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Sakhawat A, Khan MU, Rehman R, Khan S, Shan MA, Batool A, Javed MA, Ali Q. Natural compound targeting BDNF V66M variant: insights from in silico docking and molecular analysis. AMB Express 2023; 13:134. [PMID: 38015338 PMCID: PMC10684480 DOI: 10.1186/s13568-023-01640-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/09/2023] [Indexed: 11/29/2023] Open
Abstract
Brain-Derived Neurotrophic Factor (BDNF) is a neurotrophin gene family gene that encodes proteins vital for the growth, maintenance, and survival of neurons in the nervous system. The study aimed to screen natural compounds against BDNF variant (V66M), which affects memory, cognition, and mood regulation. BDNF variant (V66M) as a target structure was selected, and Vitamin D, Curcumin, Vitamin C, and Quercetin as ligands structures were taken from PubChem database. Multiple tools like AUTODOCK VINA, BIOVIA discovery studio, PyMOL, CB-dock, IMOD server, Swiss ADEMT, and Swiss predict ligands target were used to analyze binding energy, interaction, stability, toxicity, and visualize BDNF-ligand complexes. Compounds Vitamin D3, Curcumin, Vitamin C, and Quercetin with binding energies values of - 5.5, - 6.1, - 4.5, and - 6.7 kj/mol, respectively, were selected. The ligands bind to the active sites of the BDNF variant (V66M) via hydrophobic bonds, hydrogen bonds, and electrostatic interactions. Furthermore, ADMET analysis of the ligands revealed they exhibited sound pharmacokinetic and toxicity profiles. In addition, an MD simulation study showed that the most active ligand bound favorably and dynamically to the target protein, and protein-ligand complex stability was determined. The finding of this research could provide an excellent platform for discovering and rationalizing novel drugs against stress related to BDNF (V66M). Docking, preclinical drug testing and MD simulation results suggest Quercetin as a more potent BDNF variant (V66M) inhibitor and forming a more structurally stable complex.
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Affiliation(s)
- Azra Sakhawat
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan
| | - Muhammad Umer Khan
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan.
| | - Raima Rehman
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan
| | - Samiullah Khan
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan
| | - Muhammad Adnan Shan
- Centre for Applied Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Alia Batool
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Muhammad Arshad Javed
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Qurban Ali
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan.
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Bale A, Rambo R, Prior C. The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement. PLoS Comput Biol 2023; 19:e1011248. [PMID: 38011290 PMCID: PMC10703313 DOI: 10.1371/journal.pcbi.1011248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 12/07/2023] [Accepted: 11/06/2023] [Indexed: 11/29/2023] Open
Abstract
We present fast and simple-to-implement measures of the entanglement of protein tertiary structures which are appropriate for highly flexible structure comparison. These are performed using the SKMT algorithm, a novel method of smoothing the Cα backbone to achieve a minimal complexity curve representation of the manner in which the protein's secondary structure elements fold to form its tertiary structure. Its subsequent complexity is characterised using measures based on the writhe and crossing number quantities heavily utilised in DNA topology studies, and which have shown promising results when applied to proteins recently. The SKMT smoothing is used to derive empirical bounds on a protein's entanglement relative to its number of secondary structure elements. We show that large scale helical geometries dominantly account for the maximum growth in entanglement of protein monomers, and further that this large scale helical geometry is present in a large array of proteins, consistent across a number of different protein structure types and sequences. We also show how these bounds can be used to constrain the search space of protein structure prediction from small angle x-ray scattering experiments, a method highly suited to determining the likely structure of proteins in solution where crystal structure or machine learning based predictions often fail to match experimental data. Finally we develop a structural comparison metric based on the SKMT smoothing which is used in one specific case to demonstrate significant structural similarity between Rossmann fold and TIM Barrel proteins, a link which is potentially significant as attempts to engineer the latter have in the past produced the former. We provide the SWRITHE interactive python notebook to calculate these metrics.
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Affiliation(s)
- Arron Bale
- Department of Mathematical Sciences, Durham University, Durham, United Kingdom
| | - Robert Rambo
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, United Kingdom
| | - Christopher Prior
- Department of Mathematical Sciences, Durham University, Durham, United Kingdom
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Funari R, Bhalla N, Gentile L. Measuring the Radius of Gyration and Intrinsic Flexibility of Viral Proteins in Buffer Solution Using Small-Angle X-ray Scattering. ACS MEASUREMENT SCIENCE AU 2022; 2:547-552. [PMID: 36573077 PMCID: PMC9783065 DOI: 10.1021/acsmeasuresciau.2c00048] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/31/2022] [Accepted: 08/31/2022] [Indexed: 06/17/2023]
Abstract
Measuring structural features of proteins dispersed in buffer solution, in contrast to crystal form, is indispensable in understanding morphological characteristics of the biomolecule in a native environment. We report on the structure and apparent viscosity of unfolded α and β variants of SARS-CoV-2 spike proteins dispersed in buffer solutions. The radius of gyration of the β variant is found to be larger than that of the α variant, while the ab initio computation of one of the possible particle-like bodies is consistent with the small-angle X-ray scattering (SAXS) profiles resembling a conformation similar to the three-dimensional structure of the folded state of the corresponding α and β spike variant. However, a smaller radius of gyration with respect to the predicted folded state of 2.4 and 2.7 is observed for both α and β variants, respectively. Our work complements the structural characterization of spike proteins using cryo-electron microscopy techniques. The measurement/analysis discussed here might be useful for quick and cost-effective evaluation of several protein structures, let alone mutated viral proteins, which is useful for drug discovery/development applications.
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Affiliation(s)
- Riccardo Funari
- Department
of Physics “M. Merlin”, University
of Bari Aldo Moro, Via Amendola, 173, Bari 70125, Italy
- Institute
for Photonics and Nanotechnologies, CNR, Via Amendola, 173, Bari, 70125, Italy
| | - Nikhil Bhalla
- Nanotechnology
and Integrated Bioengineering Centre (NIBEC), School of Engineering, Ulster University, Jordanstown, Shore Road, Northern Ireland BT37 0QB, United Kingdom
- Healthcare
Technology Hub, Ulster University, Jordanstown Shore Road, Northern Ireland BT37 0QB, United Kingdom
| | - Luigi Gentile
- Department
of Chemistry, University of Bari Aldo Moro, Edoardo Orabona 4, Bari 70125, Italy
- Bari
unit,
Center for Colloid and Surface Science (CSGI), via della Lastruccia 3, Sesto Fiorentino 50019, Italy
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Zhang C, Wu J, Chen Q, Tan H, Huang F, Guo J, Zhang X, Yu H, Shi W. Allosteric binding on nuclear receptors: Insights on screening of non-competitive endocrine-disrupting chemicals. ENVIRONMENT INTERNATIONAL 2022; 159:107009. [PMID: 34883459 DOI: 10.1016/j.envint.2021.107009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/23/2021] [Accepted: 11/25/2021] [Indexed: 06/13/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) can compete with endogenous hormones and bind to the orthosteric site of nuclear receptors (NRs), affecting normal endocrine system function and causing severe symptoms. Recently, a series of pharmaceuticals and personal care products (PPCPs) have been discovered to bind to the allosteric sites of NRs and induce similar effects. However, it remains unclear how diverse EDCs work in this new way. Therefore, we have systematically summarized the allosteric sites and underlying mechanisms based on existing studies, mainly regarding drugs belonging to the PPCP class. Advanced methods, classified as structural biology, biochemistry and computational simulation, together with their advantages and hurdles for allosteric site recognition and mechanism insight have also been described. Furthermore, we have highlighted two available strategies for virtual screening of numerous EDCs, relying on the structural features of allosteric sites and lead compounds, respectively. We aim to provide reliable theoretical and technical support for a broader view of various allosteric interactions between EDCs and NRs, and to drive high-throughput and accurate screening of potential EDCs with non-competitive effects.
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Affiliation(s)
- Chi Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Jinqiu Wu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Qinchang Chen
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Haoyue Tan
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Fuyan Huang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Jing Guo
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Hongxia Yu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Wei Shi
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China.
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Pesce F, Lindorff-Larsen K. Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data. Biophys J 2021; 120:5124-5135. [PMID: 34627764 PMCID: PMC8633713 DOI: 10.1016/j.bpj.2021.10.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/09/2021] [Accepted: 10/04/2021] [Indexed: 01/30/2023] Open
Abstract
Intrinsically disordered proteins and flexible regions in multidomain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modeling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model (i.e., an algorithm to calculate experimental observables from individual conformations or ensembles). In many cases, this involves one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle x-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer and displaced solvent to the background-subtracted experimental data. Often, one also needs to fit a scale factor and a constant background for the SAXS data but across the entire ensemble. Here, we present a protocol to dissect the effect of the free parameters on the calculated SAXS intensities and to identify a reliable set of values. We have implemented this procedure in our Bayesian/maximum entropy framework for ensemble refinement and demonstrate the results on four intrinsically disordered proteins and a protein with three domains connected by flexible linkers. Our results show that the resulting ensembles can depend on the parameters used for solvent effects and suggest that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.
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Affiliation(s)
- Francesco Pesce
- Structural Biology and NMR Laboratory, The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory, The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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le Maire A, Germain P, Bourguet W. Protein-protein interactions in the regulation of RAR–RXR heterodimers transcriptional activity. Methods Enzymol 2020; 637:175-207. [DOI: 10.1016/bs.mie.2020.02.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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