1
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Ge Y, Pande V, Seierstad MJ, Damm-Ganamet KL. Exploring the Application of SiteMap and Site Finder for Focused Cryptic Pocket Identification. J Phys Chem B 2024; 128:6233-6245. [PMID: 38904218 DOI: 10.1021/acs.jpcb.4c00664] [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: 06/22/2024]
Abstract
The characterization of cryptic pockets has been elusive, despite substantial efforts. Computational modeling approaches, such as molecular dynamics (MD) simulations, can provide atomic-level details of binding site motions and binding pathways. However, the time scale that MD can achieve at a reasonable cost often limits its application for cryptic pocket identification. Enhanced sampling techniques can improve the efficiency of MD simulations by focused sampling of important regions of the protein, but prior knowledge of the simulated system is required to define the appropriate coordinates. In the case of a novel, unknown cryptic pocket, such information is not available, limiting the application of enhanced sampling techniques for cryptic pocket identification. In this work, we explore the ability of SiteMap and Site Finder, widely used commercial packages for pocket identification, to detect focus points on the protein and further apply other advanced computational methods. The information gained from this analysis enables the use of computational modeling, including enhanced MD sampling techniques, to explore potential cryptic binding pockets suggested by SiteMap and Site Finder. Here, we examined SiteMap and Site Finder results on 136 known cryptic pockets from a combination of the PocketMiner dataset (a recently curated set of cryptic pockets), the Cryptosite Set (a classic set of cryptic pockets), and Natural killer group 2D (NKG2D, a protein target where a cryptic pocket is confirmed). Our findings demonstrate the application of existing, well-studied tools in efficiently mapping potential regions harboring cryptic pockets.
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Affiliation(s)
- Yunhui Ge
- Computer-Aided Drug Design, Therapeutics Discovery, Janssen Research & Development, 3210 Merryfield Row, San Diego, California 92121, United States
| | - Vineet Pande
- Computer-Aided Drug Design, Therapeutics Discovery, Janssen Research & Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Mark J Seierstad
- Computer-Aided Drug Design, Therapeutics Discovery, Janssen Research & Development, 3210 Merryfield Row, San Diego, California 92121, United States
| | - Kelly L Damm-Ganamet
- Computer-Aided Drug Design, Therapeutics Discovery, Janssen Research & Development, 3210 Merryfield Row, San Diego, California 92121, United States
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2
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Otazu K, Olivos-Ramirez GE, Fernández-Silva PD, Vilca-Quispe J, Vega-Chozo K, Jimenez-Avalos GM, Chenet-Zuta ME, Sosa-Amay FE, Cárdenas Cárdenas RG, Ropón-Palacios G, Dattani N, Camps I. The Malaria Box molecules: a source for targeting the RBD and NTD cryptic pocket of the spike glycoprotein in SARS-CoV-2. J Mol Model 2024; 30:217. [PMID: 38888748 DOI: 10.1007/s00894-024-06006-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 06/05/2024] [Indexed: 06/20/2024]
Abstract
CONTEXT SARS-CoV-2, responsible for COVID-19, has led to over 500 million infections and more than 6 million deaths globally. There have been limited effective treatments available. The study aims to find a drug that can prevent the virus from entering host cells by targeting specific sites on the virus's spike protein. METHOD We examined 13,397 compounds from the Malaria Box library against two specific sites on the spike protein: the receptor-binding domain (RBD) and a predicted cryptic pocket. Using virtual screening, molecular docking, molecular dynamics, and MMPBSA techniques, they evaluated the stability of two compounds. TCMDC-124223 showed high stability and binding energy in the RBD, while TCMDC-133766 had better binding energy in the cryptic pocket. The study also identified that the interacting residues are conserved, which is crucial for addressing various virus variants. The findings provide insights into the potential of small molecules as drugs against the spike protein.
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Affiliation(s)
- Kewin Otazu
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | - Gustavo E Olivos-Ramirez
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
- HPQC Labs, Waterloo, Canada
| | - Pablo D Fernández-Silva
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | - Julissa Vilca-Quispe
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | - Karolyn Vega-Chozo
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | | | | | - Frida E Sosa-Amay
- Laboratorio de Farmacología y Toxicología, Facultad de Farmacia y Bioquímica, Universidad Nacional de la Amazonía Peruana, Iquitos, Perú
| | | | - Georcki Ropón-Palacios
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil.
- HPQC Labs, Waterloo, Canada.
| | - Nike Dattani
- HPQC College, Waterloo, Canada.
- HPQC Labs, Waterloo, Canada.
| | - Ihosvany Camps
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil.
- HPQC Labs, Waterloo, Canada.
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3
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Kombo DC, LaMarche MJ, Konkankit CC, Rackovsky S. Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences. Proteins 2024. [PMID: 38808365 DOI: 10.1002/prot.26704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024]
Abstract
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed previously, which encodes the intrinsic dynamic properties of the naturally occurring amino acids. We Fourier analyze the resulting sequences. It is demonstrated that classification models built using several different supervised learning methods are able to successfully distinguish folded from intrinsically disordered proteins from sequence alone. It is further shown that the most important sequence property for this discrimination is the sequence mobility, which is the sequence averaged value of the residue-specific average alpha carbon B factor. This is in agreement with previous work, in which we have demonstrated the central role played by the sequence mobility in protein dynamic bioinformatics and biophysics. This finding opens a path to the application of dynamic bioinformatics, in combination with machine learning algorithms, to a range of significant biomedical problems.
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Affiliation(s)
- David C Kombo
- Department of Medicinal Chemistry, Integrated Drug Discovery, Cambridge, Massachusetts, USA
| | - Matthew J LaMarche
- Department of Medicinal Chemistry, Integrated Drug Discovery, Cambridge, Massachusetts, USA
| | - Chilaluck C Konkankit
- Department of Chemistry and Chemical Biology, Baker Laboratory, Cornell University, Ithaca, New York, USA
| | - S Rackovsky
- Department of Chemistry and Chemical Biology, Baker Laboratory, Cornell University, Ithaca, New York, USA
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4
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Lefèbre J, Falk T, Ning Y, Rademacher C. Secondary Sites of the C-type Lectin-Like Fold. Chemistry 2024; 30:e202400660. [PMID: 38527187 DOI: 10.1002/chem.202400660] [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: 02/18/2024] [Revised: 03/23/2024] [Accepted: 03/25/2024] [Indexed: 03/27/2024]
Abstract
C-type lectins are a large superfamily of proteins involved in a multitude of biological processes. In particular, their involvement in immunity and homeostasis has rendered them attractive targets for diverse therapeutic interventions. They share a characteristic C-type lectin-like domain whose adaptability enables them to bind a broad spectrum of ligands beyond the originally defined canonical Ca2+-dependent carbohydrate binding. Together with variable domain architecture and high-level conformational plasticity, this enables C-type lectins to meet diverse functional demands. Secondary sites provide another layer of regulation and are often intricately linked to functional diversity. Located remote from the canonical primary binding site, secondary sites can accommodate ligands with other physicochemical properties and alter protein dynamics, thus enhancing selectivity and enabling fine-tuning of the biological response. In this review, we outline the structural determinants allowing C-type lectins to perform a large variety of tasks and to accommodate the ligands associated with it. Using the six well-characterized Ca2+-dependent and Ca2+-independent C-type lectin receptors DC-SIGN, langerin, MGL, dectin-1, CLEC-2 and NKG2D as examples, we focus on the characteristics of non-canonical interactions and secondary sites and their potential use in drug discovery endeavors.
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Affiliation(s)
- Jonathan Lefèbre
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport, Sciences, University of Vienna, Vienna, Austria
- Department of Microbiology, Immunology and Genetics, University of Vienna, Max F. Perutz Labs, Vienna, Austria
| | - Torben Falk
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport, Sciences, University of Vienna, Vienna, Austria
- Department of Microbiology, Immunology and Genetics, University of Vienna, Max F. Perutz Labs, Vienna, Austria
| | - Yunzhan Ning
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport, Sciences, University of Vienna, Vienna, Austria
- Department of Microbiology, Immunology and Genetics, University of Vienna, Max F. Perutz Labs, Vienna, Austria
| | - Christoph Rademacher
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
- Department of Microbiology, Immunology and Genetics, University of Vienna, Max F. Perutz Labs, Vienna, Austria
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5
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Borsatto A, Gianquinto E, Rizzi V, Gervasio FL. SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces. J Chem Theory Comput 2024; 20:3335-3348. [PMID: 38563746 PMCID: PMC11044271 DOI: 10.1021/acs.jctc.3c01318] [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: 12/01/2023] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/04/2024]
Abstract
Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyze a set of well-characterized protein-protein interactions harboring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we developed a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 β-lactamase.
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Affiliation(s)
- Alberto Borsatto
- School
of Pharmaceutical Sciences, University of
Geneva, 1205 Geneva, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1205 Geneva, Switzerland
- Swiss
Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Eleonora Gianquinto
- Department
of Drug Science and Technology, University
of Turin, 10125 Turin, Italy
| | - Valerio Rizzi
- School
of Pharmaceutical Sciences, University of
Geneva, 1205 Geneva, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1205 Geneva, Switzerland
- Swiss
Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Francesco Luigi Gervasio
- School
of Pharmaceutical Sciences, University of
Geneva, 1205 Geneva, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1205 Geneva, Switzerland
- Swiss
Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Department
of Chemistry, University College London, WC1 H0AJ London, United Kingdom
- Institute
of Structural and Molecular Biology, University
College London, WC1E7JE London, United Kingdom
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6
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Mansoor S, Baek M, Park H, Lee GR, Baker D. Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling. J Chem Theory Comput 2024; 20:2689-2695. [PMID: 38547871 PMCID: PMC11008089 DOI: 10.1021/acs.jctc.3c01057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 04/10/2024]
Abstract
Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here, we explore the use of variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out a search in this space guided by a structure quality metric, and then use RoseTTAFold guided by the sampled structural information to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, train the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assess the extent of sampling close to crystal structures withheld from training. We find that our latent space sampling procedure rapidly generates ensembles with high structural quality and is able to sample within 1 Å of held-out crystal structures, with a consistency higher than that of MD simulation or AlphaFold2 prediction. The sampled structures sufficiently recapitulate the cryptic pockets in the held-out K-Ras structures to allow for small molecule docking.
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Affiliation(s)
- Sanaa Mansoor
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Molecular
Engineering Graduate Program, University
of Washington, Seattle, Washington 98195, United States
| | - Minkyung Baek
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
- School
of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Hahnbeom Park
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Brain
Science Institute, Korea Institute of Science
and Technology, Seoul 02792, Republic of Korea
| | - Gyu Rie Lee
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - David Baker
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute
for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, United States
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7
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Zhang Y, Guo J, Liu Y, Qu Y, Li YQ, Mu Y, Li W. An allosteric mechanism for potent inhibition of SARS-CoV-2 main proteinase. Int J Biol Macromol 2024; 265:130644. [PMID: 38462102 DOI: 10.1016/j.ijbiomac.2024.130644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/25/2024] [Accepted: 03/04/2024] [Indexed: 03/12/2024]
Abstract
The main proteinase (Mpro) of SARS-CoV-2 plays a critical role in cleaving viral polyproteins into functional proteins required for viral replication and assembly, making it a prime drug target for COVID-19. It is well known that noncompetitive inhibition offers potential therapeutic options for treating COVID-19, which can effectively reduce the likelihood of cross-reactivity with other proteins and increase the selectivity of the drug. Therefore, the discovery of allosteric sites of Mpro has both scientific and practical significance. In this study, we explored the binding characteristics and inhibiting process of Mpro activity by two recently reported allosteric inhibitors, pelitinib and AT7519 which were obtained by the X-ray screening experiments, to probe the allosteric mechanism via molecular dynamic (MD) simulations. We found that pelitinib and AT7519 can stably bind to Mpro far from the active site. The binding affinity is estimated to be -24.37 ± 4.14 and - 26.96 ± 4.05 kcal/mol for pelitinib and AT7519, respectively, which is considerably stable compared with orthosteric drugs. Furthermore, the strong binding caused clear changes in the catalytic site of Mpro, thus decreasing the substrate accessibility. The community network analysis also validated that pelitinib and AT7519 strengthened intra- and inter-domain communication of Mpro dimer, resulting in a rigid Mpro, which could negatively impact substrate binding. In summary, our findings provide the detailed working mechanism for the two experimentally observed allosteric sites of Mpro. These allosteric sites greatly enhance the 'druggability' of Mpro and represent attractive targets for the development of new Mpro inhibitors.
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Affiliation(s)
- Yunju Zhang
- School of Physics, Shandong University, China
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
| | - Yang Liu
- School of Physics, Shandong University, China
| | - Yuanyuan Qu
- School of Physics, Shandong University, China
| | | | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
| | - Weifeng Li
- School of Physics, Shandong University, China.
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8
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Feidakis CP, Krivak R, Hoksza D, Novotny M. AHoJ-DB: A PDB-wide assignment of apo & holo relationships based on individual protein-ligand interactions. J Mol Biol 2024:168545. [PMID: 38508305 DOI: 10.1016/j.jmb.2024.168545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
A single protein structure is rarely sufficient to capture the conformational variability of a protein. Both bound and unbound (holo and apo) forms of a protein are essential for understanding its geometry and making meaningful comparisons. Nevertheless, docking or drug design studies often still consider only single protein structures in their holo form, which are for the most part rigid. With the recent explosion in the field of structural biology, large, curated datasets are urgently needed. Here, we use a previously developed application (AHoJ) to perform a comprehensive search for apo-holo pairs for 468,293 biologically relevant protein-ligand interactions across 27,983 proteins. In each search, the binding pocket is captured and mapped across existing structures within the same UniProt, and the mapped pockets are annotated as apo or holo, based on the presence or absence of ligands. We assemble the results into a database, AHoJ-DB (www.apoholo.cz/db), that captures the variability of proteins with identical sequences, thereby exposing the agents responsible for the observed differences in geometry. We report several metrics for each annotated pocket, and we also include binding pockets that form at the interface of multiple chains. Analysis of the database shows that about 24% of the binding sites occur at the interface of two or more chains and that less than 50% of the total binding sites processed have an apo form in the PDB. These results can be used to train and evaluate predictors, discover potentially druggable proteins, and reveal protein- and ligand-specific relationships that were previously obscured by intermittent or partial data. Availability: http://apoholo.cz/db.
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Affiliation(s)
- Christos P Feidakis
- Department of Cell Biology, Faculty of Science, Charles University, Prague 12843, Czech Republic
| | - Radoslav Krivak
- Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague 12116, Czech Republic; Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague 16000, Czech Republic
| | - David Hoksza
- Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague 12116, Czech Republic
| | - Marian Novotny
- Department of Cell Biology, Faculty of Science, Charles University, Prague 12843, Czech Republic.
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9
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Meller A, Kelly D, Smith LG, Bowman GR. Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants. Protein Sci 2024; 33:e4902. [PMID: 38358129 PMCID: PMC10868452 DOI: 10.1002/pro.4902] [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: 09/01/2023] [Revised: 12/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024]
Abstract
The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular BiophysicsWashington University in St. LouisSt. LouisMissouriUSA
- Medical Scientist Training ProgramWashington University in St. LouisSt. LouisMissouriUSA
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Devin Kelly
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Louis G. Smith
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gregory R. Bowman
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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10
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Botnari M, Tchertanov L. Synergy of Mutation-Induced Effects in Human Vitamin K Epoxide Reductase: Perspectives and Challenges for Allo-Network Modulator Design. Int J Mol Sci 2024; 25:2043. [PMID: 38396721 PMCID: PMC10889538 DOI: 10.3390/ijms25042043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
The human Vitamin K Epoxide Reductase Complex (hVKORC1), a key enzyme transforming vitamin K into the form necessary for blood clotting, requires for its activation the reducing equivalents delivered by its redox partner through thiol-disulfide exchange reactions. The luminal loop (L-loop) is the principal mediator of hVKORC1 activation, and it is a region frequently harbouring numerous missense mutations. Four L-loop hVKORC1 mutants, suggested in vitro as either resistant (A41S, H68Y) or completely inactive (S52W, W59R), were studied in the oxidised state by numerical approaches (in silico). The DYNASOME and POCKETOME of each mutant were characterised and compared to the native protein, recently described as a modular protein composed of the structurally stable transmembrane domain (TMD) and the intrinsically disordered L-loop, exhibiting quasi-independent dynamics. The DYNASOME of mutants revealed that L-loop missense point mutations impact not only its folding and dynamics, but also those of the TMD, highlighting a strong mutation-specific interdependence between these domains. Another consequence of the mutation-induced effects manifests in the global changes (geometric, topological, and probabilistic) of the newly detected cryptic pockets and the alternation of the recognition properties of the L-loop with its redox protein. Based on our results, we postulate that (i) intra-protein allosteric regulation and (ii) the inherent allosteric regulation and cryptic pockets of each mutant depend on its DYNASOME; and (iii) the recognition of the redox protein by hVKORC1 (INTERACTOME) depend on their DYNASOME. This multifaceted description of proteins produces "omics" data sets, crucial for understanding the physiological processes of proteins and the pathologies caused by alteration of the protein properties at various "omics" levels. Additionally, such characterisation opens novel perspectives for the development of "allo-network drugs" essential for the treatment of blood disorders.
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Affiliation(s)
| | - Luba Tchertanov
- Centre Borelli, École Normale Supérieure (ENS) Paris-Saclay, Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, 4 Avenue des Sciences, F-91190 Gif-sur-Yvette, France;
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11
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Lu W, Zhang J, Huang W, Zhang Z, Jia X, Wang Z, Shi L, Li C, Wolynes PG, Zheng S. DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model. Nat Commun 2024; 15:1071. [PMID: 38316797 PMCID: PMC10844226 DOI: 10.1038/s41467-024-45461-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/24/2024] [Indexed: 02/07/2024] Open
Abstract
While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they're computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.
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Affiliation(s)
- Wei Lu
- Galixir Technologies, 200100, Shanghai, China.
| | | | - Weifeng Huang
- School of Pharmaceutical Science, Sun Yat-sen University, 510006, Guangzhou, China
| | | | - Xiangyu Jia
- Galixir Technologies, 200100, Shanghai, China
| | - Zhenyu Wang
- Galixir Technologies, 200100, Shanghai, China
| | - Leilei Shi
- Galixir Technologies, 200100, Shanghai, China
| | - Chengtao Li
- Galixir Technologies, 200100, Shanghai, China
| | - Peter G Wolynes
- Center for Theoretical Biological Physics and Department of Chemistry, Rice University, Houston, TX, 77005, USA
| | - Shuangjia Zheng
- Global Institute of Future Technology, Shanghai Jiao Tong University, 200240, Shanghai, China.
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12
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Bekker GJ, Fukunishi Y, Higo J, Kamiya N. Binding Mechanism of Riboswitch to Natural Ligand Elucidated by McMD-Based Dynamic Docking Simulations. ACS OMEGA 2024; 9:3412-3422. [PMID: 38284074 PMCID: PMC10809319 DOI: 10.1021/acsomega.3c06826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/16/2023] [Accepted: 12/28/2023] [Indexed: 01/30/2024]
Abstract
Flavin mononucleotide riboswitches are common among many pathogenic bacteria and are therefore considered to be an attractive target for antibiotics development. The riboswitch binds riboflavin (RBF, also known as vitamin B2), and although an experimental structure of their complex has been solved with the ligand bound deep inside the RNA molecule in a seemingly unreachable state, the binding mechanism between these molecules is not yet known. We have therefore used our Multicanonical Molecular Dynamics (McMD)-based dynamic docking protocol to analyze their binding mechanism by simulating the binding process between the riboswitch aptamer domain and the RBF, starting from the apo state of the riboswitch. Here, the refinement stage was crucial to identify the native binding configuration, as several other binding configurations were also found by McMD-based docking simulations. RBF initially binds the interface between P4 and P6 including U61 and G62, which forms a gateway where the ligand lingers until this gateway opens sufficiently to allow the ligand to pass through and slip into the hidden binding site including A48, A49, and A85.
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Affiliation(s)
- Gert-Jan Bekker
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Yoshifumi Fukunishi
- Cellular
and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology
(AIST), 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Junichi Higo
- Graduate
School of Information Science, University
of Hyogo, 7-1-28 minatojima
Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Narutoshi Kamiya
- Graduate
School of Information Science, University
of Hyogo, 7-1-28 minatojima
Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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13
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Guo M, Li Z, Gu M, Gu J, You Q, Wang L. Targeting phosphatases: From molecule design to clinical trials. Eur J Med Chem 2024; 264:116031. [PMID: 38101039 DOI: 10.1016/j.ejmech.2023.116031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/03/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023]
Abstract
Phosphatase is a kind of enzyme that can dephosphorylate target proteins, which can be divided into serine/threonine phosphatase and tyrosine phosphatase according to its mode of action. Current evidence showed multiple phosphatases were highly correlated with diseases including various cancers, demonstrating them as potential targets. However, currently, targeting phosphatases with small molecules faces many challenges, resulting in no drug approved. In this case, phosphatases are even regarded as "undruggable" targets for a long time. Recently, a variety of strategies have been adopted in the design of small molecule inhibitors targeting phosphatases, leading many of them to enter into the clinical trials. In this review, we classified these inhibitors into 4 types, including (1) molecular glues, (2) small molecules targeting catalytic sites, (3) allosteric inhibition, and (4) bifunctional molecules (proteolysis targeting chimeras, PROTACs). These molecules with diverse strategies prove the feasibility of phosphatases as drug targets. In addition, the combination therapy of phosphatase inhibitors with other drugs has also entered clinical trials, which suggests a broad prospect. Thus, targeting phosphatases with small molecules by different strategies is emerging as a promising way in the modulation of pathogenetic phosphorylation.
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Affiliation(s)
- Mochen Guo
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing, 210009, China; Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Zekun Li
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing, 210009, China; Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Mingxiao Gu
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing, 210009, China; Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Junrui Gu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Qidong You
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing, 210009, China; Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
| | - Lei Wang
- State Key Laboratory of Natural Medicines and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing, 210009, China; Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
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14
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Li M, Lan X, Lu X, Zhang J. A Structure-Based Allosteric Modulator Design Paradigm. HEALTH DATA SCIENCE 2023; 3:0094. [PMID: 38487194 PMCID: PMC10904074 DOI: 10.34133/hds.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 10/11/2023] [Indexed: 03/17/2024]
Abstract
Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.
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Affiliation(s)
- Mingyu Li
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaobin Lan
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xun Lu
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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15
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Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. J Chem Theory Comput 2023; 19:7478-7495. [PMID: 37883810 PMCID: PMC10653122 DOI: 10.1021/acs.jctc.3c00814] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.
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Affiliation(s)
- Song Xia
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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16
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Barrera-Téllez FJ, Prieto-Martínez FD, Hernández-Campos A, Martínez-Mayorga K, Castillo-Bocanegra R. In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana. Int J Mol Sci 2023; 24:16046. [PMID: 38003236 PMCID: PMC10671491 DOI: 10.3390/ijms242216046] [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: 08/17/2023] [Revised: 10/23/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023] Open
Abstract
Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.
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Affiliation(s)
- Francisco J. Barrera-Téllez
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Fernando D. Prieto-Martínez
- Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Carretera Mérida-Tetiz, Km. 4.5, Ucú 97357, Mexico
| | - Alicia Hernández-Campos
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | - Karina Martínez-Mayorga
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Unidad Mérida, Universidad Nacional Autónoma de México, Sierra Papacal, Mérida 97302, Mexico
| | - Rafael Castillo-Bocanegra
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
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17
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Maritz-Olivier C, Ferreira M, Olivier NA, Crafford J, Stutzer C. Mining gene expression data for rational identification of novel drug targets and vaccine candidates against the cattle tick, Rhipicephalus microplus. EXPERIMENTAL & APPLIED ACAROLOGY 2023; 91:291-317. [PMID: 37755526 PMCID: PMC10562289 DOI: 10.1007/s10493-023-00838-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
Control of complex parasites via vaccination remains challenging, with the current combination of vaccines and small drugs remaining the choice for an integrated control strategy. Studies conducted to date, are providing evidence that multicomponent vaccines will be needed for the development of protective vaccines against endo- and ectoparasites, though multicomponent vaccines require an in-depth understanding of parasite biology which remains insufficient for ticks. With the rapid development and spread of acaricide resistance in ticks, new targets for acaricide development also remains to be identified, along with novel targets that can be exploited for the design of lead compounds. In this study, we analysed the differential gene expression of Rhipicephalus microplus ticks that were fed on cattle vaccinated with a multi-component vaccine (Bm86 and 3 putative Bm86-binding proteins). The data was scrutinised for the identification of vaccine targets, small drug targets and novel pathways that can be evaluated in future studies. Limitations associated with targeting novel proteins for vaccine and/or drug design is also discussed and placed into the context of challenges arising when targeting large protein families and intracellular localised proteins. Lastly, this study provide insight into how Bm86-based vaccines may reduce successful uptake and digestion of the bloodmeal and overall tick fecundity.
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Affiliation(s)
- Christine Maritz-Olivier
- Department of Biochemistry, Genetics and Microbiology, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, Gauteng, South Africa.
| | - Mariëtte Ferreira
- Department of Biochemistry, Genetics and Microbiology, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, Gauteng, South Africa
| | - Nicholas A Olivier
- DNA Microarray Laboratory, Department of Plant Sciences, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, Gauteng, South Africa
| | - Jan Crafford
- Department of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria, Pretoria, Gauteng, South Africa
| | - Christian Stutzer
- Department of Biochemistry, Genetics and Microbiology, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, Gauteng, South Africa.
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18
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Verkhivker G, Alshahrani M, Gupta G. Exploring Conformational Landscapes and Cryptic Binding Pockets in Distinct Functional States of the SARS-CoV-2 Omicron BA.1 and BA.2 Trimers: Mutation-Induced Modulation of Protein Dynamics and Network-Guided Prediction of Variant-Specific Allosteric Binding Sites. Viruses 2023; 15:2009. [PMID: 37896786 PMCID: PMC10610873 DOI: 10.3390/v15102009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/23/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
A significant body of experimental structures of SARS-CoV-2 spike trimers for the BA.1 and BA.2 variants revealed a considerable plasticity of the spike protein and the emergence of druggable binding pockets. Understanding the interplay of conformational dynamics changes induced by the Omicron variants and the identification of cryptic dynamic binding pockets in the S protein is of paramount importance as exploring broad-spectrum antiviral agents to combat the emerging variants is imperative. In the current study, we explore conformational landscapes and characterize the universe of binding pockets in multiple open and closed functional spike states of the BA.1 and BA.2 Omicron variants. By using a combination of atomistic simulations, a dynamics network analysis, and an allostery-guided network screening of binding pockets in the conformational ensembles of the BA.1 and BA.2 spike conformations, we identified all experimentally known allosteric sites and discovered significant variant-specific differences in the distribution of binding sites in the BA.1 and BA.2 trimers. This study provided a structural characterization of the predicted cryptic pockets and captured the experimentally known allosteric sites, revealing the critical role of conformational plasticity in modulating the distribution and cross-talk between functional binding sites. We found that mutational and dynamic changes in the BA.1 variant can induce the remodeling and stabilization of a known druggable pocket in the N-terminal domain, while this pocket is drastically altered and may no longer be available for ligand binding in the BA.2 variant. Our results predicted the experimentally known allosteric site in the receptor-binding domain that remains stable and ranks as the most favorable site in the conformational ensembles of the BA.2 variant but could become fragmented and less probable in BA.1 conformations. We also uncovered several cryptic pockets formed at the inter-domain and inter-protomer interface, including functional regions of the S2 subunit and stem helix region, which are consistent with the known role of pocket residues in modulating conformational transitions and antibody recognition. The results of this study are particularly significant for understanding the dynamic and network features of the universe of available binding pockets in spike proteins, as well as the effects of the Omicron-variant-specific modulation of preferential druggable pockets. The exploration of predicted druggable sites can present a new and previously underappreciated opportunity for therapeutic interventions for Omicron variants through the conformation-selective and variant-specific targeting of functional sites involved in allosteric changes.
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Affiliation(s)
- Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
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19
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Odstrcil RE, Dutta P, Liu J. Prediction of the Peptide-TIM3 Binding Site in Inhibiting TIM3-Galectin 9 Binding Pathways. J Chem Theory Comput 2023; 19:6500-6509. [PMID: 37649156 DOI: 10.1021/acs.jctc.3c00487] [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: 09/01/2023]
Abstract
T-cell immunoglobulin and mucin domain-containing protein-3 (TIM3) is an important receptor protein that modulates the immune system. The binding of TIM3 with Galectin 9 (GAL9) triggers immune system suppression, but the TIM3-GAL9 binding can be inhibited by binding of the peptide P26 to TIM3. A fast and accurate prediction of the P26-TIM3 binding site is crucial and a prerequisite for the investigation of P26-TIM3 interactions and TIM3-GAL9 binding pathways. Here, we present a machine learning approach, which considers protein conformational changes, to quickly identify the ligand-binding site on TIM3. Our results show that the P26 binding site is located near the C″-D loop of TIM3. Further simulations show that the binding pose is stabilized by a variety of electrostatic and hydrophobic interactions. Binding of P26 can alter the conformations of nearby glycan side chains on TIM3, providing possible mechanisms of how P26 inhibits TIM3-GAL9 binding pathways. The insights from this work will facilitate the identification of other peptides or antibodies that may also inhibit the TIM3-GAL9 pathways and eventually lead to improved attempts in the modulation of the TIM3-GAL9 immunosuppression pathways. The strategies and machine learning method can be generalized to study ligand-receptor binding when the conformational changes during the binding are important.
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Affiliation(s)
- Ryan E Odstrcil
- School of Mechanical and Materials Engineering, Washington State University, Pullman ,Washington 99164, United States
| | - Prashanta Dutta
- School of Mechanical and Materials Engineering, Washington State University, Pullman ,Washington 99164, United States
| | - Jin Liu
- School of Mechanical and Materials Engineering, Washington State University, Pullman ,Washington 99164, United States
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20
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Hellemann E, Durrant JD. Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling. J Chem Theory Comput 2023; 19:5677-5689. [PMID: 37585617 PMCID: PMC10500992 DOI: 10.1021/acs.jctc.3c00478] [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: 05/05/2023] [Indexed: 08/18/2023]
Abstract
Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.
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Affiliation(s)
- Erich Hellemann
- Department of Biological
Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Jacob D. Durrant
- Department of Biological
Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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21
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Hommel U, Hurth K, Rondeau JM, Vulpetti A, Ostermeier D, Boettcher A, Brady JP, Hediger M, Lehmann S, Koch E, Blechschmidt A, Yamamoto R, Tundo Dottorello V, Haenni-Holzinger S, Kaiser C, Lehr P, Lingel A, Mureddu L, Schleberger C, Blank J, Ramage P, Freuler F, Eder J, Bornancin F. Discovery of a selective and biologically active low-molecular weight antagonist of human interleukin-1β. Nat Commun 2023; 14:5497. [PMID: 37679328 PMCID: PMC10484922 DOI: 10.1038/s41467-023-41190-0] [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: 12/12/2022] [Accepted: 08/25/2023] [Indexed: 09/09/2023] Open
Abstract
Human interleukin-1β (hIL-1β) is a pro-inflammatory cytokine involved in many diseases. While hIL-1β directed antibodies have shown clinical benefit, an orally available low-molecular weight antagonist is still elusive, limiting the applications of hIL-1β-directed therapies. Here we describe the discovery of a low-molecular weight hIL-1β antagonist that blocks the interaction with the IL-1R1 receptor. Starting from a low affinity fragment-based screening hit 1, structure-based optimization resulted in a compound (S)-2 that binds and antagonizes hIL-1β with single-digit micromolar activity in biophysical, biochemical, and cellular assays. X-ray analysis reveals an allosteric mode of action that involves a hitherto unknown binding site in hIL-1β encompassing two loops involved in hIL-1R1/hIL-1β interactions. We show that residues of this binding site are part of a conformationally excited state of the mature cytokine. The compound antagonizes hIL-1β function in cells, including primary human fibroblasts, demonstrating the relevance of this discovery for future development of hIL-1β directed therapeutics.
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Affiliation(s)
- Ulrich Hommel
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland.
| | - Konstanze Hurth
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland.
| | - Jean-Michel Rondeau
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Anna Vulpetti
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Daniela Ostermeier
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Andreas Boettcher
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Jacob Peter Brady
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Michael Hediger
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Sylvie Lehmann
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Elke Koch
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Anke Blechschmidt
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Rina Yamamoto
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | | | | | - Christian Kaiser
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Philipp Lehr
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Andreas Lingel
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Luca Mureddu
- Leicester Institute of Structural and Chemical Biology, Department of Molecular and Cell Biology, University of Leicester, Leicester, LE1 7RH, UK
| | - Christian Schleberger
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Jutta Blank
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Paul Ramage
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Felix Freuler
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Joerg Eder
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Frédéric Bornancin
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland.
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22
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Yang WC, Gong DH, Hong Wu, Gao YY, Hao GF. Grasping cryptic binding sites to neutralize drug resistance in the field of anticancer. Drug Discov Today 2023; 28:103705. [PMID: 37453458 DOI: 10.1016/j.drudis.2023.103705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/09/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
Drug resistance is a significant obstacle to successful cancer treatment. The utilization and development of cryptic binding sites (CBSs) in proteins involved in cancer-related drug-resistance (CRDR) could help to overcome that drug resistance. However, there is no comprehensive review of the successful use of CBSs in addressing CRDR. Here, we have systematically summarized and analyzed the opportunities and challenges of using CBSs in addressing CRDR and revealed the key role that CBSs have in targeting CRDR. First, we have identified the CRDR targets and the corresponding CBSs. Second, we discuss the mechanisms by which CBSs can overcome CRDR. Finally, we have provided examples of successful CBS applications in addressing CRDR. We hope that this approach will provide guidance to biologists and chemists in effectively utilizing CBSs for the development of new drugs to alleviate CRDR.
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Affiliation(s)
- Wei-Cheng Yang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Dao-Hong Gong
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Hong Wu
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan 430079, China.
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23
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Abstract
Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.
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Affiliation(s)
- Christoph Gorgulla
- Harvard Medical School and Physics Department, Harvard University, Boston, Massachusetts, USA;
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Current affiliation: Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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24
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Hellemann E, Durrant JD. Worth the weight: Sub-Pocket EXplorer (SubPEx), a weighted-ensemble method to enhance binding-pocket conformational sampling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.03.539330. [PMID: 37251500 PMCID: PMC10214482 DOI: 10.1101/2023.05.03.539330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.
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Affiliation(s)
- Erich Hellemann
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, 15260, United States
| | - Jacob D. Durrant
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, 15260, United States
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25
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Graef J, Ehrt C, Rarey M. Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3. J Chem Inf Model 2023; 63:3128-3137. [PMID: 37130052 DOI: 10.1021/acs.jcim.3c00336] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.
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Affiliation(s)
- Joel Graef
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- 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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26
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Mattheisen JM, Limberakis C, Ruggeri RB, Dowling MS, Am Ende CW, Ceraudo E, Huber T, McClendon CL, Sakmar TP. Bioorthogonal Tethering Enhances Drug Fragment Affinity for G Protein-Coupled Receptors in Live Cells. J Am Chem Soc 2023; 145:11173-11184. [PMID: 37116188 DOI: 10.1021/jacs.3c00972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
G protein-coupled receptors (GPCRs) modulate diverse cellular signaling pathways and are important drug targets. Despite the availability of high-resolution structures, the discovery of allosteric modulators remains challenging due to the dynamic nature of GPCRs in native membranes. We developed a strategy to covalently tether drug fragments adjacent to allosteric sites in GPCRs to enhance their potency and enable fragment-based drug screening in cell-based systems. We employed genetic code expansion to site-specifically introduce noncanonical amino acids with reactive groups in C-C chemokine receptor 5 (CCR5) near an allosteric binding site for the drug maraviroc. We then used molecular dynamics simulations to design heterobifunctional maraviroc analogues consisting of a drug fragment connected by a flexible linker to a reactive moiety capable of undergoing a bioorthogonal coupling reaction. We synthesized a library of these analogues and employed the bioorthogonal inverse electron demand Diels-Alder reaction to couple the analogues to the engineered CCR5 in live cells, which were then assayed using cell-based signaling assays. Tetherable low-affinity maraviroc fragments displayed an increase in potency for CCR5 engineered with reactive unnatural amino acids that were adjacent to the maraviroc binding site. The strategy we describe to tether novel drug fragments to GPCRs should prove useful to probe allosteric or cryptic binding site functionality in fragment-based GPCR-targeted drug discovery.
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Affiliation(s)
- Jordan M Mattheisen
- Laboratory of Chemical Biology and Signal Transduction, The Rockefeller University, New York, New York 10065, United States
- Tri-Institutional PhD Program in Chemical Biology, New York, New York 10065, United States
| | - Chris Limberakis
- Pfizer Worldwide Research, Development, and Medical, Groton, Connecticut 06340, United States
| | - Roger B Ruggeri
- Pfizer Worldwide Research, Development, and Medical, Groton, Connecticut 06340, United States
| | - Matthew S Dowling
- Pfizer Worldwide Research, Development, and Medical, Groton, Connecticut 06340, United States
| | - Christopher W Am Ende
- Pfizer Worldwide Research, Development, and Medical, Groton, Connecticut 06340, United States
| | - Emilie Ceraudo
- Laboratory of Chemical Biology and Signal Transduction, The Rockefeller University, New York, New York 10065, United States
| | - Thomas Huber
- Laboratory of Chemical Biology and Signal Transduction, The Rockefeller University, New York, New York 10065, United States
| | - Christopher L McClendon
- Pfizer Worldwide Research, Development, and Medical, Cambridge, Massachusetts 02139, United States
| | - Thomas P Sakmar
- Laboratory of Chemical Biology and Signal Transduction, The Rockefeller University, New York, New York 10065, United States
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27
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Bekker GJ, Araki M, Oshima K, Okuno Y, Kamiya N. Mutual induced-fit mechanism drives binding between intrinsically disordered Bim and cryptic binding site of Bcl-xL. Commun Biol 2023; 6:349. [PMID: 36997643 PMCID: PMC10063584 DOI: 10.1038/s42003-023-04720-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
The intrinsically disordered region (IDR) of Bim binds to the flexible cryptic site of Bcl-xL, a pro-survival protein involved in cancer progression that plays an important role in initiating apoptosis. However, their binding mechanism has not yet been elucidated. We have applied our dynamic docking protocol, which correctly reproduced both the IDR properties of Bim and the native bound configuration, as well as suggesting other stable/meta-stable binding configurations and revealed the binding pathway. Although the cryptic site of Bcl-xL is predominantly in a closed conformation, initial binding of Bim in an encounter configuration leads to mutual induced-fit binding, where both molecules adapt to each other; Bcl-xL transitions to an open state as Bim folds from a disordered to an α-helical conformation while the two molecules bind each other. Finally, our data provides new avenues to develop novel drugs by targeting newly discovered stable conformations of Bcl-xL.
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Affiliation(s)
- Gert-Jan Bekker
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Kanji Oshima
- Bio-Pharma Research Laboratories, KANEKA CORPORATION, 1-8 Miyamae-cho, Takasago-cho, Takasago, Hyogo, 676-8688, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Narutoshi Kamiya
- Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.
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28
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Zheng W. Predicting allosteric sites using fast conformational sampling as guided by coarse-grained normal modes. J Chem Phys 2023; 158:124127. [PMID: 37003737 PMCID: PMC10066797 DOI: 10.1063/5.0141630] [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: 01/07/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023] Open
Abstract
To computationally identify cryptic binding sites for allosteric modulators, we have developed a fast and simple conformational sampling scheme guided by coarse-grained normal modes solved from the elastic network models followed by atomistic backbone and sidechain reconstruction. Despite the complexity of conformational changes associated with ligand binding, we previously showed that simply sampling along each of the lowest 30 modes can adequately restructure cryptic sites so they are detectable by pocket finding programs like Concavity. Here, we applied this method to study four classical examples of allosteric regulation (GluR2 receptor, GroEL chaperonin, GPCR, and myosin). Our method along with alternative methods has been utilized to locate known allosteric sites and predict new promising allosteric sites. Compared with other sampling methods based on extensive molecular dynamics simulation, our method is both faster (1-2 h for an average-size protein of ∼400 residues) and more flexible (it can be easily integrated with any structure-based pocket finding methods), so it is suitable for high-throughput screening of large datasets of protein structures at the genome scale.
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Affiliation(s)
- Wenjun Zheng
- Department of Physics, University at Buffalo, 239 Fronczak Hall, Buffalo, New York 14260, USA
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29
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Allardyce D, Adu Mantey P, Szalecka M, Nkwo R, Loizidou EZ. Identification of a new class of proteasome inhibitors based on a naphthyl-azotricyclic-urea-phenyl scaffold. RSC Med Chem 2023; 14:573-582. [PMID: 36970145 PMCID: PMC10034219 DOI: 10.1039/d2md00404f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/06/2023] [Indexed: 02/09/2023] Open
Abstract
Proteasomes play an important role in protein degradation and regulation of many cellular pathways by maintaining protein balance. Inhibitors of proteasomes disrupt this balance affecting proteins that are key in malignancies and as such have found applications in the treatment of multiple myeloma and mantle cell lymphoma. However, resistance mechanisms have been reported for these proteasome inhibitors including mutations at the β5 site which necessitates the constant development of new inhibitors. In this work, we report the identification of a new class of proteasome inhibitors, polycyclic molecules bearing a naphthyl-azotricyclic-urea-phenyl scaffold, from screening of the ZINC library of natural products. The most potent of these compounds showed evidence of dose dependency through proteasome assays with IC50 values in the low micromolar range, and kinetic analysis revealed competitive binding at the β5c site with an estimated inhibition constant, K i, of 1.15 μM. Inhibition was also shown for the β5i site of the immunoproteasome at levels similar to those of the constitutive proteasome. Structure-activity relationship studies identified the naphthyl substituent to be crucial for activity and this was attributed to enhanced hydrophobic interactions within β5c. Further to this, halogen substitution within the naphthyl ring enhanced the activity and allowed for π-π interactions with Y169 in β5c and Y130 and F124 in β5i. The combined data highlight the importance of hydrophobic and halogen interactions in β5 binding and assist in the design of next generation inhibitors of proteasomes.
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Affiliation(s)
- Duncan Allardyce
- Faculty of Science and Technology, Department of Natural Sciences, Middlesex University The Burroughs London NW4 4BT UK
| | - Priscilla Adu Mantey
- Faculty of Science and Technology, Department of Natural Sciences, Middlesex University The Burroughs London NW4 4BT UK
| | - Monika Szalecka
- Faculty of Science and Technology, Department of Natural Sciences, Middlesex University The Burroughs London NW4 4BT UK
| | - Robert Nkwo
- Faculty of Science and Technology, Department of Natural Sciences, Middlesex University The Burroughs London NW4 4BT UK
| | - Eriketi Z Loizidou
- Faculty of Science and Technology, Department of Natural Sciences, Middlesex University The Burroughs London NW4 4BT UK
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30
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Meller A, Ward M, Borowsky J, Kshirsagar M, Lotthammer JM, Oviedo F, Ferres JL, Bowman GR. Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network. Nat Commun 2023; 14:1177. [PMID: 36859488 PMCID: PMC9977097 DOI: 10.1038/s41467-023-36699-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/09/2023] [Indexed: 03/03/2023] Open
Abstract
Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly curated dataset of 39 experimentally confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures likely contain cryptic pockets, vastly expanding the potentially druggable proteome.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA
- Medical Scientist Training Program, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, MO, 63110, USA
| | - Michael Ward
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA
| | - Jonathan Borowsky
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA
| | | | - Jeffrey M Lotthammer
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA
| | - Felipe Oviedo
- AI for Good Research Lab, Microsoft, Redmond, WA, USA
| | | | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., Box 8231, St. Louis, MO, 63110, USA.
- Department of Biochemistry and Molecular Biophysics, University of Pennsylvania, 3620 Hamilton Walk, Philadelphia, PA, 19104, USA.
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31
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Development of Novel siRNA Therapeutics: A Review with a Focus on Inclisiran for the Treatment of Hypercholesterolemia. Int J Mol Sci 2023; 24:ijms24044019. [PMID: 36835426 PMCID: PMC9966809 DOI: 10.3390/ijms24044019] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 02/19/2023] Open
Abstract
Over the past two decades, it was discovered that introducing synthetic small interfering RNAs (siRNAs) into the cytoplasm facilitates effective gene-targeted silencing. This compromises gene expression and regulation by repressing transcription or stimulating sequence-specific RNA degradation. Substantial investments in developing RNA therapeutics for disease prevention and treatment have been made. We discuss the application to proprotein convertase subtilisin/kexin type 9 (PCSK9), which binds to and degrades the low-density lipoprotein cholesterol (LDL-C) receptor, interrupting the process of LDL-C uptake into hepatocytes. PCSK9 loss-of-function modifications show significant clinical importance by causing dominant hypocholesterolemia and lessening the risk of cardiovascular disease (CVD). Monoclonal antibodies and small interfering RNA (siRNA) drugs targeting PCSK9 are a significant new option for managing lipid disorders and improving CVD outcomes. In general, monoclonal antibodies are restricted to binding with cell surface receptors or circulating proteins. Similarly, overcoming the intracellular and extracellular defenses that prevent exogenous RNA from entering cells must be achieved for the clinical application of siRNAs. N-acetylgalactosamine (GalNAc) conjugates are a simple solution to the siRNA delivery problem that is especially suitable for treating a broad spectrum of diseases involving liver-expressed genes. Inclisiran is a GalNAc-conjugated siRNA molecule that inhibits the translation of PCSK9. The administration is only required every 3 to 6 months, which is a significant improvement over monoclonal antibodies for PCSK9. This review provides an overview of siRNA therapeutics with a focus on detailed profiles of inclisiran, mainly its delivery strategies. We discuss the mechanisms of action, its status in clinical trials, and its prospects.
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32
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Meller A, Lotthammer JM, Smith LG, Novak B, Lee LA, Kuhn CC, Greenberg L, Leinwand LA, Greenberg MJ, Bowman GR. Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains. eLife 2023; 12:83602. [PMID: 36705568 PMCID: PMC9995120 DOI: 10.7554/elife.83602] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/23/2023] [Indexed: 01/28/2023] Open
Abstract
The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least six of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 ms of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatin's binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R2=0.82). In a blind prediction for an isoform (Myh7b) whose blebbistatin sensitivity was unknown, we find good agreement between predicted and measured IC50s (0.67 μM vs. 0.36 μM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Medical Scientist Training Program, Washington University in St. LouisPhiladelphiaUnited States
| | - Jeffrey M Lotthammer
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Louis G Smith
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Department of Biochemistry and Biophysics, University of PennsylvaniaPhiladelphiaUnited States
| | - Borna Novak
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Medical Scientist Training Program, Washington University in St. LouisPhiladelphiaUnited States
| | - Lindsey A Lee
- Molecular, Cellular, and Developmental Biology Department, University of Colorado BoulderBoulderUnited States
- BioFrontiers InstituteBoulderUnited States
| | - Catherine C Kuhn
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Lina Greenberg
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Leslie A Leinwand
- Molecular, Cellular, and Developmental Biology Department, University of Colorado BoulderBoulderUnited States
- BioFrontiers InstituteBoulderUnited States
| | - Michael J Greenberg
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Department of Biochemistry and Biophysics, University of PennsylvaniaPhiladelphiaUnited States
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33
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Sharma S, Ebrahim A, Keedy DA. Room-temperature serial synchrotron crystallography of the human phosphatase PTP1B. Acta Crystallogr F Struct Biol Commun 2023; 79:23-30. [PMID: 36598353 PMCID: PMC9813971 DOI: 10.1107/s2053230x22011645] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 12/04/2022] [Indexed: 12/24/2022] Open
Abstract
Room-temperature X-ray crystallography provides unique insights into protein conformational heterogeneity, but obtaining sufficiently large protein crystals is a common hurdle. Serial synchrotron crystallography (SSX) helps to address this hurdle by allowing the use of many medium- to small-sized crystals. Here, a recently introduced serial sample-support chip system has been used to obtain the first SSX structure of a human phosphatase, specifically protein tyrosine phosphatase 1B (PTP1B) in the unliganded (apo) state. In previous apo room-temperature structures, the active site and allosteric sites adopted alternate conformations, including open and closed conformations of the active-site WPD loop and of a distal allosteric site. By contrast, in our SSX structure the active site is best fitted with a single conformation, but the distal allosteric site is best fitted with alternate conformations. This observation argues for additional nuance in interpreting the nature of allosteric coupling in this protein. Overall, our results illustrate the promise of serial methods for room-temperature crystallography, as well as future avant-garde crystallography experiments, for PTP1B and other proteins.
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Affiliation(s)
- Shivani Sharma
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031, USA,PhD Program in Biology, CUNY Graduate Center, New York, NY 10016, USA
| | - Ali Ebrahim
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031, USA
| | - Daniel A. Keedy
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031, USA,Department of Chemistry and Biochemistry, City College of New York, New York, NY 10031, USA,PhD Programs in Biochemistry, Biology and Chemistry, CUNY Graduate Center, New York, NY 10016, USA,Correspondence e-mail:
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34
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Blanes-Mira C, Fernández-Aguado P, de Andrés-López J, Fernández-Carvajal A, Ferrer-Montiel A, Fernández-Ballester G. Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening. Molecules 2022; 28:molecules28010175. [PMID: 36615367 PMCID: PMC9821981 DOI: 10.3390/molecules28010175] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.
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35
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Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A Guide to In Silico Drug Design. Pharmaceutics 2022; 15:pharmaceutics15010049. [PMID: 36678678 PMCID: PMC9867171 DOI: 10.3390/pharmaceutics15010049] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022] Open
Abstract
The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.
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Affiliation(s)
- Yiqun Chang
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Bryson A. Hawkins
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Jonathan J. Du
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Paul W. Groundwater
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - David E. Hibbs
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Felcia Lai
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence:
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36
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Fukunishi Y, Higo J, Kasahara K. Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles. Biophys Rev 2022; 14:1423-1447. [PMID: 36465086 PMCID: PMC9703445 DOI: 10.1007/s12551-022-01015-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/06/2022] [Indexed: 11/29/2022] Open
Abstract
Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.
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Affiliation(s)
- Yoshifumi Fukunishi
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Junichi Higo
- Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima Minamimachi, Chuo-Ku, Kobe, Hyogo 650-0047 Japan ,Research Organization of Science and Technology, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
| | - Kota Kasahara
- College of Life Sciences, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
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37
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Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets. Comput Struct Biotechnol J 2022; 21:46-57. [PMID: 36514341 PMCID: PMC9732000 DOI: 10.1016/j.csbj.2022.11.042] [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: 08/26/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.
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38
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Feidakis CP, Krivak R, Hoksza D, Novotny M. AHoJ: rapid, tailored search and retrieval of apo and holo protein structures for user-defined ligands. Bioinformatics 2022; 38:5452-5453. [PMID: 36282546 PMCID: PMC9750100 DOI: 10.1093/bioinformatics/btac701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/29/2022] [Accepted: 10/24/2022] [Indexed: 12/25/2022] Open
Abstract
SUMMARY Understanding the mechanism of action of a protein or designing better ligands for it, often requires access to a bound (holo) and an unbound (apo) state of the protein. Resources for the quick and easy retrieval of such conformations are severely limited. Apo-Holo Juxtaposition (AHoJ), is a web application for retrieving apo-holo structure pairs for user-defined ligands. Given a query structure and one or more user-specified ligands, it retrieves all other structures of the same protein that feature the same binding site(s), aligns them, and examines the superimposed binding sites to determine whether each structure is apo or holo, in reference to the query. The resulting superimposed datasets of apo-holo pairs can be visualized and downloaded for further analysis. AHoJ accepts multiple input queries, allowing the creation of customized apo-holo datasets. AVAILABILITY AND IMPLEMENTATION Freely available for non-commercial use at http://apoholo.cz. Source code available at https://github.com/cusbg/AHoJ-project. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christos P Feidakis
- Department of Cell Biology, Faculty of Science, Charles University, Prague 12843, Czech Republic
| | - Radoslav Krivak
- Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague 12116, Czech Republic
| | - David Hoksza
- Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague 12116, Czech Republic
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39
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Strömich L, Wu N, Barahona M, Yaliraki SN. Allosteric Hotspots in the Main Protease of SARS-CoV-2. J Mol Biol 2022; 434:167748. [PMID: 35843284 PMCID: PMC9288249 DOI: 10.1016/j.jmb.2022.167748] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 02/06/2023]
Abstract
Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.
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Affiliation(s)
- Léonie Strömich
- Department of Chemistry Imperial College London, United Kingdom
| | - Nan Wu
- Department of Chemistry Imperial College London, United Kingdom
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40
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Kochnev Y, Durrant JD. FPocketWeb: protein pocket hunting in a web browser. J Cheminform 2022; 14:58. [PMID: 36008829 PMCID: PMC9414105 DOI: 10.1186/s13321-022-00637-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/04/2022] [Indexed: 11/10/2022] Open
Abstract
Detecting macromolecular (e.g., protein) cavities where small molecules bind is an early step in computer-aided drug discovery. Multiple pocket-detection algorithms have been developed over the past several decades. Among them, fpocket, created by Schmidtke and Le Guilloux, is particularly popular. Like many programs used in computational-biology research, fpocket requires users to download and install an executable file. That file must also be run via a command-line interface, further complicating use. An existing fpocket server application effectively addresses these challenges, but it requires users to upload their possibly proprietary structures to a third-party server. The FPocketWeb web app builds on this prior work. It runs the fpocket3 executable entirely in a web browser without requiring installation. The pocket-finding calculations occur on the user's computer rather than on a remote server. A working version of the open-source FPocketWeb app can be accessed free of charge from http://durrantlab.com/fpocketweb .
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Affiliation(s)
- Yuri Kochnev
- Department of Biological Sciences, University of Pittsburgh, 15260, Pittsburgh, PA, USA
| | - Jacob D Durrant
- Department of Biological Sciences, University of Pittsburgh, 15260, Pittsburgh, PA, USA.
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41
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Wakefield AE, Kozakov D, Vajda S. Mapping the binding sites of challenging drug targets. Curr Opin Struct Biol 2022; 75:102396. [PMID: 35636004 PMCID: PMC9790766 DOI: 10.1016/j.sbi.2022.102396] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 02/03/2023]
Abstract
An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.
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Affiliation(s)
- Amanda E. Wakefield
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA NY, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
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42
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Egbert M, Jones G, Collins MR, Kozakov D, Vajda S. FTMove: A Web Server for Detection and Analysis of Cryptic and Allosteric Binding Sites by Mapping Multiple Protein Structures. J Mol Biol 2022; 434:167587. [PMID: 35662465 PMCID: PMC9789685 DOI: 10.1016/j.jmb.2022.167587] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/25/2022] [Accepted: 04/07/2022] [Indexed: 12/27/2022]
Abstract
Protein mapping distributes many copies of different molecular probes on the surface of a target protein in order to determine binding hot spots, regions that are highly preferable for ligand binding. While mapping of X-ray structures by the FTMap server is inherently static, this limitation can be overcome by the simultaneous analysis of multiple structures of the protein. FTMove is an automated web server that implements this approach. From the input of a target protein, by PDB code, the server identifies all structures of the protein available in the PDB, runs mapping on them, and combines the results to form binding hot spots and binding sites. The user may also upload their own protein structures, bypassing the PDB search for similar structures. Output of the server consists of the consensus binding sites and the individual mapping results for each structure - including the number of probes located in each binding site, for each structure. This level of detail allows the users to investigate how the strength of a binding site relates to the protein conformation, other binding sites, and the presence of ligands or mutations. In addition, the structures are clustered on the basis of their binding properties. The use of FTMove is demonstrated by application to 22 proteins with known allosteric binding sites; the orthosteric and allosteric binding sites were identified in all but one case, and the sites were typically ranked among the top five. The FTMove server is publicly available at https://ftmove.bu.edu.
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Affiliation(s)
- Megan Egbert
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Matthew R Collins
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Department of Chemistry, Boston University, Boston, MA 02215, USA.
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43
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Kumar A, Khade PM, Dorman KS, Jernigan RL. Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier. Bioinformatics 2022; 38:2727-2733. [PMID: 35561187 PMCID: PMC9113273 DOI: 10.1093/bioinformatics/btac159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/15/2022] [Accepted: 03/16/2022] [Indexed: 02/03/2023] Open
Abstract
SUMMARY A new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ambuj Kumar
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Pranav M Khade
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Karin S Dorman
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Department of Statistics, Iowa State University, Ames, IA 50011, USA
| | - Robert L Jernigan
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
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44
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Cruz MA, Frederick TE, Mallimadugula UL, Singh S, Vithani N, Zimmerman MI, Porter JR, Moeder KE, Amarasinghe GK, Bowman GR. A cryptic pocket in Ebola VP35 allosterically controls RNA binding. Nat Commun 2022; 13:2269. [PMID: 35477718 PMCID: PMC9046395 DOI: 10.1038/s41467-022-29927-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 04/07/2022] [Indexed: 11/08/2022] Open
Abstract
Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola's replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins.
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Affiliation(s)
- Matthew A Cruz
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Thomas E Frederick
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Upasana L Mallimadugula
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Sukrit Singh
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Neha Vithani
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Maxwell I Zimmerman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Justin R Porter
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Katelyn E Moeder
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Gaya K Amarasinghe
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Gregory R Bowman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, 63110, USA.
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45
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Ropón-Palacios G, Pérez-Silva J, Rojas-Humpire R, Olivos-Ramírez GE, Chenet-Zuta M, Cornejo-Villanueva V, Carmen-Sifuentes S, Otazu K, Ramirez-Díaz YL, Chozo KV, Camps I. Glycosylation is key for enhancing drug recognition into spike glycoprotein of SARS-CoV-2. Comput Biol Chem 2022; 98:107668. [PMID: 35339763 PMCID: PMC8941845 DOI: 10.1016/j.compbiolchem.2022.107668] [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: 09/27/2021] [Revised: 03/14/2022] [Accepted: 03/17/2022] [Indexed: 12/04/2022]
Abstract
The emergence of COVID-19 caused by SARS-CoV-2 and its spread since 2019 represents the major public health problem worldwide nowadays, which has generated a high number of infections and deaths. The spike protein (S protein) is the most studied protein of SARS-CoV-2, and key to host-cell entry through ACE2 receptor. This protein presents a large pattern of glycosylations with important roles in immunity and infection mechanisms. Therefore, understanding key aspects of the molecular mechanisms of these structures, during drug recognition in SARS-CoV-2, may contribute to therapeutic alternatives. In this work, we explored the impact of glycosylations on the drug recognition on two domains of the S protein, the receptor-binding domain (RBD) and the N-terminal domain (NTD) through molecular dynamics simulations and computational biophysics analysis. Our results show that glycosylations in the S protein induce structural stability and changes in rigidity/flexibility related to the number of glycosylations in the structure. These structural changes are important for its biological activity as well as the correct interaction of ligands in the RBD and NTD regions. Additionally, we evidenced a roto-translation phenomenon in the interaction of the ligand with RBD in the absence of glycosylation, which disappears due to the influence of glycosylation and the convergence of metastable states in RBM. Similarly, glycosylations in NTD promote an induced fit phenomenon, which is not observed in the absence of glycosylations; this process is decisive for the activity of the ligand at the cryptic site. Altogether, these results provide an explanation of glycosylation relevance in biophysical properties and drug recognition to S protein of SARS-CoV-2, which must be considered in the rational drug development and virtual screening targeting S protein.
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Affiliation(s)
- Georcki Ropón-Palacios
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil.
| | - Jhon Pérez-Silva
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil
| | - Ricardo Rojas-Humpire
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil
| | - Gustavo E Olivos-Ramírez
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil
| | | | | | - Sheyla Carmen-Sifuentes
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil
| | - Kewin Otazu
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil
| | - Yaritza L Ramirez-Díaz
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil
| | - Karolyn Vega Chozo
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil
| | - Ihosvany Camps
- Laboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil; High Performance & Quantum Computing Labs, Waterloo, Canada.
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46
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Tze-Yang Ng J, Tan YS. Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites. J Chem Theory Comput 2022; 18:1969-1981. [PMID: 35175753 DOI: 10.1021/acs.jctc.1c01177] [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 identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.
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Affiliation(s)
- Justin Tze-Yang Ng
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore
| | - Yaw Sing Tan
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore
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47
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Zha J, Li M, Kong R, Lu S, Zhang J. Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques. J Mol Biol 2022; 434:167481. [DOI: 10.1016/j.jmb.2022.167481] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/25/2022] [Accepted: 01/31/2022] [Indexed: 12/17/2022]
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48
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Margreiter MA, Witzenberger M, Wasser Y, Davydova E, Janowski R, Metz J, Habib P, Sahnoun SEM, Sobisch C, Poma B, Palomino-Hernandez O, Wagner M, Carell T, Jon Shah N, Schulz JB, Niessing D, Voigt A, Rossetti G. Small-molecule modulators of TRMT2A decrease PolyQ aggregation and PolyQ-induced cell death. Comput Struct Biotechnol J 2022; 20:443-458. [PMID: 35070167 PMCID: PMC8759985 DOI: 10.1016/j.csbj.2021.12.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 11/17/2022] Open
Abstract
Polyglutamine (polyQ) diseases are characterized by an expansion of cytosine-adenine-guanine (CAG) trinucleotide repeats encoding for an uninterrupted prolonged polyQ tract. We previously identified TRMT2A as a strong modifier of polyQ-induced toxicity in an unbiased large-scale screen in Drosophila melanogaster. This work aimed at identifying and validating pharmacological TRMT2A inhibitors as treatment opportunities for polyQ diseases in humans. Computer-aided drug discovery was implemented to identify human TRMT2A inhibitors. Additionally, the crystal structure of one protein domain, the RNA recognition motif (RRM), was determined, and Biacore experiments with the RRM were performed. The identified molecules were validated for their potency to reduce polyQ aggregation and polyQ-induced cell death in human HEK293T cells and patient derived fibroblasts. Our work provides a first step towards pharmacological inhibition of this enzyme and indicates TRMT2A as a viable drug target for polyQ diseases.
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Affiliation(s)
- Michael A Margreiter
- Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Juelich GmbH, Germany.,Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen, 52425 Aachen, Germany
| | - Monika Witzenberger
- Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Structural Biology, 85764 Neuherberg, Germany
| | - Yasmine Wasser
- Department of Neurology, RWTH University Aachen, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Elena Davydova
- Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Structural Biology, 85764 Neuherberg, Germany
| | - Robert Janowski
- Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Structural Biology, 85764 Neuherberg, Germany
| | - Jonas Metz
- Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Juelich GmbH, Germany
| | - Pardes Habib
- Department of Neurology, RWTH University Aachen, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany.,JARA - BRAIN - Translational Medicine, Aachen, Germany
| | - Sabri E M Sahnoun
- Department of Nuclear Medicine, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany
| | - Carina Sobisch
- Department of Neurology, RWTH University Aachen, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Benedetta Poma
- Department of Neurology, RWTH University Aachen, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Oscar Palomino-Hernandez
- Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Juelich GmbH, Germany.,Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen, 52425 Aachen, Germany
| | - Mirko Wagner
- Department Chemie, Ludwig-Maximilians-Universität München, Butenandtstraße 5-13, 81377 München, Germany
| | - Thomas Carell
- Department Chemie, Ludwig-Maximilians-Universität München, Butenandtstraße 5-13, 81377 München, Germany
| | - N Jon Shah
- JARA - BRAIN - Translational Medicine, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Juelich GmbH, Germany.,Institute of Neuroscience and Medicine (INM-11), Forschungszentrum Juelich GmbH, Germany
| | - Jörg B Schulz
- Department of Neurology, RWTH University Aachen, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany.,JARA - BRAIN - Translational Medicine, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-11), Forschungszentrum Juelich GmbH, Germany
| | - Dierk Niessing
- Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Structural Biology, 85764 Neuherberg, Germany.,Institute of Pharmaceutical Biotechnology, Ulm University, 89081 Ulm, Germany
| | - Aaron Voigt
- Department of Neurology, RWTH University Aachen, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany.,JARA - BRAIN - Translational Medicine, Aachen, Germany
| | - Giulia Rossetti
- Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Juelich GmbH, Germany.,Department of Neurology, RWTH University Aachen, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany.,Juelich Supercomputing Center (JSC), Forschungszentrum Juelich GmbH, Germany.,Department of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
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What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets? INFORMATICS 2022. [DOI: 10.3390/informatics9010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Small-molecule drug design aims to identify inhibitors that can specifically bind to a functionally important region on the target, i.e., an active site of an enzyme. Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce and select a dynamic binding pocket that is not visible in the apo protein, which presents a well-recognized challenge for structure-based drug discovery. Here, we assessed whether it is possible to identify features in molecules, which we refer to as inducers, that can induce the opening of cryptic pockets. The volume change between apo and bound protein conformations was used as a metric to differentiate chemical features in inducers vs. non-inducers. Based on the dataset of holo–apo pairs, classification models were built to determine an optimum threshold. The model analysis suggested that inducers preferred to be more hydrophobic and aromatic. The impact of sulfur was ambiguous, while phosphorus and halogen atoms were overrepresented in inducers. The fragment analysis showed that small changes in the structures of molecules can strongly affect the potential to induce a cryptic pocket. This analysis and developed model can be used to design inducers that can potentially open cryptic pockets for undruggable proteins.
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50
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Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD). Biochem Soc Trans 2022; 50:241-252. [PMID: 35076690 PMCID: PMC9022974 DOI: 10.1042/bst20211240] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/23/2021] [Accepted: 12/23/2021] [Indexed: 12/18/2022]
Abstract
There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.
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