JanusDDG: A Thermodynamics-Compliant Model for Sequence-Based Protein Stability via Two-Fronts Multi-Head Attention

• URL: http://arxiv.org/abs/2504.03278v2
• Date: Mon, 14 Apr 2025 18:11:58 GMT
• Title: JanusDDG: A Thermodynamics-Compliant Model for Sequence-Based Protein Stability via Two-Fronts Multi-Head Attention
• Authors: Guido Barducci, Ivan Rossi, Francesco Codicè, Cesare Rollo, Valeria Repetto, Corrado Pancotti, Virginia Iannibelli, Tiziana Sanavia, Piero Fariselli,
• Abstract summary: understanding how residue variations affect protein stability is crucial for designing functional proteins.<n>Recent advances in protein language models (PLMs) have revolutionized computational protein analysis.<n>We introduce JanusDDG, a deep learning framework that leverages PLM-derived embeddings and a bidirectional cross-attention transformer architecture.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Understanding how residue variations affect protein stability is crucial for designing functional proteins and deciphering the molecular mechanisms underlying disease-related mutations. Recent advances in protein language models (PLMs) have revolutionized computational protein analysis, enabling, among other things, more accurate predictions of mutational effects. In this work, we introduce JanusDDG, a deep learning framework that leverages PLM-derived embeddings and a bidirectional cross-attention transformer architecture to predict $\Delta \Delta G$ of single and multiple-residue mutations while simultaneously being constrained to respect fundamental thermodynamic properties, such as antisymmetry and transitivity. Unlike conventional self-attention, JanusDDG computes queries (Q) and values (V) as the difference between wild-type and mutant embeddings, while keys (K) alternate between the two. This cross-interleaved attention mechanism enables the model to capture mutation-induced perturbations while preserving essential contextual information. Experimental results show that JanusDDG achieves state-of-the-art performance in predicting $\Delta \Delta G$ from sequence alone, matching or exceeding the accuracy of structure-based methods for both single and multiple mutations. Code Availability:https://github.com/compbiomed-unito/JanusDDG

Related papers

• A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer [53.85265022754878]
  We propose a lightweight DDG predictor (Light-DDG) for fast mutation screening. We also release a large-scale dataset containing millions of mutation data for pre-training Light-DDG. For the target antibody, we propose a novel Mutation Explainer to learn mutation preferences.
  arXiv  Detail & Related papers  (2025-02-10T09:26:57Z)
• AlgoRxplorers | Precision in Mutation: Enhancing Drug Design with Advanced Protein Stability Prediction Tools [0.6749750044497732]
  Predicting the impact of single-point amino acid mutations on protein stability is essential for understanding disease mechanisms and advancing drug development.<n>Protein stability, quantified by changes in Gibbs free energy ($DeltaDelta G$), is influenced by these mutations.<n>This study proposes the application of deep neural networks, leveraging transfer learning and fusing complementary information from different models, to create a feature-rich representation of the protein stability landscape.
  arXiv  Detail & Related papers  (2025-01-13T02:17:01Z)
• SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation [97.99658944212675]
  We introduce a novel pre-training strategy for protein foundation models. It emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features. Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability.
  arXiv  Detail & Related papers  (2024-10-31T15:22:03Z)
• Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model [3.4494754789770186]
  Deep learning methods for protein modeling have demonstrated superior results at lower costs compared to traditional approaches. In mutation effect prediction, the key to pre-training deep learning models lies in accurately interpreting the complex relationships among protein sequence, structure, and function. This study introduces a retrieval-enhanced protein language model for comprehensive analysis of native properties from sequence and local structural interactions.
  arXiv  Detail & Related papers  (2024-10-28T15:28:51Z)
• Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions [48.58317905849438]
  Predicting the change in binding free energy ($Delta Delta G$) is crucial for understanding and modulating protein-protein interactions. We propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to $Delta Delta G$ prediction.
  arXiv  Detail & Related papers  (2024-10-12T14:13:42Z)
• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)
• Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning [78.38442423223832]
  We develop a novel codebook pre-training task, namely masked microenvironment modeling. We demonstrate superior performance and training efficiency over state-of-the-art pre-training-based methods in mutation effect prediction.
  arXiv  Detail & Related papers  (2024-05-16T03:53:21Z)
• Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL [1.840390797252648]
  Deep learning is increasingly recognized as a powerful tool capable of bridging the gap between in-silico predictions and in-vitro observations. We propose eGRAL, a novel graph neural network architecture designed for predicting binding affinity changes from amino acid substitutions in protein complexes. eGRAL leverages residue, atomic and evolutionary scales, thanks to features extracted from protein large language models.
  arXiv  Detail & Related papers  (2024-05-03T10:33:19Z)
• Protein Conformation Generation via Force-Guided SE(3) Diffusion Models [48.48934625235448]
  Deep generative modeling techniques have been employed to generate novel protein conformations. We propose a force-guided SE(3) diffusion model, ConfDiff, for protein conformation generation.
  arXiv  Detail & Related papers  (2024-03-21T02:44:08Z)
• Predicting loss-of-function impact of genetic mutations: a machine learning approach [0.0]
  This paper aims to train machine learning models on the attributes of a genetic mutation to predict LoFtool scores. These attributes included, but were not limited to, the position of a mutation on a chromosome, changes in amino acids, and changes in codons caused by the mutation. Models were evaluated using five-fold cross-validated averages of r-squared, mean squared error, root mean squared error, mean absolute error, and explained variance.
  arXiv  Detail & Related papers  (2024-01-26T19:27:38Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• Score-based Causal Representation Learning with Interventions [54.735484409244386]
  This paper studies the causal representation learning problem when latent causal variables are observed indirectly. The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables.
  arXiv  Detail & Related papers  (2023-01-19T18:39:48Z)
• PhyloTransformer: A Discriminative Model for Mutation Prediction Based on a Multi-head Self-attention Mechanism [10.468453827172477]
  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing pandemic infecting 219 million people as of 10/19/21, with a 3.6% mortality rate. Here we developed PhyloTransformer, a Transformer-based discriminative model that engages a multi-head self-attention mechanism to model genetic mutations that may lead to viral reproductive advantage.
  arXiv  Detail & Related papers  (2021-11-03T01:30:57Z)
• Sparse generative modeling via parameter-reduction of Boltzmann machines: application to protein-sequence families [0.0]
  Boltzmann machines (BM) are widely used as generative models. We introduce a general parameter-reduction procedure for BMs. For several protein families, our procedure allows one to remove more than $90%$ of the PM couplings.
  arXiv  Detail & Related papers  (2020-11-23T08:01:09Z)

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.