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Showing 1–9 of 9 results

Advanced filters: Author: Sheng-You Huang Clear advanced filters

An interaction-derived graph learning framework for scoring protein–peptide complexes

GraphPep presents an interaction-derived and protein language model-powered graph learning framework for robust scoring of protein–peptide complexes, substantially enhancing the binding mode prediction of protein–peptide docking.

Huanyu Tao
Xiaoyu Wang
Sheng-You Huang
Research23 Oct 2025
Nature Machine Intelligence

Volume: 7, P: 1858-1869
EMProt improves structure determination from cryo-EM maps

Here the authors present an artificial-intelligence-based automated method for improved protein structure determination from cryo-EM density maps by efficiently integrating map information and structure prediction.

Tao Li
Ji Chen
Sheng-You Huang
Research08 Dec 2025
Nature Structural & Molecular Biology

P: 1-10
Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps

Modeling DNA/RNA from cryo-EM maps is challenging because they often have lower resolutions than proteins. Here, the authors propose a deep learning-based method for accurate detection and de novo structure determination of DNA/RNA from cryo-EM maps.

Tao Li
Hong Cao
Sheng-You Huang
ResearchOpen Access30 Oct 2024
Nature Communications

Volume: 15, P: 1-13
All-atom RNA structure determination from cryo-EM maps

RNA structures are built from cryogenic electron microscopy maps using deep learning and backbone tracing.

Tao Li
Jiahua He
Sheng-You Huang
Research23 Feb 2024
Nature Biotechnology

Volume: 43, P: 97-105
Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes

Membrane proteins are encoded by approximately a quarter of human genes. Here, the authors propose a deep transfer learning method for predicting inter-chain residue-residue contacts of transmembrane protein complexes.

Peicong Lin
Yumeng Yan
Sheng-You Huang
ResearchOpen Access15 Aug 2023
Nature Communications

Volume: 14, P: 1-16
Protein–protein contact prediction by geometric triangle-aware protein language models

Contact prediction between two proteins is still computationally challenging, but is vital for understanding multi-protein complexes. Lin et al. use a geometric deep learning approach to provide accurate predictions of inter-protein residue–residue contacts.

Peicong Lin
Huanyu Tao
Sheng-You Huang
Research19 Oct 2023
Nature Machine Intelligence

Volume: 5, P: 1275-1284
Improvement of cryo-EM maps by simultaneous local and non-local deep learning

Map post-processing is crucial for cryo-EM modeling building. Here, the authors present a deep learning approach to improve both the quality and interpretability of cryo-EM maps by simultaneously considering local and non-local effects.

Jiahua He
Tao Li
Sheng-You Huang
ResearchOpen Access03 Jun 2023
Nature Communications

Volume: 14, P: 1-16
Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly

One challenge in cryo-EM is to build atomic models into intermediate resolution maps. Here, the authors present a deep learning-guided iterative assembling method by integrating AlphaFold, FFTbased fitting, and domain-based refinement.

Jiahua He
Peicong Lin
Sheng-You Huang
ResearchOpen Access13 Jul 2022
Nature Communications

Volume: 13, P: 1-16
The HDOCK server for integrated protein–protein docking

The HDOCK server is developed for template-based and template-free protein–protein docking, using amino acid sequences or PDB structures as inputs. HDOCK can incorporate SAXS data and can be applied to protein–RNA/DNA docking.

Yumeng Yan
Huanyu Tao
Sheng-You Huang
Protocols08 Apr 2020
Nature Protocols

Volume: 15, P: 1829-1852