Sonntag, Juli 31, 2022
StartBiochemistryVariations in ligand-induced protein dynamics extracted from an unsupervised deep studying method...

Variations in ligand-induced protein dynamics extracted from an unsupervised deep studying method correlate with protein–ligand binding affinities


  • Wang, L. et al. Correct and dependable prediction of relative ligand binding efficiency in potential drug discovery by the use of a contemporary free-energy calculation protocol and power subject. J. Am. Chem. Soc. 137, 2695–2703 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S. & Biggin, P. C. Correct calculation of absolutely the free vitality of binding for drug molecules. Chem. Sci. 7, 207–218 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Music, L. F., Lee, T.-S., Zhu, C., York, D. M. & Merz Jr, Ok. M. Utilizing amber18 for relative free vitality calculations. J. Chem. Inf. Mannequin. 59, 3128–3135 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • He, X. et al. Quick, correct, and dependable protocols for routine calculations of protein–ligand binding affinities in drug design initiatives utilizing amber gpu-ti with ff14sb/gaff. ACS Omega 5, 4611–4619 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Gapsys, V. et al. Massive scale relative protein ligand binding affinities utilizing non-equilibrium alchemy. Chem. Sci. 11, 1140–1152 (2020).

    CAS 
    Article 

    Google Scholar
     

  • Abel, R., Wang, L., Tougher, E. D., Berne, B. & Friesner, R. A. Advancing drug discovery by means of enhanced free vitality calculations. Acc. Chem. Res. 50, 1625–1632 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Zhang, L., Tan, J., Han, D. & Zhu, H. From machine studying to deep studying: progress in machine intelligence for rational drug discovery. Drug Discov At present 22, 1680–1685 (2017).

    PubMed 
    Article 

    Google Scholar
     

  • Shen, C. et al. From machine studying to deep studying: Advances in scoring features for protein–ligand docking. Wiley Interdisciplinary Critiques Computational Molecular Sci 10, e1429 (2020).

    CAS 
    Article 

    Google Scholar
     

  • Gomes, J., Ramsundar, B., Feinberg, E. N. & Pande, V. S. Atomic convolutional networks for predicting protein-ligand binding affinity. arXiv preprint arXiv:1703.10603 (2017).

  • Jiménez, J., Skalic, M., Martinez-Rosell, G. & De Fabritiis, G. Ok deep: protein–ligand absolute binding affinity prediction by way of 3d-convolutional neural networks. J. Chem. Inf. Mannequin. 58, 287–296 (2018).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • Cang, Z., Mu, L. & Wei, G.-W. Representability of algebraic topology for biomolecules in machine studying based mostly scoring and digital screening. PLoS Comp. Biol. 14, e1005929 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Stepniewska-Dziubinska, M. M., Zielenkiewicz, P. & Siedlecki, P. Improvement and analysis of a deep studying mannequin for protein–ligand binding affinity prediction. Bioinformatics 34, 3666–3674 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Öztürk, H., Özgür, A. & Ozkirimli, E. Deepdta: deep drug–goal binding affinity prediction. Bioinformatics 34, i821–i829 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Karimi, M., Wu, D., Wang, Z. & Shen, Y. Deepaffinity: interpretable deep studying of compound–protein affinity by means of unified recurrent and convolutional neural networks. Bioinformatics 35, 3329–3338 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Wang, R., Fang, X., Lu, Y. & Wang, S. The pdbbind database: Assortment of binding affinities for protein- ligand complexes with recognized three-dimensional buildings. J. Med. Chem. 47, 2977–2980 (2004).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Huang, N., Shoichet, B. Ok. & Irwin, J. J. Benchmarking units for molecular docking. J. Med. Chem. 49, 6789–6801 (2006).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Shi, Q., Chen, W., Huang, S., Wang, Y. & Xue, Z. Deep studying for mining protein knowledge. Briefings Bioinform. 22, 194–218 (2021).

    CAS 
    Article 

    Google Scholar
     

  • Fernandez-Leiro, R. & Scheres, S. H. Unravelling organic macromolecules with cryo-electron microscopy. Nature 537, 339–346 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Lewandowski, J. R., Halse, M. E., Blackledge, M. & Emsley, L. Direct commentary of hierarchical protein dynamics. Science 348, 578–581 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Kmiecik, S. et al. Coarse-grained protein fashions and their purposes. Chem. Rev. 116, 7898–7936 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Yang, J.-F., Wang, F., Chen, Y.-Z., Hao, G.-F. & Yang, G.-F. Larmd: integration of bioinformatic sources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor. Briefings Bioinform. 21, 2206–2218 (2020).

    CAS 
    Article 

    Google Scholar
     

  • Jin, Y. et al. Communication between the ligand-binding pocket and the activation function-2 area of androgen receptor revealed by molecular dynamics simulations. J. Chem Inform. Mannequin. 59, 842–857 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Yamamoto, E., Akimoto, T., Mitsutake, A. & Metzler, R. Common relation between instantaneous diffusivity and radius of gyration of proteins in aqueous resolution. Phys. Rev. Lett. 126, 128101 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Mitsutake, A., Iijima, H. & Takano, H. Leisure mode evaluation of a peptide system: Comparability with principal part evaluation. J. Chem. Phys. 135, 10B623 (2011).

    Article 
    CAS 

    Google Scholar
     

  • Stanley, N., Pardo, L. & De Fabritiis, G. The pathway of ligand entry from the membrane bilayer to a lipid g protein-coupled receptor. Sci. Rep. 6, 1–9 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Souza, P. C. et al. Protein–ligand binding with the coarse-grained martini mannequin. Nat. Commun. 11, 1–11 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Plattner, N., Doerr, S., De Fabritiis, G. & Noé, F. Full protein–protein affiliation kinetics in atomic element revealed by molecular dynamics simulations and markov modelling. Nat. Chem. 9, 1005–1011 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Amaral, M. et al. Protein conformational flexibility modulates kinetics and thermodynamics of drug binding. Nat. Commun. 8, 1–14 (2017).

    CAS 
    Article 

    Google Scholar
     

  • Noé, F., Tkatchenko, A., Müller, Ok.-R. & Clementi, C. Machine studying for molecular simulation. Ann. Rev. Phys. Chem. 71, 361–390 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Lemke, T. & Peter, C. Encodermap: dimensionality discount and era of molecule conformations. J. Chem. Concept Comp. 15, 1209–1215 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Endo, Ok., Yuhara, D., Tomobe, Ok. & Yasuoka, Ok. Detection of molecular habits that characterizes techniques utilizing a deep studying method. Nanoscale 11, 10064–10071 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Xie, T., France-Lanord, A., Wang, Y., Shao-Horn, Y. & Grossman, J. C. Graph dynamical networks for unsupervised studying of atomic scale dynamics in supplies. Nat. Commun. 10, 1–9 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Häse, F., Galván, I. F., Aspuru-Guzik, A., Lindh, R. & Vacher, M. How machine studying can help the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry. Chem. Sci. 10, 2298–2307 (2019).

    PubMed 
    Article 

    Google Scholar
     

  • Mardt, A., Pasquali, L., Wu, H. & Noé, F. Vampnets for deep studying of molecular kinetics. Nat. Commun. 9, 1–11 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Tsuchiya, Y., Taneishi, Ok. & Yonezawa, Y. Autoencoder-based detection of dynamic allostery triggered by ligand binding based mostly on molecular dynamics. J. Chem. Inform. Mannequin. 59, 4043–4051 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Lemke, T., Berg, A., Jain, A. & Peter, C. Encodermap (ii): Visualizing necessary molecular motions with improved era of protein conformations. J. Chem Inform. Mannequin. 59, 4550–4560 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Website positioning, M.-H., Park, J., Kim, E., Hohng, S. & Kim, H.-S. Protein conformational dynamics dictate the binding affinity for a ligand. Nat. Commun. 5, 1–7 (2014).


    Google Scholar
     

  • Cui, D. S., Lipchock, J. M., Brookner, D. & Loria, J. P. Uncovering the molecular interactions within the catalytic loop that modulate the conformational dynamics in protein tyrosine phosphatase 1b. J. Am. Chem. Soc. 141, 12634–12647 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Ferraro, M. et al. Machine studying of allosteric results: the evaluation of ligand-induced dynamics to foretell useful results in trap1. J. Phys. Chem. B 125, 101–114 (2020).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Riniker, S. Molecular dynamics fingerprints (mdfp): machine studying from md knowledge to foretell free-energy variations. J. Chem. Inform. Mannequin. 57, 726–741 (2017).

    CAS 
    Article 

    Google Scholar
     

  • Fujisawa, T. & Filippakopoulos, P. Capabilities of bromodomain-containing proteins and their roles in homeostasis and most cancers. Nat. Rev. Mol. Cell Biol. 18, 246–262 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Cochran, A. G., Conery, A. R. & Sims, R. J. Bromodomains: a brand new goal class for drug growth. Nat. Rev. Drug Discov. 18, 609–628 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Johnson, T. O., Ermolieff, J. & Jirousek, M. R. Protein tyrosine phosphatase 1b inhibitors for diabetes. Nat. Rev. Drug Discov. 1, 696–709 (2002).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Verma, M., Gupta, S. J., Chaudhary, A. & Garg, V. Ok. Protein tyrosine phosphatase 1b inhibitors as antidiabetic brokers–a short evaluation. Bioorganic Chem. 70, 267–283 (2017).

    CAS 
    Article 

    Google Scholar
     

  • Villani, C. Optimum transport: previous and new, vol. 338 (Springer, 2009).

  • Arjovsky, M., Chintala, S. & Bottou, L. Wasserstein generative adversarial networks. In Worldwide convention on machine studying, 214–223 (PMLR,2017).

  • Urick, A. Ok., Calle, L. P., Espinosa, J. F., Hu, H. & Pomerantz, W. C. Protein-observed fluorine nmr is a complementary ligand discovery methodology to 1h cpmg ligand-observed nmr. ACS Chem. Biol. 11, 3154–3164 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Ran, T. et al. Perception into the important thing interactions of bromodomain inhibitors based mostly on molecular docking, interplay fingerprinting, molecular dynamics and binding free vitality calculation. Mol. Biosyst. 11, 1295–1304 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Wang, L., Wang, Y., Solar, H., Zhao, J. & Wang, Q. Theoretical perception into molecular mechanisms of inhibitor bindings to bromodomain-containing protein 4 utilizing molecular dynamics simulations and calculations of binding free energies. Chem. Phys. Lett. 736, 136785 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Picaud, S. et al. Rvx-208, an inhibitor of wager transcriptional regulators with selectivity for the second bromodomain. Proc. Natl. Acad. Sci. 110, 19754–19759 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Liu, M., Wang, L., Solar, X. & Zhao, X. Investigating the affect of asp181 level mutations on interactions between ptp1b and phosphotyrosine substrate. Sci. Rep. 4, 1–8 (2014).


    Google Scholar
     

  • Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparability of easy potential features for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).

    CAS 
    Article 

    Google Scholar
     

  • Abraham, M. J. et al. Gromacs: Excessive efficiency molecular simulations by means of multi-level parallelism from laptops to supercomputers. SoftwareX 1, 19–25 (2015).

    Article 

    Google Scholar
     

  • Hess, B., Bekker, H., Berendsen, H. J. & Fraaije, J. G. Lincs: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).

    CAS 
    Article 

    Google Scholar
     

  • Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling by means of velocity rescaling. J. Chem. Phys. 126, 014101 (2007).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • Gros, P., van Gunsteren, W. F. & Hol, W. Inclusion of thermal movement in crystallographic buildings by restrained molecular dynamics. Science 249, 1149–1152 (1990).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: A brand new molecular dynamics methodology. J. Appl. Phys. 52, 7182–7190 (1981).

    CAS 
    Article 

    Google Scholar
     

  • Inc, C. C. G. Molecular working setting (moe) (2016).

  • Maier, J. A. et al. ff14sb: bettering the accuracy of protein facet chain and spine parameters from ff99sb. J. Chem. Concept Comput. 11, 3696–3713 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Wang, J., Wang, W., Kollman, P. A. & Case, D. A. Automated atom sort and bond sort notion in molecular mechanical calculations. J. Mol. Graphics Mannequin. 25, 247–260 (2006).

    Article 
    CAS 

    Google Scholar
     

  • Paszke, A. et al. Pytorch: An crucial type, high-performance deep studying library. In Wallach, H.et al. (eds.) Advances in Neural Info Processing Techniques 32, 8024–8035 (Curran Associates, Inc., 2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.

  • Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. & Courville, A. C. Improved coaching of wasserstein gans. In Advances in neural data processing techniques, 5767–5777 (2017).

  • Kingma, D. P. & Ba, J. Adam: A way for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

  • Whittier, S. Ok., Hengge, A. C. & Loria, J. P. Conformational motions regulate phosphoryl switch in associated protein tyrosine phosphatases. Science 341, 899–903 (2013).

  • Salmeen, A. et al. Redox regulation of protein tyrosine phosphatase 1b entails a sulphenyl-amide intermediate. Nature 423, 769–773 (2003).

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