Aadyot Bhatnagar

E1: Retrieval-Augmented Protein Encoder Models

The paper introduces Profluent-E1, retrieval-augmented protein language models that improve performance by conditioning on homologous sequences, achieving state-of-the-art results in fitness prediction and contact-map prediction.

Engineering of CRISPR-Cas PAM recognition using deep learning of vast evolutionary data

This paper introduces Protein2PAM, a deep learning model that predicts CRISPR-Cas PAM specificity from protein sequences, enabling the design of Cas variants with tailored PAM recognition.

Scaling unlocks broader generation and deeper functional understanding of proteins

Scaling sparse Mixture-of-Experts protein language models up to 46B parameters on 1.5T curated tokens broadens viable protein generation across families and amplifies gains from laboratory preference alignment.

Adapting protein language models for structure-conditioned design

This paper introduces proseLM, a protein design method that adapts language models with structural and functional context, improving residue recovery and experimental performance in genome editing and antibody design.

Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences

Language models trained on over one million mined CRISPR operons generated Cas9-like effectors and matching guide RNAs, and an AI-designed editor is reported to edit the human genome precisely.