Further Reading and Resources

This page contains additional resources related to protein structure analysis, structure prediction, and molecular visualisation. These materials are intended for readers who would like to explore the topics covered in the materials in greater depth.

ChimeraX help and tutorials

If you need help using ChimeraX, the following resources are useful.

Official documentation

ChimeraX user guide
https://www.cgl.ucsf.edu/chimerax/docs/user/index.html

ChimeraX tutorials
https://www.cgl.ucsf.edu/chimerax/tutorials.html

The documentation contains detailed descriptions of all commands used in ChimeraX.

Built-in help

ChimeraX also provides a built-in help system.

help
help <command>
help commands

For example:

help surface
help color

ChimeraX user forum

Questions about ChimeraX can be sent to the user mailing list:

chimerax-users@cgl.ucsf.edu

You can also browse the mailing list archive:

https://mail.cgl.ucsf.edu/mailman/archives/list/chimerax-users@cgl.ucsf.edu/

Many common questions have already been answered there.

Structure prediction literature

Core AlphaFold papers

Jumper, J., Evans, R., Pritzel, A., et al. (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589. https://doi.org/10.1038/s41586-021-03819-2

Abramson, J., Adler, J., Dunger, J., et al. (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493-500. https://doi.org/10.1038/s41586-024-07487-w

Other structure prediction and design methods

Du, Z., Su, H., Wang, W., et al. (2021) The trRosetta server for fast and accurate protein structure prediction. Nature Protocols 16, 5634-5651. https://doi.org/10.1038/s41596-021-00628-9

Watson, J. L., Juergens, D., Bennett, N. R., et al. (2023) De novo design of protein structure and function with RFdiffusion. Nature 620, 1089-1100. https://doi.org/10.1038/s41586-023-06415-8

Krishna, R., Wang, J., Ahern, W., et al. (2024) Generalized biomolecular modelling and design with RoseTTAFold-All-Atom. Science 384, eadl2528. https://doi.org/10.1126/science.adl2528

Wohlwend, J., et al. (2024) Boltz-1: Democratizing biomolecular interaction modelling. bioRxiv. https://doi.org/10.1101/2024.11.19.624167

Chai Discovery (2024) Chai-1: Decoding the molecular interactions of life. bioRxiv. https://doi.org/10.1101/2024.10.10.615955

Applications and extensions of AlphaFold

Wayment-Steele, H. K., Ojoawo, A., Otten, R., et al. (2024) Predicting multiple conformations via sequence clustering and AlphaFold2. Nature 625, 832-839. https://doi.org/10.1038/s41586-023-06832-9

Stahl, K., Warneke, R., Demann, L., et al. (2024) Modelling protein complexes with crosslinking mass spectrometry and deep learning. Nature Communications 15, 7866. https://doi.org/10.1038/s41467-024-51771-2

Homma, F., Lyu, J., van der Hoorn, R. A. L. (2024) Using AlphaFold Multimer to discover interkingdom protein-protein interactions. Plant Journal 120, 19-28. https://doi.org/10.1111/tpj.16969

Yu, D., Chojnowski, G., Rosenthal, M., Kosinski, J. (2023) AlphaPulldown: a Python package for protein-protein interaction screens using AlphaFold-Multimer. Bioinformatics 39(1), btac749. https://doi.org/10.1093/bioinformatics/btac749

Brotzakis, Z. F., Zhang, S., Murtada, M. H., et al. (2025) AlphaFold prediction of structural ensembles of disordered proteins. Nature Communications 16, 1632. https://doi.org/10.1038/s41467-025-56572-9

Humphreys, I. R., Pei, J., Baek, M., et al. (2021) Computed structures of core eukaryotic protein complexes. Science 374(6573), 1340. https://doi.org/10.1126/science.abm4805

Model evaluation and benchmarking

Wang, H., Sun, M., Xie, L., Liu, D., Zhang, G. (2025) Physical-aware model accuracy estimation for protein complex using deep learning method. Computational and Structural Biotechnology Journal 27, 478-487. https://doi.org/10.1016/j.csbj.2025.01.017

Williams, J., Gagnon, I. A., Sachleben, J. R. (2025) NMR spectroscopy for the validation of AlphaFold2 structures. bioRxiv. https://doi.org/10.1101/2025.02.04.636507

Method comparisons

Eshak, F., Goupil-Lamy, A. (2025) Advancements in nanobody epitope prediction: A comparative study of AlphaFold2-Multimer vs AlphaFold3. Journal of Chemical Information and Modeling 65(4), 1782-1797. https://doi.org/10.1021/acs.jcim.4c01877

Malhotra, Y., John, J., Yadav, D., Sharma, D., Vanshika, Rawal, K., Mishra, V., Chaturvedi, N. (2025) Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives. Computers in Biology and Medicine 188, 109842. https://doi.org/10.1016/j.compbiomed.2025.109842

Sharma, R., et al. (2024) Structural biology of RNA and protein-RNA complexes after AlphaFold3. ChemBioChem. https://doi.org/10.1002/cbic.202401047

Databases and model comparison tools

Manfredi, M., Savojardo, C., Iardukhin, G., Salomoni, D., Costantini, A., Martelli, P. L., Casadio, R. (2024) Alpha&ESMhFolds: A web server for comparing AlphaFold2 and ESMFold models of the human reference proteome. Journal of Molecular Biology 436(17), 168593. https://doi.org/10.1016/j.jmb.2024.168593

Manfredi, M., Vazzana, G., Savojardo, C., Martelli, P. L., Casadio, R. (2026) Alpha&ESMhFolds: An updated web server for the comparison, evaluation, and annotation of human AlphaFold2 and ESMFold models. Journal of Molecular Biology. https://doi.org/10.1016/j.jmb.2026.169663

Reviews

Szczepski, K., Jaremko, L. (2025) AlphaFold and what is next: bridging functional, systems and structural biology. Expert Review of Proteomics 22(2), 45-58. https://doi.org/10.1080/14789450.2025.2456046

Kovalevskiy, O., Mateos-Garcia, J., Tunyasuvunakool, K. (2024) AlphaFold two years on: validation and impact. Proceedings of the National Academy of Sciences 121(34), e2315002121. https://doi.org/10.1073/pnas.2315002121

Bertoline, L. M. F., Lima, A. N., Krieger, J. E., Teixeira, S. K. (2023) Before and after AlphaFold2: an overview of protein structure prediction. Frontiers in Bioinformatics 3, 1120370. https://doi.org/10.3389/fbinf.2023.1120370

Chaaban, S., Ratkevičiūtė, G., Lau, C. (2024) AI told you so: navigating protein structure prediction in the era of machine learning. Biochemist 46(2), 7-12. https://doi.org/10.1042/bio_2024_118

Textbooks on protein structure

Berg, J. M., Gatto, G. J., Hines, J. K., Tymoczko, J. L., Stryer, L. (2024) Biochemistry, 10th edition.

Fersht, A. (1999) Structure and mechanism in protein science: A guide to enzyme catalysis and protein folding.

Schweitzer-Stenner, R. (2024) The physics of protein structure and dynamics.

Protein databases

UniProt

UniProt Consortium UniProt https://www.uniprot.org/

UniProtKB statistics https://www.uniprot.org/uniprotkb/statistics

Structural classification databases

CATH https://cathdb.info/

Predicted structure databases

AlphaFold Protein Structure Database https://alphafold.ebi.ac.uk/

Historical resources in structural bioinformatics

Dayhoff, M. O. (1965) Atlas of Protein Sequence and Structure. National Biomedical Research Foundation. https://ntrs.nasa.gov/api/citations/19660014530/downloads/19660014530.pdf

Bernstein, F. C., et al. (1977) The Protein Data Bank: A computer-based archival file for macromolecular structures. Journal of Molecular Biology 112, 535-542.

Berman, H. M. (2008) The first years of the Protein Data Bank. Protein Science 17, 177-180. https://pmc.ncbi.nlm.nih.gov/articles/instance/2143743/pdf/9232661.pdf

Strasser, B. J. (2010) Collecting, comparing, and computing sequences: The making of Margaret O. Dayhoff’s Atlas of Protein Sequence and Structure, 1954-1965. Journal of the History of Biology 43, 623-660. https://doi.org/10.1007/s10739-009-9221-0

Tools for working with structure files

R

bio3d (Grant Lab) http://thegrantlab.org/bio3d/

Python

pdb-tools (Bonvin Lab) https://www.bonvinlab.org/pdb-tools/

Bio.PDB (Biopython structural bioinformatics module) https://biopython.org/wiki/The_Biopython_Structural_Bioinformatics_FAQ

Educational resources

Blogs and explainers

Oxford Protein Informatics Group (Charlotte Deane lab) AlphaFold2 is here: what’s behind the structure prediction miracle? https://www.blopig.com/blog/2021/07/alphafold-2-is-here-whats-behind-the-structure-prediction-miracle/

Has AlphaFold2 solved the protein structure problem? https://medium.com/towards-data-science/how-to-solve-the-protein-folding-problem-alphafold2-6c81faba670d

Oxford Protein Informatics Group (Charlotte Deane lab) Architectural highlights of AlphaFold3 https://www.blopig.com/blog/2024/08/architectural-highlights-of-alphafold3/

AlphaFold3 and its improvements compared to AlphaFold2 https://medium.com/@falk_hoffmann/alphafold3-and-its-improvements-in-comparison-to-alphafold2-96815ffbb044

Has AlphaFold3 reached success for RNAs? https://medium.com/@clement.bernard.these/has-alphafold-3-reached-its-success-for-rnas-benchmark-on-five-datasets-bcc0e11809cb

Sparks of chemical intuition and limitations in AlphaFold3 https://towardsdatascience.com/sparks-of-chemical-intuition-and-gross-limitations-in-alphafold-3-8487ba4dfb53/

Illustrated AlphaFold https://elanapearl.github.io/blog/2024/the-illustrated-alphafold/

An opinionated AlphaFold3 field guide https://research.dimensioncap.com/p/an-opinionated-alphafold3-field-guide

Video resources

5-minute AlphaFold overview https://www.youtube.com/watch?v=7q8Uw3rmXyE

Introduction to machine learning https://www.youtube.com/watch?v=Gv9_4yMHFhI

MIT introduction to machine learning https://www.youtube.com/watch?v=h0e2HAPTGF4

Deep learning and neural networks https://www.youtube.com/watch?v=aircAruvnKk

AlphaFold: Use and Applications (Sami Chaaban, LMB) https://www.youtube.com/watch?v=yJKfn6rvHmg

LookingGlassUniverse YouTube channel https://www.youtube.com/@LookingGlassUniverse

Boston Protein Structure Modelling and Design Club seminar series https://www.bpdmc.org/

Additional tools and databases

InterPro https://www.ebi.ac.uk/interpro/

IntFOLD https://www.reading.ac.uk/bioinf/IntFOLD/

RFDiffusion tutorial https://meilerlab.org/wp-content/uploads/2023/12/RFDiffusion_tutorial.pdf

Bioinformatic Sweeties https://bioinformaticsweeties.biocomp.unibo.it/