Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison

By Mindaugas Margelevičius, Č. Venclovas. 2010

54 citations recorded by the source corpus.

BackgroundDetection of common evolutionary origin (homology) is a primary means of inferring protein structure and function. At present, comparison of protein families represented as sequence profiles is arguably the most effective homology detection strategy. However, finding the best way to represent evolutionary information of a protein sequence family in the profile, to compare profiles and to estimate the biological significance of such comparisons, remains an active area of research.ResultsHere, we present a new homology detection method based on sequence profile-profile comparison. The method has a number of new features including position-dependent gap penalties and a global score system. Position-dependent gap penalties provide a more biologically relevant way to represent and align protein families as sequence profiles. The global score system enables an analytical solution of the statistical parameters needed to estimate the statistical significance of profile-profile similarities. The new method, together with other state-of-the-art profile-based methods (HHsearch, COMPASS and PSI-BLAST), is benchmarked in all-against-all comparison of a challenging set of SCOP domains that share at most 20% sequence identity. For benchmarking, we use a reference ("gold standard") free model-based evaluation framework. Evaluation results show that at the level of protein domains our method compares favorably to all other tested methods. We also provide examples of the new method outperforming structure-based similarity detection and alignment. The implementation of the new method both as a standalone software package and as a web server is available at http://www.ibt.lt/bioinformatics/coma.ConclusionDue to a number of developments, the new profile-profile comparison method shows an improved ability to match distantly related protein domains. Therefore, the method should be useful for annotation and homology modeling of uncharacterized proteins.

Subfields: Molecular Biology

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Citations in the corpus

IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “The IntFOLD5-TS method was newly developed for CASP13 and worked via iterative multi-template based modelling (19) using the target-template alignments from 14 alternative methods (SP3 (31), SPARKS2 (31), HHsearch (32), COMA (33), SPARKSX (34), CNFsearch (35) and the eight alternative threading meth”
Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method (2011, methodology): “In addition, two newer alignment methods, HHsearch22 and COMA,23 were also integrated into the pipeline.”
The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction (2011, methodology): “The nFOLD4 algorithm, combines alignment output from in-house versions of several proﬁle-based fold recognition methods namely SP3 (9), SPARKS (9), HHsearch (10) and COMA (11), generating up to 40 alternative 3D models from bespoke 40% and 70% non-redundant template libraries.”
Protein sequence comparison and fold recognition: progress and good-practice benchmarking. (2011, unknown): “lt:8085/coma/ [63]”