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Ding T, Guseinov AA, Milligan G, Plouffe B, Tikhonova IG. Exploring an Intracellular Allosteric Site of CC-Chemokine Receptor 4 from 3D Models, Probe Simulations, and Mutagenesis. ACS Pharmacol Transl Sci 2024; 7:2516-2526. [PMID: 39144548 PMCID: PMC11320731 DOI: 10.1021/acsptsci.4c00330] [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: 06/01/2024] [Revised: 07/04/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
We applied our previously developed probe confined dynamic mapping protocol, which combines enhanced sampling molecular dynamics (MD) simulations and fragment-based approaches, to identify the binding site of GSK2239633A (N-[[3-[[3-[(5-chlorothiophen-2-yl)sulfonylamino]-4-methoxyindazol-1-yl]methyl]phenyl]methyl]-2-hydroxy-2-methylpropanamide), a selective CC-chemokine receptor type 4 (CCR4) negative allosteric modulator, using CCR4 homology and AlphaFold models. By comparing the performance across five computational models, we identified conserved (K3108.49 and Y3047.53) and non-conserved (M2436.36) residue hotspots for GSK2239633A binding, which were validated by mutagenesis and bioluminescence resonance energy transfer assay. Further analysis of 3D models and MD simulations highlighted the pair of residues 6.36 and 7.56 that might account for antagonist selectivity among chemokine receptors. Our in silico protocol provides a promising approach for characterizing ligand binding sites in membrane proteins, considering receptor dynamics and adaptability and guiding protein template selection for ligand design.
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Affiliation(s)
- Tianyi Ding
- School
of Pharmacy, Queen’s University Belfast, Belfast Bt9 7BL, Northern Ireland, U.K.
| | - Abdul-Akim Guseinov
- School
of Pharmacy, Queen’s University Belfast, Belfast Bt9 7BL, Northern Ireland, U.K.
| | - Graeme Milligan
- Centre
for Translational Pharmacology, School of Molecular Biosciences, College
of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, Scotland G12 8QQ, U.K.
| | - Bianca Plouffe
- Wellcome-Wolfson
Institute for Experimental Medicine, School of Medicine, Dentistry
and Biomedical Sciences, Queen’s
University Belfast, Belfast Bt9 7BL, Northern Ireland, U.K.
| | - Irina G. Tikhonova
- School
of Pharmacy, Queen’s University Belfast, Belfast Bt9 7BL, Northern Ireland, U.K.
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2
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Zhao M, Yu W, MacKerell AD. Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms. J Phys Chem B 2024; 128:7362-7375. [PMID: 39031121 PMCID: PMC11294009 DOI: 10.1021/acs.jpcb.4c03045] [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] [Indexed: 07/22/2024]
Abstract
In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.
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Affiliation(s)
- Mingtian Zhao
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
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3
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Sinha P, Yadav AK. Identification of 3, 4-dihydroxy complexes as potential antiviral via DFT, molecular docking, molecular dynamics and MM/PBSA against rabies and dengue receptors. J Biomol Struct Dyn 2024; 42:7037-7053. [PMID: 37580968 DOI: 10.1080/07391102.2023.2246572] [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: 05/08/2023] [Accepted: 07/12/2023] [Indexed: 08/16/2023]
Abstract
The quest to identify antiviral drug candidates for dengue and rabies viral diseases is a great challenge for the researchers. While different research is being conducted on the repurposed drugs against these two viruses, no drug compound has gained success in treating them. Therefore, in this study, 3, 4-dihydroxy complexes have been virtually designed to investigate their antiviral properties and analyze their efficiency in interaction with the concerned viral diseases. DFT calculations are carried out to study the electronic and thermodynamic properties to understand the stability and reactivity of the reported compounds. These compounds were subjected to molecular docking studies to understand the binding interactions with NS5 Dengue virus mRNA 2'-O-methyltransferase and phosphoprotein C-terminal domain of Rabies virus. MD simulation, hydrogen bond analysis, and MM/PBSA were performed at 100 ns to support the obtained docking results.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Prashasti Sinha
- Department of Physics, School of Physical & Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, India
| | - Anil Kumar Yadav
- Department of Physics, School of Physical & Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, India
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Hurley MFD, Raddi RM, Pattis JG, Voelz VA. Expanded ensemble predictions of absolute binding free energies in the SAMPL9 host-guest challenge. Phys Chem Chem Phys 2023; 25:32393-32406. [PMID: 38009066 PMCID: PMC10760931 DOI: 10.1039/d3cp02197a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2023]
Abstract
As part of the SAMPL9 community-wide blind host-guest challenge, we implemented an expanded ensemble workflow to predict absolute binding free energies for 13 small molecules against pillar[6]arene. Notable features of our protocol include consideration of a variety of protonation and enantiomeric states for both host and guests, optimization of alchemical intermediates, and analysis of free energy estimates and their uncertainty using large numbers of simulation replicates performed using distributed computing. Our predictions of absolute binding free energies resulted in a mean absolute error of 2.29 kcal mol-1 and an R2 of 0.54. Overall, results show that expanded ensemble calculations using all-atom molecular dynamics simulations are a valuable and efficient computational tool in predicting absolute binding free energies.
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Affiliation(s)
| | - Robert M Raddi
- Department of Chemistry, Temple University, Philadelphia, PA, USA.
| | - Jason G Pattis
- Department of Chemistry, Temple University, Philadelphia, PA, USA.
| | - Vincent A Voelz
- Department of Chemistry, Temple University, Philadelphia, PA, USA.
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5
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Yu W, Weber DJ, MacKerell AD. Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation. J Chem Theory Comput 2023; 19:3007-3021. [PMID: 37115781 PMCID: PMC10205696 DOI: 10.1021/acs.jctc.3c00232] [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] [Indexed: 04/29/2023]
Abstract
Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group-protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.
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Affiliation(s)
- Wenbo Yu
- Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - David J. Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Alexander D. MacKerell
- Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
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Yu W, Weber DJ, MacKerell AD. Computer-Aided Drug Design: An Update. Methods Mol Biol 2023; 2601:123-152. [PMID: 36445582 PMCID: PMC9838881 DOI: 10.1007/978-1-0716-2855-3_7] [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] [Indexed: 11/30/2022]
Abstract
Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.
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Affiliation(s)
- Wenbo Yu
- Department of Pharmaceutical Sciences, Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA.
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA.
| | - David J Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA.
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA.
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In silico identification of a β 2-adrenoceptor allosteric site that selectively augments canonical β 2AR-Gs signaling and function. Proc Natl Acad Sci U S A 2022; 119:e2214024119. [PMID: 36449547 PMCID: PMC9894167 DOI: 10.1073/pnas.2214024119] [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] [Indexed: 12/03/2022] Open
Abstract
Activation of β2-adrenoceptors (β2ARs) causes airway smooth muscle (ASM) relaxation and bronchodilation, and β2AR agonists (β-agonists) are front-line treatments for asthma and other obstructive lung diseases. However, the therapeutic efficacy of β-agonists is limited by agonist-induced β2AR desensitization and noncanonical β2AR signaling involving β-arrestin that is shown to promote asthma pathophysiology. Accordingly, we undertook the identification of an allosteric site on β2AR that could modulate the activity of β-agonists to overcome these limitations. We employed the site identification by ligand competitive saturation (SILCS) computational method to comprehensively map the entire 3D structure of in silico-generated β2AR intermediate conformations and identified a putative allosteric binding site. Subsequent database screening using SILCS identified drug-like molecules with the potential to bind to the site. Experimental assays in HEK293 cells (expressing recombinant wild-type human β2AR) and human ASM cells (expressing endogenous β2AR) identified positive and negative allosteric modulators (PAMs and NAMs) of β2AR as assessed by regulation of β-agonist-stimulation of cyclic AMP generation. PAMs/NAMs had no effect on β-agonist-induced recruitment of β-arrestin to β2AR- or β-agonist-induced loss of cell surface expression in HEK293 cells expressing β2AR. Mutagenesis analysis of β2AR confirmed the SILCS identified site based on mutants of amino acids R131, Y219, and F282. Finally, functional studies revealed augmentation of β-agonist-induced relaxation of contracted human ASM cells and bronchodilation of contracted airways. These findings identify a allosteric binding site on the β2AR, whose activation selectively augments β-agonist-induced Gs signaling, and increases relaxation of ASM cells, the principal therapeutic effect of β-agonists.
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Kognole AA, Hazel A, MacKerell AD. SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules. J Chem Theory Comput 2022; 18:5672-5691. [PMID: 35913731 PMCID: PMC9474704 DOI: 10.1021/acs.jctc.2c00381] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.
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Affiliation(s)
- Abhishek A Kognole
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
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Goel H, Yu W, MacKerell AD. hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties. CHEMISTRY (BASEL, SWITZERLAND) 2022; 4:630-646. [PMID: 36712295 PMCID: PMC9881610 DOI: 10.3390/chemistry4030045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Human ether-a-go-go-related gene (hERG) potassium channel is well-known contributor to drug-induced cardiotoxicity and therefore an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. Availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect for using structure-based simulation and docking approaches for hERG drug liability predictions. In recent time, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson's R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to blockade, thereby facilitating the rational ligand design to minimize hERG liability.
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Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St. Baltimore, MD 21201, United States
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St. Baltimore, MD 21201, United States
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St. Baltimore, MD 21201, United States
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Mayol GF, Defelipe LA, Arcon JP, Turjanski AG, Marti MA. Solvent Sites Improve Docking Performance of Protein–Protein Complexes and Protein–Protein Interface-Targeted Drugs. J Chem Inf Model 2022; 62:3577-3588. [DOI: 10.1021/acs.jcim.2c00264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Gonzalo F. Mayol
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
| | - Lucas A. Defelipe
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
- European Molecular Biology Laboratory - Hamburg Unit, Notkestrasse 85, Hamburg 22607, Germany
| | - Juan Pablo Arcon
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
- Institute for Research in Biomedicine (IRB), 08028 Barcelona, Spain
- The Barcelona Institute of Science and Technology, 08036 Barcelona, Spain
| | - Adrian G. Turjanski
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
| | - Marcelo A. Marti
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
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