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Characterizing Physicochemical Selection in Protein Evolution with Property-Informed Models (PRIME).

Hannah Kim1,2, Konrad Scheffler3, Anton Nekrutenko4

  • 1Institute for Genomics and Evolutionary Medicine, Temple Universitya, Philadelphia, PA, USA.

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Summary
This summary is machine-generated.

We developed PRIME, a new method to understand protein evolution by linking genetic changes to amino acid properties. This framework reveals the biophysical basis of selection, improving our understanding of protein diversity.

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Area of Science:

  • Evolutionary Biology
  • Molecular Evolution
  • Biophysics

Background:

  • Standard models of coding sequence evolution identify selection but not its mechanistic basis.
  • Understanding the biophysical drivers of protein evolution is crucial for deciphering molecular mechanisms.

Purpose of the Study:

  • Introduce PRIME (Property Informed Models of Evolution), a framework for codon-level maximum likelihood methods.
  • Explicitly model amino acid exchangeability based on physicochemical properties to reveal the biophysical basis of selective constraint.
  • Enhance the understanding of protein evolution by integrating biophysical realism into evolutionary models.

Main Methods:

  • Developed global (G-PRIME), episodic (E-PRIME), and site-specific (S-PRIME) implementations of PRIME.
  • Parameterized amino acid exchangeability using physicochemical attributes like molecular volume, hydropathy, and secondary structure propensities.
  • Analyzed 24 diverse datasets and performed a genome-wide screen of 18,944 mammalian genes.

Main Results:

  • PRIME significantly improves model fit by incorporating biophysical realism, synergizing with rate variation to explain complex evolutionary patterns.
  • Site-specific analysis (S-PRIME) precisely categorizes residues based on properties, revealing selective signals missed by traditional metrics.
  • E-PRIME identified a biophysical hierarchy: core packing and beta-sheets are conserved, while alpha-helix propensity and surface electrostatics are key for adaptation.

Conclusions:

  • PRIME transforms abstract evolutionary rates into interpretable biophysical rules, providing a mechanistic framework for protein evolution.
  • The study demonstrates that biophysical realism is essential for accurately modeling protein sequence evolution.
  • PRIME's findings align with deep learning representations and experimental fitness landscapes, offering a unified view of protein diversity drivers.