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Rosilan NF, Jamali MAM, Sufira SA, Waiho K, Fazhan H, Ismail N, Sung YY, Mohamed-Hussein ZA, Hamid AAA, Afiqah-Aleng N. Molecular docking and dynamics simulation studies uncover the host-pathogen protein-protein interactions in Penaeus vannamei and Vibrio parahaemolyticus. PLoS One 2024; 19:e0297759. [PMID: 38266027 PMCID: PMC10807825 DOI: 10.1371/journal.pone.0297759] [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: 08/03/2023] [Accepted: 01/11/2024] [Indexed: 01/26/2024] Open
Abstract
Shrimp aquaculture contributes significantly to global economic growth, and the whiteleg shrimp, Penaeus vannamei, is a leading species in this industry. However, Vibrio parahaemolyticus infection poses a major challenge in ensuring the success of P. vannamei aquaculture. Despite its significance in this industry, the biological knowledge of its pathogenesis remains unclear. Hence, this study was conducted to identify the interaction sites and binding affinity between several immune-related proteins of P. vannamei with V. parahaemolyticus proteins associated with virulence factors. Potential interaction sites and the binding affinity between host and pathogen proteins were identified using molecular docking and dynamics (MD) simulation. The P. vannamei-V. parahaemolyticus protein-protein interaction of Complex 1 (Ferritin-HrpE/YscL family type III secretion apparatus protein), Complex 2 (Protein kinase domain-containing protein-Chemotaxis CheY protein), and Complex 3 (GPCR-Chemotaxis CheY protein) was found to interact with -4319.76, -5271.39, and -4725.57 of the docked score and the formation of intermolecular bonds at several interacting residues. The docked scores of Complex 1, Complex 2, and Complex 3 were validated using MD simulation analysis, which revealed these complexes greatly contribute to the interactions between P. vannamei and V. parahaemolyticus proteins, with binding free energies of -22.50 kJ/mol, -30.20 kJ/mol, and -26.27 kJ/mol, respectively. This finding illustrates the capability of computational approaches to search for molecular binding sites between host and pathogen, which could increase the knowledge of Vibrio spp. infection on shrimps, which then can be used to assist in the development of effective treatment.
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Affiliation(s)
- Nur Fathiah Rosilan
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Muhamad Arif Mohamad Jamali
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Nilai, Negeri Sembilan, Malaysia
| | - Siti Aishah Sufira
- Research Unit for Bioinformatics and Computational Biology (RUBIC), Kuliyyah of Science, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan, Pahang, Malaysia
| | - Khor Waiho
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Centre for Chemical Biology, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Hanafiah Fazhan
- Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Centre for Chemical Biology, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Noraznawati Ismail
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Yeong Yik Sung
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- UKM Medical Molecular Biology Institute, UKM Medical Centre, Jalan Yaacob Latiff, Cheras, Kuala Lumpur, Malaysia
- Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia
| | - Azzmer Azzar Abdul Hamid
- Research Unit for Bioinformatics and Computational Biology (RUBIC), Kuliyyah of Science, International Islamic University Malaysia, Bandar Indera Mahkota, Kuantan, Pahang, Malaysia
| | - Nor Afiqah-Aleng
- Institute of Climate Adaptation and Marine Biotechnology (ICAMB), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
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Li X, Zhao Y, Pu Q, He W, Yang H, Hou J, Li Y. Microplastics in cultivated soil environment: Construction of toxicity grading evaluation system, development of priority control checklist, and toxicity mechanism analysis. JOURNAL OF HAZARDOUS MATERIALS 2023; 459:132046. [PMID: 37467609 DOI: 10.1016/j.jhazmat.2023.132046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 06/05/2023] [Accepted: 07/10/2023] [Indexed: 07/21/2023]
Abstract
The present study aimed to comprehensively evaluate the toxicological effects of microplastics (MPs) on cultivated soil quality. Based on improved G1 evaluation method, we first constructed a grading evaluation system comprising of the indicators of toxicological effects of cultivated soil quality under MPs exposure, while focusing on types of MPs that had significant/non-significant toxicity effects. Furthermore, we verified reliability of screening results of significance-links at each level, using several data processing methods. Then, using natural breakpoint classification method, a priority control checklist of toxicological effects of 18 types of MPs on cultivated soil was developed to determine the types of MPs having significant toxicity risks and cultivated soil quality links significantly affected by the toxicity of MPs exposure. Finally, quantum-mechanics/molecular-mechanics (QM/MM) methods were used to carry out the differential toxicity mechanism analysis. The results showed that MPs with higher non-polar surface area may lead to stronger toxicity effect to the cultivated soil quality. Notably, the MPs that have abundant binding sites enhance the binding affinity, and less polar MPs bind more strongly to the non-polar amino acids of target receptors. Our study provides a new theoretical perspective for multi-dimensional analysis toxicological effects of different MPs exposure on cultivated soil quality.
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Affiliation(s)
- Xinao Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Yuanyuan Zhao
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Qikun Pu
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Wei He
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Hao Yang
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Jing Hou
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China.
| | - Yu Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
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Qiu Y, O’Connor MS, Xue M, Liu B, Huang X. An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes. J Chem Theory Comput 2023; 19:4728-4742. [PMID: 37382437 PMCID: PMC11042546 DOI: 10.1021/acs.jctc.3c00318] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes.
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Affiliation(s)
- Yunrui Qiu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Michael S. O’Connor
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Mingyi Xue
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Bojun Liu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
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Nagel D, Sartore S, Stock G. Selecting Features for Markov Modeling: A Case Study on HP35. J Chem Theory Comput 2023. [PMID: 37167425 DOI: 10.1021/acs.jctc.3c00240] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Markov state models represent a popular means to interpret molecular dynamics trajectories in terms of memoryless transitions between metastable conformational states. To provide a mechanistic understanding of the considered biomolecular process, these states should reflect structurally distinct conformations and ensure a time scale separation between fast intrastate and slow interstate dynamics. Adopting the folding of villin headpiece (HP35) as a well-established model problem, here we discuss the selection of suitable input coordinates or "features", such as backbone dihedral angles and interresidue distances. We show that dihedral angles account accurately for the structure of the native energy basin of HP35, while the unfolded region of the free energy landscape and the folding process are best described by tertiary contacts of the protein. To construct a contact-based model, we consider various ways to define and select contact distances and introduce a low-pass filtering of the feature trajectory as well as a correlation-based characterization of states. Relying on input data that faithfully account for the mechanistic origin of the studied process, the states of the resulting Markov model are clearly discriminated by the features, describe consistently the hierarchical structure of the free energy landscape, and─as a consequence─correctly reproduce the slow time scales of the process.
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Affiliation(s)
- Daniel Nagel
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
| | - Sofia Sartore
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
| | - Gerhard Stock
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
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González-Delgado J, Sagar A, Zanon C, Lindorff-Larsen K, Bernadó P, Neuvial P, Cortés J. WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins. J Mol Biol 2023:168053. [PMID: 36934808 DOI: 10.1016/j.jmb.2023.168053] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/10/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, one ensemble being characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the interest of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations, and discuss other potential applications such as in machine-learning-based approaches. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.
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Affiliation(s)
- Javier González-Delgado
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France; Institut de Mathématiques de Toulouse, Université de Toulouse, CNRS, Toulouse, France
| | - Amin Sagar
- Centre de Biologie Structurale, Université de Montpellier, INSERM, CNRS, Montpellier, France
| | | | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Denmark
| | - Pau Bernadó
- Centre de Biologie Structurale, Université de Montpellier, INSERM, CNRS, Montpellier, France
| | - Pierre Neuvial
- Institut de Mathématiques de Toulouse, Université de Toulouse, CNRS, Toulouse, France
| | - Juan Cortés
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
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Klem H, Hocky GM, McCullagh M. Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories. J Chem Theory Comput 2022; 18:3218-3230. [PMID: 35483073 DOI: 10.1021/acs.jctc.1c01290] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Determining the optimal number and identity of structural clusters from an ensemble of molecular configurations continues to be a challenge. Recent structural clustering methods have focused on the use of internal coordinates due to the innate rotational and translational invariance of these features. The vast number of possible internal coordinates necessitates a feature space supervision step to make clustering tractable but yields a protocol that can be system type-specific. Particle positions offer an appealing alternative to internal coordinates but suffer from a lack of rotational and translational invariance, as well as a perceived insensitivity to regions of structural dissimilarity. Here, we present a method, denoted shape-GMM, that overcomes the shortcomings of particle positions using a weighted maximum likelihood alignment procedure. This alignment strategy is then built into an expectation maximization Gaussian mixture model (GMM) procedure to capture metastable states in the free-energy landscape. The resulting algorithm distinguishes between a variety of different structures, including those indistinguishable by root-mean-square displacement and pairwise distances, as demonstrated on several model systems. Shape-GMM results on an extensive simulation of the fast-folding HP35 Nle/Nle mutant protein support a four-state folding/unfolding mechanism, which is consistent with previous experimental results and provides kinetic details comparable to previous state-of-the art clustering approaches, as measured by the VAMP-2 score. Currently, training of shape-GMMs is recommended for systems (or subsystems) that can be represented by ≲200 particles and ≲100k configurations to estimate high-dimensional covariance matrices and balance computational expense. Once a shape-GMM is trained, it can be used to predict the cluster identities of millions of configurations.
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Affiliation(s)
- Heidi Klem
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Glen M Hocky
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Martin McCullagh
- Department of Chemistry, Oklahoma State University, Stillwater, Oklahoma 74078, United States
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