Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolver
Open accessAbstract
Computational generation of new proteins with a predetermined three-dimensional shape and computational optimization of existing proteins while maintaining their shape are challenging problems in structural biology. Here, we present a protocol that uses ProteinSolver, a pre-trained graph convolutional neural network, to quickly generate thousands of sequences matching a specific protein topology. We describe computational approaches that can be used to evaluate the generated sequences, and we show how select sequences can be validated experimentally. For complete details on the use and execution of this protocol, please refer to Strokach et al. (2020).
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The problem of predicting a protein sequence that folds into a particular structure, inverse protein folding, has seen continuous improvements.
“The problem of predicting a protein sequence that folds into a particular structure (inverse protein folding) has, for example, seen continuous improvements; state-of-the-art approaches integrate contemporary architectures that capture protein invariances while reasoning about protein residues in local and global coordinate frames [79][80][81].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [81]
State-of-the-art inverse protein folding approaches integrate contemporary architectures that capture protein invariances while reasoning about protein residues in local and global coordinate frames.
“The problem of predicting a protein sequence that folds into a particular structure (inverse protein folding) has, for example, seen continuous improvements; state-of-the-art approaches integrate contemporary architectures that capture protein invariances while reasoning about protein residues in local and global coordinate frames [79][80][81].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [81]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works
BLAST+: architecture and applications
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-421 · Cited by 22,074 · see citing works
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230 · see citing works
Ryckaert, J., Ciccotti, G., & Berendsen, H. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90098-5 · Cited by 19,525 · see citing works
(2017) · Cited by 11,625 · see citing works
ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB
Maier, J., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K., & Simmerling, C. L. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00255 · Cited by 10,971 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460 · see citing works
Protein Identification and Analysis Tools on the ExPASy Server
Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S., Wilkins, M. R., Appel, R. D., & Bairoch, A. (2005) · Humana Press eBooks · doi:10.1385/1-59259-890-0:571 · Cited by 10,133 · see citing works
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535 · see citing works
UniProt: a worldwide hub of protein knowledge
The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698 · see citing works
Protein Identification and Analysis Tools in the ExPASy Server
Wilkins, M. R., Gasteiger, E., Bairoch, A., Sanchez, J., Williams, K. L., Appel, R. D., & Hochstrasser, D. F. (2003) · Humana Press eBooks · doi:10.1385/1-59259-584-7:531 · Cited by 6,209 · see citing works
Protein secondary structure prediction based on position-specific scoring matrices.
Jones, D. C. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3091 · Cited by 5,741 · see citing works
The I-TASSER Suite: protein structure and function prediction
Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015) · Nature Methods · doi:10.1038/nmeth.3213 · Cited by 5,596 · see citing works
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840 · see citing works
Comparative Protein Structure Modeling Using Modeller
Eswar, N., Webb, B., Martı́-Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M., Pieper, U., & Šali, A. (2006) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0506s15 · Cited by 4,564 · see citing works
Comparative Protein Structure Modeling Using MODELLER
Webb, B., & Šali, A. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.3 · Cited by 4,349 · see citing works
Fast Graph Representation Learning with PyTorch GeometricPreprint
Fey, M., & Lenssen, J. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02428 · Cited by 3,403 · see citing works
MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J., Hernández, C. X., Schwantes, C. R., Wang, L., Lane, T. J., & Pande, V. S. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.08.015 · Cited by 2,358 · see citing works
Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy
Micsonai, A., Wien, F., Kernya, L., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1500851112 · Cited by 1,709 · see citing works
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532 · see citing works
Improved protein structure prediction using predicted interresidue orientations
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421 · see citing works
HH-suite3 for fast remote homology detection and deep protein annotation
Steinegger, M., Meier, M., Mirdita, M., Vöhringer, H., Haunsberger, S. J., & Söding, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3019-7 · Cited by 1,145 · see citing works
Xu, D. H., & Zhang, Y. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24065 · Cited by 853 · see citing works
Efficient particle-mesh Ewald based approach to fixed and induced dipolar interactions
Toukmaji, A. Y., Sagui, C., John Board, & Darden, T. (2000) · The Journal of Chemical Physics · doi:10.1063/1.1324708 · Cited by 492 · see citing works
Sequence‐specific determination of protein and peptide concentrations by absorbance at 205 nm
Anthis, N. J., & Clore, G. M. (2013) · Protein Science · doi:10.1002/pro.2253 · Cited by 452 · see citing works
MMDB and VAST+: tracking structural similarities between macromolecular complexes
Madej, T., Lanczycki, C. J., Zhang, D., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1208 · Cited by 339 · see citing works
Fast and Flexible Protein Design Using Deep Graph Neural Networks
Strokach, A. V., Becerra, D., Corbi‐Verge, C., Perez‐Riba, A., & Kim, P. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.016 · Cited by 218 · see citing works
Self-citationBiotite: a unifying open source computational biology framework in Python
Kunzmann, P., & Hamacher, K. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2367-z · Cited by 132 · see citing works
CLICK—topology-independent comparison of biomolecular 3D structures
Nguyen, M. N., Tan, K. P., & Madhusudhan, M. S. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr393 · Cited by 130 · see citing works
A method for rapid high-throughput biophysical analysis of proteins
Perez‐Riba, A., & Itzhaki, L. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-08664-w · Cited by 20 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
Self-citation
- Authors
- Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., & Kim, P. M.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Strokach, Alexey
- Kim, Philip M.
- Venue
- STAR Protocols
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 2
- Issue
- 2
- Pages
- 100505
- ISSN
- 2666-1667 (ISSN-L)
- PMC
- PMC8102803
- OpenAlex
- W3159252521
- Cited by
- 6
- References
- 32
- Citations by year
- 2026: 1 · 2023: 3 · 2022: 1 · 2021: 1
- FWCI
- 0.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- STAR Protocols (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- CIHR IG
- NSERC
- FRQNT
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein design, protein topology, graph convolutional networks, sequence generation, protein structure prediction, protein engineering, sequence verification
- MeSH terms
- Proteins, Software, Neural Networks, Computer, Computational Biology, Databases, Protein
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
