ArticleFundamental research2026
Multiple conformational states assembly of multidomain proteins using evolutionary algorithm based on structural analogues and sequential homologues.
Article in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- AI for biology: Catalyzing interdisciplinary innovation to unravel life's complexity and address biomedical challenges.Fundamental research · 2026Article
- A comprehensive benchmarking of the AlphaFold3 for predicting biomacromolecules and their interactions.Briefings in bioinformatics · 2025Article
- Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era.Briefings in bioinformatics · 2025Review
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6 authors.
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Abstract
With the breakthrough of AlphaFold2, nearly all single-domain protein structures can be built at experimental resolution. However, accurate modelling of full-chain structures of multidomain proteins, particularly all relevant conformations for those with multiple states remain challenging. In this study, we develop a multidomain protein assembly method, M-SADA, for assembling multiple conformational states. In M-SADA, a multiple population-based evolutionary algorithm is proposed to sample multiple conformational states under the guidance of multiple energy functions constructed by combining homologous and analogous templates with inter-domain distances predicted by deep learning. On a developed benchmark dataset containing 72 multidomain proteins with multiple conformational states, the performance of M-SADA is significantly better than that of AlphaFold2 on multiple conformational states modelling, where 29/72 (40.3%) of proteins can be assembled with a TM-score > 0.90 for two highly distinct conformational states with M-SADA. Furthermore, M-SADA is tested on a developed benchmark dataset containing 296 multidomain proteins with single conformational state, and results show that the average TM-score of M-SADA on the best models is 0.913, which is 5.2% higher than that of AlphaFold2 models (0.868). Results on CASP15 multidomain targets also show that M-SADA can predict new domain arrangements when individual domain structures are correct.
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