Modeling Resources

Disclaimer

This is not a comprehensive list of Protein Modeling resources. Rather, it contains some of the references, methods, and online servers that I found useful through the years.

Most useful links (IMHO) are highlighted in bold. Other (many) tools may be useful for you, feel free to let me know and I’ll update the list.

Note

Links checked on 28/September/2023, let me know if you find any broken link.

1 Template search

• BLAST: https://blast.ncbi.nlm.nih.gov/Blast.cgi

• HHPRED: https://toolkit.tuebingen.mpg.de/tools/hhpred

• HHblits: https://toolkit.tuebingen.mpg.de/tools/hhblits

• JackHMMER: https://www.ebi.ac.uk/Tools/hmmer/search/jackhmmer

  • Ref. https://nar.oxfordjournals.org/content/46/W1/W200

2 Secondary structure and 1D features services

• Jpred4: http://www.compbio.dundee.ac.uk/jpred/

  • Secondary structure prediction

  • Ref. http://nar.oxfordjournals.org/content/early/2015/04/16/nar.gkv332

• PsiPred : http://bioinf.cs.ucl.ac.uk/psipred/

  • Secondary structure prediction

  • MEMSAT-SVM (Membrane Helix Prediction)

  • Ref: https://academic.oup.com/nar/article/47/W1/W402/5480136

• MULTICOM Toolbox: http://sysbio.rnet.missouri.edu/multicom_toolbox/web%20services.html

  • Pulls together protein structure and structural feature prediction tools.

  • ⚠️Server currently offline. Application available on GitHub: https://github.com/multicom-toolbox

• SCRATCH: http://scratch.proteomics.ics.uci.edu/

  • Metaserver with a wide range of 1D-3D prediction tools.

• SYMPRED: http://www.ibi.vu.nl/programs/sympredwww/

  • Consensus secondary structure prediction metaserver

• MESSA: http://prodata.swmed.edu/MESSA/MESSA.cgi

  • 1D features Metaserver

  • Ref.https://bmcbiol.biomedcentral.com/articles/10.1186/1741-7007-10-82

• DeepTMHMM: https://dtu.biolib.com/DeepTMHMM

  • Deep-learning 2022 version of the classic TMHMM predictor of transmembrane helices

  • Ref. https://www.biorxiv.org/content/10.1101/2022.04.08.487609v1

• Phobius: https://www.ebi.ac.uk/Tools/pfa/phobius/

  • Prediction of transmembrane topology and signal peptides from the amino acid sequence of a protein.

• TopCons: http://topcons.cbr.su.se/

  • Consensus prediction of membrane protein topology and signal peptides

  • Ref. https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkv485

• PSORT: https://psort.hgc.jp/

  • Several localization signals

• PRED-TMMB: http://bioinformatics.biol.uoa.gr/PRED-TMBB/

  • Predicion of beta-barrel TMEs & topology (HMM-based)

• PROF-TMF: https://open.predictprotein.org/

  • Predicion of beta-barrel TMEs (PredictProtein Suite)

• NLStradamus: http://www.moseslab.csb.utoronto.ca/NLStradamus/

  • Prediction of NLS

• Sulfinator: https://web.expasy.org/sulfinator/

  • Prediction of disulfide bonds

• DTU Tech resources: https://services.healthtech.dtu.dk/

  • A collection of molecular biology tools, including a number of services for 1D features prediction, such as DeepTMHMM (Hallgren et al. 2022) or NetSurfP (Høie et al. 2022).
    

3 Protein modeling

• SwissModel (Waterhouse et al. 2018): https://swissmodel.expasy.org/

• PsiPRED (Buchan and Jones 2019): http://bioinf.cs.ucl.ac.uk/psipred/

  • Fold recognition server (among other tools)

• RaptorX (Wang et al. 2017): http://raptorx.uchicago.edu/ContactMap/

  • Distance-based Protein Folding Powered by Deep Learning

• Phyre2 (Kelley et al. 2015): http://www.sbg.bio.ic.ac.uk/~phyre2/html/page.cgi?id=index

  • Fold recognition & fragment assembler

• Robetta server: https://robetta.bakerlab.org/

  • Several methods, including RosettaCM (Song et al. 2013) and RoseTTAFOLD (Baek et al. 2021; Baek et al., n.d.)

• ModWeb (Pieper et al. 2014): https://modbase.compbio.ucsf.edu/modweb/

  • Webserver using MODELLER (free registration required)

• ColabFold (Mirdita et al. 2022): https://github.com/sokrypton/ColabFold

  • Several notebooks available

• DeepMind’s AlphaFold colab: https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb

• Openfold (Ahdritz et al., n.d.) Colab: https://colab.research.google.com/github/aqlaboratory/openfold/blob/main/notebooks/OpenFold.ipyn

• Unifold (Ziyao Li et al., n.d.) Colab: https://colab.research.google.com/github/dptech-corp/Uni-Fold/blob/main/notebooks/unifold.ipynb

• ESM Metagenomic Atlas (DDBB & tools, Lin et al. (2023)): https://esmatlas.com/about

• PymolFold: A handy Pymol script that calls the ESM-Fold API

  • https://github.com/JinyuanSun/PymolFold

  • Web version: http://103.79.77.89:8501/

• Zhang server: https://zhanggroup.org/services/

  • Multiple methods, including I-TASSER (Roy, Kucukural, and Zhang 2010), C-I-TASSER (Zheng et al. 2021) or I-TASSER-MTD, among others.

4 Model quality assessment

• QMEAN & QMEANDisCo (Studer et al. 2020): https://swissmodel.expasy.org/qmean/

• Modfold (McGuffin et al. 2021): https://www.reading.ac.uk/bioinf/ModFOLD/

• VoroMQA (Olechnovič and Venclovas 2019): http://bioinformatics.ibt.lt/wtsam/voromqa

• DeepUMQA (Guo et al. 2022): http://zhanglab-bioinf.com/DeepUMQA/

5 Other resources

• DALI (Holm 2022): http://ekhidna2.biocenter.helsinki.fi/dali/

• FoldSeek (Kempen et al. 2023): https://search.foldseek.com/search

  • Fast search for structural similarities

• FatCat (Zhanwen Li et al. 2020): http://fatcat.godziklab.org/fatcat/fatcat_pair.html

  • Structural alignment tool.

• LGA: http://proteinmodel.org/AS2TS/LGA/lga.html

  • LGA program is being developed for structure comparative analysis of two selected 3D protein structures or fragments of 3D protein structures

  • Ref. https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkg571

• Galaxy Refine: http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE

  • A server for automatically refining protein model structures.

  • Ref. http://www.ncbi.nlm.nih.gov/pubmed/23737448

• Galaxy Refine2: galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE2

  • <300 aa

  • Ref:https://academic.oup.com/nar/article/47/W1/W451/5475172

• HOMCOS: http://homcos.pdbj.org/

  • Modeling of 3D Structures of Complexes

  • Ref. http://link.springer.com/article/10.1007/s10969-016-9208-y?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst

• pyDockWEB: https://life.bsc.es/pid/pydockweb

  • Prediction of protein-protein interactions (hetero-oligomers modeling)

• Hex: http://hexserver.loria.fr/

  • Prediction of protein-protein interactions (homo- & hetero-oligomers modeling)

  • Ref. https://onlinelibrary.wiley.com/doi/full/10.1002/prot.24433

• DaReUs-Loop: https://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/

  • Modeling loops

  • Ref. https://www.nature.com/articles/s41598-018-32079-w

• GalaxyLoop: http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=LOOP

  • Loops refining

  • Ref. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113811

• Expasy proteomics tools: https://www.expasy.org/proteomics

  • List of qMultiple links and resources

• Protein modeling links @Sali Lab: https://salilab.org/bioinformatics_resources.html

• Homology modeling @Proteopedia:

  • https://proteopedia.org/wiki/index.php/Homology_model

  • https://proteopedia.org/wiki/index.php/User:Wayne_Decatur/Homology_Modeling

• List of protein structure prediction software @Wikipedia: https://en.wikipedia.org/wiki/List_of_protein_structure_prediction_software

References

Ahdritz, Gustaf, Nazim Bouatta, Christina Floristean, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J. O’Donnell, et al. n.d. “OpenFold: Retraining AlphaFold2 Yields New Insights into Its Learning Mechanisms and Capacity for Generalization.” https://doi.org/10.1101/2022.11.20.517210.

Baek, Minkyung, Ivan Anishchenko, Ian R. Humphreys, Qian Cong, David Baker, and Frank DiMaio. n.d. “Efficient and Accurate Prediction of Protein Structure Using RoseTTAFold2.” https://doi.org/10.1101/2023.05.24.542179.

Baek, Minkyung, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, et al. 2021. “Accurate prediction of protein structures and interactions using a three-track neural network.” Science (New York, N.Y.) 373 (6557): 871–76. https://doi.org/10.1126/science.abj8754.

Buchan, Daniel W A, and David T Jones. 2019. “The PSIPRED Protein Analysis Workbench: 20 Years On.” Nucleic Acids Research 47 (W1): W402–7. https://doi.org/10.1093/nar/gkz297.

Guo, Sai-Sai, Jun Liu, Xiao-Gen Zhou, and Gui-Jun Zhang. 2022. “DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning.” Bioinformatics (Oxford, England) 38 (7): 1895–1903. https://doi.org/10.1093/bioinformatics/btac056.

Hallgren, Jeppe, Konstantinos D. Tsirigos, Mads Damgaard Pedersen, José Juan Almagro Armenteros, Paolo Marcatili, Henrik Nielsen, Anders Krogh, and Ole Winther. 2022. “DeepTMHMM Predicts Alpha and Beta Transmembrane Proteins Using Deep Neural Networks.” https://doi.org/10.1101/2022.04.08.487609.

Høie, Magnus Haraldson, Erik Nicolas Kiehl, Bent Petersen, Morten Nielsen, Ole Winther, Henrik Nielsen, Jeppe Hallgren, and Paolo Marcatili. 2022. “NetSurfP-3.0: Accurate and Fast Prediction of Protein Structural Features by Protein Language Models and Deep Learning.” Nucleic Acids Research 50 (W1): W510–15. https://doi.org/10.1093/nar/gkac439.

Holm, Liisa. 2022. “Dali Server: Structural Unification of Protein Families.” Nucleic Acids Research 50 (W1): W210–15. https://doi.org/10.1093/nar/gkac387.

Kelley, L. A., S. Mezulis, C. M. Yates, M. N. Wass, and M. J. Sternberg. 2015. “The Phyre2 Web Portal for Protein Modeling, Prediction and Analysis.” Nat Protoc 10 (6): 845–58. https://doi.org/10.1038/nprot.2015.053.

Kempen, Michel van, Stephanie S. Kim, Charlotte Tumescheit, Milot Mirdita, Jeongjae Lee, Cameron L. M. Gilchrist, Johannes Söding, and Martin Steinegger. 2023. “Fast and Accurate Protein Structure Search with Foldseek.” Nature Biotechnology, May, 1–4. https://doi.org/10.1038/s41587-023-01773-0.

Li, Zhanwen, Lukasz Jaroszewski, Mallika Iyer, Mayya Sedova, and Adam Godzik. 2020. “FATCAT 2.0: towards a better understanding of the structural diversity of proteins.” Nucleic Acids Research 48 (W1): W60–64. https://doi.org/10.1093/nar/gkaa443.

Li, Ziyao, Xuyang Liu, Weijie Chen, Fan Shen, Hangrui Bi, Guolin Ke, and Linfeng Zhang. n.d. “Uni-Fold: An Open-Source Platform for Developing Protein Folding Models Beyond AlphaFold.” https://doi.org/10.1101/2022.08.04.502811.

Lin, Zeming, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, et al. 2023. “Evolutionary-Scale Prediction of Atomic-Level Protein Structure with a Language Model.” Science 379 (6637): 1123–30. https://doi.org/10.1126/science.ade2574.

McGuffin, Liam J., Fahd M. F. Aldowsari, Shuaa M. A. Alharbi, and Recep Adiyaman. 2021. “ModFOLD8: accurate global and local quality estimates for 3D protein models.” Nucleic Acids Research 49 (W1): W425–30. https://doi.org/10.1093/nar/gkab321.

Mirdita, Milot, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger. 2022. “ColabFold: making protein folding accessible to all.” Nature Methods 19 (6): 679–82. https://doi.org/10.1038/s41592-022-01488-1.

Olechnovič, Kliment, and Česlovas Venclovas. 2019. “VoroMQA web server for assessing three-dimensional structures of proteins and protein complexes.” Nucleic Acids Research 47 (W1): W437–42. https://doi.org/10.1093/nar/gkz367.

Pieper, Ursula, Benjamin M. Webb, Guang Qiang Dong, Dina Schneidman-Duhovny, Hao Fan, Seung Joong Kim, Natalia Khuri, et al. 2014. “ModBase, a database of annotated comparative protein structure models and associated resources.” Nucleic Acids Research 42 (Database issue): D336–346. https://doi.org/10.1093/nar/gkt1144.

Roy, A., A. Kucukural, and Y. Zhang. 2010. “I-TASSER: a unified platform for automated protein structure and function prediction.” Nat Protoc 5 (4): 725–38. https://doi.org/nprot.2010.5 [pii] 10.1038/nprot.2010.5.

Song, Yifan, Frank DiMaio, Ray Yu-Ruei Wang, David Kim, Chris Miles, TJ Brunette, James Thompson, and David Baker. 2013. “High-Resolution Comparative Modeling with RosettaCM.” Structure 21 (10): 1735–42. https://doi.org/https://doi.org/10.1016/j.str.2013.08.005.

Studer, Gabriel, Christine Rempfer, Andrew M. Waterhouse, Rafal Gumienny, Juergen Haas, and Torsten Schwede. 2020. “QMEANDisCo-distance constraints applied on model quality estimation.” Bioinformatics (Oxford, England) 36 (6): 1765–71. https://doi.org/10.1093/bioinformatics/btz828.

Wang, Sheng, Siqi Sun, Zhen Li, Renyu Zhang, and Jinbo Xu. 2017. “Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.” PLoS computational biology 13 (1): e1005324. https://doi.org/10.1371/journal.pcbi.1005324.

Waterhouse, Andrew, Martino Bertoni, Stefan Bienert, Gabriel Studer, Gerardo Tauriello, Rafal Gumienny, Florian T Heer, et al. 2018. “SWISS-MODEL: Homology Modelling of Protein Structures and Complexes.” Nucleic Acids Research 46 (W1): W296–303. https://doi.org/10.1093/nar/gky427.

Zheng, Wei, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, and Yang Zhang. 2021. “Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.” Cell Reports Methods 1 (3): 100014. https://doi.org/10.1016/j.crmeth.2021.100014.