Awesome IDR Prediction: Labs & Tools

Repository Introduction

🤩 Welcome to our curated list of Computational Protein Intrinsic Disorder Prediction labs AND tools!

This repository is a specialized, community-maintained resource focused exclusively on computational prediction of Intrinsically Disordered Regions (IDRs)—a core subfield of IDR research. Unlike broad IDR hubs, it zeroes in on tools, methods, and labs that solve the critical challenge of identifying IDRs from protein sequences (residue-level disorder probability, segment-level disorder boundaries, or context-specific disorder).
Our focus is on actionable computational resources: every entry includes direct access to tools (web servers/GitHub repos), links to the labs driving innovation, and supporting publications to validate method credibility. Whether you are a student new to IDR prediction or a researcher seeking state-of-the-art tools for your analysis, this repo centralizes the resources you need to avoid redundant searches and accelerate your work.

📝 Note: The "Tools Section" of the list is based primarily on CAID (a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding).

⚠️ For any collected tool that lacks literature publication details (whether papers or preprints), the source of the information we selected is highly likely to be the CAID Competition/Leaderboard.

Contributing / 贡献指南

EN

• Keep README entries short: Paper, 1‑line Abstract, 1‑line Model/Method, and Access.
• Put long Abstract/Method into docs/<surname>-lab.md with headings “ToolName Abstract” and “ToolName Model/Method”; link from README with “Read more →”.
• For unpublished tools, add a Note (⚠️ No peer‑reviewed publication yet; status: experimental) and cite a verifiable source (e.g., preprint or CAID).
• Include at least one working Access item (Web, GitHub, Docker, API docs, bio.tools, PyPI).
• Use lowercase hyphenated anchors; create/update docs/<surname>-lab.md for new labs.

中文

• README 保持精简：Paper、1 行 Abstract、1 行 Model/Method、Access。
• 长内容放到 docs/<surname>-lab.md，用 “ToolName Abstract / ToolName Model/Method” 标题，并在 README 用 “Read more →” 链接。
• 未发表工具加 Note（⚠️ 未经同行评审；实验性）并注明可核实来源（preprint / CAID）。
• 至少提供一个可访问资源（Web/GitHub/Docker/API/bio.tools/PyPI 等）。
• 标题/锚点用小写连字符；新增实验室请新建/更新对应 docs/<surname>-lab.md。

⚠️ Use the PR template when submitting: .github/pull_request_template.md.

Table of Contents

Quick links:

• Awesome IDR Prediction: Labs & Tools
  • Repository Introduction
  • Contributing / 贡献指南
  • Table of Contents
  • North America
    • United States
      • Washington University in St. Louis
      • Virginia Commonwealth University
      • Toyota Technological Institute at Chicago
      • University of New Orleans
      • University of Missouri
      • Temple University
    • Canada
      • University of British Columbia
  • South America
    • Argentina
      • IIBIO, CONICET-UNSAM
  • EMEA
    • United Kingdom
      • University College London
      • University of Exeter
      • University of Cambridge
    • Germany
      • Humboldt-Universität zu Berlin
      • Technical University of Munich
    • Hungary
      • Eötvös Loránd University
    • Italy
      • University of Padua
    • Belgium
      • Vrije Universiteit Brussel
    • Poland
      • Silesian University of Technology
    • Ireland
      • University College Dublin
    • France
      • Sorbonne University
    • Sweden
      • Linköping University
  • Asia
    • China
      • Beijing Institute of Technology 北京理工大学
      • Central South University 中南大学
      • Sun Yat-sen University 中山大学
      • Institute for Systems and Physical Biology， Shenzhen Bay Laboratory
    • Japan
      • Maebashi Institute of Technology
    • Russia
      • Institute of Protein Research, RAS
    • North Korea
      • University of Sciences, Pyongyang
  • TODO: model graph

_{Maintenance note: Quick links use GitHub heading anchors. Slugs are lowercase with hyphens; accents are stripped (ü → u, ö → o) while CJK stays as-is. When adding a new school, ensure a “#### University …” heading exists, then add it here using its slug. For entries without a heading inside details, add a small heading above to enable linking.}

North America

United States

Washington University in St. Louis

Alex S. Holehouse

• Lab website: holehouse lab

• Tools

  • Metapredict: Metapredict-v1, v2, v3
    • Paper:
      • Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure (Biophysical Journal, 2021)
      • Metapredict enables accurate disorder prediction across the Tree of Life (⚠️ bioRxiv preprint, 2024)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://metapredict.net/ | Batch disorder predictions using metapredict v3 | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/idptools/metapredict | Documentation
      • Others: https://pypi.org/project/metapredict/ | https://bio.tools/metapredict

Virginia Commonwealth University

Lukasz Kurgan

• Lab website: Biomine Kurgan Lab

• Tools

  • APOD
    • Paper: APOD: accurate sequence-based predictor of disordered flexible linkers (Bioinformatics, 2020)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://yanglab.qd.sdu.edu.cn/APOD/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://yanglab.nankai.edu.cn/tmp/CAID2_packages/APOD_20220524_CAID2.tar.gz | https://bio.tools/apod
  • flDPnn:
    • Paper: flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions (Nature Communications, 2021)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://biomine.cs.vcu.edu/server-handler/?type=servers&target=flDPnn/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: https://gitlab.com/sina.ghadermarzi/fldpnn_docker
      • GitHub repository: N/A
      • Others: source code | https://bio.tools/fldpnn
  • flDPlr、flDPlr2:
    • Paper: N/A
    • Abstract: N/A
    • Model/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A
  • flDPnn2
    • Paper: flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins (Journal of Molecular Biology, 2024)
    • Abstract: Read more →
    • Description/Method: Read more →
    • Access:
      • Web server: https://biomine.cs.vcu.edu/servers/flDPnn2/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A
  • flDPnn3: flDPnn3a, flDPnn3b
    • Paper: N/A
    • Abstract: N/A
    • Description/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A

Toyota Technological Institute at Chicago

Jinbo Xu 许锦波

• Lab website: Profile

• Tools

  • AUCpreD
    • Paper: AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields (Bioinformatics, 2016)
    • Abstract: Read more →
    • Description/Method: Read more →
    • Access:
      • Web server: http://raptorx2.uchicago.edu/StructurePropertyPred/predict/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository https://github.com/realbigws/RaptorX_Property_Fast
      • Others: https://bio.tools/aucpred

University of New Orleans

Md Tamjidul Hoque

• Lab website: PI lab

• Tools

  • DisPredict2
    • Paper: Estimation of Position Specific Energy as a Feature of Protein Residues from Sequence Alone for Structural Classification (PLoS ONE, 2016)
    • Abstract: Read more →
    • Description/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/tamjidul/DisPredict2_PSEE
      • Others: https://bio.tools/dispredict2
  • DisPredict3
    • Paper: DisPredict3.0: Prediction of intrinsically disordered regions/proteins using protein language model (Applied Mathematics and Computation, 2024)
    • Abstract: Read more →
    • Description/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/wasicse/Dispredict3.0
      • Others: N/A
  • ESMDisPred: ESMDisPred-1, ESMDisPred-2, ESMDisPred-2PDB
    • Paper: N/A
    • Abstract: Read more →
    • Description/Method: Read more →
      • Access:
        • Web server: N/A
        • REST API: N/A
        • Docker image: https://hub.docker.com/r/wasicse/esmdispred
        • GitHub repository: https://github.com/wasicse/ESMDisPred
        • Others: N/A

University of Missouri

Jianlin (Jack) Cheng 程建林

• Lab website: Bioinformatics and Machine Learning Laboratory

• Tools

  • PreDisorder
    • Paper: PreDisorder: ab initio sequence-based prediction of protein disordered regions (BMC Bioinformatics, 2009)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: http://casp.rnet.missouri.edu/predisorder.html | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository
      • Others: https://bio.tools/predisorder | https://multicom-toolbox.mu.hekademeia.org/tools.html#license

Temple University

Zoran Obradovic

• Lab website: Zoran Obradovic’s Lab

• Tools

  • VSL2
    • Paper: Length-dependent prediction of protein intrinsic disorder (BMC Bioinformatics, 2006)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://www.ist.temple.edu/disprot/predictorVSL2.php | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A

Canada

University of British Columbia

Nawar Malhis

• Lab website: Gsponer Lab - Michael Smith Laboratories | Profile

• Tools

  • IPA(IDR Probabilistic Annotation): IPA-AF2-Linker
    • Paper: Probabilistic Annotations of Protein Sequences for Intrinsically Disordered Features (⚠️ bioRxiv preprint, 2025)
  • Abstract: Read more →
  • Description/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/NawarMalhis/IPA
      • Others: orca.msl.ubc.ca/nmshare/ipa.tar.gz | https://bio.tools/ipa_idr_probabilistic_annotations

South America

Argentina

IIBIO, CONICET-UNSAM

Lucia Beatriz Chemes

• Lab website: Profile

• Tools

  • LINKER-Pred: LINKER-Pred, LINKER-Pred-Lite, LINKER-Pred2
    • Paper: LINKER-Pred: A Public Web Server for Accurate Prediction of Disordered Flexible Linkers in Proteins (⚠️ bioRxiv preprint, 2025)
  • Abstract: Read more →
  • Description/Method: Read more →
    • Access:
      • Web server: https://pcrgwd.ucd.ie/linker_pred/
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/deemeng/punch_linker
      • Others: N/A

EMEA

United Kingdom

University College London

David T. Jones

• Lab website: UCL Bioinformatics Group | Profile

• Tools

  • DISOPRED3: DISOPRED3-diso
    • Paper: DISOPRED3: precise disordered region predictions with annotated protein-binding activity (Bioinformatics, 2014)
    • Abstract: Read more →
    • Description/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/psipred/disopred
      • Others: https://bioinf.cs.ucl.ac.uk/downloads/DISOPRED/ | https://bio.tools/disopred3

University of Exeter

Zheng Rong Yang

• Lab website: [Profile](https://scholargps.com/scholars/45992467342875/zheng-rong-yang | https://www.scilit.com/scholars/172034)

• Tools

  • RONN
    • Paper: RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins (Bioinformatics, 2005)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: http://www.strubi.ox.ac.uk/RONN | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/varun-ramraj/RONN | Part of BioJava project
      • Others: Details | https://bio.tools/ronn

University of Cambridge

Michele Vendruscolo

• Lab website: [Vendruscolo lab](url link) | Centre for Misfolding Diseases

• Tools

  • s2D-2
    • Paper: The s2D Method: Simultaneous Sequence-Based Prediction of the Statistical Populations of Ordered and Disordered Regions in Proteins (Journal of Molecular Biology, 2014)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://www-cohsoftware.ch.cam.ac.uk/index.php/user_not_registered | https://www-cohsoftware.ch.cam.ac.uk// | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://bio.tools/s2d

Germany

Humboldt-Universität zu Berlin

Rune Linding

• Lab website: co-PI of the Klipp-Linding Lab

• Tools

  • DisEMBL: DisEMBL-dis465, DisEMBL-disHL
    • Paper: Protein disorder prediction: implications for structural proteomics (Structure, 2003)
    • Abstract: Read more →
    • Description/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: http://dis.embl.de/ | https://bio.tools/disembl

Technical University of Munich

Burkhard Rost

• Lab website: Rostlab

• Tools

  • SETH: SETH-0, SETH-1
    • Paper: SETH predicts nuances of residue disorder from protein embeddings (Front. Bioinform, 2022)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/DagmarIlz/SETH
      • Others: https://bio.tools/seth_1
  • UdonPred: UdonPred-combined, UdonPred-DisProt, UdonPred-TriZOD
    • Paper: N/A
    • Abstract: N/A
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/JSchlensok/udonpred
      • Others: N/A

Hungary

Eötvös Loránd University

Zsuzsanna Dosztányi

• Lab website: Dosztányi lab
• Tools:
  • AIUPred
    • Paper: AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder (Nucleic Acids Research, 2024)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://aiupred.elte.hu/ | Integrated into the CAID Prediction Portal
      • REST API: API docs
      • Docker image: N/A
      • GitHub repository: https://github.com/doszilab/AIUPred
      • Others: N/A
  • IUPred3
    • Paper: IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation (Nucleic Acids Research, 2021)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://iupred3.elte.hu/ | Integrated into the CAID Prediction Portal
      • REST API: API docs
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://bio.tools/iupred3
  • AIUPred-2: AIUPred-2-disorder
    • Paper: N/A
    • Abstract: N/A
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A

Italy

University of Padua

Silvio C. E. Tosatto

• Lab website: [Profile](https://biocomputingup.it/person/silvio-c-e-tosatto/ | https://protein.bio.unipd.it/people/silvio)

• Tools

  • AlphaFold-disorder: AlphaFold-pLDDT, AlphaFold-rsa, AlphaFold-binding
    • Paper: Intrinsic protein disorder and conditional folding in AlphaFoldDB (Protein Sci, 2022)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/BioComputingUP/AlphaFold-disorder
      • Others: N/A
  • AlphaFold3-disorder: AlphaFold-binding, AlphaFold-pLDDT, AlphaFold-rsa
    • Paper: N/A
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A
  • ESpritz: ESpritz-D, ESpritz-N, ESpritz-X
    • Paper: ESpritz: accurate and fast prediction of protein disorder (Bioinformatics, 2011)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: http://old.protein.bio.unipd.it/espritz/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://bio.tools/espritz
  • MobiDB-lite
    • Paper: MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins (Bioinformatics, 2017)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://mobidb.org/ | Integrated into the CAID Prediction Portal
      • REST API: API docs
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A
  • MobiDB-lite 4.0
    • Paper: MobiDB-lite 4.0: faster prediction of intrinsic protein disorder and structural compactness (Bioinformatics, 2025)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: https://github.com/BioComputingUP/MobiDB-lite_docker
      • GitHub repository: https://github.com/BioComputingUP/MobiDB-lite | IDRPred，a modern implementation of MobiDB-lite
      • Others: N/A

Belgium

Vrije Universiteit Brussel

Wim Vranken

• Lab website: Bio2Byte group

• Tools

  • DisoMine
    • Paper: Prediction of Disordered Regions in Proteins with Recurrent Neural Networks and Protein Dynamics (Journal of Molecular Biology, 2022)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://bio2byte.be/b2btools/disomine/ | Integrated into the CAID Prediction Portal
      • REST API: API docs
      • Docker image: N/A
      • GitHub repository: https://github.com/bio2byte/
      • Others: https://pypi.org/project/b2bTools/ (⚠️ DisoMine is one of the available predictors the package provided) | https://bio.tools/disomine

Poland

Silesian University of Technology

Katarzyna Stapor

• Lab website: Profile

• Tools

  • DisorderUnetLM
    • Paper: DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction (Computers in Biology and Medicine, 2024)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://codeocean.com/capsule/0867702/tree/v1

Ireland

University College Dublin

Gianluca Pollastri

• Lab website: Profile

• Tools

  • PUNCH2: PUNCH2-Light, PUNCH2
    • Paper: PUNCH2: Explore the strategy for intrinsically disordered protein predictor (PLoS ONE, 2025)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/deemeng/punch2 | https://github.com/deemeng/punch2_light
      • Others: Datasets

France

Sorbonne University

Isabelle Callebaut

• Lab website: Profile

• Tools

  • pyHCA: PyHCA-disorder
    • Paper: HCAtk and pyHCA: A Toolkit and Python API for the Hydrophobic Cluster Analysis of Protein Sequences (⚠️ bioRxiv preprint, 2018)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/T-B-F/pyHCA/
      • Others: https://bio.tools/pyhca

Sweden

Linköping University

Björn Wallner

• Lab website: [Profile](https://liu.se/en/employee/bjowa51 | https://www.scilifelab.se/researchers/bjorn-wallner/)

• Tools

  • rawMSA
    • Paper: rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments (PLoS One, 2019)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/yhgon/rawMSA
      • Others: https://bitbucket.org/clami66/rawmsa | https://bio.tools/rawmsa
  • rawMSA-disorder
    • Paper: N/A
    • Abstract: N/A
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A

Asia

China

Beijing Institute of Technology 北京理工大学

Bin Liu 刘滨

• Lab website: Profile

• Tools

  • DeepIDP-2L
    • Paper: DeepIDP-2L: protein intrinsically disordered region prediction by combining convolutional attention network and hierarchical attention network (Bioinformatics, 2021)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: http://bliulab.net/DeepIDP-2L/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://bio.tools/deepidp-2l
  • DisoFLAG: DisoFLAG-DFL, DisoFLAG-IDR
    • Paper: DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model (BMC Biology, 2024)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: http://bliulab.net/DisoFLAG/
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/YihePang/DisoFLAG
      • Others: N/A
  • IDP-Fusion
    • Paper: Protein intrinsically disordered region prediction by combining neural architecture search and multi-objective genetic algorithm (BMC Biology, 2023)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: http://bliulab.net/IDP-Fusion/home/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: Zenodo

Central South University 中南大学

Min Li

• Lab website: Profile

• Tools

  • DisoPred
    • Paper: N/A
    • Abstract: N/A
    • Model/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A

Sun Yat-sen University 中山大学

Yuedong Yang 杨跃东

• Lab website: Supercomputing+AI for Life science, SAIL Lab | Profile

• Tools

  • LMDisorder
    • Paper: Fast and accurate protein intrinsic disorder prediction by using a pretrained language model (Briefings in Bioinformatics, 2023)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/biomed-AI/LMDisorder
      • Others: N/A

Institute for Systems and Physical Biology， Shenzhen Bay Laboratory

Yaoqi Zhou 周耀旗

• Lab website: [Sparks lab](https://sparks-lab.org/group/ | https://zhouyq-lab.szbl.ac.cn/)

• Tools

  • SPOT-Disorder
    • Paper: Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks (Bioinformatics, 2016)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://sparks-lab.org/server/spot-disorder/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: Downloadable package | https://bio.tools/spot-disorder
  • SPOT-Disorder-Single
    • Paper: https://pubs.acs.org/doi/10.1021/acs.jcim.8b00636 (Journal of Chemical Information and Modeling, 2018)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://sparks-lab.org/server/spot-disorder-single/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: Downloadable package | https://bio.tools/spot-disorder-single
  • SPOT-Disorder2
    • Paper: SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (Genomics, Proteomics & Bioinformatics, 2020)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://sparks-lab.org/server/spot-disorder2/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: Downloadable package | https://bio.tools/spot-disorder2

Japan

Maebashi Institute of Technology

Satoshi Fukuchi

• Lab website: Profile

• Tools

  • DARUMA
    • Paper: DARUMA: Your gateway to fast and easy prediction of intrinsically disordered regions (⚠️ Research Square preprint, 2024)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://antepontem.org/daruma/
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: https://github.com/antepontem/DARUMA-Additional-Data | https://github.com/antepontem/DARUMA | https://github.com/shimizuitsuki0316/DARUMA | https://github.com/shimizuitsuki0316/DARUMA-Additional-Data_test
      • Others: https://pypi.org/project/daruma/
  • NeProc: NeProc-disorder
    • Paper: NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences (Biophysics and Physicobiology, 2020)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://antepontem.org/neproc/
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A
  • NeProc2: NeProc2-beta-disorder
    • Paper: N/A
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A

Russia

Institute of Protein Research, RAS

Oxana Valerianovna Galzitskaya

• Lab website: Profile

• Tools

  • FoldUnfold
    • Paper: FoldUnfold: web server for the prediction of disordered regions in protein chain (Bioinformatics, 2006)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: https://www.disprot.org/predictors.php | http://bioinfo.protres.ru/ogu/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://bio.tools/foldunfold
  • IsUnstruct
    • Paper: The Ising model for prediction of disordered residues from protein sequence alone (Physical Biology, 2011)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: http://bioinfo.protres.ru/IsUnstruct/ | Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://bio.tools/isunstruct

North Korea

University of Sciences, Pyongyang

Kun-Sop Han

• Lab website: Profile

• Tools

  • PredIDR: PredIDR-long, PredIDR-short
    • Paper: PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network (International Journal of Biological Macromolecules, 2024)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: Integrated into the CAID Prediction Portal
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: https://protein.bio.unipd.it/shared/caid-predictors/
  • PredIDR2-Prof: PredIDR2-Prof-Art, PredIDR2-Prof-Rnd
    • Paper: PredIDR2: Improving accuracy of protein intrinsic disorder prediction by updating deep convolutional neural network and supplementing DisProt data (International Journal of Biological Macromolecules, 2025)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A
  • PredIDR2-Seq: PredIDR2-Seq-Art, PredIDR2-Seq-Rnd
    • Paper: PredIDR2: Improving accuracy of protein intrinsic disorder prediction by updating deep convolutional neural network and supplementing DisProt data (International Journal of Biological Macromolecules, 2025)
    • Abstract: Read more →
    • Model/Method: Read more →
    • Access:
      • Web server: N/A
      • REST API: N/A
      • Docker image: N/A
      • GitHub repository: N/A
      • Others: N/A

TODO: model graph