From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions

• URL: http://arxiv.org/abs/2603.18505v1
• Date: Thu, 19 Mar 2026 05:25:17 GMT
• Title: From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions
• Abstract summary: Review systematically examines the paradigm shift in AI driven protein science across five interconnected dimensions.<n>We critically analyze current bottlenecks, including data distribution biases, limited mechanistic interpretability, and the disconnect between geometric metrics and biophysical reality.<n>This methodological transformation marks artificial intelligence's transition from a structural analysis tool into a universal simulator capable of understanding and ultimately rewriting the dynamic language of life.
• Score: 0.8880611506199769
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The protein folding problem has been fundamentally transformed by artificial intelligence, evolving from static structure prediction toward the modeling of dynamic conformational ensembles and complex biomolecular interactions. This review systematically examines the paradigm shift in AI driven protein science across five interconnected dimensions: unified multimodal representations that integrate sequences, geometries, and textual knowledge; refinement of static prediction through MSA free architectures and all atom complex modeling; generative frameworks, including diffusion models and flow matching, that capture conformational distributions consistent with thermodynamic ensembles; prediction of heterogeneous interactions spanning protein ligand, protein nucleic acid, and protein protein complexes; and functional inference of fitness landscapes, mutational effects, and text guided property prediction. We critically analyze current bottlenecks, including data distribution biases, limited mechanistic interpretability, and the disconnect between geometric metrics and biophysical reality, while identifying future directions toward physically consistent generative models, multimodal foundation architectures, and experimental closed loop systems. This methodological transformation marks artificial intelligence's transition from a structural analysis tool into a universal simulator capable of understanding and ultimately rewriting the dynamic language of life.

Related papers

• Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling [5.283983962386345]
  Recent advances in deep learning have transformed the field of protein structure prediction.<n>This review focuses on the methodological evolution of the field itself.<n>It provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.
  arXiv  Detail & Related papers  (2026-08-17T04:33:17Z)
• DynaPPI: A Large-scale Dynamic Protein Dataset for AI-driven Advances in Protein Interactomics [25.369285689319852]
  We present DynaPPI, a dynamic protein dataset comprising molecular dynamics (MD) trajectories of protein complex formation from dissociated chains to the bound state.<n>Benefiting from this dataset, diffusion models can explicitly learn the dynamic binding trajectories of known complexes and accurately predict the structures of unknown complexes based on their diverse generative properties.
  arXiv  Detail & Related papers  (2026-08-11T03:32:32Z)
• A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era [57.5440098776759]
  Molecular property prediction integrates quantum chemistry, cheminformatics, and deep learning to connect molecular structure with biological behavior.<n>This survey traces four complementary paradigms, including Quantum, Descriptor Machine Learning, Geometric Deep Learning, and Foundation Models.<n> Benchmark analyses integrate evidence from both widely used datasets and datasets reflecting industry perspectives.
  arXiv  Detail & Related papers  (2026-04-17T15:16:33Z)
• Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute [64.59043351161004]
  We propose Proteina-Complexa, a novel fully atomistic binder generation method unifying both paradigms.<n>Proteina-Complexa sets a new state of the art in computational binder design benchmarks.<n>We also demonstrate interface hydrogen bond optimization, fold class-guided binder generation, and extensions to small molecule targets and enzyme design tasks.
  arXiv  Detail & Related papers  (2026-03-30T01:54:03Z)
• Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards [3.8025175740783026]
  Generative modeling has become a central paradigm in protein research.<n>This survey aims to accelerate the transition from predictive modeling to reliable, function-driven protein engineering.
  arXiv  Detail & Related papers  (2026-03-27T13:02:16Z)
• Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles [74.32932832937618]
  We introduce $textbfRigidSSL$ ($textitRigidity-Aware Self-Supervised Learning$), a geometric pretraining framework that front-loads geometry learning prior to finetuning.<n>Phase I (RigidSSL-Perturb) learns geometric priors from 432K structures from the AlphaFold Protein Structure Database with simulated perturbations.<n>Phase II (RigidSSL-MD) refines these representations on 1.3K molecular dynamics trajectories to capture physically realistic transitions.
  arXiv  Detail & Related papers  (2026-03-02T21:32:30Z)
• TEMPO: Temporal Multi-scale Autoregressive Generation of Protein Conformational Ensembles [8.6867067594535]
  We introduce a novel hierarchical autoregressive framework for modeling protein dynamics.<n>By bridging high-level biophysical principles with state-of-the-art generative modeling, our approach provides an efficient framework for simulating protein dynamics.
  arXiv  Detail & Related papers  (2025-10-24T13:11:47Z)
• Multiscale geometrical and topological learning in the analysis of soft matter collective dynamics [0.4440432588828829]
  Joint geometric and topological data analysis (TDA) offers powerful framework for investigating such systems.<n>The method based on the analysis of fields generated from images of skyrmion ensembles offers insights into the nonlinear physical mechanisms of the system's response to external stimuli.
  arXiv  Detail & Related papers  (2025-07-28T18:40:37Z)
• Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression [45.49904590474368]
  ConfRover is an autoregressive model that simultaneously learns protein conformation and dynamics from MD trajectories.<n>It supports both time-dependent and time-independent sampling.<n>Experiments on ATLAS, a large-scale protein MD dataset, demonstrate the effectiveness of our model.
  arXiv  Detail & Related papers  (2025-05-23T05:00:15Z)
• ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics [3.6181745062052495]
  We introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE) ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs. It integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories.
  arXiv  Detail & Related papers  (2024-10-27T02:59:48Z)
• Structure Language Models for Protein Conformation Generation [66.42864253026053]
  Traditional physics-based simulation methods often struggle with sampling equilibrium conformations.<n>Deep generative models have shown promise in generating protein conformations as a more efficient alternative.<n>We introduce Structure Language Modeling as a novel framework for efficient protein conformation generation.
  arXiv  Detail & Related papers  (2024-10-24T03:38:51Z)
• Deep Signature: Characterization of Large-Scale Molecular Dynamics [29.67824486345836]
  Deep Signature is a novel computationally tractable framework that characterizes complex dynamics and interatomic interactions.<n>Our approach incorporates soft spectral clustering that locally aggregates cooperative dynamics to reduce the size of the system, as well as signature transform to provide a global characterization of the non-smooth interactive dynamics.
  arXiv  Detail & Related papers  (2024-10-03T16:37:48Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.