1
|
de Azevedo WF, Quiroga R, Villarreal MA, da Silveira NJF, Bitencourt-Ferreira G, da Silva AD, Veit-Acosta M, Oliveira PR, Tutone M, Biziukova N, Poroikov V, Tarasova O, Baud S. SAnDReS 2.0: Development of machine-learning models to explore the scoring function space. J Comput Chem 2024. [PMID: 38900052 DOI: 10.1002/jcc.27449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/04/2024] [Accepted: 06/02/2024] [Indexed: 06/21/2024]
Abstract
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
Collapse
Affiliation(s)
| | - Rodrigo Quiroga
- Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), CONICET-Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Marcos Ariel Villarreal
- Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), CONICET-Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | | | | | - Amauri Duarte da Silva
- Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
| | | | | | - Marco Tutone
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Palermo, Italy
| | | | | | | | - Stéphaine Baud
- Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Université de Reims Champagne-Ardenne, CNRS, MEDYC, Reims, France
| |
Collapse
|
2
|
Liu T, Huang S, Zhang Q, Xia Y, Zhang M, Sun B. Reconciling ASPP-p53 binding mode discrepancies through an ensemble binding framework that bridges crystallography and NMR data. PLoS Comput Biol 2024; 20:e1011519. [PMID: 38324587 PMCID: PMC10878502 DOI: 10.1371/journal.pcbi.1011519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/20/2024] [Accepted: 01/24/2024] [Indexed: 02/09/2024] Open
Abstract
ASPP2 and iASPP bind to p53 through their conserved ANK-SH3 domains to respectively promote and inhibit p53-dependent cell apoptosis. While crystallography has indicated that these two proteins employ distinct surfaces of their ANK-SH3 domains to bind to p53, solution NMR data has suggested similar surfaces. In this study, we employed multi-scale molecular dynamics (MD) simulations combined with free energy calculations to reconcile the discrepancy in the binding modes. We demonstrated that the binding mode based solely on a single crystal structure does not enable iASPP's RT loop to engage with p53's C-terminal linker-a verified interaction. Instead, an ensemble of simulated iASPP-p53 complexes facilitates this interaction. We showed that the ensemble-average inter-protein contacting residues and NMR-detected interfacial residues qualitatively overlap on ASPP proteins, and the ensemble-average binding free energies better match experimental KD values compared to single crystallgarphy-determined binding mode. For iASPP, the sampled ensemble complexes can be grouped into two classes, resembling the binding modes determined by crystallography and solution NMR. We thus propose that crystal packing shifts the equilibrium of binding modes towards the crystallography-determined one. Lastly, we showed that the ensemble binding complexes are sensitive to p53's intrinsically disordered regions (IDRs), attesting to experimental observations that these IDRs contribute to biological functions. Our results provide a dynamic and ensemble perspective for scrutinizing these important cancer-related protein-protein interactions (PPIs).
Collapse
Affiliation(s)
- Te Liu
- Research Center for Pharmacoinformatics, College of Pharmacy, Harbin Medical University, Harbin, China
| | - Sichao Huang
- Research Center for Pharmacoinformatics, College of Pharmacy, Harbin Medical University, Harbin, China
| | - Qian Zhang
- Research Center for Pharmacoinformatics, College of Pharmacy, Harbin Medical University, Harbin, China
| | - Yu Xia
- Research Center for Pharmacoinformatics, College of Pharmacy, Harbin Medical University, Harbin, China
| | - Manjie Zhang
- Research Center for Pharmacoinformatics, College of Pharmacy, Harbin Medical University, Harbin, China
| | - Bin Sun
- Research Center for Pharmacoinformatics, College of Pharmacy, Harbin Medical University, Harbin, China
| |
Collapse
|
3
|
Zuo K, Kranjc A, Capelli R, Rossetti G, Nechushtai R, Carloni P. Metadynamics simulations of ligands binding to protein surfaces: a novel tool for rational drug design. Phys Chem Chem Phys 2023; 25:13819-13824. [PMID: 37184538 DOI: 10.1039/d3cp01388j] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Structure-based drug design protocols may encounter difficulties to investigate poses when the biomolecular targets do not exhibit typical binding pockets. In this study, by providing two concrete examples from our labs, we suggest that the combination of metadynamics free energy methods (validated against affinity measurements), along with experimental structural information (by X-ray crystallography and NMR), can help to identify the poses of ligands on protein surfaces. The simulation workflow proposed here was implemented in a widely used code, namely GROMACS, and it could straightforwardly be applied to various drug-design campaigns targeting ligands' binding to protein surfaces.
Collapse
Affiliation(s)
- Ke Zuo
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
- The Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 91904, Israel
- Department of Physics, Università degli Studi di Ferrara, Ferrara 44121, Italy
| | - Agata Kranjc
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
| | - Riccardo Capelli
- Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, Milan 20133, Italy
| | - Giulia Rossetti
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Jülich Supercomputing Center (JSC), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
- Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen 52074, Germany
| | - Rachel Nechushtai
- The Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 91904, Israel
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
- JARA Institute: Molecular Neuroscience and Imaging, Institute of Neuroscience and Medicine INM-11, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| |
Collapse
|