Predicting epistasis across proteins by structural logic.
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| Title: | Predicting epistasis across proteins by structural logic. |
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| Authors: | Tang, Michelle1, Cromie, Gareth A.1, Kabir, Anowarul2, Timour, Martin S.1, Ashmead, Julee1, Lo, Russell S.1, Corley, Nathaniel3, DiMaio, Frank3,4, Morizono, Hiroki5,6, Caldovic, Ljubica5,6, Mew, Nicholas Ah5,6, Gropman, Andrea7,8, Shehu, Amarda2, Dudley, Aimée M.1 aimee.dudley@gmail.com |
| Source: | Proceedings of the National Academy of Sciences of the United States of America. 1/20/2026, Vol. 123 Issue 3, p1-10. 10p. |
| Subjects: | Complementation (Genetics), Genetic variation, Machine learning, Proteins, Individualized medicine, Enzymes |
| Abstract: | Accurately predicting the phenotypic consequences of genetic variation is a major challenge for precision medicine. The problem is exacerbated by epistatic interactions, nonadditive effects between genetic variants that produce unexpected phenotypes. Here, we explore an understudied form of positive epistasis: intragenic complementation, in which pairs of loss-of-function variants restore near wild-type protein function. Using mutational scanning in yeast, we identify thousands of such interactions in a clinically important enzyme, human argininosuccinate lyase (ASL). Restoration of protein function is not due to the biochemical properties of the substituted amino acids, but rather to a structural feature of the protein, the active site assembly. We develop a machine learning algorithm that uses protein language model embeddings to predict intragenic complementation in ASL with 99.6% accuracy. Additionally, the model trained on ASL generalizes to a structurally related but sequence-divergent enzyme, fumarase, with accuracy over 90%. Our findings reveal a structural basis for this form of epistasis and provide a predictive framework that could extend to at least 4% of human proteins. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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