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Hu C, He G, Yang Y, Wang N, Zhang Y, Su Y, Zhao F, Wu J, Wang L, Lin Y, Shao L. Nanomaterials Regulate Bacterial Quorum Sensing: Applications, Mechanisms, and Optimization Strategies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306070. [PMID: 38350718 PMCID: PMC11022734 DOI: 10.1002/advs.202306070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/19/2024] [Indexed: 02/15/2024]
Abstract
Anti-virulence therapy that interferes with bacterial communication, known as "quorum sensing (QS)", is a promising strategy for circumventing bacterial resistance. Using nanomaterials to regulate bacterial QS in anti-virulence therapy has attracted much attention, which is mainly attributed to unique physicochemical properties and excellent designability of nanomaterials. However, bacterial QS is a dynamic and multistep process, and there are significant differences in the specific regulatory mechanisms and related influencing factors of nanomaterials in different steps of the QS process. An in-depth understanding of the specific regulatory mechanisms and related influencing factors of nanomaterials in each step can significantly optimize QS regulatory activity and enhance the development of novel nanomaterials with better comprehensive performance. Therefore, this review focuses on the mechanisms by which nanomaterials regulate bacterial QS in the signal supply (including signal synthesis, secretion, and accumulation) and signal transduction cascade (including signal perception and response) processes. Moreover, based on the two key influencing factors (i.e., the nanomaterial itself and the environment), optimization strategies to enhance the QS regulatory activity are comprehensively summarized. Collectively, applying nanomaterials to regulate bacterial QS is a promising strategy for anti-virulence therapy. This review provides reference and inspiration for further research on the anti-virulence application of nanomaterials.
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Affiliation(s)
- Chen Hu
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
| | - Guixin He
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
| | - Yujun Yang
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
| | - Ning Wang
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
| | - Yanli Zhang
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
| | - Yuan Su
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
- Stomatology CenterShunde HospitalSouthern Medical University (The First People's Hospital of Shunde)Foshan528399China
| | - Fujian Zhao
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
| | - Junrong Wu
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
| | - Linlin Wang
- Hainan General Hospital·Hainan Affiliated Hospital of Hainan medical UniversityHaikou570311China
| | - Yuqing Lin
- Shenzhen Luohu People's HospitalShenzhen518000China
| | - Longquan Shao
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhou510280China
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Dandekar B, Ahalawat N, Sinha S, Mondal J. Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event. JACS AU 2023; 3:1728-1741. [PMID: 37388689 PMCID: PMC10302740 DOI: 10.1021/jacsau.3c00151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 07/01/2023]
Abstract
Ras GTPase is an enzyme that catalyzes the hydrolysis of guanosine triphosphate (GTP) and plays an important role in controlling crucial cellular signaling pathways. However, this enzyme has always been believed to be undruggable due to its strong binding affinity with its native substrate GTP. To understand the potential origin of high GTPase/GTP recognition, here we reconstruct the complete process of GTP binding to Ras GTPase via building Markov state models (MSMs) using a 0.1 ms long all-atom molecular dynamics (MD) simulation. The kinetic network model, derived from the MSM, identifies multiple pathways of GTP en route to its binding pocket. While the substrate stalls onto a set of non-native metastable GTPase/GTP encounter complexes, the MSM accurately discovers the native pose of GTP at its designated catalytic site in crystallographic precision. However, the series of events exhibit signatures of conformational plasticity in which the protein remains trapped in multiple non-native conformations even when GTP has already located itself in its native binding site. The investigation demonstrates mechanistic relays pertaining to simultaneous fluctuations of switch 1 and switch 2 residues which remain most instrumental in maneuvering the GTP-binding process. Scanning of the crystallographic database reveals close resemblance between observed non-native GTP binding poses and precedent crystal structures of substrate-bound GTPase, suggesting potential roles of these binding-competent intermediates in allosteric regulation of the recognition process.
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Affiliation(s)
| | - Navjeet Ahalawat
- Department
of Bioinformatics and Computational Biology, College of Biotechnology, CCS Haryana Agricultural University, Hisar, 125004 Haryana, India
| | - Suman Sinha
- Institute
of Pharmaceutical Research, GLA University, Mathura, 281406 Uttar Pradesh, India
| | - Jagannath Mondal
- Tata
Institute of Fundamental Research, Hyderabad, Telangana 500046, India
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Bandyopadhyay S, Mondal J. A deep encoder-decoder framework for identifying distinct ligand binding pathways. J Chem Phys 2023; 158:2890463. [PMID: 37184003 DOI: 10.1063/5.0145197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The pathway(s) that a ligand would adopt en route to its trajectory to the native pocket of the receptor protein act as a key determinant of its biological activity. While Molecular Dynamics (MD) simulations have emerged as the method of choice for modeling protein-ligand binding events, the high dimensional nature of the MD-derived trajectories often remains a barrier in the statistical elucidation of distinct ligand binding pathways due to the stochasticity inherent in the ligand's fluctuation in the solution and around the receptor. Here, we demonstrate that an autoencoder based deep neural network, trained using an objective input feature of a large matrix of residue-ligand distances, can efficiently produce an optimal low-dimensional latent space that stores necessary information on the ligand-binding event. In particular, for a system of L99A mutant of T4 lysozyme interacting with its native ligand, benzene, this deep encoder-decoder framework automatically identifies multiple distinct recognition pathways, without requiring user intervention. The intermediates involve the spatially discrete location of the ligand in different helices of the protein before its eventual recognition of native pose. The compressed subspace derived from the autoencoder provides a quantitatively accurate measure of the free energy and kinetics of ligand binding to the native pocket. The investigation also recommends that while a linear dimensional reduction technique, such as time-structured independent component analysis, can do a decent job of state-space decomposition in cases where the intermediates are long-lived, autoencoder is the method of choice in systems where transient, low-populated intermediates can lead to multiple ligand-binding pathways.
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Affiliation(s)
- Satyabrata Bandyopadhyay
- Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences, Hyderabad 500046, India
| | - Jagannath Mondal
- Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences, Hyderabad 500046, India
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Ahalawat N, Sahil M, Mondal J. Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning. J Chem Theory Comput 2023; 19:2644-2657. [PMID: 37068044 DOI: 10.1021/acs.jctc.2c00932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.
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Affiliation(s)
- Navjeet Ahalawat
- Department of Bioinformatics and Computational Biology, College of Biotechnology, CCS Haryana Agricultural University, Hisar 125 004, Haryana, India
| | - Mohammad Sahil
- Center for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Jagannath Mondal
- Center for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500046, India
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Sahil M, Sarkar S, Mondal J. Long-time-step molecular dynamics can retard simulation of protein-ligand recognition process. Biophys J 2023; 122:802-816. [PMID: 36726313 PMCID: PMC10027446 DOI: 10.1016/j.bpj.2023.01.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/31/2022] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
Molecular dynamics (MD) simulation of biologically relevant processes at realistic time scale and atomistic precision is generally limited by prohibitively large computational cost, due to its restriction of using an ultrashort integration time step (1-2 fs). A popular numerical recipe to reduce the associated computational burden is adopting schemes that would allow relatively longer-time-step for MD propagation. Here, we explore the perceived potential of one of the most frequently used long-time-step protocols, namely the hydrogen mass repartitioning (HMR) approach, in alleviating the computational overhead associated with simulation of the kinetic process of protein-ligand recognition events. By repartitioning the mass of heavier atoms to their linked hydrogen atoms, HMR leverages around twofold longer time step than regular simulation, holding promise of significant performance boost. However, our probe into direct simulation of the protein-ligand recognition event, one of the computationally most challenging processes, shows that long-time-step HMR MD simulations do not necessarily translate to a computationally affordable solution. Our investigations spanning cumulative 176 μs in three independent proteins (T4 lysozyme, sensor domain of MopR, and galectin-3) show that long-time-step HMR-based MD simulations can catch the ligand in its act of recognizing the native cavity. But, as a major caveat, the ligand is found to require significantly longer time to identify buried native protein cavity in an HMR MD simulation than regular simulation, thereby defeating the purpose of its usage for performance upgrade. A molecular analysis shows that the longer time required by a ligand to recognize the protein in HMR is rooted in faster diffusion of the ligand, which reduces the survival probability of decisive on-pathway metastable intermediates, thereby slowing down the eventual recognition process at the native cavity. Together, the investigation stresses careful assessment of pitfalls of long-time-step algorithms before attempting to utilize them for higher performance for biomolecular recognition simulations.
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Affiliation(s)
- Mohammad Sahil
- Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Susmita Sarkar
- Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Jagannath Mondal
- Tata Institute of Fundamental Research, Hyderabad 500046, India.
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Nguyen HL, Thai NQ, Li MS. Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution. J Chem Theory Comput 2022; 18:3860-3872. [PMID: 35512104 PMCID: PMC9202309 DOI: 10.1021/acs.jctc.1c01158] [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] [Indexed: 01/13/2023]
Abstract
![]()
Steered molecular
dynamics (SMD) simulation is a powerful method
in computer-aided drug design as it can be used to access the relative
binding affinity with high precision but with low computational cost.
The success of SMD depends on the choice of the direction along which
the ligand is pulled from the receptor-binding site. In most simulations,
the unidirectional pathway was used, but in some cases, this choice
resulted in the ligand colliding with the complex surface of the exit
tunnel. To overcome this difficulty, several variants of SMD with
multidirectional pulling have been proposed, but they are not completely
devoid of disadvantages. Here, we have proposed to determine the direction
of pulling with a simple scoring function that minimizes the receptor–ligand
interaction, and an optimization algorithm called differential evolution
is used for energy minimization. The effectiveness of our protocol
was demonstrated by finding expulsion pathways of Huperzine A and
camphor from the binding site of Torpedo California acetylcholinesterase
and P450cam proteins, respectively, and comparing them with the previous
results obtained using memetic sampling and random acceleration molecular
dynamics. In addition, by applying this protocol to a set of ligands
bound with LSD1 (lysine specific demethylase 1), we obtained a much
higher correlation between the work of pulling force and experimental
data on the inhibition constant IC50 compared to that obtained using
the unidirectional approach based on minimal steric hindrance.
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Affiliation(s)
- Hoang Linh Nguyen
- Life Science Lab, Institute for Computational Science and Technology, QuangTrung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City 729110, Vietnam.,Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City 740500, Vietnam.,Vietnam National University, Ho Chi Minh City 71300, Vietnam
| | - Nguyen Quoc Thai
- Life Science Lab, Institute for Computational Science and Technology, QuangTrung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City 729110, Vietnam.,Dong Thap University, 783 Pham Huu Lau Street, Ward 6, Cao Lanh City, Dong Thap 81100, Vietnam
| | - Mai Suan Li
- Institute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, Warsaw 02-668, Poland
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Maximova E, Postnikov EB, Lavrova AI, Farafonov V, Nerukh D. Protein-Ligand Dissociation Rate Constant from All-Atom Simulation. J Phys Chem Lett 2021; 12:10631-10636. [PMID: 34704768 DOI: 10.1021/acs.jpclett.1c02952] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Dissociation of a ligand isoniazid from a protein catalase was investigated using all-atom molecular dynamics (MD) simulations. Random acceleration MD (τ-RAMD) was used, in which a random artificial force applied to the ligand facilitates its dissociation. We have suggested a novel approach to extrapolate such obtained dissociation times to the zero-force limit assuming never before attempted universal exponential dependence of the bond strength on the applied force, allowing direct comparison with experimentally measured values. We have found that our calculated dissociation time was equal to 36.1 s with statistically significant values distributed in the interval of 0.2-72.0 s, which quantitatively matches the experimental value of 50 ± 8 s despite the extrapolation over 9 orders of magnitude in time.
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Affiliation(s)
- Ekaterina Maximova
- Department of Nanobiotechnology, Alferov University, Khlopina Street, 8/3 A, 194021 Saint Petersburg, Russia
- Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Helmholtzstr. 10, 01069 Dresden, Germany
| | - Eugene B Postnikov
- Department of Theoretical Physics, Kursk State University, Radishcheva Street, 33, 305000 Kursk, Russia
| | - Anastasia I Lavrova
- Saint-Petersburg State University, 7/9 Universitetskaya Emb., 199034 Saint Petersburg, Russia
- Saint-Petersburg State Research Institute of Phthisiopulmonology, 2-4 Ligovskiy Avenue, 194064 Saint-Petersburg, Russia
| | - Vladimir Farafonov
- V. N. Karazin Kharkiv National University, 4 Svobody sq., Kharkiv 61022, Ukraine
| | - Dmitry Nerukh
- Department of Mathematics, Aston University, Birmingham B4 7ET, U.K
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Souza PCT, Limongelli V, Wu S, Marrink SJ, Monticelli L. Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations. Front Mol Biosci 2021; 8:657222. [PMID: 33855050 PMCID: PMC8039319 DOI: 10.3389/fmolb.2021.657222] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/05/2021] [Indexed: 01/12/2023] Open
Abstract
Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.
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Affiliation(s)
- Paulo C. T. Souza
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, Netherlands
- PharmCADD, Busan, South Korea
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, Lyon, France
| | - Vittorio Limongelli
- Faculty of Biomedical Sciences, Institute of Computational Science, Università della Svizzera Italiana (USI), Lugano, Switzerland
- Department of Pharmacy, University of Naples “Federico II”, Naples, Italy
| | - Sangwook Wu
- PharmCADD, Busan, South Korea
- Department of Physics, Pukyong National University, Busan, South Korea
| | - Siewert J. Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, Netherlands
| | - Luca Monticelli
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS, University of Lyon, Lyon, France
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