1
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Xia C, Lou B, Fu Z, Mohsen AW, Shen AL, Vockley J, Kim JJP. Molecular mechanism of interactions between ACAD9 and binding partners in mitochondrial respiratory complex I assembly. iScience 2021; 24:103153. [PMID: 34646991 PMCID: PMC8497999 DOI: 10.1016/j.isci.2021.103153] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/09/2021] [Accepted: 09/16/2021] [Indexed: 01/05/2023] Open
Abstract
The dual function protein ACAD9 catalyzes α,β-dehydrogenation of fatty acyl-CoA thioesters in fatty acid β-oxidation and is an essential chaperone for mitochondrial respiratory complex I (CI) assembly. ACAD9, ECSIT, and NDUFAF1 interact to form the core mitochondrial CI assembly complex. Current studies examine the molecular mechanism of ACAD9/ECSIT/NDUFAF1interactions. ACAD9 binds to the carboxy-terminal half and NDUFAF1 to the amino-terminal half of ECSIT. Binary complexes are unstable and aggregate easily, while the ACAD9/ECSIT/NDUFAF1 ternary complex is soluble and highly stable. Molecular modeling and small-angle X-ray scattering studies identified intra-complex interaction sites and binding sites for other assembly factors. Binding of ECSIT at the ETF binding site in the amino-terminal domain of ACAD9 is consistent with observed loss of FAD and enzymatic activity and demonstrates that the two functions of ACAD9 are mutually exclusive. Mapping of 42 known pathogenic mutations onto the homology-modeled ACAD9 structure provides structural insights into pathomechanisms of CI deficiency.
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Affiliation(s)
- Chuanwu Xia
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Baoying Lou
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Zhuji Fu
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Al-Walid Mohsen
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA 15224, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Anna L. Shen
- McArdle Laboratory for Cancer Research, Department of Oncology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jerry Vockley
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA 15224, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jung-Ja P. Kim
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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2
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Cournia Z, Allen BK, Beuming T, Pearlman DA, Radak BK, Sherman W. Rigorous Free Energy Simulations in Virtual Screening. J Chem Inf Model 2020; 60:4153-4169. [PMID: 32539386 DOI: 10.1021/acs.jcim.0c00116] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.
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Affiliation(s)
- Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
| | - Bryce K Allen
- Silicon Therapeutics, 300 A Street, Boston, Massachusetts 02210, United States
| | - Thijs Beuming
- Latham BioPharm Group, Cambridge, Massachusetts 02142, United States
| | - David A Pearlman
- QSimulate Incorporated, 625 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Brian K Radak
- Silicon Therapeutics, 300 A Street, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Silicon Therapeutics, 300 A Street, Boston, Massachusetts 02210, United States
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3
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Parks CD, Gaieb Z, Chiu M, Yang H, Shao C, Walters WP, Jansen JM, McGaughey G, Lewis RA, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK. D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 2020; 34:99-119. [PMID: 31974851 PMCID: PMC7261493 DOI: 10.1007/s10822-020-00289-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.
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Affiliation(s)
- Conor D Parks
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Zied Gaieb
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Michael Chiu
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Huanwang Yang
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Chenghua Shao
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Johanna M Jansen
- Novartis Institutes for BioMedical Research, Emeryville, CA, 94608, USA
| | | | - Richard A Lewis
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, 4002, Basel, Switzerland
| | | | | | | | - Stephen K Burley
- RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Rommie E Amaro
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Chemistry and Biochemistry, UC San Diego, La Jolla, CA, 92093-0340, USA.
| | - Michael K Gilson
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, 9500 Gilman Drive, MC0751, La Jolla, CA, 92093, USA.
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4
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Kotelnikov S, Alekseenko A, Liu C, Ignatov M, Padhorny D, Brini E, Lukin M, Coutsias E, Dill KA, Kozakov D. Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:179-189. [PMID: 31879831 DOI: 10.1007/s10822-019-00257-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/19/2019] [Indexed: 12/25/2022]
Abstract
We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.
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Affiliation(s)
- Sergei Kotelnikov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Innopolis University, Innopolis, Russia
| | - Andrey Alekseenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Mark Lukin
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Evangelos Coutsias
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA. .,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. .,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA.
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5
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Kadukova M, Chupin V, Grudinin S. Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:191-200. [PMID: 31784861 DOI: 10.1007/s10822-019-00263-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 11/22/2019] [Indexed: 12/15/2022]
Abstract
The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.
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Affiliation(s)
- Maria Kadukova
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000, Grenoble, France
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700
| | - Vladimir Chupin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000, Grenoble, France.
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Huang S, Cao Y. Correlation of cathepsin S with coronary stenosis degree, carotid thickness, blood pressure, glucose and lipid metabolism and vascular endothelial function in atherosclerosis. Exp Ther Med 2019; 19:61-66. [PMID: 31853273 PMCID: PMC6909596 DOI: 10.3892/etm.2019.8222] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 09/12/2019] [Indexed: 02/06/2023] Open
Abstract
Correlation of cathepsin S with coronary stenosis degree, carotid thickness, blood pressure, glucose and lipid metabolism, and vascular endothelial function in patients with atherosclerosis was investigated. Data from 120 patients with increased cathepsin S levels (increased group) and 120 subjects with normal cathepsin S levels (normal group) were retrospectively analyzed. The serum cathepsin S level and Gensini score were compared between the healthy subjects and patients with coronary atherosclerotic heart disease, and the correlation between serum cathepsin S level and Gensini score was analyzed. The carotid thickness, mean arterial pressure, and indexes related to glucose and lipid metabolism, as well as vascular endothelial function were compared between the two groups. The correlation of the serum cathepsin S level with carotid intima-media thickness (IMT), mean arterial pressure, fasting blood glucose, total cholesterol (TC) and nitric oxide (NO) was also investigated. Patients with multi-vessel coronary artery disease (CAD) had higher serum cathepsin S level than those with double-vessel and single-vessel disease, and higher level than that of healthy subjects. The Gensini score of patients with multi-vessel CAD was higher than that of patients with double-vessel and single-vessel disease, as well as that of healthy subjects. The serum cathepsin S level was positively correlated with the Gensini score. Patients with increased cathepsin S level had greater IMT, higher mean arterial pressure, fasting blood glucose, fasting insulin (FINS), triglyceride (TG), TC, and endothelin-1 (ET-1), however, lower NO level than those of healthy subjects. The serum cathepsin S level was positively correlated with IMT, mean arterial pressure, fasting blood glucose, and TC, however, it was negatively correlated with the NO level. In conclusion, as the serum cathepsin S level is elevated, the coronary stenosis is aggravated, the carotid thickness and blood pressure are increased, and the glucose and lipid metabolism, as well as vascular endothelial function are significantly abnormal.
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Affiliation(s)
- Shengyang Huang
- Cardiovascular Department, The Third Xiangya Hospital of Central South University, Changsha, Hunan 410000, P.R. China
| | - Yu Cao
- Cardiovascular Department, The Third Xiangya Hospital of Central South University, Changsha, Hunan 410000, P.R. China
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7
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Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S. J Comput Aided Mol Des 2019; 33:1095-1105. [PMID: 31729618 DOI: 10.1007/s10822-019-00247-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 11/02/2019] [Indexed: 12/12/2022]
Abstract
Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for both docking pose prediction and affinity ranking, and in D3R-GC4 for binding affinity ranking. The difficulties posed by CatS inhibitors in D3R mainly come from three aspects: large size, high flexibility and similar chemical structures. We have participated in GC4; our best submitted model, which employs a similarity-based alignment docking and Vina scoring protocol, yielded Kendall's τ of 0.23 for 459 binders in GC4. In our further explorations with machine learning, by curating a CatS specific training set, adopting a similarity-based constrained docking method as well as an arm-based fragmentation strategy which can describe large inhibitors in a locality-sensitive fashion, our best structure-based ranking protocol can achieve Kendall's τ of 0.52 for all binders in GC4. In this exploration process, we have demonstrated the importance of training data, docking approaches and fragmentation strategies in inhibitor-ranking protocol development with machine learning.
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8
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Raman EP. Template-Based Method for Conformation Generation and Scoring for Congeneric Series of Ligands. J Chem Inf Model 2019; 59:2690-2701. [PMID: 31045363 DOI: 10.1021/acs.jcim.9b00032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Physics-based prediction of protein-ligand binding affinities for a congeneric series of ligands in lead optimization requires their geometries as a first step. In this paper, we report a method that uses the 3D conformation of a lead compound in complex with a protein as a template to generate conformations of a series of related analog compounds. The method uses the Maximal Common Substructure (MCS) computed between lead and analog ligands to assign coordinates for the atoms shared between the ligands. For the differing atoms, a conformation generation procedure is implemented that results in a diversity of conformations. The generated conformations are sorted using a score based on the Molecular Mechanics and Generalized Born with Solvent Accessible Surface Area contribution (MM-GBSA) method. The accuracy of the generated conformations is tested retrospectively using a cross-validation approach applied to four data sets obtained from the Drug Design Data Resource (D3R) by measuring the RMSD of the top scored conformation with respect to the crystallographic pose. The scoring ability of the method is independently assessed using data for the same protein targets to test the rank ordering ability and separating active and inactive ligands. We tested the effect of protein flexibility during structural optimization and scoring approaches with and without strain energies. Retrospective validation on data sets comprising 4 targets shows that the method outperforms random selection for all targets and outperforms a molecular weight-based null model in 3 out of 4 targets in separating active and inactive compounds. Therefore, the presented method is expected to be of utility in lead optimization for rapidly screening analog ligands and generating initial conformations for use in more detailed physics-based binding affinity prediction methods.
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Affiliation(s)
- E Prabhu Raman
- BIOVIA, Dassault Systemes, 5005 Wateridge Vista Drive , San Diego , California 92121 , United States
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