1
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Ma S, Patel H, Peeples CA, Shen J. QM/MM simulations of EFGR with afatinib reveal the role of the β-dimethylaminomethyl substitution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.18.580887. [PMID: 38766221 PMCID: PMC11100610 DOI: 10.1101/2024.02.18.580887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Acrylamides are the most commonly used warheads of targeted covalent inhibitors (TCIs) directed at cysteines; however, the reaction mechanisms of acrylamides in proteins remain controversial, particularly for those involving protonated or unreactive cysteines. Using the combined semiempirical quantum mechanics (QM)/molecular mechanics (MM) free energy simulations, we investigated the reaction between afatinib, the first TCI drug for cancer treatment, and Cys797 in the EGFR kinase. Afatinib contains a β-dimethylaminomethyl (β-DMAM) substitution which has been shown to enhance the intrinsic reactivity and potency against EGFR for related inhibitors. Two hypothesized reaction mechanisms were tested. Our data suggest that Cys797 becomes deprotonated in the presence of afatinib and the reaction proceeds via a classical Michael addition mechanism, with Asp800 stabilizing the ion-pair reactant state β-DMAM+/C797- and the transition state of the nucleophilic attack. Our work elucidates an important structure-activity relationship of acrylamides in proteins.
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Affiliation(s)
- Shuhua Ma
- Department of Chemistry, Jess and Mildred Fisher College of Science and Mathematics, Towson University, Towson, MD 21252
| | - Heeral Patel
- Department of Chemistry, Jess and Mildred Fisher College of Science and Mathematics, Towson University, Towson, MD 21252
| | - Craig A Peeples
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201
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2
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Cai Z, Peng H, Sun S, He J, Luo F, Huang Y. DeepKa Web Server: High-Throughput Protein p Ka Prediction. J Chem Inf Model 2024; 64:2933-2940. [PMID: 38530291 DOI: 10.1021/acs.jcim.3c02013] [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: 03/27/2024]
Abstract
DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.
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Affiliation(s)
- Zhitao Cai
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Hao Peng
- National Pilot School of Software, Yunnan University, Kunming 650504, China
| | - Shuo Sun
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Jiahao He
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Fangfang Luo
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Yandong Huang
- College of Computer Engineering, Jimei University, Xiamen 361021, China
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3
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Thiel A, Speranza MJ, Jadhav S, Stevens LL, Unruh DK, Ren P, Ponder JW, Shen J, Schnieders MJ. Constant-pH Simulations with the Polarizable Atomic Multipole AMOEBA Force Field. J Chem Theory Comput 2024; 20:2921-2933. [PMID: 38507252 PMCID: PMC11008096 DOI: 10.1021/acs.jctc.3c01180] [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: 10/24/2023] [Revised: 03/05/2024] [Accepted: 03/05/2024] [Indexed: 03/22/2024]
Abstract
Accurately predicting protein behavior across diverse pH environments remains a significant challenge in biomolecular simulations. Existing constant-pH molecular dynamics (CpHMD) algorithms are limited to fixed-charge force fields, hindering their application to biomolecular systems described by permanent atomic multipoles or induced dipoles. This work overcomes these limitations by introducing the first polarizable CpHMD algorithm in the context of the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field. Additionally, our implementation in the open-source Force Field X (FFX) software has the unique ability to handle titration state changes for crystalline systems including flexible support for all 230 space groups. The evaluation of constant-pH molecular dynamics (CpHMD) with the AMOEBA force field was performed on 11 crystalline peptide systems that span the titrating amino acids (Asp, Glu, His, Lys, and Cys). Titration states were correctly predicted for 15 out of the 16 amino acids present in the 11 systems, including for the coordination of Zn2+ by cysteines. The lone exception was for a HIS-ALA peptide where CpHMD predicted both neutral histidine tautomers to be equally populated, whereas the experimental model did not consider multiple conformers and diffraction data are unavailable for rerefinement. This work demonstrates the promise polarizable CpHMD simulations for pKa predictions, the study of biochemical mechanisms such as the catalytic triad of proteases, and for improved protein-ligand binding affinity accuracy in the context of pharmaceutical lead optimization.
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Affiliation(s)
- Andrew
C. Thiel
- Department
of Biomedical Engineering, University of
Iowa, Iowa City, Iowa 52242, United States
| | - Matthew J. Speranza
- Department
of Biomedical Engineering, University of
Iowa, Iowa City, Iowa 52242, United States
| | - Sanika Jadhav
- Department
of Pharmaceutical Sciences and Experimental Therapeutics, University of Iowa, Iowa City, Iowa 52242, United States
| | - Lewis L. Stevens
- Department
of Pharmaceutical Sciences and Experimental Therapeutics, University of Iowa, Iowa City, Iowa 52242, United States
| | - Daniel K. Unruh
- Office
of the Vice President for Research, University
of Iowa, Iowa City, Iowa 52242, United
States
| | - Pengyu Ren
- Department
of Biomedical Engineering, University of
Texas, Austin, Texas 78712, United States
| | - Jay W. Ponder
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United
States
| | - Jana Shen
- Department
of Pharmaceutical Sciences, University of
Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Michael J. Schnieders
- Department
of Biomedical Engineering, University of
Iowa, Iowa City, Iowa 52242, United States
- Department
of Biochemistry, University of Iowa, Iowa City, Iowa 52242, United States
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4
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Jansen A, Aho N, Groenhof G, Buslaev P, Hess B. phbuilder: A Tool for Efficiently Setting up Constant pH Molecular Dynamics Simulations in GROMACS. J Chem Inf Model 2024; 64:567-574. [PMID: 38215282 PMCID: PMC10865341 DOI: 10.1021/acs.jcim.3c01313] [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: 08/16/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 01/14/2024]
Abstract
Constant pH molecular dynamics (MD) is a powerful technique that allows the protonation state of residues to change dynamically, thereby enabling the study of pH dependence in a manner that has not been possible before. Recently, a constant pH implementation was incorporated into the GROMACS MD package. Although this implementation provides good accuracy and performance, manual modification and the preparation of simulation input files are required, which can be complicated, tedious, and prone to errors. To simplify and automate the setup process, we present phbuilder, a tool that automatically prepares constant pH MD simulations for GROMACS by modifying the input structure and topology as well as generating the necessary parameter files. phbuilder can prepare constant pH simulations from both initial structures and existing simulation systems, and it also provides functionality for performing titrations and single-site parametrizations of new titratable group types. The tool is freely available at www.gitlab.com/gromacs-constantph. We anticipate that phbuilder will make constant pH simulations easier to set up, thereby making them more accessible to the GROMACS user community.
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Affiliation(s)
- Anton Jansen
- Department
of Applied Physics and Swedish e-Science Research Center, Science
for Life Laboratory, KTH Royal Institute
of Technology, 100 44 Stockholm, Sweden
| | - Noora Aho
- Nanoscience
Center and Department of Chemistry, University
of Jyväskylä, 40014 Jyväskylä, Finland
| | - Gerrit Groenhof
- Nanoscience
Center and Department of Chemistry, University
of Jyväskylä, 40014 Jyväskylä, Finland
| | - Pavel Buslaev
- Nanoscience
Center and Department of Chemistry, University
of Jyväskylä, 40014 Jyväskylä, Finland
| | - Berk Hess
- Department
of Applied Physics and Swedish e-Science Research Center, Science
for Life Laboratory, KTH Royal Institute
of Technology, 100 44 Stockholm, Sweden
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5
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Roy RK, Patra N. Probing the pH Sensitivity of OprM: Insights into Metastable States and Semi-Open Conformation. J Phys Chem B 2024; 128:622-634. [PMID: 38047375 DOI: 10.1021/acs.jpcb.3c05384] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Efflux pumps are specialized transport proteins that play a key role in the bacterial defense against a wide spectrum of antibiotics. Hence, understanding the biophysical mechanism associated with this complex system of drug expulsion becomes crucial. This work deals with some vital aspects of the outer membrane factor (OMF) of MexAB-OprM. After being passed through MexB and MexA, efflux substrates have to go through OprM for their final judgment. Thus, it is very important to understand the periplasmic pore opening mechanism and the associated biophysical changes during this process. Our study captures a detailed analysis of the pore opening mechanism involving OprM. With powerful molecular dynamics (MD) techniques such as well-tempered metadynamics, the presence of metastable states in between open and closed states was confirmed. Also, upon mutating R376, the energy barrier for the conversion of the close to open conformation decreases, indicating an important role played by the residue. Further, constant pH MD was performed to capture the effect of pH in both conformations. OprM exhibits distinct conformational states at pH values greater than 5.5 and lower than 5.5, suggesting its pH-responsive characteristics. Overall, our study elucidates a crucial undertaking toward discovering potential inhibitors for MexAB-OprM efflux pumps.
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Affiliation(s)
- Rakesh Kumar Roy
- Department of Chemistry and Chemical Biology, Indian Institute of Technology (ISM) Dhanbad, Dhanbad 826004, India
| | - Niladri Patra
- Department of Chemistry and Chemical Biology, Indian Institute of Technology (ISM) Dhanbad, Dhanbad 826004, India
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6
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Vaissier Welborn V. Understanding Cysteine Reactivity in Protein Environments with Electric Fields. J Phys Chem B 2023; 127:9936-9942. [PMID: 37962274 DOI: 10.1021/acs.jpcb.3c05749] [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: 11/15/2023]
Abstract
The role cysteine residues play in proteins is mediated by their protonation state, whereby the thiolate form of the side chain is highly reactive while the thiol form is more inert. However, the pKa of cysteine residues is hard to predict as it can differ widely from its reference value in solution, an effect that is accentuated by local effects in the heterogeneous protein environment. Here, we present a new approach to the prediction of cysteine reactivity based on electric field calculations at the thiol/thiolate group. We validated our approach by predicting the protonation state of cysteine residues in different protein environments (in the active site, at the protein surface, and buried within the protein interior), including Cys-25 in papaya protease omega, which was proven problematic for the more traditional constant pH molecular dynamics (MD) technique. We predict pKa shifts consistent with experimental observations, and the decomposition of the electric fields into contributions from molecular fragments provides a direct handle to rationalize local pH and pKa effects in proteins without introducing parameters other than those of the force field used for MD simulations.
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Affiliation(s)
- Valerie Vaissier Welborn
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24060, United States
- Macromolecules Innovation Institute (MII),Virginia Tech, Blacksburg, Virginia 24060, United States
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7
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Hernández González JE, de Araujo AS. Alchemical Calculation of Relative Free Energies for Charge-Changing Mutations at Protein-Protein Interfaces Considering Fixed and Variable Protonation States. J Chem Inf Model 2023; 63:6807-6822. [PMID: 37851531 DOI: 10.1021/acs.jcim.3c00972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
The calculation of relative free energies (ΔΔG) for charge-changing mutations at protein-protein interfaces through alchemical methods remains challenging due to variations in the system's net charge during charging steps, the possibility of mutated and contacting ionizable residues occurring in various protonation states, and undersampling issues. In this study, we present a set of strategies, collectively termed TIRST/TIRST-H+, to address some of these challenges. Our approaches combine thermodynamic integration (TI) with the prediction of pKa shifts to calculate ΔΔG values. Moreover, special sets of restraints are employed to keep the alchemically transformed molecules separated. The accuracy of the devised approaches was assessed on a large and diverse data set comprising 164 point mutations of charged residues (Asp, Glu, Lys, and Arg) to Ala at the protein-protein interfaces of complexes with known three-dimensional structures. Mean absolute and root-mean-square errors ranging from 1.38 to 1.66 and 1.89 to 2.44 kcal/mol, respectively, and Pearson correlation coefficients of ∼0.6 were obtained when testing the approaches on the selected data set using the GPU-TI module of Amber18 suite and the ff14SB force field. Furthermore, the inclusion of variable protonation states for the mutated acid residues improved the accuracy of the predicted ΔΔG values. Therefore, our results validate the use of TIRST/TIRST-H+ in prospective studies aimed at evaluating the impact of charge-changing mutations to Ala on the stability of protein-protein complexes.
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8
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Clayton J, de Oliveira VM, Ibrahim MF, Sun X, Mahinthichaichan P, Shen M, Hilgenfeld R, Shen J. Integrative Approach to Dissect the Drug Resistance Mechanism of the H172Y Mutation of SARS-CoV-2 Main Protease. J Chem Inf Model 2023; 63:3521-3533. [PMID: 37199464 PMCID: PMC10237302 DOI: 10.1021/acs.jcim.3c00344] [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: 03/03/2023] [Indexed: 05/19/2023]
Abstract
Nirmatrelvir is an orally available inhibitor of SARS-CoV-2 main protease (Mpro) and the main ingredient of Paxlovid, a drug approved by the U.S. Food and Drug Administration for high-risk COVID-19 patients. Recently, a rare natural mutation, H172Y, was found to significantly reduce nirmatrelvir's inhibitory activity. As the COVID-19 cases skyrocket in China and the selective pressure of antiviral therapy builds in the US, there is an urgent need to characterize and understand how the H172Y mutation confers drug resistance. Here, we investigated the H172Y Mpro's conformational dynamics, folding stability, catalytic efficiency, and inhibitory activity using all-atom constant pH and fixed-charge molecular dynamics simulations, alchemical and empirical free energy calculations, artificial neural networks, and biochemical experiments. Our data suggest that the mutation significantly weakens the S1 pocket interactions with the N-terminus and perturbs the conformation of the oxyanion loop, leading to a decrease in the thermal stability and catalytic efficiency. Importantly, the perturbed S1 pocket dynamics weaken the nirmatrelvir binding in the P1 position, which explains the decreased inhibitory activity of nirmatrelvir. Our work demonstrates the predictive power of the combined simulation and artificial intelligence approaches, and together with biochemical experiments, they can be used to actively surveil continually emerging mutations of SARS-CoV-2 Mpro and assist the optimization of antiviral drugs. The presented approach, in general, can be applied to characterize mutation effects on any protein drug targets.
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Affiliation(s)
- Joseph Clayton
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Vinicius Martins de Oliveira
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | | | - Xinyuanyuan Sun
- Institute of Molecular Medicine, University of Lübeck, Lübeck 23562, Germany
| | - Paween Mahinthichaichan
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Mingzhe Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Rolf Hilgenfeld
- Institute for Molecular Medicine, University of Lübeck, Lübeck 23562, Germany
- German Center for Infection Research (DZIF), Hamburg – Lübeck – Borstel – Riems Site, University of Lübeck, Lübeck 23562, Germany
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
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9
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Cai Z, Liu T, Lin Q, He J, Lei X, Luo F, Huang Y. Basis for Accurate Protein p Ka Prediction with Machine Learning. J Chem Inf Model 2023; 63:2936-2947. [PMID: 37146199 DOI: 10.1021/acs.jcim.3c00254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pKa's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pKa prediction is crucial. Here we present a theoretical pKa data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pKa orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pKa prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pKa predictor and thus can be applied immediately to, for example, pKa database construction, protein design, drug discovery, and so on.
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Affiliation(s)
- Zhitao Cai
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Tengzi Liu
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Qiaoling Lin
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Jiahao He
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Xiaowei Lei
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Fangfang Luo
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Yandong Huang
- College of Computer Engineering, Jimei University, Xiamen 361021, China
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10
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Chakraborty S, Mandal K, Ramakrishnan R. Understanding the Role of Intramolecular Ion-Pair Interactions in Conformational Stability Using an Ab Initio Thermodynamic Cycle. J Phys Chem B 2023; 127:648-660. [PMID: 36638237 DOI: 10.1021/acs.jpcb.2c06803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Intramolecular ion-pair interactions yield shape and functionality to many molecules. With proper orientation, these interactions overcome steric factors and are responsible for the compact structures of several peptides. In this study, we present a thermodynamic cycle based on isoelectronic and alchemical mutation to estimate the intramolecular ion-pair interaction energy. We determine these energies for 26 benchmark molecules with common ion-pair combinations and compare them with results obtained using intramolecular symmetry-adapted perturbation theory. For systems with long linkers, the ion-pair energies evaluated using both approaches deviate by less than 2.5% in the vacuum phase. The thermodynamic cycle based on density functional theory facilitates calculations of salt-bridge interactions in model tripeptides with continuum/microsolvation modeling and four large peptides: 1EJG (crambin), 1BDK (bradykinin), 1L2Y (a mini-protein with a tryptophan cage), and 1SCO (a toxin from the scorpion venom).
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Affiliation(s)
| | - Kalyaneswar Mandal
- Tata Institute of Fundamental Research Hyderabad, Hyderabad500046, India
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11
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Onufriev AV. Biologically relevant small variations of intra-cellular pH can have significant effect on stability of protein-DNA complexes, including the nucleosome. Front Mol Biosci 2023; 10:1067787. [PMID: 37143824 PMCID: PMC10151541 DOI: 10.3389/fmolb.2023.1067787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/16/2023] [Indexed: 05/06/2023] Open
Abstract
Stability of a protein-ligand complex may be sensitive to pH of its environment. Here we explore, computationally, stability of a set of protein-nucleic acid complexes using fundamental thermodynamic linkage relationship. The nucleosome, as well as an essentially random selection of 20 protein complexes with DNA or RNA, are included in the analysis. An increase in intra-cellular/intra-nuclear pH destabilizes most complexes, including the nucleosome. We propose to quantify the effect by ΔΔG0.3-the change in the binding free energy due to pH increase of 0.3 units, corresponding to doubling of the H + activity; variations of pH of this amplitude can occur in living cells, including in the course of the cell cycle, and in cancer cells relative to normal ones. We suggest, based on relevant experimental findings, a threshold of biological significance of 1 2 k B T ( ∼ 0.3 k c a l / m o l ) for changes of stability of chromatin-related protein-DNA complexes: a change in the binding affinity above the threshold may have biological consequences. We find that for 70% of the examined complexes, Δ Δ G 0.3 > 1 2 k B T (for 10%, ΔΔG0.3 is between 3 and 4 k B T). Thus, small but relevant variations of intra-nuclear pH of 0.3 may have biological consequences for many protein-nucleic acid complexes. The binding affinity between the histone octamer and its DNA, which directly affects the DNA accessibility in the nucleosome, is predicted to be highly sensitive to intra-nuclear pH. A variation of 0.3 units results in ΔΔG0.3 ∼ 10k B T ( ∼ 6 k c a l / m o l ) ; for spontaneous unwrapping of 20 bp long entry/exit fragments of the nucleosomal DNA, ΔΔG0.3 = 2.2k B T; partial disassembly of the nucleosome into the tetrasome is characterized by ΔΔG0.3 = 5.2k B T. The predicted pH -induced modulations of the nucleosome stability are significant enough to suggest that they may have consequences relevant to the biological function of the nucleosome. Accessibility of the nucleosomal DNA is predicted to positively correlate with pH variations during the cell cycle; an increase in intra-cellular pH seen in cancer cells is predicted to lead to a more accessible nucleosomal DNA; a drop in pH associated with apoptosis is predicted to make nucleosomal DNA less accessible. We speculate that processes that depend on accessibility to the DNA in the nucleosomes, such as transcription or DNA replication, might become upregulated due to relatively small, but nevertheless realistic increases of intra-nuclear pH.
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Affiliation(s)
- Alexey V. Onufriev
- Department of Physics, Virginia Tech, Blacksburg, Blacksburg, VA, United States
- Department of Computer Science, Virginia Tech, Blacksburg, Blacksburg, VA, United States
- Center from Soft Matter and Biological Physics, Virginia Tech, Blacksburg, VA, United States
- *Correspondence: Alexey V. Onufriev,
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12
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Silva TD, Vila-Viçosa D, Machuqueiro M. Increasing the Realism of in Silico pHLIP Peptide Models with a Novel pH Gradient CpHMD Method. J Chem Theory Comput 2022; 18:6472-6481. [PMID: 36257921 PMCID: PMC9775217 DOI: 10.1021/acs.jctc.2c00880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The pH-low insertion peptides (pHLIP) are pH-dependent membrane inserting peptides, whose function depends on the cell microenvironment acidity. Several peptide variants have been designed to improve upon the wt-sequence, particularly the state transition kinetics and the selectivity for tumor pH. The variant 3 (Var3) peptide is a 27 residue long peptide, with a key titrating residue (Asp-13) that, despite showing a modest performance in liposomes (pKins ∼ 5.0), excelled in tumor cell experiments. To help rationalize these results, we focused on the pH gradient in the cell membrane, which is one of the crucial properties that are not present in liposomes. We extended our CpHMD-L method and its pH replica-exchange (pHRE) implementation to include a pH gradient and mimic the pHLIP-membrane microenvironment in a cell where the internal pH is fixed (pH 7.2) and the external pH is allowed to change. We showed that, by properly modeling the pH-gradient, we can correctly predict the experimentally observed loss and gain of performance in tumor cells experiments by the wt and Var3 sequences, respectively. In sum, the pH gradient implementation allowed for more accurate and realistic pKa estimations and was a pivotal step in bridging the in silico data and the in vivo cell experiments.
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13
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Sequeira JN, Rodrigues FEP, Silva TGD, Reis PBPS, Machuqueiro M. Extending the Stochastic Titration CpHMD to CHARMM36m. J Phys Chem B 2022; 126:7870-7882. [PMID: 36190807 PMCID: PMC9776569 DOI: 10.1021/acs.jpcb.2c04529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The impact of pH on proteins is significant but often neglected in molecular dynamics simulations. Constant-pH Molecular Dynamics (CpHMD) is the state-of-the-art methodology to deal with these effects. However, it still lacks widespread adoption by the scientific community. The stochastic titration CpHMD is one of such methods that, until now, only supported the GROMOS force field family. Here, we extend this method's implementation to include the CHARMM36m force field available in the GROMACS software package. We test this new implementation with a diverse group of proteins, namely, lysozyme, Staphylococcal nuclease, and human and E. coli thioredoxins. All proteins were conformationally stable in the simulations, even at extreme pH values. The RMSE values (pKa prediction vs experimental) obtained were very encouraging, in particular for lysozyme and human thioredoxin. We have also identified a few residues that challenged the CpHMD simulations, highlighting scenarios where the method still needs improvement independently of the force field. The CHARMM36m all-atom implementation was more computationally efficient when compared with the GROMOS 54A7, taking advantage of a shorter nonbonded interaction cutoff and a less frequent neighboring list update. The new extension will allow the study of pH effects in many systems for which this force field is particularly suited, i.e., proteins, membrane proteins, lipid bilayers, and nucleic acids.
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14
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Aho N, Buslaev P, Jansen A, Bauer P, Groenhof G, Hess B. Scalable Constant pH Molecular Dynamics in GROMACS. J Chem Theory Comput 2022; 18:6148-6160. [PMID: 36128977 PMCID: PMC9558312 DOI: 10.1021/acs.jctc.2c00516] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Noora Aho
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014Jyväskylä, Finland
| | - Pavel Buslaev
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014Jyväskylä, Finland
| | - Anton Jansen
- Department of Applied Physics and Swedish e-Science Research Center, Science for Life Laboratory, KTH Royal Institute of Technology, 100 44Stockholm, Sweden
| | - Paul Bauer
- Department of Applied Physics and Swedish e-Science Research Center, Science for Life Laboratory, KTH Royal Institute of Technology, 100 44Stockholm, Sweden
| | - Gerrit Groenhof
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014Jyväskylä, Finland
| | - Berk Hess
- Department of Applied Physics and Swedish e-Science Research Center, Science for Life Laboratory, KTH Royal Institute of Technology, 100 44Stockholm, Sweden
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15
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Henderson JA, Liu R, Harris JA, Huang Y, de Oliveira VM, Shen J. A Guide to the Continuous Constant pH Molecular Dynamics Methods in Amber and CHARMM [Article v1.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2022; 4:1563. [PMID: 36776714 PMCID: PMC9910290 DOI: 10.33011/livecoms.4.1.1563] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Like temperature and pressure, solution pH is an important environmental variable in biomolecular simulations. Virtually all proteins depend on pH to maintain their structure and function. In conventional molecular dynamics (MD) simulations of proteins, pH is implicitly accounted for by assigning and fixing protonation states of titratable sidechains. This is a significant limitation, as the assigned protonation states may be wrong and they may change during dynamics. In this tutorial, we guide the reader in learning and using the various continuous constant pH MD methods in Amber and CHARMM packages, which have been applied to predict pK a values and elucidate proton-coupled conformational dynamics of a variety of proteins including enzymes and membrane transporters.
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Affiliation(s)
| | - Ruibin Liu
- University of Maryland School of Pharmacy, Baltimore, MD
| | | | - Yandong Huang
- University of Maryland School of Pharmacy, Baltimore, MD
| | | | - Jana Shen
- University of Maryland School of Pharmacy, Baltimore, MD
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16
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A Proof-of-Concept Inhibitor of Endothelial Lipase Suppresses Triple-Negative Breast Cancer Cells by Hijacking the Mitochondrial Function. Cancers (Basel) 2022; 14:cancers14153763. [PMID: 35954428 PMCID: PMC9367514 DOI: 10.3390/cancers14153763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/24/2022] [Accepted: 07/30/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Endothelial lipase (EL/LIPG) is a key regulator of tumor cell metabolism. In triple-negative breast cancer (TNBC) cells, we find that the expression of LIPG is associated with long non-coding RNA DANCR and positively correlates with gene signatures of mitochondrial metabolism-oxidative phosphorylation (OXPHOS). DANCR binds to LIPG, which enables tumor cells to maintain the expression. Importantly, LIPG knockdown inhibits OXPHOS and TNBC tumor formation. Finally, our study identifies a natural compound, the LIPG inhibitor cynaroside, which provides a new therapeutic strategy against TNBC. Abstract Triple-negative breast cancer (TNBC) cells reprogram their metabolism to provide metabolic flexibility for tumor cell growth and survival in the tumor microenvironment. While our previous findings indicated that endothelial lipase (EL/LIPG) is a hallmark of TNBC, the precise mechanism through which LIPG instigates TNBC metabolism remains undefined. Here, we report that the expression of LIPG is associated with long non-coding RNA DANCR and positively correlates with gene signatures of mitochondrial metabolism-oxidative phosphorylation (OXPHOS). DANCR binds to LIPG, enabling tumor cells to maintain LIPG protein stability and OXPHOS. As one mechanism of LIPG in the regulation of tumor cell oxidative metabolism, LIPG mediates histone deacetylase 6 (HDAC6) and histone acetylation, which contribute to changes in IL-6 and fatty acid synthesis gene expression. Finally, aided by a relaxed docking approach, we discovered a new LIPG inhibitor, cynaroside, that effectively suppressed the enzyme activity and DANCR in TNBC cells. Treatment with cynaroside inhibited the OXPHOS phenotype of TNBC cells, which severely impaired tumor formation. Taken together, our study provides mechanistic insights into the LIPG modulation of mitochondrial metabolism in TNBC and a proof-of-concept that targeting LIPG is a promising new therapeutic strategy for the treatment of TNBC.
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17
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Du H, Jiang D, Gao J, Zhang X, Jiang L, Zeng Y, Wu Z, Shen C, Xu L, Cao D, Hou T, Pan P. Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network. Research (Wash D C) 2022; 2022:9873564. [PMID: 35958111 PMCID: PMC9343084 DOI: 10.34133/2022/9873564] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/27/2022] [Indexed: 11/06/2022] Open
Abstract
Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.
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Affiliation(s)
- Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Junbo Gao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Lingxiao Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Yundian Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410004 Hunan, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China
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18
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Reis PBPS, Bertolini M, Montanari F, Rocchia W, Machuqueiro M, Clevert DA. A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven p Ka Predictions in Proteins. J Chem Theory Comput 2022; 18:5068-5078. [PMID: 35837736 DOI: 10.1021/acs.jctc.2c00308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Existing computational methods for estimating pKa values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined pKa shifts to train deep learning models, which are shown to rival the physics-based predictors. These neural networks managed to infer the electrostatic contributions of different chemical groups and learned the importance of solvent exposure and close interactions, including hydrogen bonds. Although trained only using theoretical data, our pKAI+ model displayed the best accuracy in a test set of ∼750 experimental values. Inference times allow speedups of more than 1000× compared to physics-based methods. By combining speed, accuracy, and a reasonable understanding of the underlying physics, our models provide a game-changing solution for fast estimations of macroscopic pKa values from ensembles of microscopic values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations.
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Affiliation(s)
| | - Marco Bertolini
- Machine Learning Research, Bayer A.G., Berlin 13353, Germany
| | | | - Walter Rocchia
- CONCEPT Lab, Istituto Italiano di Tecnologia (IIT), Via Melen 83, B Block, Genoa 16152, Italy
| | - Miguel Machuqueiro
- Biosystems and Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisboa, Campo Grande, Lisboa 1749-016, Portugal
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19
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Liu R, Verma N, Henderson JA, Zhan S, Shen J. Profiling MAP kinase cysteines for targeted covalent inhibitor design. RSC Med Chem 2022; 13:54-63. [PMID: 35224496 PMCID: PMC8792824 DOI: 10.1039/d1md00277e] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/28/2021] [Indexed: 07/20/2023] Open
Abstract
Mitogen-activated protein kinases (MAPK) are important therapeutic targets, and yet no inhibitors have advanced to the market. Here we applied the GPU-accelerated continuous constant pH molecular dynamics (CpHMD) to calculate the pK a's and profile the cysteine reactivities of all 14 MAPKs for assisting the targeted covalent inhibitor design. The simulations not only recapitulated but also rationalized the reactive cysteines in the front pocket of JNK1/2/3 and the extended front pocket of p38α. Interestingly, the DFG - 1 cysteine in the DFG-in conformation of ERK1/ERK2 was found somewhat reactive or unreactive; however, simulations of MKK7 showed that switching to the DFG-out conformation makes the DFG - 1 cysteine reactive, suggesting the advantage of type II covalent inhibitors. Additionally, the simulations prospectively predicted several druggable cysteine and lysine sites, including the αH head cysteine in JNK1/3 and DFG + 6 cysteine in JNK2, corroborating the chemical proteomic screening data. Given the low cost and the ability to offer physics-based rationales, we envision CpHMD simulations to complement the chemo-proteomic platform for systematic profiling cysteine reactivities for targeted covalent drug discovery.
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Affiliation(s)
- Ruibin Liu
- University of Maryland School of Pharmacy Baltimore MD USA
| | - Neha Verma
- University of Maryland School of Pharmacy Baltimore MD USA
| | | | - Shaoqi Zhan
- University of Maryland School of Pharmacy Baltimore MD USA
| | - Jana Shen
- University of Maryland School of Pharmacy Baltimore MD USA
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20
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Cai Z, Luo F, Wang Y, Li E, Huang Y. Protein p K a Prediction with Machine Learning. ACS OMEGA 2021; 6:34823-34831. [PMID: 34963965 PMCID: PMC8697405 DOI: 10.1021/acsomega.1c05440] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/24/2021] [Indexed: 05/23/2023]
Abstract
Protein pK a prediction is essential for the investigation of the pH-associated relationship between protein structure and function. In this work, we introduce a deep learning-based protein pK a predictor DeepKa, which is trained and validated with the pK a values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD implemented in the Amber molecular dynamics package has been employed (Huang Y.J. Chem. Inf. Model.2018, 58, 1372-1383). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pK a predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein pK a predictors in the future. Finally, the grid charge representation is general and applicable to other topics, such as the protein-ligand binding affinity prediction.
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21
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Liu R, Zhan S, Che Y, Shen J. Reactivities of the Front Pocket N-Terminal Cap Cysteines in Human Kinases. J Med Chem 2021; 65:1525-1535. [PMID: 34647463 DOI: 10.1021/acs.jmedchem.1c01186] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The front pocket (FP) N-terminal cap (Ncap) cysteine is the most popular site of covalent modification in kinases. A long-standing hypothesis associates the Ncap position with cysteine hyper-reactivity; however, traditional computational predictions suggest that the FP Ncap cysteines are predominantly unreactive. Here we applied the state-of-the-art continuous constant pH molecular dynamics (CpHMD) to test the Ncap hypothesis. Simulations found that the Ncap cysteines of BTK/BMX/TEC/ITK/TXK, JAK3, and MKK7 are reactive to varying degrees; however, those of BLK and EGFR/ERBB2/ERBB4 possessing a Ncap+3 aspartate are unreactive. Analysis suggested that hydrogen bonding and electrostatic interactions drive the reactivity, and their absence renders the Ncap cysteine unreactive. To further test the Ncap hypothesis, we examined the FP Ncap+2 cysteines in JNK1/JNK2/JNK3 and CASK. Our work offers a systematic understanding of the cysteine structure-reactivity relationship and illustrates the use of CpHMD to differentiate cysteines toward the design of targeted covalent inhibitors with reduced chemical reactivities.
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Affiliation(s)
- Ruibin Liu
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Shaoqi Zhan
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Ye Che
- Discovery Sciences, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
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22
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Reilley DJ, Wang J, Dokholyan NV, Alexandrova AN. Titr-DMD-A Rapid, Coarse-Grained Quasi-All-Atom Constant pH Molecular Dynamics Framework. J Chem Theory Comput 2021; 17:4538-4549. [PMID: 34165292 PMCID: PMC10662685 DOI: 10.1021/acs.jctc.1c00338] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The pH-dependence of enzyme fold stability and catalytic activity is a fundamentally dynamic, structural property which is difficult to study. The challenges and expense of investigating dynamic, atomic scale behavior experimentally means that computational methods, particularly constant pH molecular dynamics (CpHMD), are well situated tools for this. However, these methods often struggle with affordable sampling of sufficiently long time scales while also obtaining accurate pKa prediction and verifying the structures they generate. We introduce Titr-DMD, an affordable CpHMD method that combines the quasi-all-atom coarse-grained discrete molecular dynamics (DMD) method for conformational sampling with Propka for pKa prediction, to circumvent these issues. The combination enables rapid sampling on limited computational resources, while simulations are still performed on the atomic scale. We benchmark the method on a set of proteins with experimentally attested pKa and on the pH triggered conformational change in a staphylococcal nuclease mutant, a rare experimental study of such behavior. Our results show Titr-DMD to be an effective and inexpensive method to study pH-coupled protein dynamics.
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Affiliation(s)
- David J Reilley
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United States
| | - Jian Wang
- Department of Pharmacology, Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania 17033, United States
| | - Nikolay V Dokholyan
- Department of Pharmacology, Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania 17033, United States
- Departments of Chemistry and Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Anastassia N Alexandrova
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United States
- California NanoSystems Institute, Los Angeles, California 90095-1569, United States
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23
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Computational Design of Novel Allosteric Inhibitors for Plasmodium falciparum DegP. Molecules 2021; 26:molecules26092742. [PMID: 34066964 PMCID: PMC8141111 DOI: 10.3390/molecules26092742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/24/2021] [Accepted: 03/31/2021] [Indexed: 11/23/2022] Open
Abstract
The serine protease, DegP exhibits proteolytic and chaperone activities, essential for cellular protein quality control and normal cell development in eukaryotes. The P. falciparum DegP is essential for the parasite survival and required to combat the oscillating thermal stress conditions during the infection, protein quality checks and protein homeostasis in the extra-cytoplasmic compartments, thereby establishing it as a potential target for drug development against malaria. Previous studies have shown that diisopropyl fluorophosphate (DFP) and the peptide SPMFKGV inhibit E. coli DegP protease activity. To identify novel potential inhibitors specific to PfDegP allosteric and the catalytic binding sites, we performed a high throughput in silico screening using Malaria Box, Pathogen Box, Maybridge library, ChEMBL library and the library of FDA approved compounds. The screening helped identify five best binders that showed high affinity to PfDegP allosteric (T0873, T2823, T2801, RJC02337, CD00811) and the catalytic binding site (T0078L, T1524, T2328, BTB11534 and 552691). Further, molecular dynamics simulation analysis revealed RJC02337, BTB11534 as the best hits forming a stable complex. WaterMap and electrostatic complementarity were used to evaluate the novel bio-isosteric chemotypes of RJC02337, that led to the identification of 231 chemotypes that exhibited better binding affinity. Further analysis of the top 5 chemotypes, based on better binding affinity, revealed that the addition of electron donors like nitrogen and sulphur to the side chains of butanoate group are more favoured than the backbone of butanoate group. In a nutshell, the present study helps identify novel, potent and Plasmodium specific inhibitors, using high throughput in silico screening and bio-isosteric replacement, which may be experimentally validated.
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24
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Hong ST, Su YC, Wang YJ, Cheng TL, Wang YT. Anti-TNF Alpha Antibody Humira with pH-dependent Binding Characteristics: A constant-pH Molecular Dynamics, Gaussian Accelerated Molecular Dynamics, and In Vitro Study. Biomolecules 2021; 11:334. [PMID: 33672169 PMCID: PMC7926962 DOI: 10.3390/biom11020334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/18/2021] [Accepted: 02/20/2021] [Indexed: 12/17/2022] Open
Abstract
Humira is a monoclonal antibody that binds to TNF alpha, inactivates TNF alpha receptors, and inhibits inflammation. Neonatal Fc receptors can mediate the transcytosis of Humira-TNF alpha complex structures and process them toward degradation pathways, which reduces the therapeutic effect of Humira. Allowing the Humira-TNF alpha complex structures to dissociate to Humira and soluble TNF alpha in the early endosome to enable Humira recycling is crucial. We used the cytoplasmic pH (7.4), the early endosomal pH (6.0), and pKa of histidine side chains (6.0-6.4) to mutate the residues of complementarity-determining regions with histidine. Our engineered Humira (W1-Humira) can bind to TNF alpha in plasma at neutral pH and dissociate from the TNF alpha in the endosome at acidic pH. We used the constant-pH molecular dynamics, Gaussian accelerated molecular dynamics, two-dimensional potential mean force profiles, and in vitro methods to investigate the characteristics of W1-Humira. Our results revealed that the proposed Humira can bind TNF alpha with pH-dependent affinity in vitro. The W1-Humira was weaker than wild-type Humira at neutral pH in vitro, and our prediction results were close to the in vitro results. Furthermore, our approach displayed a high accuracy in antibody pH-dependent binding characteristics prediction, which may facilitate antibody drug design. Advancements in computational methods and computing power may further aid in addressing the challenges in antibody drug design.
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Affiliation(s)
- Shih-Ting Hong
- Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
| | - Yu-Cheng Su
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan;
| | - Yu-Jen Wang
- Department of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan;
| | - Tian-Lu Cheng
- Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yeng-Tseng Wang
- Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
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25
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Hofer F, Kamenik AS, Fernández-Quintero ML, Kraml J, Liedl KR. pH-Induced Local Unfolding of the Phl p 6 Pollen Allergen From cpH-MD. Front Mol Biosci 2021; 7:603644. [PMID: 33511157 PMCID: PMC7835895 DOI: 10.3389/fmolb.2020.603644] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/10/2020] [Indexed: 11/13/2022] Open
Abstract
Susceptibility to endosomal degradation is a decisive contribution to a protein's immunogenicity. It is assumed that the processing kinetics of structured proteins are inherently linked to their probability of local unfolding. In this study, we quantify the impact of endosomal acidification on the conformational stability of the major timothy grass pollen allergen Phl p 6. We use state of the art sampling approaches in combination with constant pH MD techniques to profile pH-dependent local unfolding events in atomistic detail. Integrating our findings into the current view on type 1 allergic sensitization, we characterize local protein dynamics in the context of proteolytic degradation at neutral and acidic pH for the wild type protein and point mutants with varying proteolytic stability. We analyze extensive simulation data using Markov state models and retrieve highly reliable thermodynamic and kinetic information at varying pH levels. Thereby we capture the impact of endolysosomal acidification on the structure and dynamics of the Phl p 6 mutants. We find that upon protonation at lower pH values, the conformational flexibilities in key areas of the wild type protein, i.e., T-cell epitopes and early proteolytic cleavage sites, increase significantly. A decrease of the pH even leads to local unfolding in otherwise stable secondary structure elements, which is a prerequisite for proteolytic cleavage. This effect is even more pronounced in the destabilized mutant, while no unfolding was observed for the stabilized mutant. In summary, we report detailed structural models which rationalize the experimentally observed cleavage pattern during endosomal acidification.
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26
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Huang Y, Henderson JA, Shen J. Continuous Constant pH Molecular Dynamics Simulations of Transmembrane Proteins. Methods Mol Biol 2021; 2302:275-287. [PMID: 33877633 PMCID: PMC8062021 DOI: 10.1007/978-1-0716-1394-8_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Many membrane channels, transporters, and receptors utilize a pH gradient or proton coupling to drive functionally relevant conformational transitions. Conventional molecular dynamics simulations employ fixed protonation states, thus neglecting the coupling between protonation and conformational equilibria. Here we describe the membrane-enabled hybrid-solvent continuous constant pH molecular dynamics method for capturing atomic details of proton-coupled conformational dynamics of transmembrane proteins. Example protocols from our recent application studies of proton channels and ion/substrate transporters are discussed.
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Affiliation(s)
- Yandong Huang
- College of Computer Engineering, Jimei University, Xiamen, Fujian, China
| | | | - Jana Shen
- University of Maryland School of Pharmacy, Baltimore, MD, USA.
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27
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Verma N, Henderson JA, Shen J. Proton-Coupled Conformational Activation of SARS Coronavirus Main Proteases and Opportunity for Designing Small-Molecule Broad-Spectrum Targeted Covalent Inhibitors. J Am Chem Soc 2020; 142:21883-21890. [PMID: 33320670 PMCID: PMC7754784 DOI: 10.1021/jacs.0c10770] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Indexed: 02/08/2023]
Abstract
The SARS coronavirus 2 (SARS-CoV-2) main protease (Mpro) is an attractive broad-spectrum antiviral drug target. Despite the enormous progress in structure elucidation, the Mpro's structure-function relationship remains poorly understood. Recently, a peptidomimetic inhibitor has entered clinical trial; however, small-molecule orally available antiviral drugs have yet to be developed. Intrigued by a long-standing controversy regarding the existence of an inactive state, we explored the proton-coupled dynamics of the Mpros of SARS-CoV-2 and the closely related SARS-CoV using a newly developed continuous constant pH molecular dynamics (MD) method and microsecond fixed-charge all-atom MD simulations. Our data supports a general base mechanism for Mpro's proteolytic function. The simulations revealed that protonation of His172 alters a conserved interaction network that upholds the oxyanion loop, leading to a partial collapse of the conserved S1 pocket, consistent with the first and controversial crystal structure of SARS-CoV Mpro determined at pH 6. Interestingly, a natural flavonoid binds SARS-CoV-2 Mpro in the close proximity to a conserved cysteine (Cys44), which is hyper-reactive according to the CpHMD titration. This finding offers an exciting new opportunity for small-molecule targeted covalent inhibitor design. Our work represents a first step toward the mechanistic understanding of the proton-coupled structure-dynamics-function relationship of CoV Mpros; the proposed strategy of designing small-molecule covalent inhibitors may help accelerate the development of orally available broad-spectrum antiviral drugs to stop the current pandemic and prevent future outbreaks.
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Affiliation(s)
- Neha Verma
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Jack A Henderson
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
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28
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Henderson JA, Verma N, Harris RC, Liu R, Shen J. Assessment of proton-coupled conformational dynamics of SARS and MERS coronavirus papain-like proteases: Implication for designing broad-spectrum antiviral inhibitors. J Chem Phys 2020; 153:115101. [PMID: 32962355 PMCID: PMC7499820 DOI: 10.1063/5.0020458] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Broad-spectrum antiviral drugs are urgently needed to stop the Coronavirus Disease 2019 pandemic and prevent future ones. The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is related to the SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), which have caused the previous outbreaks. The papain-like protease (PLpro) is an attractive drug target due to its essential roles in the viral life cycle. As a cysteine protease, PLpro is rich in cysteines and histidines, and their protonation/deprotonation modulates catalysis and conformational plasticity. Here, we report the pKa calculations and assessment of the proton-coupled conformational dynamics of SARS-CoV-2 in comparison to SARS-CoV and MERS-CoV PLpros using the recently developed graphical processing unit (GPU)-accelerated implicit-solvent continuous constant pH molecular dynamics method with a new asynchronous replica-exchange scheme, which allows computation on a single GPU card. The calculated pKa's support the catalytic roles of the Cys-His-Asp triad. We also found that several residues can switch protonation states at physiological pH among which is C270/271 located on the flexible blocking loop 2 (BL2) of SARS-CoV-2/CoV PLpro. Simulations revealed that the BL2 can open and close depending on the protonation state of C271/270, consistent with the most recent crystal structure evidence. Interestingly, despite the lack of an analogous cysteine, BL2 in MERS-CoV PLpro is also very flexible, challenging a current hypothesis. These findings are supported by the all-atom fixed-charge simulations and provide a starting point for more detailed studies to assist the structure-based design of broad-spectrum inhibitors against CoV PLpros.
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Affiliation(s)
- Jack A Henderson
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Neha Verma
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Robert C Harris
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Ruibin Liu
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
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29
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Yao X, Chen C, Wang Y, Dong S, Liu YJ, Li Y, Cui Z, Gong W, Perrett S, Yao L, Lamed R, Bayer EA, Cui Q, Feng Y. Discovery and mechanism of a pH-dependent dual-binding-site switch in the interaction of a pair of protein modules. SCIENCE ADVANCES 2020; 6:6/43/eabd7182. [PMID: 33097546 PMCID: PMC7608827 DOI: 10.1126/sciadv.abd7182] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/08/2020] [Indexed: 05/23/2023]
Abstract
Many important proteins undergo pH-dependent conformational changes resulting in "on-off" switches for protein function, which are essential for regulation of life processes and have wide application potential. Here, we report a pair of cellulosomal assembly modules, comprising a cohesin and a dockerin from Clostridium acetobutylicum, which interact together following a unique pH-dependent switch between two functional sites rather than on-off states. The two cohesin-binding sites on the dockerin are switched from one to the other at pH 4.8 and 7.5 with a 180° rotation of the bound dockerin. Combined analysis by nuclear magnetic resonance spectroscopy, crystal structure determination, mutagenesis, and isothermal titration calorimetry elucidates the chemical and structural mechanism of the pH-dependent switching of the binding sites. The pH-dependent dual-binding-site switch not only represents an elegant example of biological regulation but also provides a new approach for developing pH-dependent protein devices and biomaterials beyond an on-off switch for biotechnological applications.
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Affiliation(s)
- Xingzhe Yao
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chao Chen
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Yefei Wang
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Sheng Dong
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Ya-Jun Liu
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Yifei Li
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Zhenling Cui
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weibin Gong
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Sarah Perrett
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Lishan Yao
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Raphael Lamed
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Edward A Bayer
- Department of Biomolecular Sciences, The Weizmann Institute of Science, Rehovot 7610001, Israel
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 8499000, Israel
| | - Qiu Cui
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yingang Feng
- CAS Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China.
- Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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30
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Chen X, Xie W, Yang Y, Hua Y, Xing G, Liang L, Deng C, Wang Y, Fan Y, Liu H, Lu T, Chen Y, Zhang Y. Discovery of Dual FGFR4 and EGFR Inhibitors by Machine Learning and Biological Evaluation. J Chem Inf Model 2020; 60:4640-4652. [DOI: 10.1021/acs.jcim.0c00652] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Xingye Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Wuchen Xie
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yan Yang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - GuoMeng Xing
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chenglong Deng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yuchen Wang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yuanrong Fan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
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31
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Aleksandrov A, Roux B, MacKerell AD. p Ka Calculations with the Polarizable Drude Force Field and Poisson-Boltzmann Solvation Model. J Chem Theory Comput 2020; 16:4655-4668. [PMID: 32464053 DOI: 10.1021/acs.jctc.0c00111] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Electronic polarization effects have been suggested to play an important role in proton binding to titratable residues in proteins. In this work, we describe a new computational method for pKa calculations, using Monte Carlo (MC) simulations to sample protein protonation states with the Drude polarizable force field and Poisson-Boltzmann (PB) continuum electrostatic solvent model. While the most populated protonation states at the selected pH, corresponding to residues that are half-protonated at that pH, are sampled using the exact relative free energies computed with Drude particles optimized in the field of the PB implicit solvation model, we introduce an approximation for the protein polarization of low-populated protonation states to reduce the computational cost. The highly populated protonation states used to compute the polarization and pKa's are then iteratively improved until convergence. It is shown that for lysozyme, when considering 9 of the 18 titratable residues, the new method converged within two iterations with computed pKa's differing only by 0.02 pH units from pKa's estimated with the exact approach. Application of the method to predict pKa's of 94 titratable side chains in 8 proteins shows the Drude-PB model to produce physically more correct results as compared to the additive CHARMM36 (C36) force field (FF). With a dielectric constant of two assigned to the protein interior the Root Mean Square (RMS) deviation between computed and experimental pKa's is 2.07 and 3.19 pH units with the Drude and C36 models, respectively, and the RMS deviation using the Drude-PB model is relatively insensitive to the choice of the internal dielectric constant in contrast to the additive C36 model. At the higher internal dielectric constant of 20, pKa's computed with the additive C36 model converge to the results obtained with the Drude polarizable force field, indicating the need to artificially overestimate electrostatic screening in a nonphysical way with the additive FF. In addition, inclusion of both syn and anti orientations of the proton in the neutral state of acidic groups is shown to yield improved agreement with experiment. The present work, which is the first example of the use of a polarizable model for the prediction of pKa's in proteins, shows that the use of a polarizable model represents a more physically correct model for the treatment of electrostatic contributions to pKa shifts in proteins.
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Affiliation(s)
- Alexey Aleksandrov
- Laboratoire d'Optique et Biosciences, Ecole Polytechnique, IP Paris, F-91128 Palaiseau, France
| | - Benoît Roux
- Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, University of Chicago, 929 E57th Street, Chicago, Illinois 60637, United States
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States
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32
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Henderson JA, Verma N, Shen J. Assessment of Proton-Coupled Conformational Dynamics of SARS and MERS Coronavirus Papain-like Proteases: Implication for Designing Broad-Spectrum Antiviral Inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.06.30.181305. [PMID: 32637952 PMCID: PMC7337382 DOI: 10.1101/2020.06.30.181305] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Broad-spectrum antiviral drugs are urgently needed to stop the COVID-19 pandemic and prevent future ones. The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is related to SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), which have caused the previous outbreaks. The papain-like protease (PLpro) is an attractive drug target due to its essential roles in the viral life cycle. As a cysteine protease, PLpro is rich in cysteines and histidines and their protonation/deprotonation modulates catalysis and conformational plasticity. Here we report the pKa calculations and assessment of the proton-coupled conformational dynamics of SARS-CoV-2 in comparison to SARS-CoV and MERS-CoV PLpros using a newly developed GPU-accelerated implicit-solvent continuous constant pH molecular dynamics method with an asynchronous replica-exchange scheme. The calculated pKa's support the catalytic roles of the Cys-His-Asp triad. We also found that several residues can switch protonation states at physiological pH, among which is C270/271 located on the flexible blocking loop 2 (BL2) of SARS-CoV-2/CoV PLpro. Simulations revealed that the BL2 conformational dynamics is coupled to the titration of C271/270, in agreement with the crystal structures of SARS-CoV-2 PLpro. Simulations also revealed that BL2 in MERS-CoV PLpro is very flexible, sampling both open and closed states despite the lack of an analogous cysteine. Our work provides a starting point for more detailed mechanistic studies to assist structure-based design of broad-spectrum inhibitors against CoV PLpros.
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Affiliation(s)
- Jack A. Henderson
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201
| | - Neha Verma
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201
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33
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Harris RC, Liu R, Shen J. Predicting Reactive Cysteines with Implicit-Solvent-Based Continuous Constant pH Molecular Dynamics in Amber. J Chem Theory Comput 2020; 16:3689-3698. [PMID: 32330035 PMCID: PMC7772776 DOI: 10.1021/acs.jctc.0c00258] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Cysteines existing in the deprotonated thiolate form or having a tendency to become deprotonated are important players in enzymatic and cellular redox functions and frequently exploited in covalent drug design; however, most computational studies assume cysteines as protonated. Thus, developing an efficient tool that can make accurate and reliable predictions of cysteine protonation states is timely needed. We recently implemented a generalized Born (GB) based continuous constant pH molecular dynamics (CpHMD) method in Amber for protein pKa calculations on CPUs and GPUs. Here we benchmark the performance of GB-CpHMD for predictions of cysteine pKa's and reactivities using a data set of 24 proteins with both down- and upshifted cysteine pKa's. We found that 10 ns single-pH or 4 ns replica-exchange CpHMD titrations gave root-mean-square errors of 1.2-1.3 and correlation coefficients of 0.8-0.9 with respect to experiment. The accuracy of predicting thiolates or reactive cysteines at physiological pH with single-pH titrations is 86 or 81% with a precision of 100 or 90%, respectively. This performance well surpasses the traditional structure-based methods, particularly a widely used empirical pKa tool that gives an accuracy less than 50%. We discuss simulation convergence, dependence on starting structures, common determinants of the pKa downshifts and upshifts, and the origin of the discrepancies from the structure-based calculations. Our work suggests that CpHMD titrations can be performed on a desktop computer equipped with a single GPU card to predict cysteine protonation states for a variety of applications, from understanding biological functions to covalent drug design.
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Affiliation(s)
- Robert C Harris
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Ruibin Liu
- ComputChem LLC, Baltimore, Maryland 21202, United States
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
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34
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Ribone SR, Ferronato MJ, Vitale C, Fall Y, Curino AC, Facchinetti MM, Quevedo MA. Vitamin D receptor exhibits different pharmacodynamic features in tumoral and normal microenvironments: A molecular modeling study. J Steroid Biochem Mol Biol 2020; 200:105649. [PMID: 32142933 DOI: 10.1016/j.jsbmb.2020.105649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 02/28/2020] [Accepted: 03/03/2020] [Indexed: 12/20/2022]
Abstract
The vitamin D receptor (VDR) constitutes a promising therapeutic target for the treatment of cancer. Unfortunately, its natural agonist calcitriol does not have clinical utility due to its potential to induce hypercalcemic effects at the concentrations required to display antitumoral activity. For this reason, the search for new calcitriol analogues with adequate therapeutic profiles has been actively pursued by the scientific community. We have previously reported the obtaining and the biological activity evaluation of new calcitriol analogues by modification of its sidechain, which exhibited relevant antiproliferative and selectivity profiles against tumoral and normal cells. In this work we conducted molecular modeling studies (i.e. molecular docking, molecular dynamics, constant pH molecular dynamics (CpHMD) and free energy of binding analysis) to elucidate at an atomistic level the molecular basis related to the potential of the new calcitriol analogues to achieve selectivity between tumoral and normal cells. Two histidine residues (His305 and His397) were found to exhibit a particular tautomeric configuration that produces the observed bioactivity. Also, different acid-based properties were observed for His305 and His307 with His305 showing an increased acidity (pKa 5.2) compared to His397 (pKa 6.8) and to the typical histidine residue. This behavior favored the pharmacodynamic interaction of the calcitriol analogues exhibiting selectivity for tumoral cells when VDR was modeled at the more acidic tumoral environment (pH ≅ 6) compared to the case when VDR was modeled at pH 7.4 (normal cell environment). On the other hand, non-selective compounds, including calcitriol, exhibited a similar interaction pattern with VDR when the receptor was modeled at both pH conditions. The results presented constitute the first evidence on the properties of the VDR receptor in different physicochemical environments and thus represent a significant contribution to the in silico screening and design of new calcitriol analogues.
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Affiliation(s)
- Sergio R Ribone
- Unidad de Investigación y Desarrollo en Tecnología Farmacéutica (UNITEFA), CONICET and Departamento de Ciencias Farmacéuticas, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba. X5000HUA, Córdoba, Argentina
| | - Maria J Ferronato
- Laboratorio de Biología del Cáncer, Instituto de Investigaciones Bioquímicas de Bahía Blanca (INIBIBB), Universidad Nacional del Sur (UNS), CONICET, Departamento de Biología, Bioquímica y Farmacia (UNS), Bahía Blanca, Argentina
| | - Cristian Vitale
- Laboratorio de Química Orgánica, Instituto de Química del Sur (INQUISUR), Universidad Nacional del Sur (UNS), CONICET, Departamento de Química (UNS), Bahía Blanca, Argentina
| | - Yagamare Fall
- Departamento de Química Orgánica, Facultad de Química e Instituto de Investigación Biomédica (IBI), Universidad de Vigo, Campus Lagoas de Marcosende, 36310, Vigo, Spain
| | - Alejandro C Curino
- Laboratorio de Biología del Cáncer, Instituto de Investigaciones Bioquímicas de Bahía Blanca (INIBIBB), Universidad Nacional del Sur (UNS), CONICET, Departamento de Biología, Bioquímica y Farmacia (UNS), Bahía Blanca, Argentina
| | - Maria M Facchinetti
- Laboratorio de Biología del Cáncer, Instituto de Investigaciones Bioquímicas de Bahía Blanca (INIBIBB), Universidad Nacional del Sur (UNS), CONICET, Departamento de Biología, Bioquímica y Farmacia (UNS), Bahía Blanca, Argentina
| | - Mario A Quevedo
- Unidad de Investigación y Desarrollo en Tecnología Farmacéutica (UNITEFA), CONICET and Departamento de Ciencias Farmacéuticas, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba. X5000HUA, Córdoba, Argentina.
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35
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Hofer F, Kraml J, Kahler U, Kamenik AS, Liedl KR. Catalytic Site p Ka Values of Aspartic, Cysteine, and Serine Proteases: Constant pH MD Simulations. J Chem Inf Model 2020; 60:3030-3042. [PMID: 32348143 PMCID: PMC7312390 DOI: 10.1021/acs.jcim.0c00190] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
![]()
Enzymatic function and activity of
proteases is closely controlled
by the pH value. The protonation states of titratable residues in
the active site react to changes in the pH value, according to their
pKa, and thereby determine the functionality
of the enzyme. Knowledge of the titration behavior of these residues
is crucial for the development of drugs targeting the active site
residues. However, experimental pKa data
are scarce, since the systems’ size and complexity make determination
of these pKa values inherently difficult.
In this study, we use single pH constant pH MD simulations as a fast
and robust tool to estimate the active site pKa values of a set of aspartic, cysteine, and serine proteases.
We capture characteristic pKa shifts of
the active site residues, which dictate the experimentally determined
activity profiles of the respective protease family. We find clear
differences of active site pKa values
within the respective families, which closely match the experimentally
determined pH preferences of the respective proteases. These shifts
are caused by a distinct network of electrostatic interactions characteristic
for each protease family. While we find convincing agreement with
experimental data for serine and aspartic proteases, we observe clear
deficiencies in the description of the titration behavior of cysteines
within the constant pH MD framework and highlight opportunities for
improvement. Consequently, with this work, we provide a concise set
of active site pKa values of aspartic
and serine proteases, which could serve as reference for future theoretical
as well as experimental studies.
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Affiliation(s)
- Florian Hofer
- Institute for General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Johannes Kraml
- Institute for General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Ursula Kahler
- Institute for General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna S Kamenik
- Institute for General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Klaus R Liedl
- Institute for General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
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36
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Harris RC, Shen J. GPU-Accelerated Implementation of Continuous Constant pH Molecular Dynamics in Amber: p Ka Predictions with Single-pH Simulations. J Chem Inf Model 2019; 59:4821-4832. [PMID: 31661616 DOI: 10.1021/acs.jcim.9b00754] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We present a GPU implementation of the continuous constant pH molecular dynamics (CpHMD) based on the most recent generalized Born implicit-solvent model in the pmemd engine of the Amber molecular dynamics package. To test the accuracy of the tool for rapid pKa predictions, a series of 2 ns single-pH simulations were performed for over 120 titratable residues in 10 benchmark proteins that were previously used to test the various continuous CpHMD methods. The calculated pKa's showed a root-mean-square deviation of 0.80 and correlation coefficient of 0.83 with respect to experiment. Also, 90% of the pKa's were converged with estimated errors below 0.1 pH units. Surprisingly, this level of accuracy is similar to our previous replica-exchange simulations with 2 ns per replica and an exchange attempt frequency of 2 ps-1 (Huang, Harris, and Shen J. Chem. Inf. Model. 2018 , 58 , 1372 - 1383 ). Interestingly, for the linked titration sites in two enzymes, although residue-specific protonation state sampling in the single-pH simulations was not converged within 2 ns, the protonation fraction of the linked residues appeared to be largely converged, and the experimental macroscopic pKa values were reproduced to within 1 pH unit. Comparison with replica-exchange simulations with different exchange attempt frequencies showed that the splitting between the two macroscopic pKa's is underestimated with frequent exchange attempts such as 2 ps-1, while single-pH simulations overestimate the splitting. The same trend is seen for the single-pH vs replica-exchange simulations of a hydrogen-bonded aspartyl dyad in a much larger protein. A 2 ns single-pH simulation of a 400-residue protein takes about 1 h on a single NVIDIA GeForce RTX 2080 graphics card, which is over 1000 times faster than a CpHMD run on a single CPU core of a high-performance computing cluster node. Thus, we envision that GPU-accelerated continuous CpHMD may be used in routine pKa predictions for a variety of applications, from assisting MD simulations with protonation state assignment to offering pH-dependent corrections of binding free energies and identifying reactive hot spots for covalent drug design.
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Affiliation(s)
- Robert C Harris
- Department of Pharmaceutical Sciences , University of Maryland School of Pharmacy , Baltimore , Maryland 21201 , United States
| | - Jana Shen
- Department of Pharmaceutical Sciences , University of Maryland School of Pharmacy , Baltimore , Maryland 21201 , United States
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37
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Hofer F, Dietrich V, Kamenik AS, Tollinger M, Liedl KR. pH-Dependent Protonation of the Phl p 6 Pollen Allergen Studied by NMR and cpH-aMD. J Chem Theory Comput 2019; 15:5716-5726. [PMID: 31476118 PMCID: PMC6994067 DOI: 10.1021/acs.jctc.9b00540] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We use state-of-the-art NMR experiments to measure apparent pKa values in the native protein environment and employ a cutting-edge combination of enhanced sampling and constant pH molecular dynamics (MD) simulations to rationalize strong pKa shifts. The major timothy grass pollen allergen Phl p 6 serves as an ideal model system for both methods due to its high number of titratable residues despite its comparably small size. We present a proton transition analysis as intuitive tool to depict the captured protonation state ensemble in atomistic detail. Combining microscopic structural details from MD simulations and macroscopic ensemble averages from NMR shifts leads to a comprehensive view on pH dependencies of protonation states and tautomers. Overall, we find striking agreement between simulation-based pKa predictions and experiment. However, our analyses suggest subtle differences in the underlying molecular origin of the observed pKa shifts. From accelerated constant pH MD simulations, we identify immediate proximity of opposite charges, followed by vicinity of equal charges as major driving forces for pKa shifts. NMR experiments on the other hand, suggest only a weak relation of pKa shifts and close contacts to charged residues, while the strongest influence derives from the dipolar character of α helices. The presented study hence pinpoints opportunities for improvements concerning the theoretical description of protonation state and tautomer probabilities. However, the coherence in the resulting apparent pKa values from simulations and experiment affirms cpH-aMD as a reliable tool to study allergen dynamics at varying pH levels.
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Affiliation(s)
- Florian Hofer
- †Institute
for General, Inorganic and Theoretical Chemistry and ‡Institute for Organic Chemistry,
Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Valentin Dietrich
- †Institute
for General, Inorganic and Theoretical Chemistry and ‡Institute for Organic Chemistry,
Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna S. Kamenik
- †Institute
for General, Inorganic and Theoretical Chemistry and ‡Institute for Organic Chemistry,
Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Martin Tollinger
- †Institute
for General, Inorganic and Theoretical Chemistry and ‡Institute for Organic Chemistry,
Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Klaus R. Liedl
- †Institute
for General, Inorganic and Theoretical Chemistry and ‡Institute for Organic Chemistry,
Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria,E-mail:
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Sakipov SN, Flores-Canales JC, Kurnikova MG. A Hierarchical Approach to Predict Conformation-Dependent Histidine Protonation States in Stable and Flexible Proteins. J Phys Chem B 2019; 123:5024-5034. [PMID: 31095377 DOI: 10.1021/acs.jpcb.9b00656] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Solution acidity measured by pH is an important environmental factor that affects protein structure. It influences the protonation state of protein residues, which in turn may be coupled to protein conformational changes, unfolding, and ligand binding. It remains difficult to compute and measure the p Ka of individual residues, as well as to relate them to pH-dependent protein transitions. This paper presents a hierarchical approach to compute the p Ka of individual protonatable residues, specifically histidines, coupled with underlying structural changes of a protein. A fast and efficient free energy perturbation (FEP) algorithm has also been developed utilizing a fast implementation of standard molecular dynamics (MD) algorithms. Specifically, a CUDA version of the AMBER MD engine is used in this paper. Eight histidine p Ka's are computed in a diverse set of pH stable proteins to demonstrate the proposed approach's utility and assess the predictive quality of the AMBER FF99SB force field. A reference molecule is carefully selected and tested for convergence. A hierarchical approach is used to model p Ka's of the six histidine residues of the diphtheria toxin translocation domain (DTT), which exhibits a diverse ensemble of individual conformations and pH-dependent unfolding. The hierarchical approach consists of first sampling equilibrium conformational ensembles of a protein with protonated and neutral histidine residues via long equilibrium MD simulations (Flores-Canales, J. C.; et al. bioRxiv, 2019, 572040). A clustering method is then used to identify sampled protein conformations, and p Ka's of histidines in each protein conformation are computed. Finally, an ensemble averaging formalism is developed to compute weighted average histidine p Ka's. These can be compared with an apparent experimentally measured p Ka of the DTT protein and thus allows us to propose a mechanism of pH-dependent unfolding of the DTT protein.
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Affiliation(s)
- Serzhan N Sakipov
- Chemistry Department , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Jose C Flores-Canales
- Chemistry Department , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Maria G Kurnikova
- Chemistry Department , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
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Liu R, Yue Z, Tsai CC, Shen J. Assessing Lysine and Cysteine Reactivities for Designing Targeted Covalent Kinase Inhibitors. J Am Chem Soc 2019; 141:6553-6560. [PMID: 30945531 DOI: 10.1021/jacs.8b13248] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Targeted covalent inhibitor design is gaining increasing interest and acceptance. A typical covalent kinase inhibitor design targets a reactive cysteine; however, this strategy is limited by the low abundance of cysteine and acquired drug resistance from point mutations. Inspired by the recent development of lysine-targeted chemical probes, we asked if nucleophilic (reactive) catalytic lysines are common on the basis of the published crystal structures of the human kinome. Using a newly developed p Ka prediction tool based on continuous constant pH molecular dynamics, the catalytic lysines of eight unique kinases from various human kinase groups were retrospectively and prospectively predicted to be nucleophilic, when kinase is in the rare DFG-out/αC-out type of conformation. Importantly, other reactive lysines as well as cysteines at various locations were also identified. On the basis of the findings, we proposed a new strategy in which selective type II reversible kinase inhibitors are modified to design highly selective, lysine-targeted covalent inhibitors. Traditional covalent drugs were discovered serendipitously; the presented tool, which can assess the reactivities of any potentially targetable residues, may accelerate the rational discovery of new covalent inhibitors. Another significant finding of the work is that lysines and cysteines in kinases may adopt neutral and charged states at physiological pH, respectively. This finding may shift the current paradigm of computational studies of kinases, which assume fixed solution protonation states.
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Affiliation(s)
- Ruibin Liu
- Department of Pharmaceutical Sciences , School of Pharmacy, University of Maryland , Baltimore , Maryland 21201 , United States
| | - Zhi Yue
- Department of Pharmaceutical Sciences , School of Pharmacy, University of Maryland , Baltimore , Maryland 21201 , United States
| | - Cheng-Chieh Tsai
- Department of Pharmaceutical Sciences , School of Pharmacy, University of Maryland , Baltimore , Maryland 21201 , United States
| | - Jana Shen
- Department of Pharmaceutical Sciences , School of Pharmacy, University of Maryland , Baltimore , Maryland 21201 , United States
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