Title: PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
Venue: NeurIPS 2025
Accepted: no
Keywords: protein flexibility

motion benchmark

SE(3)-equivariant GNN

predicting linear subspaces
Abstract: Proteins move and deform to ensure their biological functions. Despite significant progress in protein structure prediction, approximating conformational ensembles at physiological conditions remains a fundamental open problem. This paper presents a novel perspective on the problem by directly targeting continuous compact representations of protein motions inferred from sparse experimental observations. We develop a task-specific loss function enforcing data symmetries, including scaling and permutation operations. Our method PETIMOT (Protein sEquence and sTructure-based Inference of MOTions) leverages transfer learning from pre-trained protein language models through an SE(3)-equivariant graph neural network. When trained and evaluated on the Protein Data Bank, PETIMOT shows superior performance in time and accuracy, capturing protein dynamics, particularly large/slow conformational changes, compared to state-of-the-art flow-matching approaches and traditional physics-based models. Our code and protocols are available at https://anonymous.4open.science/r/PETIMOT-4ED4/.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel framework named PETIMOT (Protein sEquence and sTructure-based Inference of MOTions), which aims to infer protein motions from sparse experimental data. The framework addresses the long-standing problem of approximating protein conformational ensembles under physiological conditions by learning continuous and compact representations of protein conformational changes. PETIMOT integrates SE(3)-equivariant graph neural networks and pre-trained protein language models, optimizing data symmetries, including scaling and permutation operations, through a task-specific loss function. The training and evaluation results on the Protein Data Bank (PDB) show that PETIMOT outperforms existing flow-matching methods and traditional physics-based models in terms of both time and accuracy.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The paper presents a novel perspective on addressing the problem of protein conformational diversity by learning the linear subspaces of protein motions, which provides new tools for understanding protein functions.\n\n2. Compared to existing methods, PETIMOT demonstrates performance improvements across multiple metrics, which is of great significance to biomedical research.\n\n3. The authors designed several loss functions (such as least squares loss, squared sinusoidal loss, and independent subspace loss) that are specifically tailored for learning the linear subspaces of protein motions, showing innovation.\n\n**Weakness**\n\n1. The paper is highly specialized in its field. It is suggested to add examples of demo illustrations to showcase the scientific questions of the research.\n\nQUESTIONS:\n1. The model training only uses structural data from the PDB. Can other types of data be utilized? Or are there any data augmentation techniques being considered? For example, augmented datasets were used in AlphaFold3.\n2. Why do AlphaFlow and ESMFlow require such a long time as shown in Table 1?\n3. The PETIMOT model predicts conformational changes of proteins by learning the linear subspaces of protein motions. Do these subspaces have biological significance?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces PETIMOT, a framework for predicting linear residue motions from 3D protein structures using a SE(3)-equivariant GNN on top of protein language model embeddings. The experiments show that PETIMOT is able to recover such linear motions better than existing physics and flow-based models in this space.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is actually quite well written, and I enjoyed reading it as a whole. The different parts of the loss make sense (even if they don’t affect the final results too much, as seen in Figure 2). \nThe experiments and metrics seem reasonable.\n\nMy main concerns are on the data aspect (the authors also discuss this in the text). First, given the fact that the PDB consists of proteins which could be crystallized in the first place, (thus, more rigid proteins and not intrinsically disordered ones) what is the real conformational diversity that you aim to capture? Second, how reasonable are linear motions to describe such conformational ensembles in the PDB? My feeling is that allosteric transitions are far more complex than simple linear motions, since the motions of these residues are all correlated with one another in very complex ways. I think without answering these key questions well, the paper is a little incomplete.\n\nI think this paper could have much more impact with a study on intrinsically disordered proteins (eg. from the IDRome) or macrocycles (eg. from CREMP) that do have much more interesting conformational diversity which is currently quite challenging to model.\n \nReferences:\n- IDRome: https://pubmed.ncbi.nlm.nih.gov/38297118/\n- CREMP: https://github.com/Genentech/cremp\n\nQUESTIONS:\n- Do you have experiments on generalization across length scales, in the sense that you can learn dynamics from shorter proteins/peptides and transfer to larger ones?\n- Figure 1: typo in “Aggregarion” -> “Aggregation”?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors formulate the problem of predicting protein conformations as one of predicting the principal components of motion. Their method couples a graph neural network architecture with a collection of loss functions that encourage the learning of motion subspaces that align with reference subspaces extracted from a dataset they curate from deposited PDB structures. They demonstrate that relative to diffusion generative models and normal mode analysis, their method predicts dominant motions more accurately at a fraction of the cost.\n\nSTRENGTHS AND WEAKNESSES:\n_Strengths_:\n\nThe paper proposes an interesting new formulation of a classic NMA / dimensionality reduction approach to protein conformation analysis as a machine learning problem. The loss functions appear well-thought out, emphasizing different properties of the predicted motion subspaces, and are supported by the literature. The resulting model provides a fast way for practitioners to assess what modes of variability might be present in their system, which can be helpful in designing further experiments or validating heterogeneous structure determination workflows. The authors additionally present comprehensive ablations covering losses, graph construction, embedding models, etc.\n\n_Weaknesses_:\n\nThe authors note the ability to generate conformational ensembles by sampling along the predicted components of variation. While I understand that the method is not designed to produce ensembles in a thermodynamic sense, it would enhance the value and validation of the method to sample an ensemble and compare it against the reference using metrics like precision/recall/diversity as is done in AlphaFlow.\n\nAdditionally, as the success rate is modest (~44%), it would be helpful if the authors shared an analysis of failure modes. Are the motions in unsuccessful cases not well-described by linear subspaces?\n\nQUESTIONS:\n1. In line 168, why would the opposite not be true, that the SS loss is 0 for identical subspaces and 1 for mutually orthogonalising subspaces?\n2. The IS loss is intended to maximize the rank of the predicted subspace. Can you clarify the dependence of this loss on the reference components?\n3. In Figure C.2, I understand (b)(d) might be artificially influenced by the constraints of the matching procedure. But in (a)(c), K=8 appears to clearly outperform K=4. Why was the default chosen to be K=4?\n4. Do you note any patterns in the unsuccessful test cases? Are these motions not described well by linear subspaces?\n5. Would it be meaningful to generate an ensemble of structures from the predicted components, and compare it to the reference collection using simple metrics like precision and recall?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
PETIMOT employs an SE(3)-equivariant graph neural network to model protein dynamics, directly predicting principal components of conformational changes through its geometrically aware architecture. The authors compiled a custom dataset using protein structures from the PDB, leveraging evolutionary information from multiple sequence alignments to infer diverse conformational states for individual proteins, enabling robust dynamics modeling.

STRENGTHS AND WEAKNESSES:
**Strengths**:

1. Introduces a novel formulation to predict principal components of protein conformational changes using SE(3)-equivariant networks.
2. Proposes innovative loss functions for protein dynamics modeling that may inspire future methodologies in related fields.

**Weaknesses**:

See limitations

QUESTIONS:
1. The dataset construction process in this paper is controversial (add references for proof), using more MD simulation dataset is recommended, mayby you can test your PETIMOT system on some long MD simulation case studies?
2. More test cases could be included in this paper, including several structure subsets like Apo-Holo, Fold-Switch proteins
3. Evaluation metrics currently used are hard to understand, and only used in this paper. It is possible to add some wildly used metrics, including pairwise distance, JS divergense in tICA/PCA, pairwise RMSD, RMSF MAE, etc.
4. Add comparison to conventional long MD simulation is preferred in results, I understand that it is not possible to do large scale simulation, but it can be used for a small subset of test set.

LIMITATIONS:
1. Lack sufficient test and evaluation, the test dataset is relatively limited
2. Resutls did not show similarity and systematic differences towards known models, including ESMFlow/AlphaFlow
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 4
DATE: 2025-07-03