The quantitative characterization of residue contributions to protein-protein binding across extensive flexible interfaces poses a significant challenge for biophysical computations. It is attributable to the inherent imperfections in the experimental structures themselves, as well as to the lack of reliable computational tools for the evaluation of all types of noncovalent interactions. This study leverages recent advancements in semiempirical quantum-mechanical and implicit solvent approaches embodied in the PM6-D3H4S/COSMO2 method for the development of a hierarchical computational protocols encompassing molecular dynamics, fragmentation, and virtual glycine scan techniques for the investigation of flexible protein-protein interactions. As a model, the binding of insulin to its receptor is selected, a complex and dynamic process that has been extensively studied experimentally. The interaction energies calculated at the PM6-D3H4S/COSMO2 level in ten molecular dynamics snapshots did not correlate with molecular mechanics/generalized Born interaction energies because only the former method is able to describe nonadditive effects. This became evident by the examination of the energetics in small-model dimers featuring all the present types of noncovalent interactions with respect to DFT-D3 calculations. The virtual glycine scan has identified 15 hotspot residues on insulin and 15 on the insulin receptor, and their contributions have been quantified using PM6-D3H4S/COSMO2. The accuracy and credibility of the approach are further supported by the fact that all the insulin hotspots have previously been detected by biochemical and structural methods. The modular nature of the protocol has enabled the formulation of several variants, each tailored to specific accuracy and efficiency requirements. The developed computational strategy is firmly rooted in general biophysical chemistry and is thus offered as a general tool for the quantification of interactions across relevant flexible protein-protein interfaces.

Department of Physical Chemistry University of Chemistry and Technology Technická 5 166 28 Prague 6 Czech Republic

Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Flemingovo náměstí 542 2 166 10 Prague 6 Czech Republic

Zobrazit více v PubMed

Siebenmorgen T., Zacharias M.. Computational Prediction of Protein–Protein Binding Affinities. WIREs Comput. Mol. Sci. 2020;10:e1448. doi: 10.1002/wcms.1448. PubMed DOI

de Ruiter A., Oostenbrink C.. Advances in the calculation of binding free energies. Curr. Opin. Struct. Biol. 2020;61:207–212. doi: 10.1016/j.sbi.2020.01.016. PubMed DOI

Decherchi S., Cavalli A.. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem. Rev. 2020;120:12788. doi: 10.1021/acs.chemrev.0c00534. PubMed DOI PMC

Chen F., Liu H., Sun H., Pan P., Li Y., Li D., Hou T.. Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking. Phys. Chem. Chem. Phys. 2016;18:22129. doi: 10.1039/C6CP03670H. PubMed DOI

Clark A. J., Negron C., Hauser K., Sun M., Wang L., Abel R., Friesner R. A.. Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein–Protein Interfaces. J. Mol. Biol. 2019;431:1481. doi: 10.1016/j.jmb.2019.02.003. PubMed DOI PMC

Gumbart J. C., Roux B., Chipot C.. Standard Binding Free Energies from Computer Simulations: What Is the Best Strategy? J. Chem. Theory Comput. 2013;9:794–802. doi: 10.1021/ct3008099. PubMed DOI PMC

Christensen A. S., Kubař T., Cui Q., Elstner M.. Semiempirical Quantum Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical Applications. Chem. Rev. 2016;116:5301. doi: 10.1021/acs.chemrev.5b00584. PubMed DOI PMC

Bryce, R. A. What Next for Quantum Mechanics in Structure-Based Drug Discovery? In Quantum Mechanics in Drug Discovery; Heifetz, A. , Ed.; 2020; p 339. PubMed

Ginex T., Vásquez J., Estarellas C., Luque F. J.. Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design. Curr. Opin. Struct. Biol. 2024;87:102870. doi: 10.1016/j.sbi.2024.102870. PubMed DOI

Manathunga M., Götz A. W., Merz K. M. Jr. Computer-aided drug design, quantum-mechanical methods for biological problems. Curr. Opin. Struct. Biol. 2022;75:102417. doi: 10.1016/j.sbi.2022.102417. PubMed DOI

Řezáč J., Hobza P.. Benchmark Calculations of Interaction Energies in Noncovalent Complexes and Their Applications. Chem. Rev. 2016;116:5038. doi: 10.1021/acs.chemrev.5b00526. PubMed DOI

Řezáč J., Hobza P.. Advanced Corrections of H-bonding and Dispersion for Semiempirical Quantum Mechanical Methods. J. Chem. Theory Comput. 2012;8:141. doi: 10.1021/ct200751e. PubMed DOI

Pecina A., Fanfrlík J., Lepšík M., Řezáč J.. SQM2.20: Semiempirical Quantum-Mechanical Scoring Function Yields DFT-Quality Protein-Ligand Binding Affinity Predictions in Minutes. Nat. Commun. 2024;15:1127. doi: 10.1038/s41467-024-45431-8. PubMed DOI PMC

Kříž K., Nováček M., Řezáč J.. Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts. J. Chem. Theory Comput. 2021;17:1548. doi: 10.1021/acs.jctc.0c01341. PubMed DOI

Kříž K., Fanfrlík J., Lepšík M.. Chalcogen Bonding in Protein-Ligand Complexes: PDB Survey and Quantum Mechanical Calculations. ChemPhysChem. 2018;19:2540. doi: 10.1002/cphc.201800409. PubMed DOI

Ryde U., Söderhjelm P.. Ligand-Binding Affinity Estimates Supported by Quantum-Mechanical Methods. Chem. Rev. 2016;116:5520. doi: 10.1021/acs.chemrev.5b00630. PubMed DOI

Allen A. E. A., Lubbers N., Matin S., Smith J., Messerly R., Tretiak S., Barros K.. Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning. npj Comput. Mater. 2024;10:154. doi: 10.1038/s41524-024-01339-x. DOI

Hofer T. S., de Visser S. P.. Editorial: Quantum Mechanical/Molecular Mechanical Approaches for the Investigation of Chemical Systems – Recent Developments and Advanced Applications. Front. Chem. 2018;6:357. doi: 10.3389/fchem.2018.00357. PubMed DOI PMC

Cui Q., Pal T., Xie L.. Biomolecular QM/MM Simulations: What Are Some of the “Burning Issues”? J. Phys. Chem. B. 2021;125(3):689. doi: 10.1021/acs.jpcb.0c09898. PubMed DOI PMC

Molani F., Cho A. E.. Accurate protein-ligand binding free energy estimation using QM/MM on multi-conformers predicted from classical mining minima. Commun. Chem. 2024;7:247. doi: 10.1038/s42004-024-01328-7. PubMed DOI PMC

Kříž K., Řezáč J.. Reparametrization of the COSMO Solvent Model for Semiempirical Methods PM6 and PM7. J. Chem. Inf. Model. 2019;59:229. doi: 10.1021/acs.jcim.8b00681. PubMed DOI

Kříž K., Řezáč J.. Benchmarking of Semiempirical Quantum-Mechanical Methods on Systems Relevant to Computer-Aided Drug Design. J. Chem. Inf. Model. 2020;60:1453. doi: 10.1021/acs.jcim.9b01171. PubMed DOI

Nottoli M., Mikhalev A., Stamm B., Lipparini F.. Coarse-Graining ddCOSMO through an Interface between Tinker and the ddX Library. J. Phys. Chem. B. 2022;126(43):8827. doi: 10.1021/acs.jpcb.2c04579. PubMed DOI PMC

Li J., Park J., Mayer J. P., Webb K. J., Uchikawa E., Wu J., Li S., Zhang X., Stowell M. H. B., Choi E., Bai X.-c.. Synergistic Activation of the Insulin Receptor via Two Distinct Sites. Nat. Struct. Mol. Biol. 2022;29:357. doi: 10.1038/s41594-022-00750-6. PubMed DOI PMC

Ullrich A., Bell J. R., Chen E. Y., Herrera R., Petruzzelli L. M., Dull T. J., Gray A., Coussens L., Liao Y.-C., Tsubokawa M., Mason A., Seeburg P. H., Grunfeld C., Rosen O. M., Ramachadran J.. Human Insulin Receptor and Its Relationship to the Tyrosine Kinase Family of Oncogenes. Nature. 1985;313:756. doi: 10.1038/313756a0. PubMed DOI

Choi E., Bai X.-c.. The Activation Mechanism of the Insulin Receptor: A Structural Perspective. Annu. Rev. Biochem. 2023;92:247. doi: 10.1146/annurev-biochem-052521-033250. PubMed DOI PMC

Scapin G., Dandey V. P., Zhang Z., Prosise W., Hruza A., Kelly T., Mayhood T., Strickland C., Potter C. S., Carragher B.. Structure of the Insulin Receptor–Insulin Complex by Single-Particle Cryo-EM Analysis. Nature. 2018;556:122. doi: 10.1038/nature26153. PubMed DOI PMC

Weis F., Menting J. G., Margetts M. B., Chan S. J., Xu Y., Tennagels N., Wohlfart P., Langer T., Müller C. W., Dreyer M. K., Lawrence M. C.. The Signaling Conformation of the Insulin Receptor Ectodomain. Nat. Commun. 2018;9:4420. doi: 10.1038/s41467-018-06826-6. PubMed DOI PMC

Gutmann T., Schäfer I., Poojari C., Brankatschk B., Vattulainen I., Strauss M., Coskun Ü.. Cryo-EM Structure of the Complete and Ligand-Saturated Insulin Receptor Ectodomain. J. Cell Biol. 2020;219:e201907210. doi: 10.1083/jcb.201907210. PubMed DOI PMC

Uchikawa E., Choi E., Shang G., Yu H., Bai X.-c.. Activation Mechanism of the Insulin Receptor Revealed by Cryo-EM Structure of the Fully Liganded Receptor–Ligand Complex. eLife. 2019;8:e48630. doi: 10.7554/eLife.48630. PubMed DOI PMC

The PyMOL Molecular Graphics System, Version 2.0; Schrödinger, LLC.

De Meyts P.. Insulin/receptor binding: the last piece of the puzzle? What recent progress on the structure of the insulin/receptor complex tells us (or not) about negative cooperativity and activation. Bioessays. 2015;37:389. doi: 10.1002/bies.201400190. PubMed DOI

Fiser A., Do R. K. G., Sali A.. Modeling of loops in protein structures. Protein Science. 2000;9:1753–1773. doi: 10.1110/ps.9.9.1753. PubMed DOI PMC

Case, D. A. ; Betz, R. M. ; Cerutti, D. S. ; Cheatham, T. E., III ; Darden, T. A. ; Duke, R. E. ; Giese, T. J. ; Gohlke, H. ; Goetz, A. W. ; Homeyer, N. ; Izadi, S. ; Janowski, P. ; Kaus, J. ; Kovalenko, A. ; Lee, T. S. ; LeGrand, S. ; Li, P. ; Lin, C. ; Luchko, T. ; Luo, R. ; Madej, B. ; Mermelstein, D. ; Merz, K. M. ; Monard, G. ; Nguyen, H. ; Nguyen, H. T. ; Omelyan, I. ; Onufriev, A. ; Roe, D. R. ; Roitberg, A. ; Sagui, C. ; Simmerling, C. L. ; Botello-Smith, W. M. ; Swails, J. ; Walker, R. C. ; Wang, J. ; Wolf, R. M. ; Wu, X. ; Xiao, L. ; Kollman, P. A. . AMBER 2016; University of California, San Francisco, 2016.

Van der Spoel D., Lindahl E., Hess B., Groenhof G., Mark A. E., Berendsen H. J.. GROMACS: Fast, Flexible, and Free. J. Comput. Chem. 2005;26:1701. doi: 10.1002/jcc.20291. PubMed DOI

Maier J. A., Martinez C., Kasavajhala K., Wickstrom L., Hauser K. E., Simmerling C.. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015;11:3696. doi: 10.1021/acs.jctc.5b00255. PubMed DOI PMC

Mongan J., Simmerling C., McCammon J. A., Case D. A., Onufriev A.. Generalized Born Model with a Simple, Robust Molecular Volume Correction. J. Chem. Theory Comput. 2007;3:156. doi: 10.1021/ct600085e. PubMed DOI PMC

Izadi S., Onufriev A. V.. Accuracy Limit of Rigid 3-Point Water Models. J. Chem. Phys. 2016;145:074501. doi: 10.1063/1.4960175. PubMed DOI PMC

Yurenko, Y. P. ; Lepsik, M. . Mendeley Data, V 2; 2025, 10.17632/6twk7snmyh.2. DOI

Bussi G., Donadio D., Parrinello M.. Canonical Sampling Through Velocity Rescaling. J. Chem. Phys. 2007;126:014101. doi: 10.1063/1.2408420. PubMed DOI

Berendsen H. J. C., Postma J. P. M., van Gunsteren W. F., DiNola A., Haak J. R.. Molecular Dynamics with Coupling to an External Bath. J. Chem. Phys. 1984;81:3684. doi: 10.1063/1.448118. DOI

Stewart, J. J. P , MOPAC2016; Stewart Computational Chemistry: Colorado Springs, CO.

Řezáč, J. Cuby – ruby framework for computational chemistry, version 4, http://cuby4.molecular.cz. PubMed

Řezáč J.. Cuby: An integrative framework for computational chemistry. J. Comput. Chem. 2016;37:1230. doi: 10.1002/jcc.24312. PubMed DOI

Duan Y., Wu C., Chowdhury S., Lee M. C., Xiong G., Zhang W., Yang R., Cieplak P., Luo R., Lee T., Caldwell J., Wang J., Kollman P.. A Point-Charge Force Field for Molecular Mechanics Simulations of Proteins Based on Condensed-Phase Quantum Mechanical Calculations. J. Comput. Chem. 2003;24:1999. doi: 10.1002/jcc.10349. PubMed DOI

Onufriev A. V., Bashford D., Case D. A.. Modification of the Generalized Born Model Suitable for Macromolecules. J. Phys. Chem. B. 2000;104:3712. doi: 10.1021/jp994072s. DOI

Klamt A., Schüümann G.. COSMO: a New Approach to Dielectric Screening in Solvents with Explicit Expressions for the Screening Energy and its Gradient. J. Chem. Soc., Perkin Trans. 1993;2:799. doi: 10.1039/P29930000799. DOI

Becke A. D.. Density-Functional Thermochemistry. III. The Role of Exact Exchange. J. Chem. Phys. 1993;98:5648. doi: 10.1063/1.464913. DOI

Lee C., Yang W., Parr R. G.. Development of the Colle-Salvetti Correlation-Energy Formula into a Functional of the Electron Density. Phys. Rev. B. 1988;37:785. doi: 10.1103/PhysRevB.37.785. PubMed DOI

Grimme S., Antony J., Ehrlich S., Krieg S.. A Consistent and Accurate ab initio Parametrization of Density Functional Dispersion Correction (DFT-D) for the 94 Elements H-Pu. J. Chem. Phys. 2010;132:154104. doi: 10.1063/1.3382344. PubMed DOI

Weigend F., Ahlrichs R.. Balanced Basis Sets of Split Valence, Triple Zeta Valence and Quadruple Zeta Valence Quality for H to Rn: Design and Assessment of Accuracy. Phys. Chem. Chem. Phys. 2005;7:3297. doi: 10.1039/b508541a. PubMed DOI

TURBOMOLE V7.2 (2017), a development of University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, 1989–2007, TURBOMOLE GmbH, since 2007; available at http://www.turbomole.com.

Honda D. E., Lopes Martins J. B., Ventura M. M., Eyrilmez S. M., Lepšík M., Hobza P., Pecina A., de Freitas S. M.. Interface Interactions of the Bowman-Birk Inhibitor BTCI in a Ternary Complex with Trypsin and Chymotrypsin Evaluated by Semiempirical Quantum Mechanical Calculations. Eur. J. Org. Chem. 2018;2018:5203. doi: 10.1002/ejoc.201800754. DOI

Lawrence M. C.. Understanding Insulin and its Receptor from their Three-Dimensional Structures. Mol. Metab. 2021;52:101255. doi: 10.1016/j.molmet.2021.101255. PubMed DOI PMC

Kertisová A., Žáková L., Macháčková K., Marek A., Šácha P., Pompach P., Jiráček J., Selicharová I.. Insulin Receptor Arg717 and IGF-1 Receptor Arg704 Play a Key Role in Ligand Binding and in Receptor Activation. Open Biol. 2023;13:230142. doi: 10.1098/rsob.230142. PubMed DOI PMC

Nielsen J., Brandt J., Boesen T., Hummelshøj T., Slaaby R., Schuckelbier G., Nissen P.. Structural Investigations of Full-Length Insulin Receptor Dynamics and Signalling. J. Mol. Biol. 2022;434:167458. doi: 10.1016/j.jmb.2022.167458. PubMed DOI

Casañal A., Shakeel S., Passmore L. A.. Interpretation of Medium Resolution cryo-EM Maps of Multi-Protein Complexes. Curr. Opin. Struct. Biol. 2019;58:166. doi: 10.1016/j.sbi.2019.06.009. PubMed DOI PMC

Neijenhuis T., van Keulen S. C., Bonvin A. M. J. J.. Interface Refinement of Low- to Medium-Resolution Cryo-EM Complexes using HADDOCK2.4. Structure. 2022;30:476. doi: 10.1016/j.str.2022.02.001. PubMed DOI

Lindahl E., Friedman R.. Exploring the Impact of Protein Chain Selection in Binding Energy Calculations with DFT. ChemPhysChem. 2024;25:e202400119. doi: 10.1002/cphc.202400119. PubMed DOI PMC

Iwaoka M., Isozumi N.. Hypervalent Nonbonded Interactions of a Divalent Sulfur Atom. Implications in Protein Architectures and the Functions. Molecules. 2012;17:7266. doi: 10.3390/molecules17067266. PubMed DOI PMC

Bauzá A., Mooibroek T. D., Frontera A.. The bright future of unconventional σ/π-hole interactions. ChemPhysChem. 2015;16:2496. doi: 10.1002/cphc.201500314. PubMed DOI

Politzer P., Murray J. S., Clark T., Resnati G.. The σ-hole revisited. Phys. Chem. Chem. Phys. 2017;19:32166. doi: 10.1039/C7CP06793C. PubMed DOI

Yan X. C., Robertson M. J., Tirado-Rives J., Jorgensen W. L.. Improved Description of Sulfur Charge Anisotropy in OPLS Force Fields: Model Development and Parameterization. J. Phys. Chem. B. 2017;121:6626. doi: 10.1021/acs.jpcb.7b04233. PubMed DOI PMC

Kolář M. H., Hobza P.. Computer Modeling of Halogen Bonds and Other σ-Hole Interactions. Chem. Rev. 2016;116:5155. doi: 10.1021/acs.chemrev.5b00560. PubMed DOI

Chen F., Liu H., Sun H., Pan P., Li Y., Li D., Hou T.. Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking. Phys. Chem. Chem. Phys. 2016;18(32):22129. doi: 10.1039/C6CP03670H. PubMed DOI

Sheng Y.-j., Yin Y.-w., Ma Y.-q., Ding H.-m.. Improving the performance of MM/PBSA in protein–protein interactions via the screening electrostatic energy. J. Chem. Inf. Model. 2021;61(5):2454. doi: 10.1021/acs.jcim.1c00410. PubMed DOI

Pfeiffenberger E., Bates P. A.. Refinement of protein-protein complexes in contact map space with metadynamics simulations. Proteins. 2019;87:12. doi: 10.1002/prot.25612. PubMed DOI PMC

Rahman S., Wineman-Fisher V., Al-Hamdani Y., Tkatchenko A., Varma S.. Predictive QM/MM modeling of modulations in protein–protein binding by lysine methylation. J. Mol. Biol. 2021;433:166745. doi: 10.1016/j.jmb.2020.166745. PubMed DOI PMC

Tang T., Zhang X., Liu Y., Peng H., Zheng B., Yin Y., Zeng X.. Machine learning on protein–protein interaction prediction: models, challenges and trends. Brief. Bioinform. 2023;24:bbad076. doi: 10.1093/bib/bbad076. PubMed DOI

Casadio R., Martelli P. L., Savojardo C.. Machine learning solutions for predicting protein–protein interactions. WIREs Comput. Mol. Sci. 2022;12:e1618. doi: 10.1002/wcms.1618. DOI

Xiong X., Blakely A., Kim J. H., Menting J. G., Schäfer I. B., Schubert H. L., Agawal R.. et al. Symmetric and asymmetric receptor conformation continuum induced by a new insulin. Nature Chem. Biol. 2022;18:511. doi: 10.1038/s41589-022-00981-0. PubMed DOI PMC

Lenz V., Gattner H. G., Sievert D., Wollmer A., Engels M., Hoker M.. Semisynthetic des-(B27-B30)-insulins with modified B26-tyrosine. Biol. Chem. 1991;372:495. doi: 10.1515/bchm3.1991.372.2.495. PubMed DOI

Žáková L., Barth T., Jiráček J., Barthová J., Zorad Š.. Shortened insulin analogues: marked changes in biological activity resulting from replacement of TyrB26 and N-methylation of peptide bonds in the C-terminus of the B-chain. Biochemistry. 2004;43:2323. doi: 10.1021/bi036001w. PubMed DOI

Žáková L., Kazdová L., Hančlová I., Protivínská E., Šanda M., Buděšínský M., Jiráček J.. Insulin analogues with modifications at position B26. Divergence of binding affinity and biological activity. Biochemistry. 2008;47:5858. doi: 10.1021/bi702086w. PubMed DOI