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SYAI: Symposium AI in (Bio-)Physics

SYAI 1: AI in (Bio-)Physics

SYAI 1.1: Hauptvortrag

Donnerstag, 20. März 2025, 09:30–10:00, H1

Predicting interaction partners and generating new protein sequences using protein language models — •Anne-Florence Bitbol — École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Protein sequences are shaped by functional optimization on the one hand and by evolutionary history, i.e. phylogeny, on the other hand. A multiple sequence alignment of homologous proteins contains sequences which evolved from the same ancestral sequence and have similar structure and function. In such an alignment, statistical patterns in amino-acid usage at different sites encode structural and functional constraints.

Protein language models trained on multiple sequence alignments capture coevolution between sites and structural contacts, but also phylogenetic relationships. I will discuss a method we recently proposed that leverages these models to predict which proteins interact among the paralogs of two protein families, and improves the prediction of the structure of some protein complexes. Next, I will show that these models can be used to generate new protein sequences from given protein families.

While multiple sequence alignments are very useful, their construction is imperfect. To address these limitations, we developed ProtMamba, a homology-aware but alignment-free protein language model based on the Mamba architecture, which efficiently uses long contexts. I will show that ProtMamba has promising generative properties, and is able to predict fitness.

Keywords: Protein-protein interactions; Protein sequences; Inference; Language models; Machine learning