Brian M Farrell,Markus A Seeliger
Brian M Farrell
Drug-target residence time is a crucial determinant of pharmacological efficacy, complementing traditional equilibrium affinity measures. Variations in residence time influence drug selectivity, therapeutic windows, and resistance developme...
Liam Rashleigh,Mengqi Pan,Jamie Rossjohn et al.
Liam Rashleigh et al.
T cell receptor (TCR) diversity underpins cellular immunity. While αβ TCRs have been extensively studied in the context of major histocompatibility complex (MHC)-restricted antigen recognition, the γδ TCR system remains underexplored. U...
Rational protein design [0.03%]
理性的蛋白质设计
Joel J Chubb,Aimee L Boyle,Katherine I Albanese
Joel J Chubb
Protein design enables the creation of novel structures and functions beyond those found in nature, with recent progress accelerated by computational modeling and machine learning. However, many automated methods act as black boxes, limitin...
Drug-target residence time: Analyzing cooperativity effects in G protein-coupled receptors by mathematical modeling and molecular dynamics simulations [0.03%]
药物-靶点驻留时间：通过数学建模和分子动力学模拟分析G蛋白偶联受体中的协同效应
Antonio J Ortiz,Antoniel A S Gomes,Pedro Renault et al.
Antonio J Ortiz et al.
Drug-target residence time (τ) is reviewed from two perspectives: mathematics and molecular dynamics. The first focuses on the quantification of τ using a mathematical formalism applicable to different pharmacological mechanistic conditio...
Ashar J Malik,Stephanie Portelli,David B Ascher
Ashar J Malik
Transformers are rapidly reshaping structural biology. We argue the reason is "Emergent Latent Biology" (ELB): transformers place proteins into high-dimensional representations where hidden biophysical patterns become easier to see. We expl...
From sequence to structure: A comprehensive review of deep learning models for RNA structure prediction [0.03%]
从序列到结构：RNA 结构预测深度学习模型综述
Utkarsh Upadhyay,Anton Dorn,Christian Faber et al.
Utkarsh Upadhyay et al.
RNA structure prediction remains one of the most challenging problems in computational biology, with significant implications for understanding gene regulation, drug design, and synthetic biology. While deep learning has revolutionized prot...
Machine learning, docking, or physics for structure prediction of ligand-induced ternary complexes [0.03%]
机器学习、对接或物理学在配体诱导三元复合物结构预测中的应用
Riccardo Solazzo,Shu-Yu Chen,Sereina Riniker
Riccardo Solazzo
Proteolysis-targeting chimeras (PROTACs) and molecular glues promote targeted protein degradation by recruiting an E3 ligase to proteins of interest (POIs). An accurate 3D structure of the ternary complex formed by E3 ligase, ligand, and PO...
Decrypting cryptic pockets with physics-based simulations and artificial intelligence [0.03%]
基于物理和人工智能的模拟揭开隐秘口袋之谜
Si Zhang,Gregory R Bowman
Si Zhang
Cryptic pockets are promising targets for drug discovery that greatly expand the druggable proteome. In particular, they can provide opportunities to target proteins previously thought to be "undruggable" due to a lack of pockets in structu...
Giulio Tesei,Francesco Pesce,Kresten Lindorff-Larsen
Giulio Tesei
Protein design has the potential to revolutionize biotechnology and medicine. While most efforts have focused on proteins with well-defined structures, increased recognition of the functional significance of intrinsically disordered regions...
Generative molecular dynamics [0.03%]
生成分子动力学
Simon Olsson
Simon Olsson
Understanding biomolecular function depends on bridging experimental observables with models that capture structural, stationary, and dynamical properties. Molecular dynamics (MD) simulations, in principle provide a bridge, but the sampling...