Protein Structure and Dynamics

Latest papers

• CODesign: Consistency from Data to Trajectory in All-Atom Protein Binder Co-Design

  Yuanle Mo, Bo Qiang, Haitao Lin, Qinghan Wang, Gang Du, Odin Zhang, Pheng Ann Heng · 2 October 2026

  The central challenge in de novo protein design is generating plausible, mutually compatible structures and sequences, such that each designed sequence folds into its intended structure and the structure accommodates that sequence. Compared to typical two-stage design methods, which decouple the mod…

• Structure-aware Reinforcement Learning for Protein Directed Evolution

  Zikun Nie, Suyuan Zhao, Yizhen Luo, Siqi Fan, Zaiqing Nie · 1 October 2026

  Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely on sequence-only features, overlooking the critical spatial constraints and co-evolutionary interactions encoded in protein structures. However, dire…

• Does Learning Protein Folding Generalize to Broader Reasoning?

  Yong Liu, Zhanpeng Shi, Yizhou Dang, Zhongyue Zhang, Xiaoliang Shi, Zhijian Wei, Shuangjia Zheng · 1 October 2026

  Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed, because one solved structure yields thousands of exactly checkable spatial and topological statements. We ask: can lea…

• From Surfaces to Volumes: Registered Geometry for Protein Representation Learning

  Siyuan Chen, Cai Zhou, Jinrui Zhang, Zhaokang Liang, Taku Komura, Wojciech Matusik, Stephen Bates, Tommi Jaakkola, Wengong Jin, Peter Yichen Chen, Minghao Guo · 30 September 2026

  Existing protein geometry models typically represent molecular surfaces using local geometric features such as sampled points, normals, and curvature. While effective for capturing exposed molecular shape, these representations do not explicitly model the volumetric organization beneath the surface …

• Robust Biomolecular Complex Design Across Protein Conformational Landscapes

  Qingyuan Zeng, Zongqi Xu, Anglin Liu, Ziqi Gong, Pengxiang Cai, Zixin Guan, Yunan Chen, Sen Gao, Min Zhou, Jintai Chen · 29 September 2026

  Proteins populate conformational ensembles, yet structure-based biomolecular design typically optimizes candidates against a single target conformation. Consequently, a candidate that fits one state can lose favorable interactions or develop steric clashes when the target adopts another. We introduc…

• PhiFold: Towards Dynamic Protein Design with Physics-Structured Covariance Modeling

  Yutian Liu, Mujie Lin, LanqianZhang, Meng Fan, Chang Liu, ZhiweiNie, Siwei Ma · 29 September 2026

  Protein design is moving beyond structural correctness toward function-aware design, yet existing generative models typically treat dynamics as a downstream property estimated through simulation or prediction after structure generation. Using MD trajectories as a generative target is also undesirabl…

• Predicting Transmembrane Protein Topology from 3D Structure

  Sitong Chen, Xiaopeng Mao · 28 September 2026

  This paper presents a novel approach to infer protein topology using the state-of-the-art graph neural network (GNN), SchNet. The model is trained on the same dataset used to develop the recent DeepTMHMM model with 5-fold cross-validation. Unlike the conventional approaches based on using only the p…

• PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

  Peining Zhang, Jinbo Bi · 25 September 2026

  Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbf{PocketVE}, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that…

• SPIBER: Reconstructing Free Energy Landscapes from Short, Unconverged Trajectories with Generative Flow Networks

  Venkata Sai Sreyas Adury (Chemical Physics Program and Institute for Physical Science and Technology, University of Maryland), Pratyush Tiwary (Biophysics Program and Institute for Physical Science and Technology, University of Maryland, Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, University of Maryland Institute for Health Computing, Bethesda, USA) · 22 September 2026

  Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these variables and estimating free energies along them from limited simulation data remains a challenging, important problem. Separate short trajectories m…

• Human mutation field reveals an equilibrium-like structure with irreversible circulation

  Isabella Caranzano, Daniel Maria Busiello, Stefano Priorelli, Amos Maritan, Piero Fariselli · 9 September 2026

  The evolution of DNA sequences can be viewed as stochastic dynamics on a high-dimensional discrete space, but it is unclear when empirical transition biases reduce to an effective energy landscape versus retain irreducible non-equilibrium circulation. Human context-dependent mutation probabilities o…

• Advances in Machine Learning for Directed Evolution: A Five-Year Retrospective

  Bruce J. Wittmann · 4 September 2026

  The last five-plus years have seen many protein engineering disciplines transformed by advances in machine learning (ML), but the same cannot be said for directed evolution. Reflecting on a previously co-authored perspective, I discuss why I believe this to be the case, arguing that a disconnect bet…

• SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign

  Jiarui Lu, Yuyang Wang, Yizhe Zhang, Jiatao Gu, Navdeep Jaitly, Joshua M. Susskind, Miguel \'Angel Bautista · 4 September 2026

  Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Developing generative models capable of understanding this intrinsically multi-modal relationship is crucial for fields like …

• Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling

  Yifan Feng, Guanjie Cheng, Shihui Ying, Shaoyi Du, Yue Gao · 2 September 2026

  Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (three-dimensional geometry). What is the expressive limit of this layer class? We show that the complete bilinear operator over content-geometry outer…

• SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding

  Handong Wang, Jiaxin Qi, Baisheng Lai, Jianqiang Huang · 2 September 2026

  Protein inverse folding aims to recover amino acid sequences for a given 3D protein structure, underpinning broad applications such as enzyme engineering and drug discovery.Current methods often follow a serial pipeline, in which a structure encoder predicts a coarse sequence, which is then refined …

• AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design

  Mingquan Liu, Jiangyu Chen, Hanqun Cao, Xujun Zhang, Pengsen Ma, Xiangru Tang, Shuting Jin, Zhuo Yang, Tianfan Fu, Fang Wu, Xiangxiang Zeng · 28 August 2026

  Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomously improve large, tightly coupled scientific machine-learning systems through executable code changes and computationally expensive validation. We st…

• Off-Manifold Collapse in Guided Protein Language Models

  Shuibai Zhang, Xinchi Liu, Fred Zhangzhi Peng, Zhihan Yang, Shutong Wu, Yingzi Ma, Jiawei Zhang · 20 August 2026

  Protein language models are widely used priors for protein sequence design, and a growing body of work controls them at inference time as an alternative to fine-tuning. Such guidance faces a dilemma: mild enough to preserve natural activation statistics, it barely moves the property; strong enough t…

• Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design

  Xuefeng Liu, Mingxuan Cao, Xiao Luo, Songhao Jiang, Tobin Sosnick, Jinbo Xu, Louis Maher, Rick Stevens · 19 August 2026

  Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains challenging-especially for DNA/RNA, underexplored non-protein modalities with scarce, heterogeneous complex data and sharper geometric and chemical cons…

• Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling

  Wengan He, Yongsheng Luo, Lihong Jiang, Wenhui Xu, Yu Li · 18 August 2026

  Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation. Recent advances in deep learning have transformed the field from multiple sequence alignment (MSA)-driven monomer fol…

• Probing and steering biology across Boltz-1s trunk-diffusion boundary

  Piotr Jedryszek, Tongmeng Xie, Adam Winnifrith, Alexander Hasson, Weronika \'Slesak, George Wicks, Toby Winnifrith, Oliver M. Crook · 13 August 2026

  AlphaFold3-class structure predictors pair a representational trunk, which processes sequence and context, with a diffusion module, which generates atomic coordinates. How biological information changes as it crosses this architectural boundary remains poorly understood. We analyze per-residue activ…

• How to Spend Your Oracle Budget: Practical Guidance for Protein Structure Prediction Models

  Aleksandra Kalisz, Jack Simons, Krisztina Sinkovics, Noam Ghenassia, Shikha Surana, Henry Moss, Paul Duckworth · 13 August 2026

  Foundation models for protein structure prediction remain unreliable on certain targets. External oracles can flag and correct these failures, but biological oracles are expensive, making oracle budget a critical constraint. Existing guidance methods, such as FK-steering, DPO, and Best K-of-N sampli…

• DynaPPI: A Large-scale Dynamic Protein Dataset for AI-driven Advances in Protein Interactomics

  Jiabao Wei, Zilong Geng, Yuze Wang, Jianjun Li, Ning Ding, Bowen Zhou, Bing Zhang, Zhiyuan Ma · 12 August 2026

  Diffusion models have been widely explored in protein backbone generation due to their powerful generation capabilities.However, in today's AI-driven biological research, predicting the structure of unknown multi-chain protein aggregates (called "complexes" in biology) remains an unsolved challenge.…

• SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups

  Yikun Bai, Binghang Lu, Yikai Liu, Elaheh Akbari, Soheil Kolouri, Linxuan Wang, Ping He, Shuchan Wang, Ruqi Zhang, Guang Lin · 3 August 2026

  Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusion and flow-matching models produce high-quality backbones on SE(3)^N, but inference requires numerically integrating an ODE over hundreds of network…

• Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling

  Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang, Yunhong Lu, Chenxi Cai, Yan Zhang, Min Zhang · 28 July 2026

  The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination. However, existing approaches are predominantly implemented under a single…

• DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales

  Kaihui Cheng, Zhiqiang Cai, Peng Tu, Yisong Yao, Limei Han, Libo Wu, Siyu Zhu, Tzuhsiung Yang, Yuan Qi · 20 July 2026

  Proteins function through coordinated motion across multiple spatial and temporal scales, underpinning processes such as ligand binding, allostery, and catalysis. However, accessing long-timescale conformational change through molecular dynamics (MD) simulations remains prohibitively expensive for s…

• Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes

  Kaustav Mehta · 20 July 2026

  AlphaFold2's 93 million parameters, shaped by the evolutionary record of protein structure encoded in the Protein Data Bank and in sequence alignments, are conventionally treated only as machinery for converting sequence to structure. We propose they are also a scientific object that can be analyzed…