Protein Structure Prediction Using Rosetta

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The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

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Predicting protein structures with a multiplayer online game

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Unified rational protein engineering with sequence-based deep representation learning

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A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions

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Consistent blind protein structure generation from NMR chemical shift data

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De novo protein design by deep network hallucination

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Proof of principle for epitope-focused vaccine design

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Global analysis of protein folding using massively parallel design, synthesis, and testing

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Principles for designing ideal protein structures

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Crystal structure of a monomeric retroviral protease solved by protein folding game players

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Learning the protein language: Evolution, structure, and function

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Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details

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Progress in Modeling of Protein Structures and Interactions.

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Large-scale determination of previously unsolved protein structures using evolutionary information

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Biomimetic Chemosensor: Designing Peptide Recognition Elements for Surface Functionalization of Carbon Nanotube Field Effect Transistors

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Protein sequence design with a learned potential

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Can self‐inhibitory peptides be derived from the interfaces of globular protein–protein interactions?

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RosettaAntibody: antibody variable region homology modeling server

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Protein sequence design by conformational landscape optimization

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A Computationally Designed Inhibitor of an Epstein-Barr Viral Bcl-2 Protein Induces Apoptosis in Infected Cells

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Deep learning techniques have significantly impacted protein structure prediction and protein design

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AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges

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Assessing predictors of changes in protein stability upon mutation using self-consistency

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rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments

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Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation

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Analysis and modeling of the variable region of camelid single-domain antibodies

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Protein structure prediction by AlphaFold 2: are attention and symmetries all you need?

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Protein design via deep learning

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Structure and Function of Glycosylated Tandem Repeats from Candida albicans Als Adhesins

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Towards practical applications in quantum computational biology

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Polymerization of the backbone of the pectic polysaccharide rhamnogalacturonan I

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DeepUMQA: Ultrafast Shape Recognition-based Protein Model Quality Assessment using Deep Learning

Guo, S.-S., Liu, J., Zhou, X.-G., & Zhang, G.-J. (2021) · doi:10.1101/2021.10.02.462491 · Cited by 53

DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning

Guo, S.-S., Liu, J., Zhou, X.-G., & Zhang, G.-J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac056 · Cited by 53

Free energy perturbation calculations of the thermodynamics of protein side-chain mutations

Steinbrecher, T., Abel, R., Clark, A., & Friesner, R. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.03.002 · Cited by 46

Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs

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Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment

Iovanac, N. C., & Savoie, B. M. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b01398 · Cited by 41

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

Liu, J., Wu, T., Guo, Z., Hou, J., & Cheng, J. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26186 · Cited by 32

Are protein-protein interfaces special regions on a protein’s surface?

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Mechanical gating of the auditory transduction channel TMC1 involves the fourth and sixth transmembrane helices

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Antibody affinity maturation by computational design

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Improved model quality assessment using sequence and structural information by enhanced deep neural networks

Liu, J., Zhao, K., & Zhang, G. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac507 · Cited by 25

Development of scoring functions for antibody sequence assessment and optimization

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OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors

Xu, G., Wang, Q., & Ma, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab529 · Cited by 24

The Coral Protein CARP3 Acts from a Disordered Mineral Surface Film to Divert Aragonite Crystallization in Favor of Mg‐Calcite

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Protein oligomer modeling guided by predicted interchain contacts in CASP14

Baek, M., Anishchenko, I., Park, H., Humphreys, I. R., & Baker, D. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26197 · Cited by 18

Assessing protein model quality based on deep graph coupled networks using protein language model

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GraphGPSM: a global scoring model for protein structure using graph neural networks

He, G., Liu, J., Liu, D., & Zhang, G. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad219 · Cited by 16

A computational toolbox for the assembly yield of complex and heterogeneous structures

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Integration of Nanometer-Range Label-to-Label Distances and Their Distributions into Modelling Approaches

Jeschke, G. (2022) · Biomolecules · doi:10.3390/biom12101369 · Cited by 13

Exploring “dark-matter” protein folds using deep learning

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Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

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Integrating physics in deep learning algorithms: a force field as a PyTorch module

Orlando, G., Serrano, L., Schymkowitz, J., & Rousseau, F. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae160 · Cited by 7

Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features

Sawhney, A., Li, J., & Liao, L. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25105247 · Cited by 6

Conditional diffusion with locality-aware modal alignment for generating diverse protein conformational ensembles

Wang, B., Wang, C., Chen, J., Liu, D., Sun, C., Zhang, J., Zhang, K., & Li, H. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01198-9 · Cited by 5

Physical-aware model accuracy estimation for protein complex using deep learning method

Wang, H., Sun, M., Xie, L., Liu, D., & Zhang, G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.01.017 · Cited by 5

Construct a variable-length fragment library for de novo protein structure prediction

Feng, Q., Hou, M., Liu, J., Zhao, K., & Zhang, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac086 · Cited by 5

TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences

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Mapping adipocyte interactome networks by HaloTag-enrichment-mass spectrometry

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Unsupervised learning reveals landscape of local structural motifs across protein classes

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