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Advancements in protein language models (PLMs) are revolutionizing protein design and biological research. InterPLM, a new framework, utilizes sparse autoencoders (SAEs) to uncover over 2,500 interpretable features per model layer, providing insights into how PLMs represent protein structure and function. This interpretability allows for the discovery of new biological concepts and fills gaps in existing databases. Additionally, PROPEND, another innovative framework, leverages 'pre-train and prompt' methodologies for multi-purpose protein generation, enabling applications like structure-based, function-based, and sequence-based design. These advancements signify a shift towards programmable biology, where proteins can be designed as easily as writing code, with potential applications in enzyme engineering, antimicrobial peptide design, and the generation of D-peptides using Chat-GPT.
SEQPROFT: Applying LoRA Finetuning for Sequence-Only Protein Property Predictions • SEQPROFT introduces a parameter-efficient finetuning (PEFT) method using LoRA (Low-Rank Adaptation) to enhance protein language models (PLMs) like ESM-2 for sequence-only protein property… https://t.co/RWdhtSeI1U
ConfFlow: Transformer-Based Flow Model for Molecular Conformation Generation • Introducing ConfFlow, a novel flow-based model utilizing transformer architectures for molecular conformation generation. Unlike previous methods, ConfFlow directly samples 3D atomic coordinates… https://t.co/AFhrZLa0g5
Reinforcement Learning for Sequence Design Leveraging Protein Language Models • This study presents a novel framework combining reinforcement learning (RL) with protein language models (PLMs) for efficient de novo protein sequence design, optimizing sequence diversity and… https://t.co/W6c8Rvyy31
