CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning
Driggers, E. M., Hale, S. P., Lee, J. & Terrett, N. K. The exploration of macrocycles for drug discovery–an underexploited structural class. Nat. Rev. Drug Discov. 7, 608–624 (2008).
Google Scholar
Muttenthaler, M., King, G. F., Adams, D. J. & Alewood, P. F. Trends in peptide drug discovery. Nat. Rev. Drug Discov. 20, 309–325 (2021).
Google Scholar
Huang, Y., Wiedmann, M. M. & Suga, H. RNA display methods for the discovery of bioactive macrocycles. Chem. Rev. 119, 10360–10391 (2019).
Google Scholar
Vinogradov, A. A., Yin, Y. & Suga, H. Macrocyclic peptides as drug candidates: Recent progress and remaining challenges. J. Am. Chem. Soc. 141, 4167–4181 (2019).
Google Scholar
Shinbara, K., Liu, W., van Neer, R. H. P., Katoh, T. & Suga, H. Methodologies for backbone macrocyclic peptide synthesis compatible with screening technologies. Front. Chem. 8, 447 (2020).
Google Scholar
Villar, E. A. et al. How proteins bind macrocycles. Nat. Chem. Biol. 10, 723–731 (2014).
Google Scholar
Whitty, A. et al. Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs. Drug Discov. Today 21, 712–717 (2016).
Google Scholar
Bhardwaj, G. et al. Accurate de novo design of membrane-traversing macrocycles. Cell 185, 3520–3532.e26 (2022).
Google Scholar
Linker, S. M. et al. Lessons for oral bioavailability: How conformationally flexible cyclic peptides enter and cross lipid membranes. J. Med. Chem. 66, 2773–2788 (2023).
Google Scholar
Landrum, G. RDKit: Open-source cheminformatics (2006).
Riniker, S. & Landrum, G. A. Better informed distance geometry: Using what we know to improve conformation generation. J. Chem. Inf. Model. 55, 2562–2574 (2015).
Google Scholar
Wang, S., Witek, J., Landrum, G. A. & Riniker, S. Improving conformer generation for small rings and macrocycles based on distance geometry and experimental torsional-angle preferences. J. Chem. Inf. Model. 60, 2044–2058 (2020).
Google Scholar
Wang, S. et al. Incorporating NOE-Derived distances in conformer generation of cyclic peptides with distance geometry. J. Chem. Inf. Model. 62, 472–485 (2022).
Google Scholar
Hawkins, P. C. D., Skillman, A. G., Warren, G. L., Ellingson, B. A. & Stahl, M. T. Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and cambridge structural database. J. Chem. Inf. Model. 50, 572–584 (2010).
Google Scholar
Hawkins, P. C. D. & Nicholls, A. Conformer generation with OMEGA: learning from the data set and the analysis of failures. J. Chem. Inf. Model. 52, 2919–2936 (2012).
Google Scholar
Halgren, T. A. Merck molecular force field. v. extension of MMFF94 using experimental data, additional computational data, and empirical rules. J. Comput. Chem. 17, 616–641 (1996).
Google Scholar
Kolossváry, I. & Guida, W. C. Low mode search. an efficient, automated computational method for conformational analysis: Application to cyclic and acyclic alkanes and cyclic peptides. J. Am. Chem. Soc. 118, 5011–5019 (1996).
Google Scholar
Kolossváry, I. & Guida, W. C. Low-mode conformational search elucidated: Application to C39H80 and flexible docking of 9-deazaguanine inhibitors into PNP. J. Comput. Chem. 20, 1671–1684 (1999).
Google Scholar
Chang, G., Guida, W. C. & Still, W. C. An internal-coordinate monte carlo method for searching conformational space. J. Am. Chem. Soc. 111, 4379–4386 (1989).
Google Scholar
Watts, K. S., Dalal, P., Tebben, A. J., Cheney, D. L. & Shelley, J. C. Macrocycle conformational sampling with MacroModel. J. Chem. Inf. Model. 54, 2680–2696 (2014).
Google Scholar
Sindhikara, D. et al. Improving accuracy, diversity, and speed with prime macrocycle conformational sampling. J. Chem. Inf. Model. 57, 1881–1894 (2017).
Google Scholar
Damjanovic, J., Miao, J., Huang, H. & Lin, Y.-S. Elucidating solution structures of cyclic peptides using molecular dynamics simulations. Chem. Rev. 121, 2292–2324 (2021).
Google Scholar
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Precup, D. & Teh, Y. W. (eds.) Proceedings of the 34th International Conference on Machine Learning, vol. 70 of Proceedings of Machine Learning Research, 1263–1272 (PMLR, 2017).
Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192–3203 (2017).
Google Scholar
Schütt, K. et al. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. In Guyon, I. et al. (eds.) Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., 2017).
Gasteiger, J., Groß, J. & Günnemann, S. Directional message passing for molecular graphs. In International Conference on Learning Representations (2020).
Liu, Y. et al. Spherical message passing for 3d molecular graphs. In International Conference on Learning Representations (2022).
Mansimov, E., Mahmood, O., Kang, S. & Cho, K. Molecular geometry prediction using a deep generative graph neural network. Sci. Rep. 9, 20381 (2019).
Google Scholar
Simm, G. & Hernandez-Lobato, J. M. A generative model for molecular distance geometry. In III, H. D. & Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning, vol. 119 of Proceedings of Machine Learning Research, 8949–8958 (PMLR, 2020).
Xu, M., Luo, S., Bengio, Y., Peng, J. & Tang, J. Learning neural generative dynamics for molecular conformation generation. In International Conference on Learning Representations (2021).
Xu, M. et al. Geodiff: A geometric diffusion model for molecular conformation generation. In International Conference on Learning Representations (2022).
Stärk, H., Ganea, O., Pattanaik, L., Barzilay, R. & Jaakkola, T. EquiBind: Geometric deep learning for drug binding structure prediction. In Chaudhuri, K. et al. (eds.) Proceedings of the 39th International Conference on Machine Learning, vol. 162 of Proceedings of Machine Learning Research, 20503–20521 (PMLR, 2022).
Jing, B., Corso, G., Chang, J., Barzilay, R. & Jaakkola, T. Torsional diffusion for molecular conformer generation. In Koyejo, S. et al. (eds.) Advances in Neural Information Processing Systems, vol. 35, 24240–24253 (Curran Associates, Inc., 2022).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Google Scholar
Wu, R. et al. High-resolution de novo structure prediction from primary sequence (2022).
Anand, N. & Achim, T. Protein structure and sequence generation with equivariant denoising diffusion probabilistic models (2022).
Yim, J. et al. SE(3) diffusion model with application to protein backbone generation (2023) 2302.02277.
Wu, K. E. et al. Protein structure generation via folding diffusion. Nat. Commun. 15, 1059 (2024).
Google Scholar
Groom, C. R., Bruno, I. J., Lightfoot, M. P. & Ward, S. C. The cambridge structural database. Acta Crystallogr B Struct Sci Cryst Eng Mater 72, 171–179 (2016).
Google Scholar
Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).
Google Scholar
Ruddigkeit, L., van Deursen, R., Blum, L. C. & Reymond, J.-L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model. 52, 2864–2875 (2012).
Google Scholar
Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data 1 (2014).
Axelrod, S. & Gómez-Bombarelli, R. GEOM, energy-annotated molecular conformations for property prediction and molecular generation. Sci Data 9, 185 (2022).
Prasad, V. K., Otero-de-la Roza, A. & DiLabio, G. A. PEPCONF, a diverse data set of peptide conformational energies. Sci Data 6, 180310 (2019).
Google Scholar
Eastman, P. et al. SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials. Sci Data 10, 11 (2023).
Google Scholar
Pracht, P., Bohle, F. & Grimme, S. Automated exploration of the low-energy chemical space with fast quantum chemical methods. Phys. Chem. Chem. Phys. 22, 7169–7192 (2020).
Google Scholar
Bannwarth, C., Ehlert, S. & Grimme, S. GFN2-xTB-an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 15, 1652–1671 (2019).
Google Scholar
Ehlert, S., Stahn, M., Spicher, S. & Grimme, S. Robust and efficient implicit solvation model for fast semiempirical methods. J. Chem. Theory Comput. 17, 4250–4261 (2021).
Google Scholar
Li, J. et al. CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides. Journal of Chemical Information and Modeling. (2023).
Chan, L., Morris, G. M. & Hutchison, G. R. Understanding conformational entropy in small molecules. J. Chem. Theory Comput. 17, 2099–2106 (2021).
Google Scholar
Grambow, C. A., Weir, H., Cunningham, C. N., Biancalani, T. & Chuang, K. V. CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning. Zenodo (2023).
Grambow, C. A., Weir, H., Cunningham, C. N., Biancalani, T. & Chuang, K. V. CREMP-CycPeptMPDB: Conformer-Rotamer Ensembles of Macrocyclic Peptides with Permeability Annotations. Zenodo (2024).
McInnes, L., Healy, J. & Melville, J. Umap: Uniform manifold approximation and projection for dimension reduction (2020). 1802.03426.
Ramachandran, G. N. & Sasisekharan, V. Conformation of polypeptides and proteins. Adv. Protein Chem. 23, 283–438 (1968).
Google Scholar
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