Deep Learning for Protein Complex Prediction and Design

• URL: http://arxiv.org/abs/2605.11189v1
• Date: Mon, 11 May 2026 19:53:18 GMT
• Title: Deep Learning for Protein Complex Prediction and Design
• Abstract summary: This thesis investigates two fundamental aspects of this problem using deep learning: domain-specific architectures that capture the hierarchical nature of protein structures, and search algorithms that efficiently navigate the vast sequence spaces of protein complexes.
• Score: 1.2918076675053387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately modeling and designing protein complex structures is a central problem in computational structural biology, with broad implications for understanding cellular function and developing therapeutics. This thesis investigates two fundamental aspects of this problem using deep learning: domain-specific architectures that capture the hierarchical nature of protein structures, and search algorithms that efficiently navigate the vast sequence spaces of protein complexes to identify interacting homologs for improving complex structure prediction and to design protein sequences.

Related papers

• Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition [13.468392186646382]
  ProtDiS is a knowledge-guided framework that decomposes pretrained protein micro-environment embeddings into biologically grounded dimensions.<n>Inspired by the information bottleneck principle, ProtDiS learns representations that balance informativeness and compression.<n>Protein- and residue-level analyses show that ProtDiS differentiates proteins with similar folds but divergent functions and captures fine-grained biophysical signals critical.
  arXiv  Detail & Related papers  (2026-05-12T07:12:12Z)
• A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design [3.5897534810405403]
  Deep learning has demonstrated remarkable performance in fields such as computer vision and natural language processing.<n>It has been increasingly applied in recent years to the data-rich domain of protein sequences with great success.<n>The performance improvements achieved by deep learning unlocks new possibilities in the field of protein bioinformatics.
  arXiv  Detail & Related papers  (2025-01-02T05:21:34Z)
• ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design [61.19456204667385]
  We introduce ProteinWeaver, a two-stage framework for protein backbone design.<n>ProteinWeaver generates high-quality, novel protein backbones through versatile domain assembly.<n>By introducing a divide-and-assembly' paradigm, ProteinWeaver advances protein engineering and opens new avenues for functional protein design.
  arXiv  Detail & Related papers  (2024-11-08T08:10:49Z)
• CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation [7.161099050722313]
  We develop a structure-sensitive supervised deep learning model, Crystal vs Predicted Evaluator for Protein Structure (CPE-Pro) CPE-Pro learns the structural information of proteins and captures inter-structural differences to achieve accurate traceability on four data classes. We utilize Foldseek to encode protein structures into "structure-sequences" and trained a protein Structural Sequence Language Model, SSLM.
  arXiv  Detail & Related papers  (2024-10-21T02:21:56Z)
• A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration [4.909112037834705]
  This paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction. The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization.
  arXiv  Detail & Related papers  (2024-02-29T12:24:20Z)
• Progressive Multi-Modality Learning for Inverse Protein Folding [47.095862120116976]
  We propose a novel protein design paradigm called MMDesign, which leverages multi-modality transfer learning. MMDesign is the first framework that combines a pretrained structural module with a pretrained contextual module, using an auto-encoder (AE) based language model to incorporate prior protein semantic knowledge. Experimental results, only training with the small dataset, demonstrate that MMDesign consistently outperforms baselines on various public benchmarks.
  arXiv  Detail & Related papers  (2023-12-11T10:59:23Z)
• Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks [68.90692290665648]
  We integrate knowledge learned by protein language models into several state-of-the-art geometric networks. Our findings show an overall improvement of 20% over baselines. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin.
  arXiv  Detail & Related papers  (2022-12-07T04:04:04Z)
• 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)
• Transfer Learning for Protein Structure Classification at Low Resolution [124.5573289131546]
  We show that it is possible to make accurate ($geq$80%) predictions of protein class and architecture from structures determined at low ($leq$3A) resolution. We provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function.
  arXiv  Detail & Related papers  (2020-08-11T15:01:32Z)
• Deep Learning in Protein Structural Modeling and Design [6.282267356230666]
  Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources. Protein structural modeling is critical to understand and engineer biological systems at the molecular level. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.
  arXiv  Detail & Related papers  (2020-07-16T14:59:38Z)
• BERTology Meets Biology: Interpreting Attention in Protein Language Models [124.8966298974842]
  We demonstrate methods for analyzing protein Transformer models through the lens of attention. We show that attention captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure. We also present a three-dimensional visualization of the interaction between attention and protein structure.
  arXiv  Detail & Related papers  (2020-06-26T21:50:17Z)

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.