Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration
Abstract Protein–protein interactions (PPIs) play essential roles in most biological processes. The binding interfaces between interacting proteins impose evolutionary constraints that have successfully been employed to predict PPIs from multiple sequence alignments (MSAs). To construct MSAs, critic...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-55655-9 |
| _version_ | 1797259348434485248 |
|---|---|
| author | Tao Fang Damian Szklarczyk Radja Hachilif Christian von Mering |
| author_facet | Tao Fang Damian Szklarczyk Radja Hachilif Christian von Mering |
| author_sort | Tao Fang |
| collection | DOAJ |
| description | Abstract Protein–protein interactions (PPIs) play essential roles in most biological processes. The binding interfaces between interacting proteins impose evolutionary constraints that have successfully been employed to predict PPIs from multiple sequence alignments (MSAs). To construct MSAs, critical choices have to be made: how to ensure the reliable identification of orthologs, and how to optimally balance the need for large alignments versus sufficient alignment quality. Here, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed under distinct clades in the tree of life. Coevolutionary signals are searched separately within these clades, and are only subsequently integrated using machine learning techniques. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. Given the recent successes of AlphaFold in predicting direct PPIs at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates—thus reducing false positives as well as computation time. |
| first_indexed | 2024-04-24T23:08:00Z |
| format | Article |
| id | doaj.art-4aa43a5ba80f4728a24bce72ba0cab7a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T23:08:00Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4aa43a5ba80f4728a24bce72ba0cab7a2024-03-17T12:21:35ZengNature PortfolioScientific Reports2045-23222024-03-0114111710.1038/s41598-024-55655-9Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integrationTao Fang0Damian Szklarczyk1Radja Hachilif2Christian von Mering3Department of Molecular Life Sciences, University of ZurichDepartment of Molecular Life Sciences, University of ZurichDepartment of Molecular Life Sciences, University of ZurichDepartment of Molecular Life Sciences, University of ZurichAbstract Protein–protein interactions (PPIs) play essential roles in most biological processes. The binding interfaces between interacting proteins impose evolutionary constraints that have successfully been employed to predict PPIs from multiple sequence alignments (MSAs). To construct MSAs, critical choices have to be made: how to ensure the reliable identification of orthologs, and how to optimally balance the need for large alignments versus sufficient alignment quality. Here, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed under distinct clades in the tree of life. Coevolutionary signals are searched separately within these clades, and are only subsequently integrated using machine learning techniques. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. Given the recent successes of AlphaFold in predicting direct PPIs at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates—thus reducing false positives as well as computation time.https://doi.org/10.1038/s41598-024-55655-9 |
| spellingShingle | Tao Fang Damian Szklarczyk Radja Hachilif Christian von Mering Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration Scientific Reports |
| title | Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration |
| title_full | Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration |
| title_fullStr | Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration |
| title_full_unstemmed | Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration |
| title_short | Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration |
| title_sort | enhancing coevolutionary signals in protein protein interaction prediction through clade wise alignment integration |
| url | https://doi.org/10.1038/s41598-024-55655-9 |
| work_keys_str_mv | AT taofang enhancingcoevolutionarysignalsinproteinproteininteractionpredictionthroughcladewisealignmentintegration AT damianszklarczyk enhancingcoevolutionarysignalsinproteinproteininteractionpredictionthroughcladewisealignmentintegration AT radjahachilif enhancingcoevolutionarysignalsinproteinproteininteractionpredictionthroughcladewisealignmentintegration AT christianvonmering enhancingcoevolutionarysignalsinproteinproteininteractionpredictionthroughcladewisealignmentintegration |