1
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Sequeiros-Borja C, Surpeta B, Thirunavukarasu AS, Dongmo Foumthuim CJ, Marchlewski I, Brezovsky J. Water will Find Its Way: Transport through Narrow Tunnels in Hydrolases. J Chem Inf Model 2024. [PMID: 38669675 DOI: 10.1021/acs.jcim.4c00094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
An aqueous environment is vital for life as we know it, and water is essential for nearly all biochemical processes at the molecular level. Proteins utilize water molecules in various ways. Consequently, proteins must transport water molecules across their internal network of tunnels to reach the desired action sites, either within them or by functioning as molecular pipes to control cellular osmotic pressure. Despite water playing a crucial role in enzymatic activity and stability, its transport has been largely overlooked, with studies primarily focusing on water transport across membrane proteins. The transport of molecules through a protein's tunnel network is challenging to study experimentally, making molecular dynamics simulations the most popular approach for investigating such events. In this study, we focused on the transport of water molecules across three different α/β-hydrolases: haloalkane dehalogenase, epoxide hydrolase, and lipase. Using a 5 μs adaptive simulation per system, we observed that only a few tunnels were responsible for the majority of water transport in dehalogenase, in contrast to a higher diversity of tunnels in other enzymes. Interestingly, water molecules could traverse narrow tunnels with subangstrom bottlenecks, which is surprising given the commonly accepted water molecule radius of 1.4 Å. Our analysis of the transport events in such narrow tunnels revealed a markedly increased number of hydrogen bonds formed between the water molecules and protein, likely compensating for the steric penalty of the process. Overall, these commonly disregarded narrow tunnels accounted for ∼20% of the total water transport observed, emphasizing the need to surpass the standard geometrical limits on the functional tunnels to properly account for the relevant transport processes. Finally, we demonstrated how the obtained insights could be applied to explain the differences in a mutant of the human soluble epoxide hydrolase associated with a higher incidence of ischemic stroke.
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Affiliation(s)
- Carlos Sequeiros-Borja
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań 61-614, Poland
| | - Bartlomiej Surpeta
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań 61-614, Poland
| | - Aravind Selvaram Thirunavukarasu
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań 61-614, Poland
| | | | - Igor Marchlewski
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań 61-614, Poland
| | - Jan Brezovsky
- International Institute of Molecular and Cell Biology, Warsaw 02-109, Poland
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań 61-614, Poland
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2
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Durairaj J, Follonier OM, Leuzinger K, Alexander LT, Wilhelm M, Pereira J, Hillenbrand CA, Weissbach FH, Schwede T, Hirsch HH. Structural implications of BK polyomavirus sequence variations in the major viral capsid protein Vp1 and large T-antigen: a computational study. mSphere 2024; 9:e0079923. [PMID: 38501831 PMCID: PMC11036806 DOI: 10.1128/msphere.00799-23] [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: 12/20/2023] [Accepted: 02/21/2024] [Indexed: 03/20/2024] Open
Abstract
BK polyomavirus (BKPyV) is a double-stranded DNA virus causing nephropathy, hemorrhagic cystitis, and urothelial cancer in transplant patients. The BKPyV-encoded capsid protein Vp1 and large T-antigen (LTag) are key targets of neutralizing antibodies and cytotoxic T-cells, respectively. Our single-center data suggested that variability in Vp1 and LTag may contribute to failing BKPyV-specific immune control and impact vaccine design. We, therefore, analyzed all available entries in GenBank (1516 VP1; 742 LTAG) and explored potential structural effects using computational approaches. BKPyV-genotype (gt)1 was found in 71.18% of entries, followed by BKPyV-gt4 (19.26%), BKPyV-gt2 (8.11%), and BKPyV-gt3 (1.45%), but rates differed according to country and specimen type. Vp1-mutations matched a serotype different than the assigned one or were serotype-independent in 43%, 18% affected more than one amino acid. Notable Vp1-mutations altered antibody-binding domains, interactions with sialic acid receptors, or were predicted to change conformation. LTag-sequences were more conserved, with only 16 mutations detectable in more than one entry and without significant effects on LTag-structure or interaction domains. However, LTag changes were predicted to affect HLA-class I presentation of immunodominant 9mers to cytotoxic T-cells. These global data strengthen single center observations and specifically our earlier findings revealing mutant 9mer epitopes conferring immune escape from HLA-I cytotoxic T cells. We conclude that variability of BKPyV-Vp1 and LTag may have important implications for diagnostic assays assessing BKPyV-specific immune control and for vaccine design. IMPORTANCE Type and rate of amino acid variations in BKPyV may provide important insights into BKPyV diversity in human populations and an important step toward defining determinants of BKPyV-specific immunity needed to protect vulnerable patients from BKPyV diseases. Our analysis of BKPyV sequences obtained from human specimens reveals an unexpectedly high genetic variability for this double-stranded DNA virus that strongly relies on host cell DNA replication machinery with its proof reading and error correction mechanisms. BKPyV variability and immune escape should be taken into account when designing further approaches to antivirals, monoclonal antibodies, and vaccines for patients at risk of BKPyV diseases.
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Affiliation(s)
- Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Océane M. Follonier
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- Transplantation & Clinical Virology, Department of Biomedicine, Medical Faculty, University of Basel, Basel, Switzerland
| | - Karoline Leuzinger
- Transplantation & Clinical Virology, Department of Biomedicine, Medical Faculty, University of Basel, Basel, Switzerland
- Clinical Virology, Laboratory Medicine, Department Theragnostic, University Hospital Basel, Basel, Switzerland
| | - Leila T. Alexander
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Maud Wilhelm
- Transplantation & Clinical Virology, Department of Biomedicine, Medical Faculty, University of Basel, Basel, Switzerland
| | - Joana Pereira
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Caroline A. Hillenbrand
- Transplantation & Clinical Virology, Department of Biomedicine, Medical Faculty, University of Basel, Basel, Switzerland
| | - Fabian H. Weissbach
- Transplantation & Clinical Virology, Department of Biomedicine, Medical Faculty, University of Basel, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Hans H. Hirsch
- Transplantation & Clinical Virology, Department of Biomedicine, Medical Faculty, University of Basel, Basel, Switzerland
- Infectious Diseases & Hospital Epidemiology, Department Acute Medicine, University Hospital Basel, Basel, Switzerland
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3
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Edholm F, Nandy A, Reinhardt CR, Kastner DW, Kulik HJ. Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify. J Comput Chem 2024; 45:352-361. [PMID: 37873926 DOI: 10.1002/jcc.27242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/27/2023] [Accepted: 10/03/2023] [Indexed: 10/25/2023]
Abstract
Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.
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Affiliation(s)
- Freya Edholm
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Clorice R Reinhardt
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - David W Kastner
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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4
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Flachsenberg F, Ehrt C, Gutermuth T, Rarey M. Redocking the PDB. J Chem Inf Model 2024; 64:219-237. [PMID: 38108627 DOI: 10.1021/acs.jcim.3c01573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).
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Affiliation(s)
- Florian Flachsenberg
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Torben Gutermuth
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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5
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Goullieux M, Zoete V, Röhrig UF. Two-Step Covalent Docking with Attracting Cavities. J Chem Inf Model 2023; 63:7847-7859. [PMID: 38049143 PMCID: PMC10751798 DOI: 10.1021/acs.jcim.3c01055] [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: 07/12/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 12/06/2023]
Abstract
Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.
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Affiliation(s)
- Mathilde Goullieux
- SIB
Swiss Institute of Bioinformatics, Molecular Modeling Group, CH-1015 Lausanne, Switzerland
| | - Vincent Zoete
- SIB
Swiss Institute of Bioinformatics, Molecular Modeling Group, CH-1015 Lausanne, Switzerland
- Department
of Oncology UNIL-CHUV, Lausanne University, Ludwig Institute for Cancer Research
Lausanne Branch, CH-1066 Epalinges, Switzerland
| | - Ute F. Röhrig
- SIB
Swiss Institute of Bioinformatics, Molecular Modeling Group, CH-1015 Lausanne, Switzerland
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6
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Bello-Vargas E, Leyva-Peralta MA, Gómez-Sandoval Z, Ordóñez M, Razo-Hernández RS. A Computational Method for the Binding Mode Prediction of COX-1 and COX-2 Inhibitors: Analyzing the Union of Coxibs, Oxicams, Propionic and Acetic Acids. Pharmaceuticals (Basel) 2023; 16:1688. [PMID: 38139814 PMCID: PMC10747940 DOI: 10.3390/ph16121688] [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: 10/06/2023] [Revised: 11/21/2023] [Accepted: 11/29/2023] [Indexed: 12/24/2023] Open
Abstract
Among the biological targets extensively investigated to improve inflammation and chronic inflammatory conditions, cyclooxygenase enzymes (COXs) occupy a prominent position. The inhibition of these enzymes, essential for mitigating inflammatory processes, is chiefly achieved through Non-Steroidal Anti-Inflammatory Drugs (NSAIDs). In this work, we introduce a novel method-based on computational molecular docking-that could aid in the structure-based design of new compounds or the description of the anti-inflammatory activity of already-tested compounds. For this, we used eight crystal complexes (four COX-1 and COX-2 each), and each pair had a specific NSAID: Celecoxib, Meloxicam, Ibuprofen, and Indomethacin. This selection was based on the ligand selectivity towards COX-1 or COX-2 and their binding mode. An interaction profile of each NSAID was compiled to detect the residues that are key for their binding mode, highlighting the interaction made by the Me group. Furthermore, we rigorously validated our models based on structural accuracy (RMSD < 1) and (R2 > 70) using eight NSAIDs and thirteen compounds with IC50 values for each enzyme. Therefore, this model can be used for the binding mode prediction of small and structurally rigid compounds that work as COX inhibitors or the prediction of new compounds that are designed by means of a structure-based approach.
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Affiliation(s)
- Estefany Bello-Vargas
- Centro de Investigaciones Químicas, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Mexico;
| | - Mario Alberto Leyva-Peralta
- Departamento de Ciencias Químico Biológicas y Agropecuarias, Universidad de Sonora, H. Caborca, Sonora 83621, Mexico;
| | - Zeferino Gómez-Sandoval
- Facultad de Ciencias Químicas, Universidad de Colima, km 9 Carretera Colima-Coquimatlán, Coquimatlán 28400, Mexico;
| | - Mario Ordóñez
- Centro de Investigaciones Químicas, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Mexico;
| | - Rodrigo Said Razo-Hernández
- Laboratorio de Quimioinformática y Diseño de Fármacos, Centro de Investigación en Dinámica Celular, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Mexico
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7
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Wang Z, Zhong H, Zhang J, Pan P, Wang D, Liu H, Yao X, Hou T, Kang Y. Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data Sets. J Chem Inf Model 2023; 63:6525-6536. [PMID: 37883143 DOI: 10.1021/acs.jcim.3c01519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Small-molecule conformer generation (SMCG) is an extremely important task in both ligand- and structure-based computer-aided drug design, especially during the hit discovery phase. Recently, a multitude of artificial intelligence (AI) models tailored for SMCG have emerged. Despite developers typically furnishing performance evaluation data upon releasing their AI models, a comprehensive and equitable performance comparison between AI models and conventional methods is still lacking. In this study, we curated a new benchmarking data set comprising 3354 high-quality ligand bioactive conformations. Subsequently, we conducted a systematic assessment of the performance of four widely adopted traditional methods (i.e., ConfGenX, Conformator, OMEGA, and RDKit ETKDG) and five AI models (i.e., ConfGF, DMCG, GeoDiff, GeoMol, and torsional diffusion) in the tasks of reproducing bioactive and low-energy conformations of small molecules. In the former task, the AI models have no advantage, particularly with a maximum ensemble size of 1. Even the best-performing AI model GeoMol is still worse than any of the tested traditional methods. Conversely, in the latter task, the torsional diffusion model shows obvious advantages, surpassing the best-performing traditional method ConfGenX by 26.09 and 12.97% on the COV-R and COV-P metrics, respectively. Furthermore, the influence of force field-based fine-tuning on the quality of the generated conformers was also discussed. Finally, a user-friendly Web server called fastSMCG was developed to enable researchers to rapidly and flexibly generate small-molecule conformers using both traditional and AI methods. We anticipate that our work will offer valuable practical assistance to the scientific community in this field.
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Affiliation(s)
- Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Haiyang Zhong
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jintu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao SAR 999078, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao SAR 999078, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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8
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Pletzer-Zelgert J, Ehrt C, Fender I, Griewel A, Flachsenberg F, Klebe G, Rarey M. LifeSoaks: a tool for analyzing solvent channels in protein crystals and obstacles for soaking experiments. Acta Crystallogr D Struct Biol 2023; 79:837-856. [PMID: 37561404 PMCID: PMC10478636 DOI: 10.1107/s205979832300582x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/03/2023] [Indexed: 08/11/2023] Open
Abstract
Due to the structural complexity of proteins, their corresponding crystal arrangements generally contain a significant amount of solvent-occupied space. These areas allow a certain degree of intracrystalline protein flexibility and mobility of solutes. Therefore, knowledge of the geometry of solvent-filled channels and cavities is essential whenever the dynamics inside a crystal are of interest. Especially in soaking experiments for structure-based drug design, ligands must be able to traverse the crystal solvent channels and reach the corresponding binding pockets. Unsuccessful screenings are sometimes attributed to the geometry of the crystal packing, but the underlying causes are often difficult to understand. This work presents LifeSoaks, a novel tool for analyzing and visualizing solvent channels in protein crystals. LifeSoaks uses a Voronoi diagram-based periodic channel representation which can be efficiently computed. The size and location of channel bottlenecks, which might hinder molecular diffusion, can be directly derived from this representation. This work presents the calculated bottleneck radii for all crystal structures in the PDB and the analysis of a new, hand-curated data set of structures obtained by soaking experiments. The results indicate that the consideration of bottleneck radii and the visual inspection of channels are beneficial for planning soaking experiments.
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Affiliation(s)
| | - Christiane Ehrt
- Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Inken Fender
- Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Axel Griewel
- Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Florian Flachsenberg
- Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Gerhard Klebe
- Institut für Pharmazeutische Chemie, Universität Marburg, Marbacher Weg 6-10, 35032 Marburg, Germany
| | - Matthias Rarey
- Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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9
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Röhrig UF, Goullieux M, Bugnon M, Zoete V. Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking. J Chem Inf Model 2023; 63:3925-3940. [PMID: 37285197 PMCID: PMC10305763 DOI: 10.1021/acs.jcim.3c00054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Indexed: 06/08/2023]
Abstract
Molecular docking is a computational approach for predicting the most probable position of a ligand in the binding site of a target macromolecule. Our docking algorithm Attracting Cavities (AC) has been shown to compare favorably to other widely used docking algorithms [Zoete, V.; et al. J. Comput. Chem. 2016, 37, 437]. Here we describe several improvements of AC, making the sampling more robust and providing more flexibility for either fast or high-accuracy docking. We benchmark the performance of AC 2.0 using the 285 complexes of the PDBbind Core set, version 2016. For redocking from randomized ligand conformations, AC 2.0 reaches a success rate of 73.3%, compared to 63.9% for GOLD and 58.0% for AutoDock Vina. Due to its force-field-based scoring function and its thorough sampling procedure, AC 2.0 also performs well for blind docking on the entire receptor surface. The accuracy of its scoring function allows for the detection of problematic experimental structures in the benchmark set. For cross-docking, the AC 2.0 success rate is about 30% lower than for redocking (42.5%), similar to GOLD (42.8%) and better than AutoDock Vina (33.1%), and it can be improved by an informed choice of flexible protein residues. For selected targets with a high success rate in cross-docking, AC 2.0 also achieves good enrichment factors in virtual screening.
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Affiliation(s)
- Ute F. Röhrig
- Molecular
Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Mathilde Goullieux
- Molecular
Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Marine Bugnon
- Molecular
Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Vincent Zoete
- Molecular
Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
- Department
of Oncology UNIL-CHUV, Lausanne University,
Ludwig Institute for Cancer Research Lausanne Branch, CH-1066 Epalinges, Switzerland
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10
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Jain AN, Brueckner AC, Cleves AE, Reibarkh M, Sherer EC. A Distributional Model of Bound Ligand Conformational Strain: From Small Molecules up to Large Peptidic Macrocycles. J Med Chem 2023; 66:1955-1971. [PMID: 36701387 PMCID: PMC9923749 DOI: 10.1021/acs.jmedchem.2c01744] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The internal conformational strain incurred by ligands upon binding a target site has a critical impact on binding affinity, and expectations about the magnitude of ligand strain guide conformational search protocols. Estimates for bound ligand strain begin with modeled ligand atomic coordinates from X-ray co-crystal structures. By deriving low-energy conformational ensembles to fit X-ray diffraction data, calculated strain energies are substantially reduced compared with prior approaches. We show that the distribution of expected global strain energy values is dependent on molecular size in a superlinear manner. The distribution of strain energy follows a rectified normal distribution whose mean and variance are related to conformational complexity. The modeled strain distribution closely matches calculated strain values from experimental data comprising over 3000 protein-ligand complexes. The distributional model has direct implications for conformational search protocols as well as for directions in molecular design.
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Affiliation(s)
- Ajay N. Jain
- Research
& Development, BioPharmics LLC, Sonoma County, California95404, United States,
| | - Alexander C. Brueckner
- Molecular
Structure & Design, Bristol Myers Squibb, Princeton, New Jersey08543, United States
| | - Ann E. Cleves
- Research
& Development, BioPharmics LLC, Sonoma County, California95404, United States
| | - Mikhail Reibarkh
- Analytical
Research and Development, Merck & Co.
Inc., Rahway, New Jersey07065, United States
| | - Edward C. Sherer
- Analytical
Research and Development, Merck & Co.
Inc., Rahway, New Jersey07065, United States,
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11
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Samways M, Bruce Macdonald HE, Taylor RD, Essex JW. Water Networks in Complexes between Proteins and FDA-Approved Drugs. J Chem Inf Model 2023; 63:387-396. [PMID: 36469670 PMCID: PMC9832485 DOI: 10.1021/acs.jcim.2c01225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.
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Affiliation(s)
- Marley
L. Samways
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.
| | - Hannah E. Bruce Macdonald
- Computational
and Systems Biology Program, Memorial Sloan
Kettering Cancer Center, New York, New York 10065, United States
| | | | - Jonathan W. Essex
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.,
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12
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Hahn DF, Bayly CI, Boby ML, Macdonald HEB, Chodera JD, Gapsys V, Mey ASJS, Mobley DL, Benito LP, Schindler CEM, Tresadern G, Warren GL. Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2022; 4:1497. [PMID: 36382113 PMCID: PMC9662604 DOI: 10.33011/livecoms.4.1.1497] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.
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Affiliation(s)
- David F. Hahn
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Melissa L. Boby
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- MSD R&D Innovation Centre, 120 Moorgate, London EC2M 6UR, United Kingdom
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, CA USA
| | - Laura Perez Benito
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Gary Tresadern
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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13
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Kirchweger B, Wasilewicz A, Fischhuber K, Tahir A, Chen Y, Heiss EH, Langer T, Kirchmair J, Rollinger JM. In Silico and In Vitro Approach to Assess Direct Allosteric AMPK Activators from Nature. PLANTA MEDICA 2022; 88:794-804. [PMID: 35915889 DOI: 10.1055/a-1797-3030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The 5'-adenosine monophosphate-activated protein kinase (AMPK) is an important metabolic regulator. Its allosteric drug and metabolite binding (ADaM) site was identified as an attractive target for direct AMPK activation and holds promise as a novel mechanism for the treatment of metabolic diseases. With the exception of lusianthridin and salicylic acid, no natural product (NP) is reported so far to directly target the ADaM site. For the streamlined assessment of direct AMPK activators from the pool of NPs, an integrated workflow using in silico and in vitro methods was applied. Virtual screening combining a 3D shape-based approach and docking identified 21 NPs and NP-like molecules that could potentially activate AMPK. The compounds were purchased and tested in an in vitro AMPK α 1 β 1 γ 1 kinase assay. Two NP-like virtual hits were identified, which, at 30 µM concentration, caused a 1.65-fold (± 0.24) and a 1.58-fold (± 0.17) activation of AMPK, respectively. Intriguingly, using two different evaluation methods, we could not confirm the bioactivity of the supposed AMPK activator lusianthridin, which rebuts earlier reports.
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Affiliation(s)
- Benjamin Kirchweger
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, Vienna, Austria
| | - Andreas Wasilewicz
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, Vienna, Austria
| | - Katrin Fischhuber
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Ammar Tahir
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Ya Chen
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Elke H Heiss
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Thierry Langer
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Johannes Kirchmair
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Judith M Rollinger
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
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14
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Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors. Molecules 2022; 27:molecules27154718. [PMID: 35897894 PMCID: PMC9330098 DOI: 10.3390/molecules27154718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/13/2022] [Accepted: 07/15/2022] [Indexed: 11/19/2022] Open
Abstract
Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20−25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.
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15
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Parker MI, Meyer JE, Golemis EA, Dunbrack RL. Delineating The RAS Conformational Landscape. Cancer Res 2022; 82:2485-2498. [PMID: 35536216 DOI: 10.1158/0008-5472.can-22-0804] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 11/16/2022]
Abstract
Mutations in RAS isoforms (KRAS, NRAS, and HRAS) are among the most frequent oncogenic alterations in many cancers, making these proteins high priority therapeutic targets. Effectively targeting RAS isoforms requires an exact understanding of their active, inactive, and druggable conformations. However, there is no structural catalog of RAS conformations to guide therapeutic targeting or examining the structural impact of RAS mutations. Here we present an expanded classification of RAS conformations based on analyses of the catalytic switch 1 (SW1) and switch 2 (SW2) loops. From 721 human KRAS, NRAS, and HRAS structures available in the Protein Data Bank (206 RAS-protein co-complexes, 190 inhibitor-bound, and 325 unbound, including 204 WT and 517 mutated structures), we created a broad conformational classification based on the spatial positions of Y32 in SW1 and Y71 in SW2. Clustering all well-modeled SW1 and SW2 loops using a density-based machine learning algorithm defined additional conformational subsets, some previously undescribed. Three SW1 conformations and nine SW2 conformations were identified, each associated with different nucleotide states (GTP-bound, nucleotide-free, and GDP-bound) and specific bound proteins or inhibitor sites. The GTP-bound SW1 conformation could be further subdivided based on the hydrogen bond type made between Y32 and the GTP γ-phosphate. Further analysis clarified the catalytic impact of G12D and G12V mutations and the inhibitor chemistries that bind to each druggable RAS conformation. Overall, this study has expanded our understanding of RAS structural biology, which could facilitate future RAS drug discovery.
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Affiliation(s)
- Mitchell I Parker
- Drexel University College of Medicine, Philadelphia, PA, United States
| | - Joshua E Meyer
- Fox Chase Cancer Center, Philadelphia, PA, United States
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16
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Gehlhaar DK, Luty BA, Cheung PP, Litman AH, Owen RM, Rose PW. The Pfizer Crystal Structure Database: An essential tool for structure-based design at Pfizer. J Comput Chem 2022; 43:1053-1062. [PMID: 35394655 DOI: 10.1002/jcc.26862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/07/2022] [Accepted: 03/23/2022] [Indexed: 11/10/2022]
Abstract
Pfizer's Crystal Structure Database (CSDB) is a key enabling technology that allows scientists on structure-based projects rapid access to Pfizer's vast library of in-house crystal structures, as well as a significant number of structures imported from the Protein Data Bank. In addition to capturing basic information such as the asymmetric unit coordinates, reflection data, and the like, CSDB employs a variety of automated methods to first ensure a standard level of annotations and error checking, and then to add significant value for design teams by processing the structures through a sequence of algorithms that prepares the structures for use in modeling. The structures are made available, both as the original asymmetric unit as submitted, as well as the final prepared structures, through REST-based web services that are consumed by several client desktop applications. The structures can be searched by keyword, sequence, submission date, ligand substructure and similarity search, and other common queries.
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Affiliation(s)
| | | | | | | | | | - Peter W Rose
- Structural Bioinformatics Laboratory, San Diego Supercomputer Center, San Diego Supercomputer Center, La Jolla, California, USA
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17
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Kirchweger B, Klein-Junior LC, Pretsch D, Chen Y, Cretton S, Gasper AL, Heyden YV, Christen P, Kirchmair J, Henriques AT, Rollinger JM. Azepine-Indole Alkaloids From Psychotria nemorosa Modulate 5-HT 2A Receptors and Prevent in vivo Protein Toxicity in Transgenic Caenorhabditis elegans. Front Neurosci 2022; 16:826289. [PMID: 35360162 PMCID: PMC8963987 DOI: 10.3389/fnins.2022.826289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/18/2022] [Indexed: 11/26/2022] Open
Abstract
Nemorosine A (1) and fargesine (2), the main azepine-indole alkaloids of Psychotria nemorosa, were explored for their pharmacological profile on neurodegenerative disorders (NDs) applying a combined in silico–in vitro–in vivo approach. By using 1 and 2 as queries for similarity-based searches of the ChEMBL database, structurally related compounds were identified to modulate the 5-HT2A receptor; in vitro experiments confirmed an agonistic effect for 1 and 2 (24 and 36% at 10 μM, respectively), which might be linked to cognition-enhancing properties. This and the previously reported target profile of 1 and 2, which also includes BuChE and MAO-A inhibition, prompted the evaluation of these compounds in several Caenorhabditis elegans models linked to 5-HT modulation and proteotoxicity. On C. elegans transgenic strain CL4659, which expresses amyloid beta (Aβ) in muscle cells leading to a phenotypic paralysis, 1 and 2 reduced Aβ proteotoxicity by reducing the percentage of paralyzed worms to 51%. Treatment of the NL5901 strain, in which α-synuclein is yellow fluorescent protein (YFP)-tagged, with 1 and 2 (10 μM) significantly reduced the α-synuclein expression. Both alkaloids were further able to significantly extend the time of metallothionein induction, which is associated with reduced neurodegeneration of aged brain tissue. These results add to the multitarget profiles of 1 and 2 and corroborate their potential in the treatment of NDs.
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Affiliation(s)
- Benjamin Kirchweger
- Department of Pharmaceutical Sciences, Division of Pharmacognosy, University of Vienna, Vienna, Austria
| | - Luiz C Klein-Junior
- School of Health Sciences, Universidade do Vale do Itajaí (UNIVALI), Itajaí, Brazil.,Laboratory of Pharmacognosy and Quality Control of Phytomedicines, Faculty of Pharmacy, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Dagmar Pretsch
- Department of Pharmaceutical Sciences, Division of Pharmacognosy, University of Vienna, Vienna, Austria
| | - Ya Chen
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Sylvian Cretton
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - André L Gasper
- Herbarium Dr. Roberto Miguel Klein, Department of Natural Sciences, Universidade Regional de Blumenau (FURB), Blumenau, Brazil
| | - Yvan Vander Heyden
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modeling, Center for Pharmaceutical Research (CePhaR), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Philippe Christen
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Johannes Kirchmair
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Amélia T Henriques
- Laboratory of Pharmacognosy and Quality Control of Phytomedicines, Faculty of Pharmacy, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Judith M Rollinger
- Department of Pharmaceutical Sciences, Division of Pharmacognosy, University of Vienna, Vienna, Austria
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18
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Penner P, Guba W, Schmidt R, Meyder A, Stahl M, Rarey M. The Torsion Library: Semiautomated Improvement of Torsion Rules with SMARTScompare. J Chem Inf Model 2022; 62:1644-1653. [PMID: 35318851 DOI: 10.1021/acs.jcim.2c00043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Torsion Library is a collection of torsion motifs associated with angle distributions, derived from crystallographic databases. It is used in strain assessment, conformer generation, and geometry optimization. A hierarchical structure of expert curated SMARTS defines the chemical environments of rotatable bonds and associates these with preferred angles. SMARTS can be very complex and full of implications, which make them difficult to maintain manually. Recent developments in automatically comparing SMARTS patterns can be applied to the Torsion Library to ensure its correctness. We specifically discuss the implementation and the limits of such a procedure in the context of torsion motifs and show several examples of how the Torsion Library benefits from this. All automated changes are validated manually and then shown to have an effect on the angle distributions by correcting matching behavior. The corrected Torsion Library itself is available including both PDB as well as CSD histograms in the Supporting Information and can be used to evaluate rotatable bonds at https://torsions.zbh.uni-hamburg.de.
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Affiliation(s)
- Patrick Penner
- Universität Hamburg,ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Wolfgang Guba
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., CH-4070 Basel, Switzerland
| | - Robert Schmidt
- Universität Hamburg,ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Agnes Meyder
- Universität Hamburg,ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Martin Stahl
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., CH-4070 Basel, Switzerland
| | - Matthias Rarey
- Universität Hamburg,ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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19
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Modi V, Dunbrack RL. Kincore: a web resource for structural classification of protein kinases and their inhibitors. Nucleic Acids Res 2022; 50:D654-D664. [PMID: 34643709 PMCID: PMC8728253 DOI: 10.1093/nar/gkab920] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
The active form of kinases is shared across different family members, as are several commonly observed inactive forms. We previously performed a clustering of the conformation of the activation loop of all protein kinase structures in the Protein Data Bank (PDB) into eight classes based on the dihedral angles that place the Phe side chain of the DFG motif at the N-terminus of the activation loop. Our clusters are strongly associated with the placement of the activation loop, the C-helix, and other structural elements of kinases. We present Kincore, a web resource providing access to our conformational assignments for kinase structures in the PDB. While other available databases provide conformational states or drug type but not both, KinCore includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric) for each kinase chain. The user can query and browse the database using these attributes or determine the conformational labels of a kinase structure using the web server or a standalone program. The database and labeled structure files can be downloaded from the server. Kincore will help in understanding the conformational dynamics of these proteins and guide development of inhibitors targeting specific states. Kincore is available at http://dunbrack.fccc.edu/kincore.
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Affiliation(s)
- Vivek Modi
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19148, USA
| | - Roland L Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19148, USA
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20
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Röhrig UF, Michielin O, Zoete V. Structure and Plasticity of Indoleamine 2,3-Dioxygenase 1 (IDO1). J Med Chem 2021; 64:17690-17705. [PMID: 34907770 DOI: 10.1021/acs.jmedchem.1c01665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Since the discovery of the implication of indoleamine 2,3-dioxygenase 1 (IDO1) in tumoral immune resistance in 2003, the search for inhibitors has been intensely pursued both in academia and in pharmaceutical companies, supported by the publication of the first crystal structure of IDO1 in 2006. More recently, it has become clear that IDO1 is an important player in various biological pathways and diseases ranging from neurodegenerative diseases to infection and autoimmunity. Its inhibition may lead to clinical benefit in different therapeutic settings. At present, over 50 experimental structures of IDO1 in complex with different ligands are available in the Protein Data Bank. Our analysis of this wealth of structural data sheds new light on several open issues regarding IDO1's structure and function.
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Affiliation(s)
- Ute F Röhrig
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Olivier Michielin
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.,Department of Oncology, University Hospital of Lausanne (CHUV), Ludwig Cancer Research─Lausanne Branch, 1011 Lausanne, Switzerland
| | - Vincent Zoete
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.,Department of Oncology UNIL-CHUV, Ludwig Lausanne Branch, 1066 Epalinges, Switzerland
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21
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Kelow SP, Adolf-Bryfogle J, Dunbrack RL. Hiding in plain sight: structure and sequence analysis reveals the importance of the antibody DE loop for antibody-antigen binding. MAbs 2021; 12:1840005. [PMID: 33180672 PMCID: PMC7671036 DOI: 10.1080/19420862.2020.1840005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Antibody variable domains contain “complementarity-determining regions” (CDRs), the loops that form the antigen binding site. CDRs1-3 are recognized as the canonical CDRs. However, a fourth loop sits adjacent to CDR1 and CDR2 and joins the D and E strands on the antibody v-type fold. This “DE loop” is usually treated as a framework region, even though mutations in the loop affect the conformation of the CDRs and residues in the DE loop occasionally contact antigen. We analyzed the length, structure, and sequence features of all DE loops in the Protein Data Bank (PDB), as well as millions of sequences from HIV-1 infected and naïve patients. We refer to the DE loop as H4 and L4 in the heavy and light chains, respectively. Clustering the backbone conformations of the most common length of L4 (6 residues) reveals four conformations: two κ-only clusters, one λ-only cluster, and one mixed κ/λ cluster. Most H4 loops are length-8 and exist primarily in one conformation; a secondary conformation represents a small fraction of H4-8 structures. H4 sequence variability exceeds that of the antibody framework in naïve human high-throughput sequences, and both L4 and H4 sequence variability from λ and heavy germline sequences exceed that of germline framework regions. Finally, we identified dozens of structures in the PDB with insertions in the DE loop, all related to broadly neutralizing HIV-1 antibodies (bNabs), as well as antibody sequences from high-throughput sequencing studies of HIV-infected individuals, illuminating a possible role in humoral immunity to HIV-1.
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Affiliation(s)
- Simon P Kelow
- Institute for Cancer Research, Fox Chase Cancer Center , Philadelphia, PA, USA.,Department of Biochemistry and Molecular Biophysics, University of Pennsylvania , Philadelphia, PA, USA
| | - Jared Adolf-Bryfogle
- Protein Design Lab, Institute for Protein Innovation , Boston, MA, USA.,Division of Hematology/Oncology, Boston Children's Hospital , Boston, MA, USA.,Department of Pediatrics, Harvard Medical School , Boston, MA, USA
| | - Roland L Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center , Philadelphia, PA, USA
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22
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' All That Glitters Is Not Gold': High-Resolution Crystal Structures of Ligand-Protein Complexes Need Not Always Represent Confident Binding Poses. Int J Mol Sci 2021; 22:ijms22136830. [PMID: 34202053 PMCID: PMC8268033 DOI: 10.3390/ijms22136830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/24/2021] [Accepted: 05/24/2021] [Indexed: 01/09/2023] Open
Abstract
Our understanding of the structure–function relationships of biomolecules and thereby applying it to drug discovery programs are substantially dependent on the availability of the structural information of ligand–protein complexes. However, the correct interpretation of the electron density of a small molecule bound to a crystal structure of a macromolecule is not trivial. Our analysis involving quality assessment of ~0.28 million small molecule–protein binding site pairs derived from crystal structures corresponding to ~66,000 PDB entries indicates that the majority (65%) of the pairs might need little (54%) or no (11%) attention. Out of the remaining 35% of pairs that need attention, 11% of the pairs (including structures with high/moderate resolution) pose serious concerns. Unfortunately, most users of crystal structures lack the training to evaluate the quality of a crystal structure against its experimental data and, in general, rely on the resolution as a ‘gold standard’ quality metric. Our work aims to sensitize the non-crystallographers that resolution, which is a global quality metric, need not be an accurate indicator of local structural quality. In this article, we demonstrate the use of several freely available tools that quantify local structural quality and are easy to use from a non-crystallographer’s perspective. We further propose a few solutions for consideration by the scientific community to promote quality research in structural biology and applied areas.
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23
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Liebeschuetz JW. The Good, the Bad, and the Twisted Revisited: An Analysis of Ligand Geometry in Highly Resolved Protein-Ligand X-ray Structures. J Med Chem 2021; 64:7533-7543. [PMID: 34060310 DOI: 10.1021/acs.jmedchem.1c00228] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
An analysis of the rotatable bond geometry of drug-like ligand models is reported for high-resolution (<1.1 Å) crystallographic protein-ligand complexes. In cases where the ligand fit to the electron density is very good, unusual torsional geometry is rare and, most often, though not exclusively, associated with strong polar, metal, or covalent ligand-protein interactions. It is rarely associated with a torsional strain of greater than 2 kcal mol-1 by calculation. An unusual torsional geometry is more prevalent where the fit to electron density is not perfect. Multiple low-strain conformer bindings were observed in 21% of the set and, it is suggested, may also lie behind many of the 35% of single-occupancy cases, where a poor fit to the e-density was found. It is concluded that multiple conformer ligand binding is an under-recognized phenomenon in structure-based drug design and that there is a need for more robust crystallographic refinement methods to better handle such cases.
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Affiliation(s)
- John W Liebeschuetz
- Skilos Chemoinformatics, 159 Water Street, Cambridge CB4 1PB, United Kingdom.,Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Milton, Cambridge CB4 0QA, United Kingdom
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24
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Chakraborti S, Chakraborty M, Bose A, Srinivasan N, Visweswariah SS. Identification of Potential Binders of Mtb Universal Stress Protein (Rv1636) Through an in silico Approach and Insights Into Compound Selection for Experimental Validation. Front Mol Biosci 2021; 8:599221. [PMID: 34012976 PMCID: PMC8126637 DOI: 10.3389/fmolb.2021.599221] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/01/2021] [Indexed: 12/28/2022] Open
Abstract
Millions of deaths caused by Mycobacterium tuberculosis (Mtb) are reported worldwide every year. Treatment of tuberculosis (TB) involves the use of multiple antibiotics over a prolonged period. However, the emergence of resistance leading to multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) is the most challenging aspect of TB treatment. Therefore, there is a constant need to search for novel therapeutic strategies that could tackle the growing problem of drug resistance. One such strategy could be perturbing the functions of novel targets in Mtb, such as universal stress protein (USP, Rv1636), which binds to cAMP with a higher affinity than ATP. Orthologs of these proteins are conserved in all mycobacteria and act as “sink” for cAMP, facilitating the availability of this second messenger for signaling when required. Here, we have used the cAMP-bound crystal structure of USP from Mycobacterium smegmatis, a closely related homolog of Mtb, to conduct a structure-guided hunt for potential binders of Rv1636, primarily employing molecular docking approach. A library of 1.9 million compounds was subjected to virtual screening to obtain an initial set of ~2,000 hits. An integrative strategy that uses the available experimental data and consensus indications from other computational analyses has been employed to prioritize 22 potential binders of Rv1636 for experimental validations. Binding affinities of a few compounds among the 22 prioritized compounds were tested through microscale thermophoresis assays, and two compounds of natural origin showed promising binding affinities with Rv1636. We believe that this study provides an important initial guidance to medicinal chemists and biochemists to synthesize and test an enriched set of compounds that have the potential to inhibit Mtb USP (Rv1636), thereby aiding the development of novel antitubercular lead candidates.
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Affiliation(s)
- Sohini Chakraborti
- Molecular Biophysics Unit, Indian Institute of Science, Bengaluru, India
| | - Moubani Chakraborty
- Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bengaluru, India
| | - Avipsa Bose
- Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bengaluru, India
| | | | - Sandhya S Visweswariah
- Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bengaluru, India
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25
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Tong J, Zhao S. Large-Scale Analysis of Bioactive Ligand Conformational Strain Energy by Ab Initio Calculation. J Chem Inf Model 2021; 61:1180-1192. [PMID: 33630603 DOI: 10.1021/acs.jcim.0c01197] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Ligand conformational strain energy (LCSE) plays an important role in virtual screening and lead optimization. While various studies have provided insights into LCSE for small-molecule ligands in the Protein Data Bank (PDB), conclusions are inconsistent mainly due to small datasets, poor quality control of crystal structures, and molecular mechanics (MM) or low-level quantum mechanics (QM) calculations. Here, we built a high-quality dataset (LigBoundConf) of 8145 ligand-bound conformations from PDB crystal structures and calculated LCSE at the M062X-D3/ma-TZVPP (SMD)//M062X-D3/def2-SVP(SMD) level for each case in the dataset. The mean/median LCSE is 4.6/3.7 kcal/mol for 6672 successfully calculated cases, which is significantly lower than the estimates based on molecular mechanics in many previous analyses. Especially, when removing ligands with nonaromatic ring(s) that are prone to have large LCSEs due to electron density overfitting, the mean/median LCSE was reduced to 3.3/2.5 kcal/mol. We further reveal that LCSE is correlated with several ligand properties, including formal atomic charge, molecular weight, number of rotatable bonds, and number of hydrogen-bond donors and acceptors. In addition, our results show that although summation of torsion strains is a good approximation of LCSE for most cases, for a small fraction (about 6%) of our dataset, it underestimates LCSEs if ligands could form nonlocal intramolecular interactions in the unbound state. Taken together, our work provides a comprehensive profile of LCSE for ligands in PDB, which could help ligand conformation generation, ligand docking pose evaluation, and lead optimization.
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Affiliation(s)
- Jiahui Tong
- iHuman Institute, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China.,School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China.,University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China.,Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China.,School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
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26
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Vennelakanti V, Qi HW, Mehmood R, Kulik HJ. When are two hydrogen bonds better than one? Accurate first-principles models explain the balance of hydrogen bond donors and acceptors found in proteins. Chem Sci 2021; 12:1147-1162. [PMID: 35382134 PMCID: PMC8908278 DOI: 10.1039/d0sc05084a] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/18/2020] [Indexed: 01/02/2023] Open
Abstract
Hydrogen bonds (HBs) play an essential role in the structure and catalytic action of enzymes, but a complete understanding of HBs in proteins challenges the resolution of modern structural (i.e., X-ray diffraction) techniques and mandates computationally demanding electronic structure methods from correlated wavefunction theory for predictive accuracy. Numerous amino acid sidechains contain functional groups (e.g., hydroxyls in Ser/Thr or Tyr and amides in Asn/Gln) that can act as either HB acceptors or donors (HBA/HBD) and even form simultaneous, ambifunctional HB interactions. To understand the relative energetic benefit of each interaction, we characterize the potential energy surfaces of representative model systems with accurate coupled cluster theory calculations. To reveal the relationship of these energetics to the balance of these interactions in proteins, we curate a set of 4000 HBs, of which >500 are ambifunctional HBs, in high-resolution protein structures. We show that our model systems accurately predict the favored HB structural properties. Differences are apparent in HBA/HBD preference for aromatic Tyr versus aliphatic Ser/Thr hydroxyls because Tyr forms significantly stronger O–H⋯O HBs than N–H⋯O HBs in contrast to comparable strengths of the two for Ser/Thr. Despite this residue-specific distinction, all models of residue pairs indicate an energetic benefit for simultaneous HBA and HBD interactions in an ambifunctional HB. Although the stabilization is less than the additive maximum due both to geometric constraints and many-body electronic effects, a wide range of ambifunctional HB geometries are more favorable than any single HB interaction. Correlated wavefunction theory predicts and high-resolution crystal structure analysis confirms the important, stabilizing effect of simultaneous hydrogen bond donor and acceptor interactions in proteins.![]()
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Affiliation(s)
- Vyshnavi Vennelakanti
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
- Department of Chemistry
| | - Helena W. Qi
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
- Department of Chemistry
| | - Rimsha Mehmood
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
- Department of Chemistry
| | - Heather J. Kulik
- Department of Chemical Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
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27
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Chachulski L, Windshügel B. LEADS-FRAG: A Benchmark Data Set for Assessment of Fragment Docking Performance. J Chem Inf Model 2020; 60:6544-6554. [PMID: 33289563 DOI: 10.1021/acs.jcim.0c00693] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Fragment-based drug design is a popular approach in drug discovery, which makes use of computational methods such as molecular docking. To assess fragment placement performance of molecular docking programs, we constructed LEADS-FRAG, a benchmark data set containing 93 high-quality protein-fragment complexes that were selected from the Protein Data Bank using a rational and unbiased process. The data set contains fully prepared protein and fragment structures and is publicly available. Moreover, we used LEADS-FRAG for evaluating the small-molecule docking programs AutoDock, AutoDock Vina, FlexX, and GOLD for their fragment docking performance. GOLD in combination with the scoring function ChemPLP and AutoDock Vina performed best and generated near-native conformations (root mean square deviation <1.5 Å) for more than 50% of the data set considering the top-ranked docking pose. Taking into account all docking poses, the tested programs generated near-native conformations for up to 86% of the fragments in LEADS-FRAG. By rescoring all docking poses with the GOLD scoring functions and the Protein-Ligand Informatics force field, the number of near-native conformations increased up to 40% with respect to the top-rescored poses. Our results show that conventional small-molecule docking programs achieve a satisfactory fragment docking performance when utilizing rescoring.
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Affiliation(s)
- Laura Chachulski
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, ScreeningPort, Hamburg 22525, Germany.,Jacobs University Bremen gGmbH, Bremen 28759, Germany
| | - Björn Windshügel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, ScreeningPort, Hamburg 22525, Germany.,Institute for Biochemistry and Molecular Biology, Department of Chemistry, Universität Hamburg, Hamburg 20146, Germany
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28
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Schöning-Stierand K, Diedrich K, Fährrolfes R, Flachsenberg F, Meyder A, Nittinger E, Steinegger R, Rarey M. ProteinsPlus: interactive analysis of protein-ligand binding interfaces. Nucleic Acids Res 2020; 48:W48-W53. [PMID: 32297936 PMCID: PMC7319454 DOI: 10.1093/nar/gkaa235] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 03/19/2020] [Accepted: 04/14/2020] [Indexed: 01/22/2023] Open
Abstract
Due to the increasing amount of publicly available protein structures searching, enriching and investigating these data still poses a challenging task. The ProteinsPlus web service (https://proteins.plus) offers a broad range of tools addressing these challenges. The web interface to the tool collection focusing on protein–ligand interactions has been geared towards easy and intuitive access to a large variety of functionality for life scientists. Since our last publication, the ProteinsPlus web service has been extended by additional services as well as it has undergone substantial infrastructural improvements. A keyword search functionality was added on the start page of ProteinsPlus enabling users to work on structures without knowing their PDB code. The tool collection has been augmented by three tools: StructureProfiler validates ligands and active sites using selection criteria of well-established protein–ligand benchmark data sets, WarPP places water molecules in the ligand binding sites of a protein, and METALizer calculates, predicts and scores coordination geometries of metal ions based on surrounding complex atoms. Additionally, all tools provided by ProteinsPlus are available through a REST service enabling the automated integration in structure processing and modeling pipelines.
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Affiliation(s)
| | - Konrad Diedrich
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Rainer Fährrolfes
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Florian Flachsenberg
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Agnes Meyder
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Eva Nittinger
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Ruben Steinegger
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
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29
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A chemical interpretation of protein electron density maps in the worldwide protein data bank. PLoS One 2020; 15:e0236894. [PMID: 32785279 PMCID: PMC7423092 DOI: 10.1371/journal.pone.0236894] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/15/2020] [Indexed: 11/29/2022] Open
Abstract
High-quality three-dimensional structural data is of great value for the functional interpretation of biomacromolecules, especially proteins; however, structural quality varies greatly across the entries in the worldwide Protein Data Bank (wwPDB). Since 2008, the wwPDB has required the inclusion of structure factors with the deposition of x-ray crystallographic structures to support the independent evaluation of structures with respect to the underlying experimental data used to derive those structures. However, interpreting the discrepancies between the structural model and its underlying electron density data is difficult, since derived sigma-scaled electron density maps use arbitrary electron density units which are inconsistent between maps from different wwPDB entries. Therefore, we have developed a method that converts electron density values from sigma-scaled electron density maps into units of electrons. With this conversion, we have developed new methods that can evaluate specific regions of an x-ray crystallographic structure with respect to a physicochemical interpretation of its corresponding electron density map. We have systematically compared all deposited x-ray crystallographic protein models in the wwPDB with their underlying electron density maps, if available, and characterized the electron density in terms of expected numbers of electrons based on the structural model. The methods generated coherent evaluation metrics throughout all PDB entries with associated electron density data, which are consistent with visualization software that would normally be used for manual quality assessment. To our knowledge, this is the first attempt to derive units of electrons directly from electron density maps without the aid of the underlying structure factors. These new metrics are biochemically-informative and can be extremely useful for filtering out low-quality structural regions from inclusion into systematic analyses that span large numbers of PDB entries. Furthermore, these new metrics will improve the ability of non-crystallographers to evaluate regions of interest within PDB entries, since only the PDB structure and the associated electron density maps are needed. These new methods are available as a well-documented Python package on GitHub and the Python Package Index under a modified Clear BSD open source license.
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30
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Chakraborti S, Bheemireddy S, Srinivasan N. Repurposing drugs against the main protease of SARS-CoV-2: mechanism-based insights supported by available laboratory and clinical data. Mol Omics 2020; 16:474-491. [PMID: 32696772 DOI: 10.1039/d0mo00057d] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The ongoing global pandemic of COVID-19 has brought life to almost a standstill with the implementation of lockdowns and social distancing as some of the preventive measures in the absence of any approved specific therapeutic interventions. To combat this crisis, research communities worldwide are falling back on the existing repertoire of approved/investigational drugs to probe into their anti-coronavirus properties. In this report, we describe our unique efforts in identifying potential drugs that could be repurposed against the main protease of SARS-CoV-2 (SARS-CoV-2 Mpro). To achieve this goal, we have primarily exploited the principles of 'neighbourhood behaviour' in the protein 3D (workflow-I) and chemical 2D structural space (workflow-II) coupled with docking simulations and insights into the possible modes of action of the selected candidates from the available literature. This integrative approach culminated in prioritizing 29 potential repurpose-able agents (20 approved drugs and 9 investigational molecules) against SARS-CoV-2 Mpro. Apart from the approved/investigational anti-viral drugs, other notable hits include anti-bacterial, anti-inflammatory, anti-cancer and anti-coagulant drugs. Our analysis suggests that some of these drugs have the potential to simultaneously modulate the functions of viral proteins and the host response system. Interestingly, many of these identified candidates (12 molecules from workflow-I and several molecules, belonging to the chemical classes of alkaloids, tetracyclines, peptidomimetics, from workflow-II) are suggested to possess anti-viral properties, which is supported by laboratory and clinical data. Furthermore, this work opens a new avenue of research to probe into the molecular mechanism of action of many drugs, which are known to demonstrate anti-viral activity but are so far not known to target viral proteases.
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Affiliation(s)
- Sohini Chakraborti
- Molecular Biophysics Unit, Indian Institute of Science, Bengaluru 560012, India.
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31
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Pintilie G, Zhang K, Su Z, Li S, Schmid MF, Chiu W. Measurement of atom resolvability in cryo-EM maps with Q-scores. Nat Methods 2020; 17:328-334. [PMID: 32042190 PMCID: PMC7446556 DOI: 10.1038/s41592-020-0731-1] [Citation(s) in RCA: 181] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 12/31/2019] [Indexed: 01/18/2023]
Abstract
Cryogenic electron microscopy (cryo-EM) maps are now at the point where resolvability of individual atoms can be achieved. However, resolvability is not necessarily uniform throughout the map. We introduce a quantitative parameter to characterize the resolvability of individual atoms in cryo-EM maps, the map Q-score. Q-scores can be calculated for atoms in proteins, nucleic acids, water, ligands and other solvent atoms, using models fitted to or derived from cryo-EM maps. Q-scores can also be averaged to represent larger features such as entire residues and nucleotides. Averaged over entire models, Q-scores correlate very well with the estimated resolution of cryo-EM maps for both protein and RNA. Assuming the models they are calculated from are well fitted to the map, Q-scores can be used as a measure of resolvability in cryo-EM maps at various scales, from entire macromolecules down to individual atoms. Q-score analysis of multiple cryo-EM maps of the same proteins derived from different laboratories confirms the reproducibility of structural features from side chains down to water and ion atoms.
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Affiliation(s)
- Grigore Pintilie
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, USA.
| | - Kaiming Zhang
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, USA
| | - Zhaoming Su
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, USA
| | - Shanshan Li
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, USA
| | - Michael F Schmid
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA
| | - Wah Chiu
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, USA.
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA.
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32
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Meyder A, Kampen S, Sieg J, Fährrolfes R, Friedrich NO, Flachsenberg F, Rarey M. StructureProfiler: an all-in-one tool for 3D protein structure profiling. Bioinformatics 2019; 35:874-876. [PMID: 30124779 DOI: 10.1093/bioinformatics/bty692] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 07/05/2018] [Accepted: 08/15/2018] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Three-dimensional protein structures are important starting points for elucidating protein function and applications like drug design. Computational methods in this area rely on high quality validation datasets which are usually manually assembled. Due to the increase in published structures as well as the increasing demand for specially tailored validation datasets, automatic procedures should be adopted. RESULTS StructureProfiler is a new tool for automatic, objective and customizable profiling of X-ray protein structures based on the most frequently applied selection criteria currently in use to assemble benchmark datasets. As examples, four dataset configurations (Astex, Iridium, Platinum, combined), all results of the combined tests and the list of all PDB Ids passing the combined criteria set are attached in the Supplementary Material. AVAILABILITY AND IMPLEMENTATION StructureProfiler is available as part of the ProteinsPlus web service http://proteins.plus and as standalone tool in the NAOMI ChemBio Suite. Dataset updates together with the tool can be found on http://www.zbh.uni-hamburg.de/structureprofiler. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Agnes Meyder
- ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Stefanie Kampen
- ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Jochen Sieg
- ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Rainer Fährrolfes
- ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | | | | | - Matthias Rarey
- ZBH-Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
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33
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Moreno-Chicano T, Ebrahim A, Axford D, Appleby MV, Beale JH, Chaplin AK, Duyvesteyn HME, Ghiladi RA, Owada S, Sherrell DA, Strange RW, Sugimoto H, Tono K, Worrall JAR, Owen RL, Hough MA. High-throughput structures of protein-ligand complexes at room temperature using serial femtosecond crystallography. IUCRJ 2019; 6:1074-1085. [PMID: 31709063 PMCID: PMC6830213 DOI: 10.1107/s2052252519011655] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 08/21/2019] [Indexed: 05/09/2023]
Abstract
High-throughput X-ray crystal structures of protein-ligand complexes are critical to pharmaceutical drug development. However, cryocooling of crystals and X-ray radiation damage may distort the observed ligand binding. Serial femtosecond crystallography (SFX) using X-ray free-electron lasers (XFELs) can produce radiation-damage-free room-temperature structures. Ligand-binding studies using SFX have received only modest attention, partly owing to limited beamtime availability and the large quantity of sample that is required per structure determination. Here, a high-throughput approach to determine room-temperature damage-free structures with excellent sample and time efficiency is demonstrated, allowing complexes to be characterized rapidly and without prohibitive sample requirements. This yields high-quality difference density maps allowing unambiguous ligand placement. Crucially, it is demonstrated that ligands similar in size or smaller than those used in fragment-based drug design may be clearly identified in data sets obtained from <1000 diffraction images. This efficiency in both sample and XFEL beamtime opens the door to true high-throughput screening of protein-ligand complexes using SFX.
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Affiliation(s)
- Tadeo Moreno-Chicano
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, England
| | - Ali Ebrahim
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, England
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, England
| | - Danny Axford
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, England
| | - Martin V. Appleby
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, England
| | - John H. Beale
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, England
| | - Amanda K. Chaplin
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, England
| | - Helen M. E. Duyvesteyn
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, England
- Division of Structural Biology (STRUBI), University of Oxford, The Henry Wellcome Building for Genomic Medicine, Roosevelt Drive, Oxford OX3 7BN, England
| | - Reza A. Ghiladi
- Department of Chemistry, North Carolina State University, Raleigh, NC 27695-8204, USA
| | - Shigeki Owada
- RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo, Hyogo 679-5148, Japan
- Japan Synchrotron Radiation Research Institute, 1-1-1 Kouto, Sayo, Hyogo 679-5198, Japan
| | - Darren A. Sherrell
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, England
| | - Richard W. Strange
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, England
| | | | - Kensuke Tono
- RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo, Hyogo 679-5148, Japan
- Japan Synchrotron Radiation Research Institute, 1-1-1 Kouto, Sayo, Hyogo 679-5198, Japan
| | - Jonathan A. R. Worrall
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, England
| | - Robin L. Owen
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, England
| | - Michael A. Hough
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, England
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34
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Qi HW, Kulik HJ. Reply to "Comment on 'Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis'". J Chem Inf Model 2019; 59:3609-3610. [PMID: 31424928 DOI: 10.1021/acs.jcim.9b00606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Helena W Qi
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States.,Department of Chemistry , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States
| | - Heather J Kulik
- Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States
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35
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Zhang Y, Sanner MF. Docking Flexible Cyclic Peptides with AutoDock CrankPep. J Chem Theory Comput 2019; 15:5161-5168. [PMID: 31505931 DOI: 10.1021/acs.jctc.9b00557] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep (ADCP), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.
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Affiliation(s)
- Yuqi Zhang
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037 , United States
| | - Michel F Sanner
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037 , United States
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36
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Jones L, Tynes M, Smith P. Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2019; 75:696-717. [PMID: 31373570 PMCID: PMC6677017 DOI: 10.1107/s2059798319008933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 06/22/2019] [Indexed: 11/11/2022]
Abstract
Current software tools for the automated building of models for macromolecular X-ray crystal structures are capable of assembling high-quality models for ordered macromolecule and small-molecule scattering components with minimal or no user supervision. Many of these tools also incorporate robust functionality for modelling the ordered water molecules that are found in nearly all macromolecular crystal structures. However, no current tools focus on differentiating these ubiquitous water molecules from other frequently occurring multi-atom solvent species, such as sulfate, or the automated building of models for such species. PeakProbe has been developed specifically to address the need for such a tool. PeakProbe predicts likely solvent models for a given point (termed a `peak') in a structure based on analysis (`probing') of its local electron density and chemical environment. PeakProbe maps a total of 19 resolution-dependent features associated with electron density and two associated with the local chemical environment to a two-dimensional score space that is independent of resolution. Peaks are classified based on the relative frequencies with which four different classes of solvent (including water) are observed within a given region of this score space as determined by large-scale sampling of solvent models in the Protein Data Bank. Designed to classify peaks generated from difference density maxima, PeakProbe also incorporates functionality for identifying peaks associated with model errors or clusters of peaks likely to correspond to multi-atom solvent, and for the validation of existing solvent models using solvent-omit electron-density maps. When tasked with classifying peaks into one of four distinct solvent classes, PeakProbe achieves greater than 99% accuracy for both peaks derived directly from the atomic coordinates of existing solvent models and those based on difference density maxima. While the program is still under development, a fully functional version is publicly available. PeakProbe makes extensive use of cctbx libraries, and requires a PHENIX licence and an up-to-date phenix.python environment for execution.
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Affiliation(s)
- Laurel Jones
- Department of Chemistry, Fordham University, Bronx, NY 10458, USA
| | - Michael Tynes
- Department of Computer and Information Science, Fordham University, Bronx, NY 10458, USA
| | - Paul Smith
- Department of Chemistry, Fordham University, Bronx, NY 10458, USA
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37
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Yoshida T, Hirono S. A 3D-QSAR Analysis of CDK2 Inhibitors Using FMO Calculations and PLS Regression. Chem Pharm Bull (Tokyo) 2019; 67:546-555. [DOI: 10.1248/cpb.c18-00990] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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38
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van Beusekom B, Wezel N, Hekkelman ML, Perrakis A, Emsley P, Joosten RP. Building and rebuilding N-glycans in protein structure models. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2019; 75:416-425. [PMID: 30988258 PMCID: PMC6465985 DOI: 10.1107/s2059798319003875] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 03/20/2019] [Indexed: 01/16/2023]
Abstract
Carbohydrates are automatically built and rebuilt using Coot in the PDB-REDO pipeline. N-Glycosylation is one of the most common post-translational modifications and is implicated in, for example, protein folding and interaction with ligands and receptors. N-Glycosylation trees are complex structures of linked carbohydrate residues attached to asparagine residues. While carbohydrates are typically modeled in protein structures, they are often incomplete or have the wrong chemistry. Here, new tools are presented to automatically rebuild existing glycosylation trees, to extend them where possible, and to add new glycosylation trees if they are missing from the model. The method has been incorporated in the PDB-REDO pipeline and has been applied to build or rebuild 16 452 carbohydrate residues in 11 651 glycosylation trees in 4498 structure models, and is also available from the PDB-REDO web server. With better modeling of N-glycosylation, the biological function of this important modification can be better and more easily understood.
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Affiliation(s)
- Bart van Beusekom
- Department of Biochemistry, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Natasja Wezel
- Department of Biochemistry, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Maarten L Hekkelman
- Department of Biochemistry, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Anastassis Perrakis
- Department of Biochemistry, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Paul Emsley
- MRC Laboratory for Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, England
| | - Robbie P Joosten
- Department of Biochemistry, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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39
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Jacquemard C, Drwal MN, Desaphy J, Kellenberger E. Binding mode information improves fragment docking. J Cheminform 2019; 11:24. [PMID: 30903304 PMCID: PMC6431075 DOI: 10.1186/s13321-019-0346-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 03/13/2019] [Indexed: 12/11/2022] Open
Abstract
Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.![]()
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Affiliation(s)
- Célien Jacquemard
- Laboratoire d'innovation thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400, Illkirch, France
| | - Malgorzata N Drwal
- Laboratoire d'innovation thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400, Illkirch, France
| | - Jérémy Desaphy
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, 46285, USA
| | - Esther Kellenberger
- Laboratoire d'innovation thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400, Illkirch, France.
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40
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Qi HW, Kulik HJ. Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis. J Chem Inf Model 2019; 59:2199-2211. [DOI: 10.1021/acs.jcim.9b00144] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Helena W. Qi
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J. Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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41
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Abstract
Protein kinases play important roles in signaling pathways and are widely studied as drug targets. Their active site exhibits remarkable structural variation as observed in the large number of available crystal structures. We have developed a clustering scheme and nomenclature to categorize and label all the observed conformations in human protein kinases. This has enabled us to clearly define the geometry of the active state and to distinguish closely related inactive states which were previously not characterized. Our classification of kinase conformations will help in better understanding the conformational dynamics of these proteins and the development of inhibitors against them. Targeting protein kinases is an important strategy for intervention in cancer. Inhibitors are directed at the active conformation or a variety of inactive conformations. While attempts have been made to classify these conformations, a structurally rigorous catalog of states has not been achieved. The kinase activation loop is crucial for catalysis and begins with the conserved DFGmotif. This motif is observed in two major classes of conformations, DFGin—a set of active and inactive conformations where the Phe residue is in contact with the C-helix of the N-terminal lobe—and DFGout—an inactive form where Phe occupies the ATP site exposing the C-helix pocket. We have developed a clustering of kinase conformations based on the location of the Phe side chain (DFGin, DFGout, and DFGinter or intermediate) and the backbone dihedral angles of the sequence X-D-F, where X is the residue before the DFGmotif, and the DFG-Phe side-chain rotamer, utilizing a density-based clustering algorithm. We have identified eight distinct conformations and labeled them based on the Ramachandran regions (A, alpha; B, beta; L, left) of the XDF motif and the Phe rotamer (minus, plus, trans). Our clustering divides the DFGin group into six clusters including BLAminus, which contains active structures, and two common inactive forms, BLBplus and ABAminus. DFGout structures are predominantly in the BBAminus conformation, which is essentially required for binding type II inhibitors. The inactive conformations have specific features that make them unable to bind ATP, magnesium, and/or substrates. Our structurally intuitive nomenclature will aid in understanding the conformational dynamics of kinases and structure-based development of kinase drugs.
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42
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A new clustering and nomenclature for beta turns derived from high-resolution protein structures. PLoS Comput Biol 2019; 15:e1006844. [PMID: 30845191 PMCID: PMC6424458 DOI: 10.1371/journal.pcbi.1006844] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 03/19/2019] [Accepted: 01/31/2019] [Indexed: 11/20/2022] Open
Abstract
Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I’, II, II’, VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta-turn conformations from a set of 13,030 turns from 1074 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN and k-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., ‘A’ and ‘D’ for alpha regions on the left side of the Ramachandran map and ‘a’ and ‘d’ for equivalent regions on the right-hand side; classical Type I turns are ‘AD’ turns and Type I’ turns are ‘ad’). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta-turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types, BetaTurnLib18, and cross-platform software, BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable from dunbrack.fccc.edu/betaturn and github.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs. Folded proteins consist of elements of regular secondary structure, such as alpha helices and beta sheets connected by irregular structures called loops. Loops have a varying length and typically contain U-shaped beta turns which abruptly change the direction of the chain. Beta turns are formed by four sequential amino acid residues and adopt specific conformations which have been classified into eight types since the 1970s. Based on a larger set of very detailed protein structures and thorough statistical data analysis, the previous set of beta-turn types was revised to include 7 existing turn types, 5 subtypes of the existing turns, and 6 new types. Their properties and amino-acid sequence propensities are analyzed. We propose a self-explanatory turn nomenclature, based on the conformations of residues 2 and 3 of the beta turn, that is much easier to remember than the old nomenclature. We developed a library of 18 turn types and software to assign them to an input protein structure. The software and new turn types should advance fundamental understanding of protein structure as well as benefit applications for protein structure prediction, determination, and refinement.
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43
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Friedrich NO, Flachsenberg F, Meyder A, Sommer K, Kirchmair J, Rarey M. Conformator: A Novel Method for the Generation of Conformer Ensembles. J Chem Inf Model 2019; 59:731-742. [PMID: 30747530 DOI: 10.1021/acs.jcim.8b00704] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0.47 Å with no significant difference to the highest-ranked commercial algorithm OMEGA and significantly higher accuracy than seven free algorithms, including the RDKit DG algorithm. Conformator is freely available for noncommercial use and academic research.
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Affiliation(s)
- Nils-Ole Friedrich
- Center for Bioinformatics , Universität Hamburg , Bundesstrasse 43 , 20146 Hamburg , Germany
| | - Florian Flachsenberg
- Center for Bioinformatics , Universität Hamburg , Bundesstrasse 43 , 20146 Hamburg , Germany
| | - Agnes Meyder
- Center for Bioinformatics , Universität Hamburg , Bundesstrasse 43 , 20146 Hamburg , Germany
| | - Kai Sommer
- Center for Bioinformatics , Universität Hamburg , Bundesstrasse 43 , 20146 Hamburg , Germany
| | - Johannes Kirchmair
- Center for Bioinformatics , Universität Hamburg , Bundesstrasse 43 , 20146 Hamburg , Germany.,Department of Chemistry , University of Bergen , N-5020 Bergen , Norway.,Computational Biology Unit (CBU) , University of Bergen , N-5020 Bergen , Norway
| | - Matthias Rarey
- Center for Bioinformatics , Universität Hamburg , Bundesstrasse 43 , 20146 Hamburg , Germany
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44
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Nittinger E, Gibbons P, Eigenbrot C, Davies DR, Maurer B, Yu CL, Kiefer JR, Kuglstatter A, Murray J, Ortwine DF, Tang Y, Tsui V. Water molecules in protein–ligand interfaces. Evaluation of software tools and SAR comparison. J Comput Aided Mol Des 2019; 33:307-330. [DOI: 10.1007/s10822-019-00187-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 01/24/2019] [Indexed: 01/08/2023]
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45
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van Zundert GCP, Hudson BM, de Oliveira SHP, Keedy DA, Fonseca R, Heliou A, Suresh P, Borrelli K, Day T, Fraser JS, van den Bedem H. qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X-Ray Electron Density Maps. J Med Chem 2018; 61:11183-11198. [PMID: 30457858 DOI: 10.1021/acs.jmedchem.8b01292] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.
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Affiliation(s)
| | - Brandi M Hudson
- Department of Bioengineering and Therapeutic Sciences , UCSF , San Francisco , California 94158 , United States
| | - Saulo H P de Oliveira
- SLAC National Accelerator Laboratory , Stanford University , Menlo Park , California 94025 United States
| | - Daniel A Keedy
- Department of Bioengineering and Therapeutic Sciences , UCSF , San Francisco , California 94158 , United States
| | - Rasmus Fonseca
- Department of Molecular and Cellular Physiology , Stanford University , Stanford , California 94305 , United States
| | - Amelie Heliou
- LIX, Ecole Polytechnique, CNRS, Inria , Université Paris-Saclay , 91128 Palaiseau , France
| | - Pooja Suresh
- Department of Bioengineering and Therapeutic Sciences , UCSF , San Francisco , California 94158 , United States
| | | | - Tyler Day
- Schrödinger , New York , New York 10036 , United States
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences , UCSF , San Francisco , California 94158 , United States
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic Sciences , UCSF , San Francisco , California 94158 , United States.,SLAC National Accelerator Laboratory , Stanford University , Menlo Park , California 94025 United States
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46
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Su M, Yang Q, Du Y, Feng G, Liu Z, Li Y, Wang R. Comparative Assessment of Scoring Functions: The CASF-2016 Update. J Chem Inf Model 2018; 59:895-913. [DOI: 10.1021/acs.jcim.8b00545] [Citation(s) in RCA: 208] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Minyi Su
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Qifan Yang
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Yu Du
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Guoqin Feng
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Zhihai Liu
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
| | - Yan Li
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
| | - Renxiao Wang
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- Shanxi Key Laboratory of Innovative Drugs for the Treatment of Serious Diseases Basing on Chronic Inflammation, College of Traditional Chinese Medicines, Shanxi University of Chinese Medicine, Taiyuan, Shanxi 030619, People’s Republic of China
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47
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Nittinger E, Flachsenberg F, Bietz S, Lange G, Klein R, Rarey M. Placement of Water Molecules in Protein Structures: From Large-Scale Evaluations to Single-Case Examples. J Chem Inf Model 2018; 58:1625-1637. [DOI: 10.1021/acs.jcim.8b00271] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Eva Nittinger
- Universität Hamburg, ZBH − Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Florian Flachsenberg
- Universität Hamburg, ZBH − Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Stefan Bietz
- Universität Hamburg, ZBH − Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Gudrun Lange
- Bayer CropScience AG, Industriepark Hoechst G836, 65926 Frankfurt am Main, Germany
| | - Robert Klein
- Bayer CropScience AG, Industriepark Hoechst G836, 65926 Frankfurt am Main, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH − Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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48
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Drwal MN, Bret G, Perez C, Jacquemard C, Desaphy J, Kellenberger E. Structural Insights on Fragment Binding Mode Conservation. J Med Chem 2018; 61:5963-5973. [PMID: 29906118 DOI: 10.1021/acs.jmedchem.8b00256] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Aiming at a deep understanding of fragment binding to ligandable targets, we performed a large scale analysis of the Protein Data Bank. Binding modes of 1832 drug-like ligands and 1079 fragments to 235 proteins were compared. We observed that the binding modes of fragments and their drug-like superstructures binding to the same protein are mostly conserved, thereby providing experimental evidence for the preservation of fragment binding modes during molecular growing. Furthermore, small chemical changes in the fragment are tolerated without alteration of the fragment binding mode. The exceptions to this observation generally involve conformational variability of the molecules. Our data analysis also suggests that, provided enough fragments have been crystallized within a protein, good interaction coverage of the binding pocket is achieved. Last, we extended our study to 126 crystallization additives and discuss in which cases they provide information relevant to structure-based drug design.
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Affiliation(s)
- Malgorzata N Drwal
- Laboratoire d'Innovation Thérapeutique , UMR7200, Université de Strasbourg , 74 Route du Rhin , 67401 Illkirch , France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique , UMR7200, Université de Strasbourg , 74 Route du Rhin , 67401 Illkirch , France
| | - Carlos Perez
- Eli Lilly Research Laboratories , Avenida de la Industria, 30 , 28108 Alcobendas , Madrid , Spain
| | - Célien Jacquemard
- Laboratoire d'Innovation Thérapeutique , UMR7200, Université de Strasbourg , 74 Route du Rhin , 67401 Illkirch , France
| | - Jérémy Desaphy
- Lilly Research Laboratories, Eli Lilly and Company , Lilly Corporate Center , Indianapolis , Indiana 46285 , United States
| | - Esther Kellenberger
- Laboratoire d'Innovation Thérapeutique , UMR7200, Université de Strasbourg , 74 Route du Rhin , 67401 Illkirch , France
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49
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Kalinowsky L, Weber J, Balasupramaniam S, Baumann K, Proschak E. A Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions. ACS OMEGA 2018; 3:5704-5714. [PMID: 31458770 PMCID: PMC6641919 DOI: 10.1021/acsomega.7b01194] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 01/22/2018] [Indexed: 06/10/2023]
Abstract
The prediction of protein-ligand interactions and their corresponding binding free energy is a challenging task in structure-based drug design and related applications. Docking and scoring is broadly used to propose the binding mode and underlying interactions as well as to provide a measure for ligand affinity or differentiate between active and inactive ligands. Various studies have revealed that most docking software packages reliably predict the binding mode, although scoring remains a challenge. Here, a diverse benchmark data set of 99 matched molecular pairs (3D-MMPs) with experimentally determined X-ray structures and corresponding binding affinities is introduced. This data set was used to study the predictive power of 13 commonly used scoring functions to demonstrate the applicability of the 3D-MMP data set as a valuable tool for benchmarking scoring functions.
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Affiliation(s)
- Lena Kalinowsky
- Institute
of Pharmaceutical Chemistry, Goethe University
Frankfurt, Max-von-Laue
Str. 9, Frankfurt am Main D-60438, Germany
| | - Julia Weber
- Institute
of Pharmaceutical Chemistry, Goethe University
Frankfurt, Max-von-Laue
Str. 9, Frankfurt am Main D-60438, Germany
| | - Shantheya Balasupramaniam
- Institute
of Medicinal and Pharmaceutical Chemistry, University of Technology of Braunschweig, Beethovenstr. 55, Braunschweig D-38106, Germany
| | - Knut Baumann
- Institute
of Medicinal and Pharmaceutical Chemistry, University of Technology of Braunschweig, Beethovenstr. 55, Braunschweig D-38106, Germany
| | - Ewgenij Proschak
- Institute
of Pharmaceutical Chemistry, Goethe University
Frankfurt, Max-von-Laue
Str. 9, Frankfurt am Main D-60438, Germany
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50
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Friedrich NO, Simsir M, Kirchmair J. How Diverse Are the Protein-Bound Conformations of Small-Molecule Drugs and Cofactors? Front Chem 2018; 6:68. [PMID: 29637066 PMCID: PMC5880911 DOI: 10.3389/fchem.2018.00068] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 03/05/2018] [Indexed: 12/19/2022] Open
Abstract
Knowledge of the bioactive conformations of small molecules or the ability to predict them with theoretical methods is of key importance to the design of bioactive compounds such as drugs, agrochemicals, and cosmetics. Using an elaborate cheminformatics pipeline, which also evaluates the support of individual atom coordinates by the measured electron density, we compiled a complete set ("Sperrylite Dataset") of high-quality structures of protein-bound ligand conformations from the PDB. The Sperrylite Dataset consists of a total of 10,936 high-quality structures of 4,548 unique ligands. Based on this dataset, we assessed the variability of the bioactive conformations of 91 small molecules-each represented by a minimum of ten structures-and found it to be largely independent of the number of rotatable bonds. Sixty-nine molecules had at least two distinct conformations (defined by an RMSD greater than 1 Å). For a representative subset of 17 approved drugs and cofactors we observed a clear trend for the formation of few clusters of highly similar conformers. Even for proteins that share a very low sequence identity, ligands were regularly found to adopt similar conformations. For cofactors, a clear trend for extended conformations was measured, although in few cases also coiled conformers were observed. The Sperrylite Dataset is available for download from http://www.zbh.uni-hamburg.de/sperrylite_dataset.
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Affiliation(s)
- Nils-Ole Friedrich
- Department of Informatics, Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Méliné Simsir
- Department of Informatics, Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.,Molécules Thérapeutiques In Silico, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Johannes Kirchmair
- Department of Informatics, Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
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