Importance of higher-order epistasis in large protein sequence-function relationships

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 24, 2024

Epistasis complicates our understanding of protein sequence-function relationships and impedes ability to build accurate predictive models for novel genotypes. Although pairwise epistasis has been extensively studied in proteins, the significance higher-order remains contentious, largely due challenges fitting epistatatic interactions full-length proteins. Here, we introduce a transformer-based approach. The key feature method is that can adjust order fit by model changing number attention layers while also accounting any global nonlinearity induced experimental conditions. This allows us test if inclusion leads enhanced performance. Applying 10 large datasets, found importance differs substantially between up 60% total variance attributed epistasis. We including particularly important generalizing locally sampled fitness data distant regions sequence space modeling an additional multipeak landscape derived from combining mutagenesis 4 orthologous green fluorescencent Our findings suggest often does play role relationships, thus should be properly incorporated during engineering evolutionary analysis.

Language: Английский

Epistasis facilitates functional evolution in an ancient transcription factor

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: April 20, 2023

A protein’s genetic architecture – the set of causal rules by which its sequence produces functions also determines possible evolutionary trajectories. Prior research has proposed that proteins is very complex, with pervasive epistatic interactions constrain evolution and make function difficult to predict from sequence. Most this work analyzed only direct paths between two interest excluding vast majority genotypes trajectories considered a single protein function, leaving unaddressed functional specificity impact on new functions. Here we develop method based ordinal logistic regression directly characterize global determinants multiple 20-state combinatorial deep mutational scanning (DMS) experiments. We use it dissect transcription factor’s for DNA, using data DMS an ancient steroid hormone receptor’s capacity activate biologically relevant DNA elements. show recognition consists dense main pairwise effects involve virtually every amino acid state in protein-DNA interface, but higher-order epistasis plays tiny role. Pairwise enlarge sequences are primary different They massively expand number opportunities single-residue mutations switch one target another. By bringing variants close together space, therefore facilitates rather than constrains

Language: Английский