HyperEvoGen: Exploring deep phylogeny using non-Euclidean variational inference

TL;DR

HyperEvoGen introduces a hyperbolic variational autoencoder with adversarial training for improved modeling of protein evolution, capturing long-range co-evolutionary patterns and phylogenetic relationships more accurately.

Contribution

It presents a novel hyperbolic latent space model that better preserves evolutionary distances and hierarchical structures in protein sequence data.

Findings

Outperforms traditional methods in ancestral sequence reconstruction.

Provides higher-quality sequence generation with less training time.

Accurately models deep evolutionary divergence in benchmarks.

Abstract

Homologous proteins evolve from a common ancestral sequence, constrained by intricate patterns of co-evolving residues. Accurate reconstruction of evolutionary histories remains a challenge, primarily due to the inability of the existing approaches to capture long-range coevolutionary ties and lack of a precise metric to represent the evolutionary distance between sequences. Standard approaches are based on p-distance or substitution-corrected measures such as Jukes-Cantor. These methods saturate in cases of deep evolutionary divergence, losing all evolutionary signal after enough time. We present HyperEvoGen, a Poincar\'e variational autoencoder with adversarial training, hyperbolic latent geometry, and a compound loss function that learns evolutionarily meaningful representations from single-family alignments. The arrangement of protein sequences in HyperEvoGen's hyperbolic embedding aims to preserve phylogenetic structure and produce latent distances which scale with true evolutionary divergence. HyperEvoGen enables fast, scalable modeling of protein evolution while preserving hierarchical relatedness in a geometry-aware representation. On Potts-coupled simulation benchmarks, it produces more accurate ancestral reconstructions than conventional baselines, and offers higher-quality sequence generation with less training time than Potts models. This combination of accuracy and throughput supports large-family evolutionary studies and accelerates design-oriented applications.