Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
What are the fundamental units of protein sequences? Most protein language models treat amino acids as tokens, yet biological functions are not encoded at the single-residue level. Instead, they emerge from combinations of residues that form functional units that corresponds to conserved sequence motifs. Just like how...
Boon How Low, Wen Y. Goh, Bo-Yang Li et al.· Proceedings of the 32nd ACM...· 0 citations
Testing the ability of common large language models to consider design principles to generate de novo proteins that bind metals and lipophilic small molecules without copying existing sequences highlights the utility of LLMs in making protein design more comprehensible and accessible to users without sophisticated desi...
Nam Hyeong Kim, A. K. Hatstat, Hyunil Jo et al.· bioRxiv· 0 citations
RFO formulates binder improvement as a residue-wise mutational search problem, sampling candidate substitutions alternately based on gradient-guided sequence optimization using all-atom structure prediction models and a cycling-based sequence redesign strategy that alternates structure generation with an orthogonal pre...
Odin Zhang, Jia-Qi Wang, T. Thompson et al.· bioRxiv· 0 citations
An approach to create novel, functional proteins through the integration of deep mutational scanning, structural analysis, and evolutionary mining within prompts for a generative protein language model (PLM) is described and the utility of this approach is demonstrated with the generation of novel compact RNA-guided nu...
Nicholas W. Hughes, Sourab Kulkarni, Grant Goldman et al.· bioRxiv· 0 citations
De novo protein design is pivotal for revolutionizing protein engineering and advancing life sciences. Protein co-design aims to simultaneously create a novel protein sequence and structure with tailored functions, addressing the insufficient consistency between sequence and structure of two-stage design. Current AI-as...
Ming Yang, Xin Zheng, Yi Li et al.· Proceedings of the 32nd ACM...· 0 citations
How solid-phase peptide synthesis, genetically encoded libraries, and high-throughput selection and screening enable systematic exploration of vast, noncanonical landscapes largely inaccessible to traditional engineering is discussed.
Filip Buchel, V. G. Giacobelli, K. Hlouchová· TIBS -Trends in Biochemical...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.