1
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Chen Y, Forster L, Wang K, Gupta HS, Li X, Huang J, Rui Y. Investigation of collagen reconstruction mechanism in skin wound through dual-beam laser welding: Insights from multi-spectroscopy, molecular dynamics simulation, and finite element multiphysics simulation. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY. B, BIOLOGY 2024; 255:112927. [PMID: 38701631 DOI: 10.1016/j.jphotobiol.2024.112927] [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: 01/18/2024] [Revised: 03/07/2024] [Accepted: 04/25/2024] [Indexed: 05/05/2024]
Abstract
Since the mechanism underlying real-time acquisition of mechanical strength during laser-induced skin wound fusion remains unclear, and collagen is the primary constituent of skin tissue, this study investigates the structural and mechanical alterations in collagen at temperatures ranging from 40 °C to 60 °C using various spectroscopic techniques and molecular dynamics calculations. The COMSOL Multiphysics coupling is employed to simulate the three-dimensional temperature field, stress-strain relationship, and light intensity distribution in the laser thermal affected zone of skin wounds during dual-beam laser welding process. Raman spectroscopy, synchronous fluorescence spectroscopy and circular dichroism measurement results confirm that laser energy activates biological activity in residues, leading to a transformation in the originally fractured structure of collagen protein for enhanced mechanical strength. Molecular dynamics simulations reveal that stable hydrogen bonds form at amino acid residues within the central region of collagen protein when the overall temperature peak around the wound reaches 60 °C, thereby providing stability to previously fractured skin incisions and imparting instantaneous strength. However, under a 55 °C system, Type I collagen ensures macrostructural stability while activating biological properties at amino acid bases to promote wound healing function; this finding aligns with experimental analysis results. The COMSOL simulation outcomes also correspond well with macroscopic morphology after laser welding samples, confirming that by maintaining temperatures between 55 °C-60 °C during laser welding of skin incisions not only can certain instantaneous mechanical strength be achieved but irreversible thermal damage can also be effectively controlled. It is anticipated that these findings will provide valuable insights into understanding the healing mechanism for laser-welded skin wounds.
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Affiliation(s)
- Yuxin Chen
- School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Institute of Bioengineering and School of Engineering and Material Science, Queen Mary University of London, London E1 4NS, UK.
| | - Laura Forster
- Institute of Bioengineering and School of Engineering and Material Science, Queen Mary University of London, London E1 4NS, UK
| | - Kehong Wang
- School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
| | - Himadri S Gupta
- Institute of Bioengineering and School of Engineering and Material Science, Queen Mary University of London, London E1 4NS, UK
| | - Xiaopeng Li
- School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
| | - Jun Huang
- School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yunfeng Rui
- Clinical Medical School, Southeast University, Nanjing 211189, China
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2
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Ishizone T, Matsunaga Y, Fuchigami S, Nakamura K. Representation of Protein Dynamics Disentangled by Time-Structure-Based Prior. J Chem Theory Comput 2024; 20:436-450. [PMID: 38151233 DOI: 10.1021/acs.jctc.3c01025] [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: 12/29/2023]
Abstract
Representation learning (RL) is a universal technique for deriving low-dimensional disentangled representations from high-dimensional observations, aiding in a multitude of downstream tasks. RL has been extensively applied to various data types, including images and natural language. Here, we analyze molecular dynamics (MD) simulation data of biomolecules in terms of RL. Currently, state-of-the-art RL techniques, mainly motivated by the variational principle, try to capture slow motions in the representation (latent) space. Here, we propose two methods based on an alternative perspective on the disentanglement in the latent space. By disentanglement, we here mean the separation of underlying factors in the simulation data, aiding in detecting physically important coordinates for conformational transitions. The proposed methods introduce a simple prior that imposes temporal constraints in the latent space, serving as a regularization term to facilitate the capture of disentangled representations of dynamics. Comparison with other methods via the analysis of MD simulation trajectories for alanine dipeptide and chignolin validates that the proposed methods construct Markov state models (MSMs) whose implied time scales are comparable to those of the state-of-the-art methods. Using a measure based on total variation, we quantitatively evaluated that the proposed methods successfully disentangle physically important coordinates, aiding the interpretation of folding/unfolding transitions of chignolin. Overall, our methods provide good representations of complex biomolecular dynamics for downstream tasks, allowing for better interpretations of the conformational transitions.
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Affiliation(s)
- Tsuyoshi Ishizone
- Mathematical Sciences Program, Graduate School of Advanced Mathematical Sciences, Meiji University, Nakano 4-21-1, Nakano-ku, Tokyo 164-8525, Japan
| | - Yasuhiro Matsunaga
- Graduate School of Science and Engineering, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama-shi, Saitama 338-8570, Japan
| | - Sotaro Fuchigami
- Physical Biochemistry Laboratory, Division of Pharmaceutical Sciences, School of Pharmaceutical Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka 422-8526, Japan
| | - Kazuyuki Nakamura
- Department of Mathematical Sciences Based on Modeling and Analysis, School of Interdisciplinary Mathematical Sciences, Meiji University, Nakano 4-21-1, Nakano-ku, Tokyo 164-8525, Japan
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3
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Mikhailovskii O, Izmailov SA, Xue Y, Case DA, Skrynnikov NR. X-ray Crystallography Module in MD Simulation Program Amber 2023. Refining the Models of Protein Crystals. J Chem Inf Model 2024; 64:18-25. [PMID: 38147516 DOI: 10.1021/acs.jcim.3c01531] [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: 12/28/2023]
Abstract
The MD simulation package Amber offers an attractive platform to refine crystallographic structures of proteins: (i) state-of-the-art force fields help to regularize protein coordinates and reconstruct the poorly diffracting elements of the structure, such as flexible loops; (ii) MD simulations restrained by the experimental diffraction data provide an effective strategy to optimize structural models of protein crystals, including explicitly modeled interstitial solvent as well as crystal contacts. Here, we present the new crystallography module xray, released as a part of the Amber 2023 package. This module contains functions to calculate and scale structure factors (including the contributions from bulk solvent), evaluate the maximum-likelihood-type crystallographic potential, and compute its derivative forces. The X-ray functionality of Amber no longer relies on external dependencies so that the full advantage of GPU acceleration can be taken. This makes it possible to refine in a short time hundreds of crystal models, including supercell models comprised of multiple unit cells. The new automated Amber-based refinement procedure leads to an appreciable improvement in Rfree (in some cases, by as much as 0.067) as well as MolProbity scores.
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Affiliation(s)
- Oleg Mikhailovskii
- Laboratory of Biomolecular NMR, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Sergei A Izmailov
- Laboratory of Biomolecular NMR, St. Petersburg State University, St. Petersburg 199034, Russia
| | - Yi Xue
- School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - David A Case
- Department of Chemistry & Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Nikolai R Skrynnikov
- Laboratory of Biomolecular NMR, St. Petersburg State University, St. Petersburg 199034, Russia
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
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4
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Ge F, Zhang L, Hou YF, Chen Y, Ullah A, Dral PO. Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models. J Phys Chem Lett 2023; 14:7732-7743. [PMID: 37606602 DOI: 10.1021/acs.jpclett.3c01592] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model, which for the given initial conditions (nuclear positions and velocities at time zero) can predict nuclear positions and velocities as a continuous function of time up to the distant future. Such models of molecules can be unrolled in the time dimension to yield long-time high-resolution molecular dynamics trajectories with high efficiency and accuracy. 4D-spacetime models can make predictions for different times in any order and do not need a stepwise evaluation of forces and integration of the equations of motions at discretized time steps, which is a major advance over traditional, cost-inefficient molecular dynamics. These models can be used to speed up dynamics, simulate vibrational spectra, and obtain deeper insight into nuclear motions, as we demonstrate for a series of organic molecules.
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Affiliation(s)
- Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Lina Zhang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Yi-Fan Hou
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Yuxinxin Chen
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Arif Ullah
- School of Physics and Optoelectronic Engineering, Anhui University, Hefei 230601, China
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
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5
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Alfonso-Prieto M, Capelli R. Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study. J Chem Inf Model 2023; 63:2911-2917. [PMID: 37145455 DOI: 10.1021/acs.jcim.3c00380] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Atomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here, we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D2.50 and E3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other ∼400 members of this family. Given the almost concurrent publication of a CryoEM structure of the same receptor in the active state, we propose this protocol as an in silico complement to the growing field of ORs structure determination.
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Affiliation(s)
- Mercedes Alfonso-Prieto
- Computational Biomedicine, Institute for Advanced Simulation IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, D-52428 Jülich, Germany
| | - Riccardo Capelli
- Dipartimento di Bioscienze, Università degli Studi di Milano, Via Celoria 26, I-20133 Milan, Italy
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6
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Oliva F, Musiani F, Giorgetti A, De Rubeis S, Sorokina O, Armstrong DJ, Carloni P, Ruggerone P. Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases. Front Chem 2023; 10:1059593. [PMID: 36700074 PMCID: PMC9868658 DOI: 10.3389/fchem.2022.1059593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 12/06/2022] [Indexed: 01/11/2023] Open
Abstract
The seamless integration of human disease-related mutation data into protein structures is an essential component of any attempt to correctly assess the impact of the mutation. The key step preliminary to any structural modelling is the identification of the isoforms onto which mutations should be mapped due to there being several functionally different protein isoforms from the same gene. To handle large sets of data coming from omics techniques, this challenging task needs to be automatized. Here we present the MoNvIso (Modelling eNvironment for Isoforms) code, which identifies the most useful isoform for computational modelling, balancing the coverage of mutations of interest and the availability of templates to build a structural model of both the wild-type isoform and the related variants.
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Affiliation(s)
- Francesco Oliva
- Department of Physics, University of Cagliari, Monserrato (CA), Italy,Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany
| | - Francesco Musiani
- Laboratory of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Alejandro Giorgetti
- Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany,Department of Biotechnology, University of Verona, Verona, Italy
| | - Silvia De Rubeis
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, United States,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States,The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Oksana Sorokina
- The School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Douglas J. Armstrong
- Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany,The School of Informatics, University of Edinburgh, Edinburgh, United Kingdom,Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
| | - Paolo Carloni
- Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany,Department of Physics, RWTH Aachen University, Aachen, Germany,JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Paolo Ruggerone
- Department of Physics, University of Cagliari, Monserrato (CA), Italy,*Correspondence: Paolo Ruggerone,
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7
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Liu N, Mikhailovskii O, Skrynnikov NR, Xue Y. Simulating diffraction photographs based on molecular dynamics trajectories of a protein crystal: a new option to examine structure-solving strategies in protein crystallography. IUCRJ 2023; 10:16-26. [PMID: 36598499 PMCID: PMC9812212 DOI: 10.1107/s2052252522011198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
A molecular dynamics (MD)-based pipeline has been designed and implemented to emulate the entire process of collecting diffraction photographs and calculating crystallographic structures of proteins from them. Using a structure of lysozyme solved in-house, a supercell comprising 125 (5 × 5 × 5) crystal unit cells containing a total of 1000 protein molecules and explicit interstitial solvent was constructed. For this system, two 300 ns MD trajectories at 298 and 250 K were recorded. A series of snapshots from these trajectories were then used to simulate a fully realistic set of diffraction photographs, which were further fed into the standard pipeline for structure determination. The resulting structures show very good agreement with the underlying MD model not only in terms of coordinates but also in terms of B factors; they are also consistent with the original experimental structure. The developed methodology should find a range of applications, such as optimizing refinement protocols to solve crystal structures and extracting dynamics information from diffraction data or diffuse scattering.
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Affiliation(s)
- Ning Liu
- School of Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
| | - Oleg Mikhailovskii
- Laboratory of Biomolecular NMR, St Petersburg State University, St Petersburg, Russian Federation
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | - Nikolai R. Skrynnikov
- Laboratory of Biomolecular NMR, St Petersburg State University, St Petersburg, Russian Federation
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | - Yi Xue
- School of Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, People’s Republic of China
- Tsinghua University–Peking University Joint Center for Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
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8
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Mookherjee T, Bagchi A, Ghosh R. In-silico studies to analyse the possible interactions of CircPPP1R12A translated peptide with Mst proteins. Biochem Biophys Res Commun 2022; 635:108-113. [DOI: 10.1016/j.bbrc.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/01/2022] [Indexed: 11/29/2022]
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9
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Hayashi S, Koseki J, Shimamura T. Bayesian statistical method for detecting structural and topological diversity in polymorphic proteins. Comput Struct Biotechnol J 2022; 20:6519-6525. [DOI: 10.1016/j.csbj.2022.11.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/22/2022] Open
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10
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Wang Y, Liu T, Xie J, Cheng M, Sun L, Zhang S, Xin J, Zhang N. A review on application of molecular simulation technology in food molecules interaction. Curr Res Food Sci 2022; 5:1873-1881. [PMID: 36276243 PMCID: PMC9579209 DOI: 10.1016/j.crfs.2022.10.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022] Open
Abstract
Molecular simulation is a new technology to analyze the interaction between molecules. This review mainly summarizes the application of molecular simulation technology in the food industry. This technology has been employed to assess structural changes of biomolecules, the interaction between components, and the mechanism of physical and chemical property alterations. These conclusions provide a deeper understanding of the molecular interaction mechanism in foods, break through the limitations of scientific experiments and avoid blind and time-consuming scientific research. In this paper, the advantages and development trends of molecular simulation technology in the food research field are described. This methodology can be used to contribute to further studies of the mechanism of molecular interactions in food, confirm experimental results and provide new ideas for research in the field of food sciences.
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Affiliation(s)
- Yan Wang
- Key Laboratory for Food Science & Engineering, Harbin University of Commerce, Harbin, 150076, PR China
| | - Tianjiao Liu
- Key Laboratory for Food Science & Engineering, Harbin University of Commerce, Harbin, 150076, PR China
| | - Jinhui Xie
- Key Laboratory for Food Science & Engineering, Harbin University of Commerce, Harbin, 150076, PR China
| | - Meijia Cheng
- Key Laboratory for Food Science & Engineering, Harbin University of Commerce, Harbin, 150076, PR China
| | - Lirui Sun
- Key Laboratory for Food Science & Engineering, Harbin University of Commerce, Harbin, 150076, PR China
| | - Shuai Zhang
- Key Laboratory for Food Science & Engineering, Harbin University of Commerce, Harbin, 150076, PR China
| | - Jiaying Xin
- Key Laboratory for Food Science & Engineering, Harbin University of Commerce, Harbin, 150076, PR China,State Key Laboratory for Oxo Synthesis & Selective Oxidation, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou, 730000, PR China
| | - Na Zhang
- Key Laboratory for Food Science & Engineering, Harbin University of Commerce, Harbin, 150076, PR China,Corresponding author.
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11
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Characterizations of a novel peptide encoded by a circular RNA using in-silico analyses. Biochem Biophys Res Commun 2022; 630:36-40. [PMID: 36137323 DOI: 10.1016/j.bbrc.2022.09.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 09/07/2022] [Indexed: 12/23/2022]
Abstract
CircRNAs have gained importance in recent times due to their involvement in gene regulation and also in the prognosis of cancer. Generally, the circRNA directly interact with miRNA or RNA binding proteins to exert their action, but some of them can be translated. These translated peptides often participate in the regulation of cellular processes. The circPPP1R12A translated peptide has been shown to influence the functioning of the Mst pathway. The Mst signaling is noteworthy for its role in the process of development, but it also has a function as a regulator of apoptosis, which is significant for regulation of cancer. Overexpression of this novel peptide deactivates the Mst signaling to induce the expression of the proliferative oncogene, Yap. Its molecular interaction with the molecules in the Mst pathway is hitherto unknown. In this short report we present our findings from in-silico studies the plausible structure of the peptide through bioinformatics and dynamics simulation studies. This is the first such report on the structure of the novel peptide encoded by circPPP1R12A, which could be important to predict in future its molecular interactions to understand its functionality.
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12
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Mikhailovskii O, Xue Y, Skrynnikov NR. Modeling a unit cell: crystallographic refinement procedure using the biomolecular MD simulation platform Amber. IUCRJ 2022; 9:114-133. [PMID: 35059216 PMCID: PMC8733891 DOI: 10.1107/s2052252521011891] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/09/2021] [Indexed: 06/14/2023]
Abstract
A procedure has been developed for the refinement of crystallographic protein structures based on the biomolecular simulation program Amber. The procedure constructs a model representing a crystal unit cell, which generally contains multiple protein molecules and is fully hydrated with TIP3P water. Periodic boundary conditions are applied to the cell in order to emulate the crystal lattice. The refinement is conducted in the form of a specially designed short molecular-dynamics run controlled by the Amber ff14SB force field and the maximum-likelihood potential that encodes the structure-factor-based restraints. The new Amber-based refinement procedure has been tested on a set of 84 protein structures. In most cases, the new procedure led to appreciably lower R free values compared with those reported in the original PDB depositions or obtained by means of the industry-standard phenix.refine program. In particular, the new method has the edge in refining low-accuracy scrambled models. It has also been successful in refining a number of molecular-replacement models, including one with an r.m.s.d. of 2.15 Å. In addition, Amber-refined structures consistently show superior MolProbity scores. The new approach offers a highly realistic representation of protein-protein interactions in the crystal, as well as of protein-water interactions. It also offers a realistic representation of protein crystal dynamics (akin to ensemble-refinement schemes). Importantly, the method fully utilizes the information from the available diffraction data, while relying on state-of-the-art molecular-dynamics modeling to assist with those elements of the structure that do not diffract well (for example mobile loops or side chains). Finally, it should be noted that the protocol employs no tunable parameters, and the calculations can be conducted in a matter of several hours on desktop computers equipped with graphical processing units or using a designated web service.
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Affiliation(s)
- Oleg Mikhailovskii
- Laboratory of Biomolecular NMR, St Petersburg State University, St Petersburg 199034, Russian Federation
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | - Yi Xue
- School of Life Sciences, Tsinghua University, Beijing 100084, People's Republic of China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, People's Republic of China
- Tsinghua University-Peking University Joint Center for Life Sciences, Tsinghua University, Beijing 100084, People's Republic of China
| | - Nikolai R Skrynnikov
- Laboratory of Biomolecular NMR, St Petersburg State University, St Petersburg 199034, Russian Federation
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
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13
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Wang D, Wang Y, Chang J, Zhang L, Wang H, E W. Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics. NATURE COMPUTATIONAL SCIENCE 2022; 2:20-29. [PMID: 38177702 DOI: 10.1038/s43588-021-00173-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 11/15/2021] [Indexed: 01/06/2024]
Abstract
Enhanced sampling methods such as metadynamics and umbrella sampling have become essential tools for exploring the configuration space of molecules and materials. At the same time, they have long faced a number of issues such as the inefficiency when dealing with a large number of collective variables (CVs) or systems with high free energy barriers. Here we show that, with clustering and adaptive tuning techniques, the reinforced dynamics (RiD) scheme can be used to efficiently explore the configuration space and free energy landscapes with a large number of CVs or systems with high free energy barriers. We illustrate this by studying various representative and challenging examples. First we demonstrate the efficiency of adaptive RiD compared with other methods and construct the nine-dimensional (9D) free energy landscape of a peptoid trimer, which has energy barriers of more than 8 kcal mol-1. We then study the folding of the protein chignolin using 18 CVs. In this case, both the folding and unfolding rates are observed to be 4.30 μs-1. Finally, we propose a protein structure refinement protocol based on RiD. This protocol allows us to efficiently employ more than 100 CVs for exploring the landscape of protein structures and it gives rise to an overall improvement of 14.6 units over the initial global distance test-high accuracy (GDT-HA) score.
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Affiliation(s)
- Dongdong Wang
- Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA
- DP Technology, Beijing, People's Republic of China
| | - Yanze Wang
- DP Technology, Beijing, People's Republic of China
- College of Chemistry and Molecular Engineering, Peking University, Beijing, People's Republic of China
| | - Junhan Chang
- DP Technology, Beijing, People's Republic of China
- College of Chemistry and Molecular Engineering, Peking University, Beijing, People's Republic of China
| | - Linfeng Zhang
- Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.
- DP Technology, Beijing, People's Republic of China.
| | - Han Wang
- Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, People's Republic of China.
| | - Weinan E
- School of Mathematical Sciences, Peking University, Beijing, People's Republic of China
- Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA
- Beijing Institute of Big Data Research, Beijing, People's Republic of China
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14
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Monza E, Gil V, Lucas MF. Computational Enzyme Design at Zymvol. Methods Mol Biol 2022; 2397:249-259. [PMID: 34813068 DOI: 10.1007/978-1-0716-1826-4_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Directed evolution is the most recognized methodology for enzyme engineering. The main drawback resides in its random nature and in the limited sequence exploration; both require screening of thousands (if not millions) of variants to achieve a target function. Computer-driven approaches can limit laboratorial screening to a few hundred candidates, enabling and accelerating the development of industrial enzymes. In this book chapter, the technology adopted at Zymvol is described. An overview of the current development and future directions in the company is also provided.
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Affiliation(s)
- Emanuele Monza
- Zymvol Biomodeling SL, Carrer Roc Boronat 117, Barcelona, Spain.
| | - Victor Gil
- Zymvol Biomodeling SL, Carrer Roc Boronat 117, Barcelona, Spain
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15
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Linciano P, Nasti R, Listro R, Amadio M, Pascale A, Potenza D, Vasile F, Minneci M, Ann J, Lee J, Zhou X, Mitchell GA, Blumberg PM, Rossi D, Collina S. Chiral 2-phenyl-3-hydroxypropyl esters as PKC-alpha modulators: HPLC enantioseparation, NMR absolute configuration assignment, and molecular docking studies. Chirality 2021; 34:498-513. [PMID: 34962318 DOI: 10.1002/chir.23406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/02/2021] [Accepted: 12/03/2021] [Indexed: 12/16/2022]
Abstract
Protein kinase C (PKC) isoforms play a pivotal role in the regulation of numerous cellular functions, making them extensively studied and highly attractive drug targets. In our previous work, we identified in racemate 1-2, based on the 2-benzyl-3-hydroxypropyl ester scaffold, two new potent and promising PKCα and PKCδ ligands, targeting the C1 domain of these two kinases. Herein, we report the resolution of the racemates by enantioselective semi-preparative HPLC. The attribution of the absolute configuration (AC) of homochirals 1 was performed by NMR, via methoxy-α-trifluoromethyl-α-phenylacetic acid derivatization (MTPA or Mosher's acid). Moreover, the match between the experimental and predicted electronic circular dichroism (ECD) spectra confirmed the assigned AC. These results proved that Mosher's esters can be properly exploited for the determination of the AC also for chiral primary alcohols. Lastly, homochiral 1 and 2 were assessed for binding affinity and functional activity against PKCα. No significative differences in the Ki of the enantiopure compounds was observed, thus suggesting that chirality does not seem to play a significant role in targeting PKC C1 domain. These results are in accordance with the molecular docking studies performed using a new homology model for the human PKCαC1B domain.
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Affiliation(s)
| | - Rita Nasti
- Department of Drug Sciences, University of Pavia, Pavia, Italy.,Department of Environmental Science and Policy, University of Milan, Milan, Italy
| | - Roberta Listro
- Department of Drug Sciences, University of Pavia, Pavia, Italy
| | | | - Alessia Pascale
- Department of Drug Sciences, University of Pavia, Pavia, Italy
| | | | | | - Marco Minneci
- Department of Chemistry, University of Milan, Milan, Italy
| | - Jihyae Ann
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Jeewoo Lee
- Laboratory of Medicinal Chemistry, College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Xiaoling Zhou
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Gary A Mitchell
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Peter M Blumberg
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Daniela Rossi
- Department of Drug Sciences, University of Pavia, Pavia, Italy
| | - Simona Collina
- Department of Drug Sciences, University of Pavia, Pavia, Italy
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16
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Heo L, Janson G, Feig M. Physics-based protein structure refinement in the era of artificial intelligence. Proteins 2021; 89:1870-1887. [PMID: 34156124 PMCID: PMC8616793 DOI: 10.1002/prot.26161] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/31/2021] [Accepted: 06/08/2021] [Indexed: 12/21/2022]
Abstract
Protein structure refinement is the last step in protein structure prediction pipelines. Physics-based refinement via molecular dynamics (MD) simulations has made significant progress during recent years. During CASP14, we tested a new refinement protocol based on an improved sampling strategy via MD simulations. MD simulations were carried out at an elevated temperature (360 K). An optimized use of biasing restraints and the use of multiple starting models led to enhanced sampling. The new protocol generally improved the model quality. In comparison with our previous protocols, the CASP14 protocol showed clear improvements. Our approach was successful with most initial models, many based on deep learning methods. However, we found that our approach was not able to refine machine-learning models from the AlphaFold2 group, often decreasing already high initial qualities. To better understand the role of refinement given new types of models based on machine-learning, a detailed analysis via MD simulations and Markov state modeling is presented here. We continue to find that MD-based refinement has the potential to improve AI predictions. We also identified several practical issues that make it difficult to realize that potential. Increasingly important is the consideration of inter-domain and oligomeric contacts in simulations; the presence of large kinetic barriers in refinement pathways also continues to present challenges. Finally, we provide a perspective on how physics-based refinement could continue to play a role in the future for improving initial predictions based on machine learning-based methods.
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Affiliation(s)
- Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Giacomo Janson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
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17
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Llanos MA, Alberca LN, Larrea SCV, Schoijet AC, Alonso GD, Bellera CL, Gavenet L, Talevi A. Homology Modeling and Molecular Dynamics Simulations of Trypanosoma cruzi Phosphodiesterase b1. Chem Biodivers 2021; 19:e202100712. [PMID: 34813143 DOI: 10.1002/cbdv.202100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/22/2021] [Indexed: 11/07/2022]
Abstract
Cyclic nucleotide phosphodiesterases have been implicated in the proliferation, differentiation and osmotic regulation of trypanosomatids; in some trypanosomatid species, they have been validated as molecular targets for the development of new therapeutic agents. Because the experimental structure of Trypanosoma cruzi PDEb1 (TcrPDEb1) has not been solved so far, an homology model of the target was created using the structure of Trypanosoma brucei PDEb1 (TbrPDEb1) as a template. The model was refined by extensive enhanced sampling molecular dynamics simulations, and representative snapshots were extracted from the trajectory by combined clustering analysis. This structural ensemble was used to develop a structure-based docking model of the target. The docking accuracy of the model was validated by redocking and cross-docking experiments using all available crystal structures of TbrPDEb1, whereas the scoring accuracy was validated through a retrospective screen, using a carefully curated dataset of compounds assayed against TbrPDEb1 and/or TcrPDEb1. Considering the results from in silico validations, the model may be applied in prospective virtual screening campaigns to identify novel hits, as well as to guide the rational design of potent and selective inhibitors targeting this enzyme.
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Affiliation(s)
- Manuel A Llanos
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata - 47 and 115, La Plata, Buenos Aires, Argentina
| | - Lucas N Alberca
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata - 47 and 115, La Plata, Buenos Aires, Argentina
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Buenos Aires, Argentina
| | - Salomé C Vilchez Larrea
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Buenos Aires, Argentina
| | - Alejandra C Schoijet
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Buenos Aires, Argentina
| | - Guillermo D Alonso
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular (INGEBI-CONICET), Buenos Aires, Argentina
| | - Carolina L Bellera
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata - 47 and 115, La Plata, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET) - CCT, La Plata, Argentina
| | - Luciana Gavenet
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata - 47 and 115, La Plata, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET) - CCT, La Plata, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata - 47 and 115, La Plata, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET) - CCT, La Plata, Argentina
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18
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Sun B, Fang X, Johnson C, Hauck G, Kou Y, Davis JP, Kekenes-Huskey PM. Non-Canonical Interaction between Calmodulin and Calcineurin Contributes to the Differential Regulation of Plant-Derived Calmodulins on Calcineurin. J Chem Inf Model 2021; 61:5223-5233. [PMID: 34615359 PMCID: PMC8867402 DOI: 10.1021/acs.jcim.1c00873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Calmodulin (CaM) serves as an important Ca2+ signaling hub that regulates many protein signaling pathways. Recently, it was demonstrated that plant CaM homologues can regulate mammalian targets, often in a manner that opposes the impact of the mammalian CaM (mCaM). However, the molecular basis of how CaM homologue mutations differentially impact target activation is unclear. To understand these mechanisms, we examined two CaM isoforms found in soybean plants that differentially regulate a mammalian target, calcineurin (CaN). These CaM isoforms, sCaM-1 and sCaM-4, share >90 and ∼78% identity with the mCaM, respectively, and activate CaN with comparable or reduced activity relative to mCaM. We used molecular dynamics (MD) simulations and fluorometric assays of CaN-dependent dephosphorylation of MUF-P to probe whether calcium and protein-protein binding interactions are altered by plant CaMs relative to mCaM as a basis for differential CaN regulation. In the presence of CaN, we found that the two sCaMs' Ca2+ binding properties, such as their predicted coordination of Ca2+ and experimentally measured EC50 [Ca2+] values are comparable to mCaM. Furthermore, the binding of CaM to the CaM binding region (CaMBR) in CaN is comparable among the three CaMs, as evidenced by MD-predicted binding energies and experimentally measured EC50 [CaM] values. However, mCaM and sCaM-1 exhibited binding with a secondary region of CaN's regulatory domain that is weakened for sCaM-4. We speculate that this secondary interaction affects the turnover rate (kcat) of CaN based on our modeling of enzyme activity, which is consistent with our experimental data. Together, our data describe how plant-derived CaM variants alter CaN activity through enlisting interactions other than those directly influencing Ca2+ binding and canonical CaMBR binding, which may additionally play a role in the differential regulation of other mammalian targets.
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Affiliation(s)
- Bin Sun
- Department of Cell and Molecular Physiology, Loyola University Chicago, Maywood, IL, USA 60153
| | - Xuan Fang
- Department of Cell and Molecular Physiology, Loyola University Chicago, Maywood, IL, USA 60153
| | - Christopher Johnson
- Department of Physiology and Cell Biology, The Ohio State University, Columbus, OH, USA 43210
- Department of Chemistry, Mississippi State University Starkville MS, 39759
| | - Garrett Hauck
- Department of Physiology and Cell Biology, The Ohio State University, Columbus, OH, USA 43210
| | - Yongjun Kou
- Department of Physiology and Cell Biology, The Ohio State University, Columbus, OH, USA 43210
| | - Jonathan P. Davis
- Department of Physiology and Cell Biology, The Ohio State University, Columbus, OH, USA 43210
| | - Peter M. Kekenes-Huskey
- Department of Cell and Molecular Physiology, Loyola University Chicago, Maywood, IL, USA 60153
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19
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James SA, Ong HS, Hari R, Khan AM. A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences. BMC Genomics 2021; 22:700. [PMID: 34583643 PMCID: PMC8477458 DOI: 10.1186/s12864-021-07657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 04/28/2021] [Indexed: 11/10/2022] Open
Abstract
Background Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications. Results This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions. Conclusion Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07657-4.
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Affiliation(s)
- Stephen Among James
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia.,Department of Biochemistry, Faculty of Science, Kaduna State University, Kaduna, 800211, Nigeria
| | - Hui San Ong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia
| | - Ranjeev Hari
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia
| | - Asif M Khan
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia. .,Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Beykoz, Istanbul, 34820, Turkey.
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20
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Farrokhzadeh A, Akher FB, Egan TJ. Molecular Mechanism Exploration of Potent Fluorinated PI3K Inhibitors with a Triazine Scaffold: Unveiling the Unusual Synergistic Effect of Pyridine-to-Pyrimidine Ring Interconversion and CF 3 Defluorination. J Phys Chem B 2021; 125:10072-10084. [PMID: 34473499 DOI: 10.1021/acs.jpcb.1c03242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The phosphatidylinostitol-3-kinase (PI3K)/AKT/mammalian target of rapamycin signaling pathway is a vital regulator of cell proliferation, growth, and survival, which is frequently overactivated in many human cancers. To this effect, PI3K, which is an important mediator of this pathway, has been pinpointed as a crucial target in cancer therapy and hence the importance of PI3K inhibitors. It was recently reported that defluorination and pyridine-to-pyrimidine ring interconversion increase the potency of specific small-molecule inhibitors of PI3K. Compound 4, an inhibitor with the difluorinated pyrimidine motif, was found to be eight times more potent against PI3K than compound 1, an inhibitor with the trifluorinated pyridine motif. This observation presents the need to rationally resolve the differential inhibitory mechanisms exhibited by both compounds. In this present work, we employed multiple computational approaches to investigate and distinguish the binding modes of 1 and 4 in addition to the effects they mediate on the secondary structure of PI3K. Likewise, we evaluated two other derivatives, compounds 2 with the difluorinated pyridine motif and 3 with the trifluorinated pyrimidine motif, to investigate the cooperativity effect between the defluorination of CF3 and pyridine-to-pyrimidine ring interconversion. Findings revealed that PI3K, upon interaction with 4, exhibited a series of structural changes that favored the binding of the inhibitor at the active-site region. Furthermore, a positive (synergistic) cooperativity effect was observed between CF3 defluorination and pyridine-to-pyrimidine ring interconversion. Moreover, there was a good correlation between the binding free energy estimated and the biological activity reported experimentally. Energy decomposition analysis revealed that the major contributing force to binding affinity variations between 1 and 4 is the electrostatic energy. Per-residue energy-based hierarchical clustering analysis further identified four hot-spot residues ASP841, TYR867, ASP964, and LYS833 and four warm-spot residues ASP836, SER806, ASP837, and LYS808, which essentially mediate the optimal and higher-affinity binding of compound 4 to PI3K relative to 1. This study therefore provides rational insights into the mechanisms by which 4 exhibited superior PI3K-inhibitory activities over 1, which is vital for future structure-based drug discovery efforts in PI3K targeting.
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Affiliation(s)
| | - Farideh Badichi Akher
- Department of Chemistry, University of Cape Town, Rondebosch, 7701 Cape Town, South Africa.,Department of Computer Science, University of Cape Town, Rondebosch, 7701 Cape Town, South Africa
| | - Timothy J Egan
- Department of Chemistry, University of Cape Town, Rondebosch, 7701 Cape Town, South Africa
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21
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The role of zinc finger linkers in zinc finger protein binding to DNA. J Comput Aided Mol Des 2021; 35:973-986. [PMID: 34350488 DOI: 10.1007/s10822-021-00413-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 07/26/2021] [Indexed: 10/20/2022]
Abstract
Zinc finger proteins (ZFP) play important roles in cellular processes. The DNA binding region of ZFP consists of 3 zinc finger DNA binding domains connected by amino acid linkers, the sequence TGQKP connects ZF1 and ZF2, and TGEKP connects ZF2 with ZF3. Linkers act to tune the zinc finger protein in the right position to bind its DNA target, the type of amino acid residues and length of linkers reflect on ZF1-ZF2-ZF3 interactions and contribute to the search and recognition process of ZF protein to its DNA target. Linker mutations and the affinity of the resulting mutants to specific and nonspecific DNA targets were studied by MD simulations and MM_GB(PB)SA. The affinity of mutants to DNA varied with type and position of amino acid residue. Mutation of K in TGQKP resulted in loss in affinity due to the loss of positive K interaction with phosphates, mutation of G showed loss in affinity to DNA, WT protein and all linker mutants showed loss in affinity to a nonspecific DNA target, this finding confirms previous reports which interpreted this loss in affinity as due to ZF1 having an anchoring role, and ZF3 playing an explorer role in the binding mechanism. The change in ZFP-DNA affinity with linker mutations is discussed in view of protein structure and role of linker residues in binding.
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22
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Kumar R. Mutations in passive residues modulate 3D-structure of NDM (New Delhi metallo-β-lactamase) protein that endue in drug resistance: a MD simulation approach. J Biomol Struct Dyn 2021; 40:9492-9508. [PMID: 34034624 DOI: 10.1080/07391102.2021.1930165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The ability of antimicrobial resistance developed by bacteria enhanced the complexity of bacterial treatment leading a serious threat to human health. Production of β-lactamase by bacteria that inactivates β-lactam is a generic cause of resistance. One such β-lactamase enzyme is New Delhi Metallo-β-lactamase (NDM) which is recently reported to have clinically more importance and recognized as an antibiotic resistance marker. Mutations in active and passive residues of NDM protein play a fateful role in the substrate and inhibitor specificity. In this study, in silico point mutations of residues near the active site and flexible regions of protein were investigated. Hybrid modelling and molecular dynamics (MD) simulations were carried to build up the mutant models and monitored structural stability. Molecular docking results articulated that mutant proteins had lesser binding affinities with methicillin, oxacillin and doripenem drugs. Further, to scrutinize the structural alterations and rescore the binding energies per-residue basis, MD simulations of wildtype (WT) and mutant (MT) NDM proteins with methicillin, oxacillin and doripenem were performed. Our results demonstrated that mutations in N193A, S217A, G219A and T262A residues led to protein destabilization and amend their binding affinities with methicillin, oxacillin and doripenem. The present study exploited computational approaches which displayed differential binding of drugs with WT and MT NDM proteins that confer resistance to oxacillin and doripenem. The study features the significance of passive residues, thus provides a clue to accelerate the process of designing an ergastic antibiotic against NDM protein. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rakesh Kumar
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
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23
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Schlick T, Portillo-Ledesma S. Biomolecular modeling thrives in the age of technology. NATURE COMPUTATIONAL SCIENCE 2021; 1:321-331. [PMID: 34423314 PMCID: PMC8378674 DOI: 10.1038/s43588-021-00060-9] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/22/2021] [Indexed: 12/12/2022]
Abstract
The biomolecular modeling field has flourished since its early days in the 1970s due to the rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase in size and timespan of biomolecular simulations has outpaced Moore's law. Here, we discuss the role of knowledge-based versus physics-based methods and hardware versus software advances in propelling the field forward. This rapid adaptation and outreach suggests a bright future for modeling, where theory, experimentation and simulation define three pillars needed to address future scientific and biomedical challenges.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, NY, USA
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
- New York University–East China Normal University Center for Computational Chemistry at New York University Shanghai, Shanghai, China
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24
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Kapla J, Rodríguez-Espigares I, Ballante F, Selent J, Carlsson J. Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models? PLoS Comput Biol 2021; 17:e1008936. [PMID: 33983933 PMCID: PMC8186765 DOI: 10.1371/journal.pcbi.1008936] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 06/08/2021] [Accepted: 04/02/2021] [Indexed: 01/14/2023] Open
Abstract
The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.
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Affiliation(s)
- Jon Kapla
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Ismael Rodríguez-Espigares
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Flavio Ballante
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
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25
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de Araújo RSA, Mendonça FJ, Scotti MT, Scotti L. Protein modeling. PHYSICAL SCIENCES REVIEWS 2021. [DOI: 10.1515/psr-2018-0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Proteins are essential and versatile polymers consisting of sequenced amino acids that often possess an organized three-dimensional arrangement, (a result of their monomeric composition), which determines their biological role in cellular function. Proteins are involved in enzymatic catalysis; they participate in genetic information decoding and transmission processes, in cell recognition, in signaling, and transport of substances, in regulation of intra and extracellular conditions, and other functions.
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Affiliation(s)
- Rodrigo S. A. de Araújo
- Biological Science Department, Laboratory of Synthesis and Drug Delivery , State University of Paraiba , 58070-450 , João Pessoa , PB , Brazil
| | - Francisco J. B. Mendonça
- Biological Science Department, Laboratory of Synthesis and Drug Delivery , State University of Paraiba , 58070-450 , João Pessoa , PB , Brazil
| | - Marcus T. Scotti
- Health Center , Federal University of Paraíba , 50670-910 , João Pessoa , PB , Brazil
| | - Luciana Scotti
- Health Center , Federal University of Paraíba , 50670-910 , João Pessoa , PB , Brazil
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26
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Sanejouand YH. On the vibrational free energy of hydrated proteins. Phys Biol 2021; 18:036003. [PMID: 33720038 DOI: 10.1088/1478-3975/abdc0f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
When the hydration shell of a protein is filled with at least 0.6 gram of water per gram of protein, a significant anti-correlation between the vibrational free energy and the potential energy of energy-minimized conformers is observed. This means that low potential energy, well-hydrated, protein conformers tend to be more rigid than high-energy ones. On the other hand, in the case of CASP target 624, when its hydration shell is filled, a significant energy gap is observed between the crystal structure and the best conformers proposed during the prediction experiment, strongly suggesting that including explicit water molecules may help identifying unlikely conformers among good-looking ones.
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27
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Heo L, Arbour CF, Janson G, Feig M. Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation. J Chem Theory Comput 2021; 17:1931-1943. [PMID: 33562962 DOI: 10.1021/acs.jctc.0c01238] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. These methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore the conformational space more broadly. Based on the insights of this analysis, we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here.
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Affiliation(s)
- Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Collin F Arbour
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Giacomo Janson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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Vlachakis D, Vlamos P. Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents. MATHEMATICS IN COMPUTER SCIENCE 2021; 15. [PMCID: PMC8205651 DOI: 10.1007/s11786-021-00517-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
COVID19 is the most impactful pandemic of recent times worldwide. It is a highly infectious disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), To date there is specific drug nor vaccination against COVID19. Therefor the need for novel and pioneering anti-COVID19 is of paramount importance. In this direction, computer-aided drug design constitutes a very promising antiviral approach for the discovery and analysis of drugs and molecules with biological activity against SARS-CoV2. In silico modelling takes advantage of the massive amounts of biological and chemical data available on the nature of the interactions between the targeted systems and molecules, as well as the rapid progress of computational tools and software. Herein, we describe the potential of the merging of mathematical modelling, artificial intelligence and learning techniques into seamless computational pipelines for the rapid and efficient discovery and design of potent anti- SARS-CoV-2 modulators.
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Affiliation(s)
- Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, Genetics and Computational Biology Group, School of Applied Biology and Biotechnology, Agricultural University of Athens, Iera Odos 75 Str. GR11855, Athens, Greece
- Laboratory of Molecular Endocrinology, Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Soranou Ephessiou Str. GR11527, Athens, Greece
- University Research Institute of Maternal and Child Health and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Thivon 1 & Papadiamantopoulou Str. GR11527, Athens, Greece
| | - Panayiotis Vlamos
- Department of Informatics, Ionian University, Plateia Tsirigoti 7, 49100 Corfu, Greece
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29
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Hempel T, Raich L, Olsson S, Azouz NP, Klingler AM, Hoffmann M, Pöhlmann S, Rothenberg ME, Noé F. Molecular mechanism of inhibiting the SARS-CoV-2 cell entry facilitator TMPRSS2 with camostat and nafamostat. Chem Sci 2021; 12:983-992. [PMID: 35382133 PMCID: PMC8906443 DOI: 10.1039/d0sc05064d] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 11/06/2020] [Indexed: 12/22/2022] Open
Abstract
The entry of the coronavirus SARS-CoV-2 into human lung cells can be inhibited by the approved drugs camostat and nafamostat. Here we elucidate the molecular mechanism of these drugs by combining experiments and simulations. In vitro assays confirm that both drugs inhibit the human protein TMPRSS2, a SARS-Cov-2 spike protein activator. As no experimental structure is available, we provide a model of the TMPRSS2 equilibrium structure and its fluctuations by relaxing an initial homology structure with extensive 330 microseconds of all-atom molecular dynamics (MD) and Markov modeling. Through Markov modeling, we describe the binding process of both drugs and a metabolic product of camostat (GBPA) to TMPRSS2, reaching a Michaelis complex (MC) state, which precedes the formation of a long-lived covalent inhibitory state. We find that nafamostat has a higher MC population than camostat and GBPA, suggesting that nafamostat is more readily available to form the stable covalent enzyme–substrate intermediate, effectively explaining its high potency. This model is backed by our in vitro experiments and consistent with previous virus cell entry assays. Our TMPRSS2–drug structures are made public to guide the design of more potent and specific inhibitors. The authors unravel the molecular action principle of nafamostat and camostat, two potential COVID-19 drugs targeting the human protein TMPRSS2.![]()
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Affiliation(s)
- Tim Hempel
- Freie Universität Berlin
- Department of Mathematics and Computer Science
- Berlin
- Germany
- Freie Universität Berlin
| | - Lluís Raich
- Freie Universität Berlin
- Department of Mathematics and Computer Science
- Berlin
- Germany
| | - Simon Olsson
- Freie Universität Berlin
- Department of Mathematics and Computer Science
- Berlin
- Germany
- Chalmers University of Technology
| | - Nurit P. Azouz
- Division of Allergy and Immunology
- Cincinnati Children's Hospital Medical Center
- Department of Pediatrics
- University of Cincinnati College of Medicine
- Cincinnati
| | - Andrea M. Klingler
- Division of Allergy and Immunology
- Cincinnati Children's Hospital Medical Center
- Department of Pediatrics
- University of Cincinnati College of Medicine
- Cincinnati
| | - Markus Hoffmann
- Infection Biology Unit
- German Primate Center – Leibniz Institute for Primate Research
- Göttingen
- Germany
- Faculty of Biology and Psychology
| | - Stefan Pöhlmann
- Infection Biology Unit
- German Primate Center – Leibniz Institute for Primate Research
- Göttingen
- Germany
- Faculty of Biology and Psychology
| | - Marc E. Rothenberg
- Division of Allergy and Immunology
- Cincinnati Children's Hospital Medical Center
- Department of Pediatrics
- University of Cincinnati College of Medicine
- Cincinnati
| | - Frank Noé
- Freie Universität Berlin
- Department of Mathematics and Computer Science
- Berlin
- Germany
- Freie Universität Berlin
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30
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Chen M, Chen X, Jin S, Lu W, Lin X, Wolynes PG. Protein Structure Refinement Guided by Atomic Packing Frustration Analysis. J Phys Chem B 2020; 124:10889-10898. [PMID: 32931278 DOI: 10.1021/acs.jpcb.0c06719] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Recent advances in machine learning, bioinformatics, and the understanding of the folding problem have enabled efficient predictions of protein structures with moderate accuracy, even for targets where there is little information from templates. All-atom molecular dynamics simulations provide a route to refine such predicted structures, but unguided atomistic simulations, even when lengthy in time, often fail to eliminate incorrect structural features that would prevent the structure from becoming more energetically favorable owing to the necessity of making large scale motions and to overcoming energy barriers for side chain repacking. In this study, we show that localizing packing frustration at atomic resolution by examining the statistics of the energetic changes that occur when the local environment of a site is changed allows one to identify the most likely locations of incorrect contacts. The global statistics of atomic resolution frustration in structures that have been predicted using various algorithms provide strong indicators of structural quality when tested over a database of 20 targets from previous CASP experiments. Residues that are more correctly located turn out to be more minimally frustrated than more poorly positioned sites. These observations provide a diagnosis of both global and local quality of predicted structures and thus can be used as guidance in all-atom refinement simulations of the 20 targets. Refinement simulations guided by atomic packing frustration turn out to be quite efficient and significantly improve the quality of the structures.
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Affiliation(s)
- Mingchen Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - Xun Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Shikai Jin
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Department of Biosciences, Rice University, Houston, Texas 77005, United States
| | - Wei Lu
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Department of Physics and Astronomy, Rice University, Houston, Texas 77030, United States
| | - Xingcheng Lin
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Peter G Wolynes
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Department of Chemistry, Rice University, Houston, Texas 77005, United States.,Department of Biosciences, Rice University, Houston, Texas 77005, United States.,Department of Physics and Astronomy, Rice University, Houston, Texas 77030, United States
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31
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Khan SU, Ahemad N, Chuah LH, Naidu R, Htar TT. G protein-coupled estrogen receptor-1: homology modeling approaches and application in screening new GPER-1 modulators. J Biomol Struct Dyn 2020; 40:3325-3335. [PMID: 33164654 DOI: 10.1080/07391102.2020.1844059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
G protein-coupled receptors (GPCRs) belong to the largest family of protein targets comprising over 800 members in which at least 500 members are the therapeutic targets. Among the GPCRs, G protein-coupled estrogen receptor-1 (GPER-1) has shown to have the ability in estrogen signaling. As GPER-1 plays a critical role in several physiological responses, GPER-1 has been considered as a potential therapeutic target to treat estrogen-based cancers and other non-communicable diseases. However, the progress in the understanding of GPER-1 structure and function is relatively slow due to the availability of a only a few selective GPER-1 modulators. As with many GPCRs, the X-ray crystal structure of GPER-1 is yet to be resolved and thus has led the researchers to search for new GPER-1 modulators using homology models of GPER-1. In this review, we aim to summarize various approaches used in the generation of GPER-1 homology model and their applications that have resulted in new GPER-1 ligands.
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Affiliation(s)
- Shafi Ullah Khan
- School of Pharmacy, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
| | - Nafees Ahemad
- School of Pharmacy, Monash University Malaysia, Subang Jaya, Selangor, Malaysia.,Tropical Medicine and Biology Multidisciplinary Platform, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
| | - Lay-Hong Chuah
- School of Pharmacy, Monash University Malaysia, Subang Jaya, Selangor, Malaysia.,Advanced Engineering Platform, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
| | - Rakesh Naidu
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
| | - Thet Thet Htar
- School of Pharmacy, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
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32
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Sahoo BR. Structure of fish Toll-like receptors (TLR) and NOD-like receptors (NLR). Int J Biol Macromol 2020; 161:1602-1617. [PMID: 32755705 PMCID: PMC7396143 DOI: 10.1016/j.ijbiomac.2020.07.293] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/26/2020] [Accepted: 07/27/2020] [Indexed: 12/23/2022]
Abstract
Innate immunity driven by pattern recognition receptor (PRR) protects the host from invading pathogens. Aquatic animals like fish where the adaptive immunity is poorly developed majorly rely on their innate immunity modulated by PRRs like toll-like receptors (TLR) and NOD-like receptors (NLR). However, current development to improve the fish immunity via TLR/NLR signaling is affected by a poor understanding of its mechanistic and structural features. This review discusses the structure of fish TLRs/NLRs and its interaction with pathogen associated molecular patterns (PAMPs) and downstream signaling molecules. Over the past one decade, significant progress has been done in studying the structure of TLRs/NLRs in higher eukaryotes; however, structural studies on fish innate immune receptors are undermined. Several novel TLR genes are identified in fish that are absent in higher eukaryotes, but the function is still poorly understood. Unlike the fundamental progress achieved in developing antagonist/agonist to modulate human innate immunity, analogous studies in fish are nearly lacking due to structural inadequacy. This underlies the importance of exploring the structural and mechanistic details of fish TLRs/NLRs at an atomic and molecular level. This review outlined the mechanistic and structural basis of fish TLR and NLR activation.
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33
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Rehman HM, Mirza MU, Ahmad MA, Saleem M, Froeyen M, Ahmad S, Gul R, Alghamdi HA, Aslam MS, Sajjad M, Bhinder MA. A Putative Prophylactic Solution for COVID-19: Development of Novel Multiepitope Vaccine Candidate against SARS-COV-2 by Comprehensive Immunoinformatic and Molecular Modelling Approach. BIOLOGY 2020; 9:E296. [PMID: 32962156 PMCID: PMC7563440 DOI: 10.3390/biology9090296] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/04/2020] [Accepted: 09/05/2020] [Indexed: 12/13/2022]
Abstract
The outbreak of 2019-novel coronavirus (SARS-CoV-2) that causes severe respiratory infection (COVID-19) has spread in China, and the World Health Organization has declared it a pandemic. However, no approved drug or vaccines are available, and treatment is mainly supportive and through a few repurposed drugs. The urgency of the situation requires the development of SARS-CoV-2-based vaccines. Immunoinformatic and molecular modelling are time-efficient methods that are generally used to accelerate the discovery and design of the candidate peptides for vaccine development. In recent years, the use of multiepitope vaccines has proved to be a promising immunization strategy against viruses and pathogens, thus inducing more comprehensive protective immunity. The current study demonstrated a comprehensive in silico strategy to design stable multiepitope vaccine construct (MVC) from B-cell and T-cell epitopes of essential SARS-CoV-2 proteins with the help of adjuvants and linkers. The integrated molecular dynamics simulations analysis revealed the stability of MVC and its interaction with human Toll-like receptors (TLRs), which trigger an innate and adaptive immune response. Later, the in silico cloning in a known pET28a vector system also estimated the possibility of MVC expression in Escherichia coli. Despite that this study lacks validation of this vaccine construct in terms of its efficacy, the current integrated strategy encompasses the initial multiple epitope vaccine design concepts. After validation, this MVC can be present as a better prophylactic solution against COVID-19.
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Affiliation(s)
- Hafiz Muzzammel Rehman
- Institute of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Punjab, Pakistan; (H.M.R.); (M.S.A.)
- Department of Human Genetics and Molecular Biology, University of Health Sciences, Lahore 54590, Punjab, Pakistan; (M.A.A.); (M.A.B.)
| | - Muhammad Usman Mirza
- Department of Pharmaceutical and Pharmacological Sciences, Rega Institute for Medical Research, Medicinal Chemistry, University of Leuven, B-3000 Leuven, Belgium; (M.U.M.); (M.F.)
| | - Mian Azhar Ahmad
- Department of Human Genetics and Molecular Biology, University of Health Sciences, Lahore 54590, Punjab, Pakistan; (M.A.A.); (M.A.B.)
- Department of Health, Government of the Punjab, Lahore 54590, Punjab, Pakistan
| | - Mahjabeen Saleem
- Institute of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Punjab, Pakistan; (H.M.R.); (M.S.A.)
| | - Matheus Froeyen
- Department of Pharmaceutical and Pharmacological Sciences, Rega Institute for Medical Research, Medicinal Chemistry, University of Leuven, B-3000 Leuven, Belgium; (M.U.M.); (M.F.)
| | - Sarfraz Ahmad
- Drug Design and Development Research Group (DDDRG), Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Roquyya Gul
- Faculty of Life Sciences, Gulab Devi Educational Complex, Lahore 54590, Punjab, Pakistan;
| | - Huda Ahmed Alghamdi
- Department of Biology, College of Sciences, King Khalid University, Abha 61413, Saudi Arabia;
| | - Muhammad Shahbaz Aslam
- Institute of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Punjab, Pakistan; (H.M.R.); (M.S.A.)
| | - Muhammad Sajjad
- School of Biological Sciences, University of the Punjab, Quaid e Azam Campus, Lahore 54590, Punjab, Pakistan;
| | - Munir Ahmad Bhinder
- Department of Human Genetics and Molecular Biology, University of Health Sciences, Lahore 54590, Punjab, Pakistan; (M.A.A.); (M.A.B.)
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34
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Dhingra S, Sowdhamini R, Cadet F, Offmann B. A glance into the evolution of template-free protein structure prediction methodologies. Biochimie 2020; 175:85-92. [DOI: 10.1016/j.biochi.2020.04.026] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/24/2020] [Accepted: 04/27/2020] [Indexed: 11/26/2022]
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35
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Tao P, Xiao Y. Using the generalized Born surface area model to fold proteins yields more effective sampling while qualitatively preserving the folding landscape. Phys Rev E 2020; 101:062417. [PMID: 32688556 DOI: 10.1103/physreve.101.062417] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/01/2020] [Indexed: 11/07/2022]
Abstract
Protein folding is a long-standing problem and has been widely investigated using molecular dynamics simulations with both explicit and implicit solvents. However, to what extent the folding mechanisms observed in two water models agree remains an open question. In this study, ab initio folding simulations of ten proteins with different topologies are performed in two combinations of force fields and water models (ff14SB+TIP3P and ff14SBonlysc+GB-Neck2). Interestingly, the latter combination not only folds more proteins but also provides a better balance of different secondary structures than the former in the same number of integration time steps. More importantly, the folding pathways found in the two types of simulations are conserved and they may only differ in their weights. Our results suggest that simulations with an implicit solvent may also be suitable for the investigation of the mechanism of protein folding.
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Affiliation(s)
- Peng Tao
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Xiao
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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36
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Bhattacharya D. refineD: improved protein structure refinement using machine learning based restrained relaxation. Bioinformatics 2020; 35:3320-3328. [PMID: 30759180 DOI: 10.1093/bioinformatics/btz101] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 01/22/2019] [Accepted: 02/11/2019] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement. RESULTS Here, we develop a machine learning based restrained relaxation protocol that uses deep discriminative learning based binary classifiers to predict multi-resolution probabilistic restraints from the starting structure and subsequently converts these restraints to be integrated into Rosetta all-atom energy function as additional scoring terms during structure refinement. We use four restraint resolutions as adopted in GDT-HA (0.5, 1, 2 and 4 Å), centered on the Cα atom of each residue that are predicted by ensemble of four deep discriminative classifiers trained using combinations of sequence and structure-derived features as well as several energy terms from Rosetta centroid scoring function. The proposed method, refineD, has been found to produce consistent and substantial structural refinement through the use of cumulative and non-cumulative restraints on 150 benchmarking targets. refineD outperforms unrestrained relaxation strategy or relaxation that is restrained to starting structures using the FastRelax application of Rosetta or atomic-level energy minimization based ModRefiner method as well as molecular dynamics (MD) simulation based FG-MD protocol. Furthermore, by adjusting restraint resolutions, the method addresses the tradeoff that exists between degree and consistency of refinement. These results demonstrate a promising new avenue for improving accuracy of template-based protein models by effectively guiding conformational sampling during structure refinement through the use of machine learning based restraints. AVAILABILITY AND IMPLEMENTATION http://watson.cse.eng.auburn.edu/refineD/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Debswapna Bhattacharya
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
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37
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Ali A, Kumar R, Khan A, Khan AU. Interaction of LysM BON family protein domain with carbapenems: A putative mechanism of carbapenem resistance. Int J Biol Macromol 2020; 160:212-223. [PMID: 32464197 DOI: 10.1016/j.ijbiomac.2020.05.172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 05/21/2020] [Accepted: 05/21/2020] [Indexed: 10/24/2022]
Abstract
Carbapenem resistance in Gram-negative pathogens has become a global concern for health workers worldwide. In one of our earlier studies, a Klebsiella pneumoniae-carbapenemase-2 producing strain was induced with meropenem to explore differentially expressed proteins under induced and uninduced conditions. There is, LysM domain BON family protein, was found over 12-fold expressed under the induced state. A hypothesis was proposed that LysM domain protein might have an affinity towards carbapenem antibiotics making them unavailable to bind with their target. Hence, we initiated a study to understand the binding mode of carbapenem with LysM domain protein. MICs of imipenem and meropenem against LysM clone were increased by several folds as compared to NP-6 clinical strain as well as DH5 α (PET-28a KPC-2) clone. This study further revealed a strong binding of both antibiotics to LysM domain protein. Molecular simulation studies of LysM domain protein with meropenem and imipenem for 80 ns has also showed stable structure. We concluded that overexpressed LysM domain under induced condition interacted with carbapenems, leading to enhanced resistance as proved by high MIC values. Hence, the study proved the proposed hypothesis that the LysM domain plays a significant role in the putative mechanism of antibiotics resistance.
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Affiliation(s)
- Abid Ali
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, UP, India
| | - Rakesh Kumar
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, Delhi 110067, India
| | - Arbab Khan
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, UP, India
| | - Asad U Khan
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, UP, India; Faculty of Science and Marine Environment, University Malysia Terengganu, Kuala Terengganu, Malaysia.
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38
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Pang WC, Ramli ANM, Hamid AAA. Comparative modelling studies of fruit bromelain using molecular dynamics simulation. J Mol Model 2020; 26:142. [PMID: 32417971 DOI: 10.1007/s00894-020-04398-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 04/28/2020] [Indexed: 12/25/2022]
Abstract
Fruit bromelain is a cysteine protease accumulated in pineapple fruits. This proteolytic enzyme has received high demand for industrial and therapeutic applications. In this study, fruit bromelain sequences QIM61759, QIM61760 and QIM61761 were retrieved from the National Center for Biotechnology Information (NCBI) Genbank Database. The tertiary structure of fruit bromelain QIM61759, QIM61760 and QIM61761 was generated by using MODELLER. The result revealed that the local stereochemical quality of the generated models was improved by using multiple templates during modelling process. Moreover, by comparing with the available papain model, structural analysis provides an insight on how pro-peptide functions as a scaffold in fruit bromelain folding and contributing to inactivation of mature protein. The structural analysis also disclosed the similarities and differences between these models. Lastly, thermal stability of fruit bromelain was studied. Molecular dynamics simulation of fruit bromelain structures at several selected temperatures demonstrated how fruit bromelain responds to elevation of temperature.
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Affiliation(s)
- Wei Cheng Pang
- Faculty of Industrial Science & Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang Darul Makmur, Malaysia
| | - Aizi Nor Mazila Ramli
- Faculty of Industrial Science & Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang Darul Makmur, Malaysia. .,Bio Aromatic Research Centre of Excellence, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang Darul Makmur, Malaysia.
| | - Azzmer Azzar Abdul Hamid
- Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia (IIUM), Bandar Indera Mahkota, 25200, Kuantan, Pahang, Malaysia.,Research Unit for Bioinformatics and Computational Biology (RUBIC), Kulliyyah of Science, International Islamic University Malaysia (IIUM), Bandar Indera Mahkota, 25200, Kuantan, Pahang, Malaysia
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39
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Anderson JS, Hernández G, LeMaster DM. 13C NMR Relaxation Analysis of Protein GB3 for the Assessment of Side Chain Dynamics Predictions by Current AMBER and CHARMM Force Fields. J Chem Theory Comput 2020; 16:2896-2913. [PMID: 32268062 DOI: 10.1021/acs.jctc.0c00050] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Molecular simulations with seven current AMBER- and CHARMM-based force fields yield markedly differing internal bond vector autocorrelation function predictions for many of the 223 methine and methylene H-C bonds of the 56-residue protein GB3. To enable quantification of accuracy, 13C R1, R2, and heteronuclear NOE relaxation rates have been determined for the methine and stereochemically assigned methylene Cα and Cβ positions. With only three experimental relaxation values for each bond vector, central to this analysis is the accuracy with which MD-derived autocorrelation curves can be represented by a 3-parameter equation which, in turn, maps onto the NMR relaxation values. In contrast to the more widely used extended Lipari-Szabo order parameter representation, 95% of these MD-derived internal autocorrelation curves for GB3 can be fitted to within 1.0% rmsd over the time frame from 30 ps to 4 ns by a biexponential Larmor frequency-selective representation (LF-S2). Applying the LF-S2 representation to the experimental relaxation rates and uncertainties serves to determine the boundary range for the autocorrelation function of each bond vector consistent with the experimental data. Not surprisingly, all seven force fields predict the autocorrelation functions for the more motionally restricted 1Hα-13Cα and 1Hβ-13Cβ bond vectors with reasonable accuracy. However, for the 1Hβ-13Cβ bond vectors exhibiting aggregate order parameter S2 values less than 0.85, only 1% of the MD-derived predictions lie with 1 σ of the experimentally determined autocorrelation functions and only 7% within 2 σ. On the other hand, substantial residue type-specific improvements in predictive performance were observed among the recent AMBER force fields. This analysis indicates considerable potential for the use of 13C relaxation measurements in guiding the optimization of the side chain dynamics characteristics of protein molecular simulations.
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Affiliation(s)
- Janet S Anderson
- Department of Chemistry, Union College, Schenectady, New York 12308, United States
| | - Griselda Hernández
- Wadsworth Center, New York State Department of Health, Empire State Plaza, Albany, New York 12201, United States
| | - David M LeMaster
- Wadsworth Center, New York State Department of Health, Empire State Plaza, Albany, New York 12201, United States
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40
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Abstract
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.
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41
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Saurabh S, Sivakumar PM, Perumal V, Khosravi A, Sugumaran A, Prabhawathi V. Molecular Dynamics Simulations in Drug Discovery and Drug Delivery. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/978-3-030-36260-7_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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42
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Jaiteh M, Rodríguez-Espigares I, Selent J, Carlsson J. Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity. PLoS Comput Biol 2020; 16:e1007680. [PMID: 32168319 PMCID: PMC7135368 DOI: 10.1371/journal.pcbi.1007680] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/06/2020] [Accepted: 01/23/2020] [Indexed: 12/15/2022] Open
Abstract
Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.
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Affiliation(s)
- Mariama Jaiteh
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Ismael Rodríguez-Espigares
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
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43
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Gagic Z, Ruzic D, Djokovic N, Djikic T, Nikolic K. In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs. Front Chem 2020; 7:873. [PMID: 31970149 PMCID: PMC6960140 DOI: 10.3389/fchem.2019.00873] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 12/04/2019] [Indexed: 12/11/2022] Open
Abstract
Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.
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Affiliation(s)
- Zarko Gagic
- Department of Pharmaceutical Chemistry, Faculty of Medicine, University of Banja Luka, Banja Luka, Bosnia and Herzegovina
| | - Dusan Ruzic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Nemanja Djokovic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Teodora Djikic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
| | - Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia
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44
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Menzer WM, Xie B, Minh DDL. On Restraints in End-Point Protein-Ligand Binding Free Energy Calculations. J Comput Chem 2019; 41:573-586. [PMID: 31821590 DOI: 10.1002/jcc.26119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 10/26/2019] [Accepted: 11/08/2019] [Indexed: 12/14/2022]
Abstract
The impact of harmonic restraints on protein heavy atoms and ligand atoms on end-point free energy calculations is systematically characterized for 54 protein-ligand complexes. We observe that stronger restraints reduce the equilibration time and statistical inefficiency, suppress conformational sampling, influence correlation with experiment, and monotonically decrease the estimated loss of entropy upon binding, leading to stronger estimated binding free energies in most systems. A statistical estimator that reweights for the biasing potential and includes data prior to the estimated equilibration time has the highest correlation with experiment. A spring constant of 20 cal mol-1 Å-2 maintains a near-native energy landscape and suppresses artifactual energy minima while minimally limiting thermal fluctuations about the crystal structure. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- William M Menzer
- Department of Biology, Illinois Institute of Technology, Chicago, Illinois, 60616
| | - Bing Xie
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
| | - David D L Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
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45
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Jonniya N, Sk MF, Kar P. Investigating Phosphorylation-Induced Conformational Changes in WNK1 Kinase by Molecular Dynamics Simulations. ACS OMEGA 2019; 4:17404-17416. [PMID: 31656913 PMCID: PMC6812135 DOI: 10.1021/acsomega.9b02187] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 09/25/2019] [Indexed: 05/10/2023]
Abstract
The With-No-Lysine (WNK) kinase is considered to be a master regulator for various cation-chloride cotransporters involved in maintaining cell-volume and ion homeostasis. Here, we have investigated the phosphorylation-induced structural dynamics of the WNK1 kinase bound to an inhibitor via atomistic molecular dynamics simulations. Results from our simulations show that the phosphorylation at Ser382 could stabilize the otherwise flexible activation loop (A-loop). The intrahelix salt-bridge formed between Arg264 and Glu268 in the unphosphorylated system is disengaged after the phosphorylation, and Glu268 reorients itself and forms a stable salt-bridge with Arg348. The dynamic cross-correlation analysis shows that phosphorylation diminishes anticorrelated motions and increases correlated motions between different domains. Structural network analysis reveals that the phosphorylation causes structural rearrangements and shortens the communication path between the αC-helix and catalytic loop, making the binding pocket more suitable for accommodating the ligand. Overall, we have characterized the structural changes in the WNK kinase because of phosphorylation in the A-loop, which might help in designing rational drugs.
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46
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Guterres H, Lee HS, Im W. Ligand-Binding-Site Structure Refinement Using Molecular Dynamics with Restraints Derived from Predicted Binding Site Templates. J Chem Theory Comput 2019; 15:6524-6535. [PMID: 31557013 DOI: 10.1021/acs.jctc.9b00751] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Accurate modeling of ligand-binding-site structures plays a critical role in structure-based virtual screening. However, the structures of the ligand-binding site in most predicted protein models are generally of low quality and need refinements. In this work, we present a ligand-binding-site structure refinement protocol using molecular dynamics simulation with restraints derived from predicted binding site templates. Our benchmark validation shows great performance for 40 diverse sets of proteins from the Astex list. The ligand-binding sites on modeled protein structures are consistently refined using our method with an average Cα RMSD improvement of 0.90 Å. Comparison of ligand binding modes from ligand docking to initial unrefined and refined structures shows an average of 1.97 Å RMSD improvement in the refined structures. These results demonstrate a promising new method of structure refinement for protein ligand-binding-site structures.
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Affiliation(s)
- Hugo Guterres
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Hui Sun Lee
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Wonpil Im
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States.,School of Computational Sciences , Korea Institute for Advanced Study , Seoul 02455 , Republic of Korea
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47
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Geng H, Chen F, Ye J, Jiang F. Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins. Comput Struct Biotechnol J 2019; 17:1162-1170. [PMID: 31462972 PMCID: PMC6709365 DOI: 10.1016/j.csbj.2019.07.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/07/2019] [Accepted: 07/23/2019] [Indexed: 12/21/2022] Open
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
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Affiliation(s)
- Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fangfang Chen
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Jing Ye
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- NanoAI Biotech Co.,Ltd., Silicon Valley Compound, Longhua District, Shenzhen 518109, China
- Corresponding author at: Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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48
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Grazioli G, Martin RW, Butts CT. Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods. Front Mol Biosci 2019; 6:42. [PMID: 31245383 PMCID: PMC6581705 DOI: 10.3389/fmolb.2019.00042] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/20/2019] [Indexed: 01/23/2023] Open
Abstract
Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (Aβ1−40) and its “Arctic” variant (E22G), systems that play a central role in the etiology of Alzheimer's disease. Our analyses demonstrate ways in which ML and related approaches can be used to elucidate subtle differences between these proteins, including transient structure that is poorly captured by conventional metrics.
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Affiliation(s)
- Gianmarc Grazioli
- California Institute for Telecommunications and Information Technology (Calit2), University of California, Irvine, Irvine, CA, United States.,Department of Chemistry, University of California, Irvine, Irvine, CA, United States
| | - Rachel W Martin
- Department of Chemistry, University of California, Irvine, Irvine, CA, United States.,Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
| | - Carter T Butts
- California Institute for Telecommunications and Information Technology (Calit2), University of California, Irvine, Irvine, CA, United States.,Department of Computer Science, University of California, Irvine, Irvine, CA, United States.,Department of Sociology, Statistics, and Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
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49
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Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 2019; 20:ijms20092301. [PMID: 31075942 PMCID: PMC6539982 DOI: 10.3390/ijms20092301] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022] Open
Abstract
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
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50
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Bignon E, Rizza S, Filomeni G, Papaleo E. Use of Computational Biochemistry for Elucidating Molecular Mechanisms of Nitric Oxide Synthase. Comput Struct Biotechnol J 2019; 17:415-429. [PMID: 30996821 PMCID: PMC6451115 DOI: 10.1016/j.csbj.2019.03.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 03/17/2019] [Accepted: 03/21/2019] [Indexed: 12/25/2022] Open
Abstract
Nitric oxide (NO) is an essential signaling molecule in the regulation of multiple cellular processes. It is endogenously synthesized by NO synthase (NOS) as the product of L-arginine oxidation to L-citrulline, requiring NADPH, molecular oxygen, and a pterin cofactor. Two NOS isoforms are constitutively present in cells, nNOS and eNOS, and a third is inducible (iNOS). Despite their biological relevance, the details of their complex structural features and reactivity mechanisms are still unclear. In this review, we summarized the contribution of computational biochemistry to research on NOS molecular mechanisms. We described in detail its use in studying aspects of structure, dynamics and reactivity. We also focus on the numerous outstanding questions in the field that could benefit from more extensive computational investigations.
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Affiliation(s)
- Emmanuelle Bignon
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Salvatore Rizza
- Redox Signaling and Oxidative Stress Group, Cell Stress and Survival Unit, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Giuseppe Filomeni
- Redox Signaling and Oxidative Stress Group, Cell Stress and Survival Unit, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark.,Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark.,Translational Disease Systems Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research University of Copenhagen, Copenhagen, Denmark
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