1
|
Salveson PJ, Moyer AP, Said MY, Gӧkçe G, Li X, Kang A, Nguyen H, Bera AK, Levine PM, Bhardwaj G, Baker D. Expansive discovery of chemically diverse structured macrocyclic oligoamides. Science 2024; 384:420-428. [PMID: 38662830 DOI: 10.1126/science.adk1687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 03/22/2024] [Indexed: 05/03/2024]
Abstract
Small macrocycles with four or fewer amino acids are among the most potent natural products known, but there is currently no way to systematically generate such compounds. We describe a computational method for identifying ordered macrocycles composed of alpha, beta, gamma, and 17 other amino acid backbone chemistries, which we used to predict 14.9 million closed cycles composed of >42,000 monomer combinations. We chemically synthesized 18 macrocycles predicted to adopt single low-energy states and determined their x-ray or nuclear magnetic resonance structures; 15 of these were very close to the design models. We illustrate the therapeutic potential of these macrocycle designs by developing selective inhibitors of three protein targets of current interest. By opening up a vast space of readily synthesizable drug-like macrocycles, our results should considerably enhance structure-based drug design.
Collapse
Affiliation(s)
- Patrick J Salveson
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Adam P Moyer
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Meerit Y Said
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Gizem Gӧkçe
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Xinting Li
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Alex Kang
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Hannah Nguyen
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Asim K Bera
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Paul M Levine
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Gaurav Bhardwaj
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
| | - David Baker
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| |
Collapse
|
2
|
Isert C, Atz K, Riniker S, Schneider G. Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning. RSC Adv 2024; 14:4492-4502. [PMID: 38312732 PMCID: PMC10835705 DOI: 10.1039/d3ra08650j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 01/19/2024] [Indexed: 02/06/2024] Open
Abstract
Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.
Collapse
Affiliation(s)
- Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland +41 44 633 73 27
| | - Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland +41 44 633 73 27
| | - Sereina Riniker
- ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland +41 44 633 73 27
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland +41 44 633 73 27
| |
Collapse
|
3
|
Jain AN, Brueckner AC, Jorge C, Cleves AE, Khandelwal P, Cortes JC, Mueller L. Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design. J Comput Aided Mol Des 2023; 37:519-535. [PMID: 37535171 PMCID: PMC10505130 DOI: 10.1007/s10822-023-00524-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2023] [Indexed: 08/04/2023]
Abstract
Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.
Collapse
Affiliation(s)
- Ajay N Jain
- Research and Development, BioPharmics LLC, Sonoma County, CA, USA.
| | | | | | - Ann E Cleves
- Research and Development, BioPharmics LLC, Sonoma County, CA, USA
| | | | | | | |
Collapse
|