References
AlQuraishi, M. (2021), ‘Machine learning in protein structure prediction’, Current Opinion in Chemical Biology 65, 1–8, doi.org/10.1016/j.cbpa.2021.04.005.
Ameh, E. (2019), ‘A review of basic crystallography and x-ray diffraction applications’, The International Journal of Advanced Manufacturing Technology 105(7), 3289–3302, doi.org/10.1007/s00170-019-04508-1.
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R. et al. (2018), ‘Relational inductive biases, deep learning, and graph networks’, arXiv preprint arXiv:1806.01261 .
Bellissent-Funel, M.-C., Hassanali, A., Havenith, M., Henchman, R., Pohl, P., Sterpone, F., Van Der Spoel, D., Xu, Y. & Garcia, A. E. (2016), ‘Water determines the structure and dynamics of proteins’, Chemical reviews 116(13), 7673–7697, dx.doi.org/10.1021%2Facs.chemrev.5b00664.
Blundell, T., Dodson, G., Dodson, E., Hodgkin, D. & Vijayan, M. (1971), X-ray analysis and the structure of insulin, in ‘Proceedings of the 1970 Laurentian Hormone Conference’, Elsevier, pp. 1–40, doi.org/10.1016/B978-0-12-571127-2.50025-0.
Bodnar, C., Frasca, F., Wang, Y. G., Otter, N., Montu ́far, G., Lio, P. & Bronstein, M. (2021), ‘Weisfeiler and lehman go topological: Message passing simplicial networks’, arXiv preprint arXiv:2103.03212.
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A. & Vandergheynst, P. (2017), ‘Geometric deep learning: going beyond euclidean data’, IEEE Signal Processing Magazine 34(4), 18–42, doi.org/10.1109/MSP.2017.2693418
Bronstein, M.M., Bruna, J., Cohen, T. and Veličković, P., 2021. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478.
Carter, P. & Wells, J. A. (1988), ‘Dissecting the catalytic triad of a serine protease’, Nature 332(6164), 564–568, doi.org/10.1038/332564a0.
Charlier, B., Feydy, J., Glaun`es, J., Collin, F.-D. & Durif, G. (2021), ‘Kernel operations on the gpu, with autodiff, without memory overflows’, Journal of Machine Learning Research 22(74), 1–6.
Cheung, M. S., Garc ́ıa, A. E. & Onuchic, J. N. (2002), ‘Protein folding mediated by solvation: water expulsion and formation of the hydrophobic core occur after the structural collapse’, Proceedings of the National Academy of Sciences 99(2), 685–690, doi.org/10.1073/pnas.022387699.
DeLano, W. L. et al. (2002), ‘Pymol: An open-source molecular graphics tool’, CCP4 Newsletter on protein crystallography 40(1), 82–92.
Doerr, S., Majewski, M., P ́erez, A., Kramer, A., Clementi, C., Noe, F., Giorgino, T. & De Fabritiis, G. (2021), ‘Torchmd: A deep learning framework for molecular simulations’, Journal of Chemical Theory and Computation 17(4), 2355–2363.
Doniach, S. & Eastman, P. (1999), ‘Protein dynamics simulations from nanoseconds to microseconds’, Current Opinion in Structural Biology 9(2), 157–163, doi.org/10.1016/S0959-440X(99)80022-0.
Fuchs, F. B., Wagstaff, E., Dauparas, J. & Posner, I. (2021), ‘Iterative se (3)-transformers’, arXiv preprint arXiv:2102.13419.
Fuchs, F. B., Worrall, D. E., Fischer, V. & Welling, M. (2020), ‘Se (3)-transformers: 3d roto-translation equivariant attention networks’, arXiv preprint arXiv:2006.10503.
Gainza, P., Sverrisson, F., Monti, F., Rodola, E., Boscaini, D., Bronstein, M. & Correia, B. (2020), ‘Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning’, Nature Methods 17(2), 184–192, doi.org/10.1038/s41592-019-0666-6.
Goel, H., Yu, W., Ustach, V. D., Aytenfisu, A. H., Sun, D. & MacKerell, A. D. (2020), ‘Impact of elec- tronic polarizability on protein-functional group interactions’, Physical Chemistry Chemical Physics 22(13), 6848–6860, doi.org/10.1039/D0CP00088D.
Greener, J. G. & Jones, D. T. (2021), ‘Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins’, bioRxiv , doi.org/10.1101/2021.02.05.429941.
Hu, Y., Anderson, L., Li, T.-M., Sun, Q., Carr, N., Ragan-Kelley, J. & Durand, F. (2019), ‘Difftaichi: Differentiable programming for physical simulation’, arXiv preprint arXiv:1910.00935 .
Hunter, J. D. (2007), ‘Matplotlib: A 2d graphics environment’, IEEE Annals of the History of Computing 9(03), 90–95, doi.ieeecomputersociety.org/10.1109/MCSE.2007.55
Ishima, R. & Torchia, D. A. (2000), ‘Protein dynamics from nmr’, Nature structural biology 7(9), 740–743, doi.org/10.1038/78963.
Jamasb, A. R., Li ́o, P. & Blundell, T. (2020), ‘Graphein-a python library for geometric deep learning and network analysis on protein structures’, bioRxiv, doi.org/10.1101/2020.07.15.204701.
Johansson, L. C., Stauch, B., Ishchenko, A. & Cherezov, V. (2017), ‘A bright future for serial femtosecond crystallography with xfels’, Trends in biochemical sciences 42(9), 749–762, doi.org/10.1016/j.tibs.2017.06.007.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Tunyasuvunakool, K., Ronneberger, O., Bates, R., Zidek, A., Bridgland, A. et al. (2020), ‘High accuracy protein structure prediction using deep learning’, Fourteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstract Book) 22, 24.
Kathuria, S.V., Chan, Y.H., Nobrega, R.P., Özen, A. and Matthews, C.R., 2016. Clusters of isoleucine, leucine, and valine side chains define cores of stability in high‐energy states of globular proteins: Sequence determinants of structure and stability. Protein Science, 25(3), pp.662-675, doi.org/10.1002/pro.2860.
Karplus, M. & McCammon, J. A. (1986), ‘The dynamics of proteins’, Scientific American 254(4), 42–51.
Lee, Y., Lazim, R., Macalino, S. J. Y. & Choi, S. (2019), ‘Importance of protein dynamics in the structure based drug discovery of class ag protein-coupled receptors (gpcrs)’, Current opinion in structural biology 55, 147–153, doi.org/10.1016/j.sbi.2019.03.015.
Loving, K.A., Lin, A. and Cheng, A.C., 2014. Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility. PLoS Comput Biol, 10(7), p.e1003741, doi.org/10.1371/journal.pcbi.1003741.
Monasse, B. & Boussinot, F. (2014), ‘Determination of forces from a potential in molecular dynamics’, arXiv preprint arXiv:1401.1181 .
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J. & Bronstein, M. M. (2017), Geometric deep learning on graphs and manifolds using mixture model cnns, in ‘Proceedings of the IEEE conference on computer vision and pattern recognition’, pp. 5115–5124.
Mortier, J., Rakers, C., Bermudez, M., Murgueitio, M. S., Riniker, S. & Wolber, G. (2015), ‘The impact of molecular dynamics on drug design: applications for the characterization of ligand–macromolecule complexes’, Drug Discovery Today 20(6), 686–702, .
Oldfield, C. J. & Dunker, A. K. (2014), ‘Intrinsically disordered proteins and intrinsically disordered protein regions’, Annual review of biochemistry 83, 553–584, doi.org/10.1146/annurev-biochem-072711-164947.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L. and Lerer, A., 2017. Automatic differentiation in pytorch.
Peng, J. W. (2009), ‘Communication breakdown: protein dynamics and drug design’, Structure 17(3), 319–320, doi.org/10.1016/j.str.2009.02.004.
Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J. & Battaglia, P. (2020), Learning to simulate complex physics with graph networks, in ‘International Conference on Machine Learning’, PMLR, pp. 8459–8468.
Satorras, V. G., Hoogeboom, E., Fuchs, F. B., Posner, I. & Welling, M. (2021), ‘E(n) equivariant normalizing flows for molecule generation in 3d’, arXiv preprint arXiv:2105.09016.
Satorras, V. G., Hoogeboom, E. & Welling, M. (2021), ‘E(n) equivariant graph neural networks’, arXiv preprint arXiv:2102.09844.
Schoenholz, S. S. & Cubuk, E. D. (2019), ‘Jax md: End-to-end differentiable, hardware accelerated, molecular dynamics in pure python’.
Schutt, K., Kessel, P., Gastegger, M., Nicoli, K., Tkatchenko, A. & Muller, K.-R. (2018), ‘Schnetpack: A deep learning toolbox for atomistic systems’, Journal of chemical theory and computation 15(1), 448– 455, /doi.org/10.1021/acs.jctc.8b00908.
Sekhar, A. & Kay, L. E. (2019), ‘An nmr view of protein dynamics in health and disease’, Annual review of biophysics 48, 297–319, doi.org/10.1146/annurev-biophys-052118-115647.
Selkoe, D. J. (2004), ‘Cell biology of protein misfolding: the examples of alzheimer’s and parkinson’s diseases’, Nature cell biology 6(11), 1054–1061, doi.org/10.1038/ncb1104-1054.
Spence, J. (2017), ‘Xfels for structure and dynamics in biology’, IUCrJ 4(4), 322–339, doi.org/10.1107/S2052252517005760.
Sverrisson, F., Feydy, J., Correia, B. & Bronstein, M. (2020), ‘Fast end-to-end learning on protein surfaces’, bioRxiv, doi.org/10.1101/2020.12.28.424589.
Wang, H., Zhang, L., Han, J. & Weinan, E. (2018), ‘Deepmd-kit: A deep learning package for many body potential energy representation and molecular dynamics’, Computer Physics Communications 228, 178–184, doi.org/10.1016/j.cpc.2018.03.016.
Wright, P. E. & Dyson, H. J. (2015), ‘Intrinsically disordered proteins in cellular signalling and regulation’, Nature reviews Molecular cell biology 16(1), 18–29, doi.org/10.1038/nrm3920.
Zhao, H. & Caflisch, A. (2015), ‘Molecular dynamics in drug design’, European journal of medicinal chemistry 91, 4–14, doi.org/10.1016/j.ejmech.2014.08.004.
Zhao, Q. (2013), ‘Nature of protein dynamics and thermodynamics’, Reviews in Theoretical Science 1(1), 83–101, doi.org/10.1166/rits.2013.1005.
Zhou, H. & Zhou, Y. (2002), ‘Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction’, Protein science 11(11), 2714– 2726, doi.org/10.1110/ps.0217002.