CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning

0
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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Huang, Y., Wiedmann, M. M. & Suga, H. RNA display methods for the discovery of bioactive macrocycles. Chem. Rev. 119, 10360–10391 (2019).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Villar, E. A. et al. How proteins bind macrocycles. Nat. Chem. Biol. 10, 723–731 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bhardwaj, G. et al. Accurate de novo design of membrane-traversing macrocycles. Cell 185, 3520–3532.e26 (2022).

    Article 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 

    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).

    Article 

    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).

    Article 

    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).

    Article 
    CAS 

    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).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Sindhikara, D. et al. Improving accuracy, diversity, and speed with prime macrocycle conformational sampling. J. Chem. Inf. Model. 57, 1881–1894 (2017).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    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).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    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).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • link

    Leave a Reply

    Your email address will not be published. Required fields are marked *