensembles are constructed using feature variables which are various scores from sequence alignments of amino acids in a candidate protein and three dimensional descriptions of a native protein representing functional and structural similarity of proteins. While prediction performances of the ensembles of models are better than the contemporary state-of-the-art ensembles and competitive to the winning procedures of the 2004 KDD cup competition, the performances of the ensemble models and metrics are found on the top of all. In this application, we have 2 Ruben H. Zamar two diverse ensembles of models constructed on two complementary evaluation metrics average precision and rank last, where the former is robust against ranking close homologs and the latter is robust against ranking distant homologs. The advantage of using ensemble of models and metrics is that it is robust against both close and distant homologs.

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Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space

Abstract: mini@ncbs.res.in.

Cited by 6 publications

References 28 publications

Evidence for the preferential reuse of sub‐domain motifs in primordial protein folds

Evidence for the preferential reuse of sub‐domain motifs in primordial protein folds

PASV: Automatic protein partitioning and validation using conserved residues

Ensembles of phalanxes across assessment metrics for robust ranking of homologous proteins

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