A mouse testis serine protease, TESP1, as the potential SPINK3 receptor protein on mouse sperm acrosome

2021 ◽  
Vol 27 (10) ◽  
Author(s):  
Shiyam Sundar Ramachandran ◽  
Rubhadevi Balu ◽  
Ravikumar Vilwanathan ◽  
Jeyakanthan Jeyaraman ◽  
Sudhakar Gandhi Paramasivam
Keyword(s):  
Serine Protease ◽  
Binding Affinity ◽  
Trypsin Inhibitor ◽  
Homology Modelling ◽  
Protein Structures ◽  
Protein Docking ◽  
Female Reproductive Tract ◽  
Dynamics Simulation ◽  
Receptor Protein ◽  
Sperm Acrosome

Abstract Serine protease inhibitor Kazal type 3 (SPINK3) from mouse seminal vesicles is a Kazal-type trypsin inhibitor. It has been shown to bind to the sperm acrosome and modify sperm activity by influencing the sub-cellular Ca2+ influx. Previously, SPINK3 was reported to suppress in vitro sperm capacitation. However, under natural coitus, SPINK3 is removed from the mouse acrosome in the female reproductive tract, leading to successful fertilisation. Identification of the SPINK3 binding partner becomes essential to develop a contraceptive that works by prolonging the binding of SPINK3 to the sperm acrosome. We identified the SPINK3 receptor by using recombinant SPINK3 (rSPINK3). Testicular serine protease 1 (TESP1) was identified as the receptor for SPINK3 by 2D gel electrophoresis coupled with western blot analysis. To authenticate TESP1 as the receptor for SPINK3, sperm cells were incubated with TESP1 peptide antibody followed by determining the intracellular [Ca2+]i concentration by flow cytometry using Fluo-3 AM as a calcium probe. Furthermore, the 3D structures of SPINK3 and TESP1 were predicted by homology modelling (Schrodinger suite) using the crystal structure of pancreatic secretory trypsin inhibitor (PDB ID—1TGS) and human prostasin (PDB ID—3DFJ) as templates. The modelled protein structures were validated and subjected to molecular dynamics simulation (MDS) using GROMACS v5.0.5. Protein–protein docking was performed using HDOCK and the complex was validated by MDS. The results predicted that SPINK3 and TESP1 had strong binding affinity, with a dock score of −430.70 and 14 hydrogen bonds as key active site residues. If the binding affinity between SPINK3 and TESP1 could be increased, the SPINK3-TESP1 association will be prolonged, which will be helpful in the development of a male contraceptive.

scholarly journals In silico comparison of SARS-CoV-2 spike protein-ACE2 binding affinities across species and implications for virus origin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakshi Piplani ◽  
Puneet Kumar Singh ◽  
David A. Winkler ◽  
Nikolai Petrovsky
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Sequence Similarity ◽  
Homology Modelling ◽  
Protein Docking ◽  
Dynamics Simulation ◽  
Spike Protein ◽  
High Sequence Similarity ◽  
Virus Origin ◽  
Species Specific

AbstractThe devastating impact of the COVID-19 pandemic caused by SARS–coronavirus 2 (SARS-CoV-2) has raised important questions about its origins and the mechanism of its transfer to humans. A further question was whether companion or commercial animals could act as SARS-CoV-2 vectors, with early data suggesting susceptibility is species specific. To better understand SARS-CoV-2 species susceptibility, we undertook an in silico structural homology modelling, protein–protein docking, and molecular dynamics simulation study of SARS-CoV-2 spike protein’s ability to bind angiotensin converting enzyme 2 (ACE2) from relevant species. Spike protein exhibited the highest binding to human (h)ACE2 of all the species tested, forming the highest number of hydrogen bonds with hACE2. Interestingly, pangolin ACE2 showed the next highest binding affinity despite having a relatively low sequence homology, whereas the affinity of monkey ACE2 was much lower despite its high sequence similarity to hACE2. These differences highlight the power of a structural versus a sequence-based approach to cross-species analyses. ACE2 species in the upper half of the predicted affinity range (monkey, hamster, dog, ferret, cat) have been shown to be permissive to SARS-CoV-2 infection, supporting a correlation between binding affinity and infection susceptibility. These findings show that the earliest known SARS-CoV-2 isolates were surprisingly well adapted to bind strongly to human ACE2, helping explain its efficient human to human respiratory transmission. This study highlights how in silico structural modelling methods can be used to rapidly generate information on novel viruses to help predict their behaviour and aid in countermeasure development.

scholarly journals Repurposing the McoTI-II Rigid Molecular Scaffold in to Inhibitor of ‘Papain Superfamily’ Cysteine Proteases

2020 ◽  
Vol 14 (1) ◽  
pp. 7
Author(s):  
Manasi Mishra ◽  
Vigyasa Singh ◽  
Meenakshi B. Tellis ◽  
Rakesh S. Joshi ◽  
Shailja Singh
Keyword(s):  
Cyclic Peptide ◽  
Homology Modelling ◽  
Cathepsin L ◽  
Cysteine Proteases ◽  
Protein Docking ◽  
Dynamics Simulation ◽  
Cysteine Protease Inhibitor ◽  
Full Potential ◽  
Molecular Scaffold ◽  
Mechanism Of Inhibition

Clan C1A or ‘papain superfamily’ cysteine proteases are key players in many important physiological processes and diseases in most living systems. Novel approaches towards the development of their inhibitors can open new avenues in translational medicine. Here, we report a novel design of a re-engineered chimera inhibitor Mco-cysteine protease inhibitor (CPI) to inhibit the activity of C1A cysteine proteases. This was accomplished by grafting the cystatin first hairpin loop conserved motif (QVVAG) onto loop 1 of the ultrastable cyclic peptide scaffold McoTI-II. The recombinantly expressed Mco-CPI protein was able to bind with micromolar affinity to papain and showed remarkable thermostability owing to the formation of multi-disulphide bonds. Using an in silico approach based on homology modelling, protein–protein docking, the calculation of the free-energy of binding, the mechanism of inhibition of Mco-CPI against representative C1A cysteine proteases (papain and cathepsin L) was validated. Furthermore, molecular dynamics simulation of the Mco-CPI–papain complex validated the interaction as stable. To conclude, in this McoTI-II analogue, the specificity had been successfully redirected towards C1A cysteine proteases while retaining the moderate affinity. The outcomes of this study pave the way for further modifications of the Mco-CPI design for realizing its full potential in therapeutics. This study also demonstrates the relevance of ultrastable peptide-based scaffolds for the development of novel inhibitors via grafting.

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

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 (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those 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 an 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 conformational space more broadly. Based on the insight 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. <br>

Structure prediction of SPAK C-terminal domain and analysis of its binding to RFXV/I motifs by homology modelling, docking, and molecular dynamics simulation studies

2020 ◽  
Vol 16 ◽  
Author(s):  
Mubarak A. Alamri ◽  
Ahmed D. Alafnan ◽  
Obaid Afzal ◽  
Alhumaidi B. Alabbas ◽  
Safar M. Alqahtani
Keyword(s):  
Molecular Dynamic ◽  
Dynamic Stability ◽  
Md Simulation ◽  
Homology Modelling ◽  
Antihypertensive Agents ◽  
Structure And Function ◽  
Dynamics Simulation ◽  
Dimensional Structure ◽  
Terminal Domain ◽  
And Function

Background: The STE20/SPS1-related proline/alanine-rich kinase (SPAK) is a component of WNKSPAK/OSR1 signaling pathway that plays an essential role in blood pressure regulation. The function of SPAK is mediated by its highly conserved C-terminal domain (CTD) that interacts with RFXV/I motifs of upstream activators, WNK kinases, and downstream substrate, cation-chloride cotransporters. Objective: To determine and validate the three-dimensional structure of the CTD of SPAK and to study and analyze its interaction with the RFXV/I motifs. Methods: A homology model of SPAK CTD was generated and validated through multiple approaches. The model was based on utilizing the OSR1 protein kinase as a template. This model was subjected to 100 ns molecular dynamic (MD) simulation to evaluate its dynamic stability. The final equilibrated model was used to dock the RFQV-peptide derived from WNK4 into the primary pocket that was determined based on the homology sequence between human SPAK and OSR1 CTDs. The mechanism of interaction, conformational rearrangement and dynamic stability of the binding of RFQV-peptide to SPAK CTD were characterized by molecular docking and molecular dynamic simulation. Results: The MD simulation suggested that the binding of RFQV induces a large conformational change due to the distribution of salt bridge within the loop regions. These results may help in understanding the relation between the structure and function of SPAK CTD and to support drug design of potential SPAK kinase inhibitors as antihypertensive agents. Conclusion: This study provides deep insight into SPAK CTD structure and function relationship.

Proteomic Analysis of Medicinal Plant Calotropis Gigantea by insilico Peptide Mass Fingerprinting

2020 ◽  
Vol 16 ◽  
Author(s):  
Saad Ur Rehman ◽  
Muhammad Rizwan ◽  
Sajid Khan ◽  
Azhar Mehmood ◽  
Anum Munir
Keyword(s):  
Medicinal Plant ◽  
Structure Prediction ◽  
3D Structure ◽  
Peptide Mass Fingerprinting ◽  
Protein Docking ◽  
Receptor Protein ◽  
Human Leukemia ◽  
Protein Database ◽  
Calotropis Gigantea ◽  
Peptide Mass

: Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. Background: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in a database and identified. Method: The calculation of insilico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from MASCOT online server. Anticancer probability is calculated from the iACP server, docking of active peptides is done by CABS-dock the server. Objective: The purpose of the study is to identify the peptides having anti-cancerous properties by in-silico peptide mass fingerprinting. Results : The anti-cancerous peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the Active chain, the human leukemia protein docking with peptides shows the accurate binding. Conclusion : These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

scholarly journals Molecular Docking and Molecular Dynamics Simulation Studies of Triterpenes from Vernonia patula with the Cannabinoid Type 1 Receptor

2021 ◽  
Vol 22 (7) ◽  
pp. 3595
Author(s):  
Md Afjalus Afjalus Siraj ◽  
Md. Sajjadur Rahman ◽  
Ghee T. Tan ◽  
Veronique Seidel
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Binding Affinity ◽  
Docking Studies ◽  
Dynamics Simulation ◽  
Simulation Studies ◽  
Cannabinoid Type 1 Receptor ◽  
Docking Approach

A molecular docking approach was employed to evaluate the binding affinity of six triterpenes, namely epifriedelanol, friedelin, α-amyrin, α-amyrin acetate, β-amyrin acetate, and bauerenyl acetate, towards the cannabinoid type 1 receptor (CB1). Molecular docking studies showed that friedelin, α-amyrin, and epifriedelanol had the strongest binding affinity towards CB1. Molecular dynamics simulation studies revealed that friedelin and α-amyrin engaged in stable non-bonding interactions by binding to a pocket close to the active site on the surface of the CB1 target protein. The studied triterpenes showed a good capacity to penetrate the blood–brain barrier. These results help to provide some evidence to justify, at least in part, the previously reported antinociceptive and sedative properties of Vernonia patula.

scholarly journals Design of peptides with strong binding affinity to poly(methyl methacrylate) resin by use of molecular simulation-based materials informatics

2021 ◽  
Author(s):  
Tomio Iwasaki ◽  
Masashi Maruyama ◽  
Tatsuya Niwa ◽  
Toshiki Sawada ◽  
Takeshi Serizawa
Keyword(s):  
Methyl Methacrylate ◽  
Binding Affinity ◽  
Molecular Simulation ◽  
Dynamics Simulation ◽  
Binding Affinities ◽  
Materials Informatics ◽  
Poly Methyl Methacrylate ◽  
Strong Binding ◽  
Experimental Values ◽  
Pmma Resin

AbstractPeptides with strong binding affinities for poly(methyl methacrylate) (PMMA) resin were designed by use of materials informatics technology based on molecular dynamics simulation for the purpose of covering the resin surface with adhesive peptides, which were expected to result in eco-friendly and biocompatible biomaterials. From the results of binding affinity obtained with this molecular simulation, it was confirmed that experimental values could be predicted with errors <10%. By analyzing the simulation data with the response-surface method, we found that three peptides (RWWRPWW, EWWRPWR, and RWWRPWR), which consist of arginine (R), tryptophan (W), and proline (P), have strong binding affinity to the PMMA resin. These amino acids were effective because arginine and tryptophan have strong binding affinities for methoxycarbonyl groups and methyl groups, which are the main constituents of the PMMA resin, and proline stabilizes the flat zigzag structures of the peptides in water. The strong binding affinities of the three peptides were confirmed by experiments (surface plasmon resonance methods).

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