Search · four archives
Search · four archives
9 papers · ranked by Valyu relevance
Alp Tartici, Mihajlo Stojkovic, Anru Tian, Michael C. Jewett + 2 more
Protein engineering has important implications in the bioeconomy, enabling applications in materials, medicine, and energy. A key challenge is designing protein sequences that have a specific form and function. Protein inverse folding seeks to address this challenge by identifying amino acid sequences compatible with a…
Odin Zhang, Jiaqi Wang, Tuscan Rock Thompson, Ziyi You + 3 more
Biomolecular interactions, including protein–protein interactions, protein–nucleic acid recognition, and protein–small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent de novo design methods can generate candidate binders for diverse molecular targets…
Jakub Lála, Stefano Angioletti-Uberti
The antigen-binding segment of chimeric antigen receptors (CARs) in CAR-T therapy has emerged as a compelling application of de novo AI protein design. In the Bits to Binders competition, our group submitted 414 BAGEL-CAR designs for CD20-directed CAR binding segments, 38.4% of which were statistically enriched in a…
Kunyu Wang, Jon Paul Janet, Alessandro Tibo
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Kapali Suri, Anshul Yadav, Abhishek Tripathi, N. Arul Murugan
Protein-ligand pose prediction is central to structure-based drug discovery, yet the relative performance of physics-based and AI-driven methods under realistic cross-docking conditions remains insufficiently characterized. Here, we compare physics-based docking methods (AutoDock4, AutoDock Vina, and DOCK 6) with…
Minha Park, Shinwoo Kim, Seokhyun Moon, Hyeongwoo Kim + 2 more
Co-folding models accurately predict biomolecular complexes, but how their internal representations support joint structure prediction remains unclear. We analyze the Boltz-1 trunk by decomposing its pair representation into intra-chain and inter-chain blocks and applying ablation, layer-wise representation geometry…
Seonghwan Seo, Hyeongwoo Kim, Seokhyun Moon, Woo Youn Kim
Protein language models (PLMs) trained on evolutionary sequences learn representations that encode protein structure, enabling direct structure prediction without multiple-sequence alignments (MSAs). Here we present the Atlas model family, an open and trainable system spanning protein language modeling, monomer…
Nele P. Quast, Matthew I. J. Raybould, Charlotte M. Deane
The development of highly accurate deep learning models for protein structure prediction has transformed the landscape of T-cell receptor (TCR) structure data, which can now be accessed at repertoire scale. We provide a perspective on the growing field of structural TCR immunoinformatics, summarizing core principles of…
Yiming Xue, Xiaojian Liu, Weimin Zhu, Shengfan Wang + 2 more
While protein-RNA interactions are fundamental to post-transcriptional processes, achieving a holistic understanding of their regulatory logic remains challenging. Current computational models often treat binding affinity, interface mapping, and RNA design as isolated tasks, thereby failing to provide a unified…