High fitness paths can connect proteins with low sequence overlap
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingProtein Language ModelSimilar Papers 제목 키워드 기반
Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains
Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutiona…
Scaling properties of evolutionary paths in a biophysical model of protein adaptation
The enormous size and complexity of genotypic sequence space frequently requires consideration of coarse-grained sequences in empirical models. We develop scaling relations to quantify the effect of this coarse-graining …
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using …
DecoderLanguage ModelingLanguage ModellingProtein Language ModelMutational paths with sequence-based models of proteins: from sampling to mean-field characterisation
Identifying and characterizing mutational paths is an important issue in evolutionary biology and in bioengineering. We here introduce a generic description of mutational paths in terms of the goodness of sequences and o…
How pairwise coevolutionary models capture the collective residue variability in proteins
Global coevolutionary models of homologous protein families, as constructed by direct coupling analysis (DCA), have recently gained popularity in particular due to their capacity to accurately predict residue-residue con…
Protein Structure Prediction