High fitness paths can connect proteins with low sequence overlap

TL;DR

This paper introduces an algorithm that constructs high-fitness evolutionary paths connecting distantly related proteins with low sequence overlap, using AI-based structure prediction and sequence viability evaluation.

Contribution

The study develops a novel method to generate viable protein paths with minimal sequence changes, expanding understanding of protein morphospace connectivity.

Findings

Paths often connect proteins with different structural folds.

High-fitness paths can be generated between distantly related proteins.

The approach may serve as a proxy for homology likelihood.

Abstract

The structure and function of a protein are determined by its amino acid sequence. While random mutations change a protein's sequence, evolutionary forces shape its structural fold and biological activity. Studies have shown that neutral networks can connect a local region of sequence space by single residue mutations that preserve viability. However, the larger-scale connectedness of protein morphospace remains poorly understood. Recent advances in artificial intelligence have enabled us to computationally predict a protein's structure and quantify its functional plausibility. Here we build on these tools to develop an algorithm that generates viable paths between distantly related extant protein pairs. The intermediate sequences in these paths differ by single residue changes over subsequent steps - substitutions, insertions and deletions are admissible moves. Their fitness is evaluated using the protein language model ESM2, and maintained as high as possible subject to the constraints of the traversal. We document the qualitative variation across paths generated between progressively divergent protein pairs, some of which do not even acquire the same structural fold. The ease of interpolating between two sequences could be used as a proxy for the likelihood of homology between them.