Assessment of protein-ligand complexes in CASP15.
CASP
protein structure prediction
protein-ligand complex
Journal
Proteins
ISSN: 1097-0134
Titre abrégé: Proteins
Pays: United States
ID NLM: 8700181
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
revised:
14
09
2023
received:
11
04
2023
accepted:
19
09
2023
medline:
24
11
2023
pubmed:
5
10
2023
entrez:
5
10
2023
Statut:
ppublish
Résumé
CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.
Substances chimiques
Ligands
0
Ions
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1811-1821Informations de copyright
© 2023 Wiley Periodicals LLC.
Références
Kryshtafovych A, Antczak M, Szachniuk M, et al. New Prediction Categories In CASP15. Proteins. 2023;91(12):1550-1557. doi: 10.1002/prot.26515
Weininger D. SMILES: a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28(1):31-36.
Weininger D, Weininger A, Weininger JL. SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inform Comput Sci. 1989;29(2):97-101. doi:10.1021/ci00062a008
Jansen JM, Cornell W, Tseng YJ, Amaro RE. Teach-discover-treat (TDT): collaborative computational drug discovery for neglected diseases. J Mol Graph Model. 2012;38:360-362.
Wagner JR, Churas CP, Liu S, et al. Continuous evaluation of ligand protein predictions: a weekly community challenge for drug docking. Structure. 2019;27(8):1326-1335.e4.
Gathiaka S, Liu S, Chiu M, et al. D3R grand challenge 2015: evaluation of protein-ligand pose and affinity predictions. J Comput Aided Mol des. 2016;30(9):651-668.
Gaieb Z, Liu S, Gathiaka S, et al. D3R grand challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol des. 2018;32(1):1-20.
Gaieb Z, Parks CD, Chiu M, et al. D3R grand challenge 3: blind prediction of protein-ligand poses and affinity rankings. J Comput Aided Mol Des. 2019;33(1):1-18.
Parks CD, Gaieb Z, Chiu M, et al. D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des. 2020;34(2):99-119.
Damm-Ganamet KL, Smith RD, Dunbar JB Jr, Stuckey JA, Carlson HA. CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model. 2013;53(8):1853-1870.
Smith RD, Damm-Ganamet KL, Dunbar JB Jr, et al. CSAR benchmark exercise 2013: evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge. J Chem Inf Model. 2016;56(6):1022-1031.
Dalby A, Nourse JG, Grier WDD, Leland BA. Description of several Chemical structure file formats developed at molecular design. J Chem Inf Comput Sci. 1992;32:244-255.
Mariani V, Biasini M, Barbato A, Schwede T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics. 2013;29(21):2722-2728.
Méndez R, Leplae R, De Maria L, Wodak SJ. Assessment of blind predictions of protein-protein interactions: current status of docking methods. Proteins. 2003;52(1):51-67.
Meli R, Biggin PC. Spyrmsd: symmetry-corrected RMSD calculations in python. J Chem. 2020;12(1):49.
Hagberg AA, Schult DA, Swart PJ. Exploring network structure, dynamics, and function using NetworkX. In: Varoquaux G, Vaught T, Millman J, eds. Proceedings of the 7th Python in Science Conference (SciPy 2008);2008:11-15. https://conference.scipy.org/proceedings/SciPy2008/
Biasini M, Schmidt T, Bienert S, et al. OpenStructure: an integrated software framework for computational structural biology. Acta Crystallogr D Biol Crystallogr. 2013;69(Pt 5):701-709.
Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004;47(7):1739-1749.
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461.
Olanders G, Alogheli H, Brandt P, Karlén A. Conformational analysis of macrocycles: comparing general and specialized methods. J Comput Aided Mol des. 2020;34(3):231-252.
Jain AN, Cleves AE, Gao Q, et al. Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen. J Comput Aided Mol des. 2019;33(6):531-558.
O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open babel: an open Chemical toolbox. J Chem. 2011;3:33.
Steinegger M, Meier M, Mirdita M, Vöhringer H, Haunsberger SJ, Söding J. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics. 2019;20(1):473.
Stärk H, Ganea O-E, Pattanaik L, Barzilay R, Jaakkola T. EquiBind: geometric deep learning for drug binding structure prediction. arXiv [q-bio.BM], Preprint posted online. 2022 http://arxiv.org/abs/2202.05146
Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv [q-bio.BM], Preprint posted online. 2022 http://arxiv.org/abs/2210.01776
Lu W, Wu Q, Zhang J. TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction. doi:10.1101/2022.06.06.495043