Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction. Issue 20 (1st October 2021)

• Record Type: Journal Article
• Authors: Sanchez-Pulido, Luis; Ponting, Chris P.
• Is Part Of: Journal of molecular biology. Volume 433:Issue 20(2021)
• Publication Date: 2021-10-01
• Subjects: remote homology -- CFAP298 -- C21ORF59 -- DISC1 -- coevolution; BLAST Basic Local Alignment Search Tool -- C21ORF59 Chromosome 21 Open Reading Frame 59 -- CATH Class Architecture Topology Homologous -- CFAP298 Cilia- and flagella-associated protein 298 -- DISC1 Disrupted in schizophrenia 1 -- MON1A Monensin sensitivity protein 1A -- MSA Multiple sequence alignment -- PDB Protein Data Bank -- Pfam Protein Families Database -- STRING Search Tool for the Retrieval of Interacting Genes/Proteins; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2021.167106
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: Co-evolution-based methods extend the homology detection horizon of protein sequence analysis. Predicted contact maps help predict domain boundaries and tertiary structures. Such maps also aid in identifying internal repeats and large insertions. Coevolution-based methods could yield insights into protein dynamics and conformational change. Abstract: Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a "beads-on-a-string" model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.