Calculating the force-dependent unbinding rate of biological macromolecular bonds from force-ramp optical trapping assays

The non-covalent biological bonds that constitute protein–protein or protein–ligand interactions play crucial roles in many cellular functions, including mitosis, motility, and cell–cell adhesion. The effect of external force ($$F$$ F ) on the unbinding rate ($${k}_{\text{off}}\left(F\right)$$ k off F ) of macromolecular interactions is a crucial parameter to understanding the mechanisms behind these functions. Optical tweezer-based single-molecule force spectroscopy is frequently used to obtain quantitative force-dependent dissociation data on slip, catch, and ideal bonds. However, analyses of this data using dissociation time or dissociation force histograms often quantitatively compare bonds without fully characterizing their underlying biophysical properties. Additionally, the results of histogram-based analyses can depend on the rate at which force was applied during the experiment and the experiment’s sensitivity. Here, we present an analytically derived cumulative distribution function-like approach to analyzing force-dependent dissociation force spectroscopy data. We demonstrate the benefits and limitations of the technique using stochastic simulations of various bond types. We show that it can be used to obtain the detachment rate and force sensitivity of biological macromolecular bonds from force spectroscopy experiments by explicitly accounting for loading rate and noisy data. We also discuss the implications of our results on using optical tweezers to collect force-dependent dissociation data.

Virtual Docking of VBP1 with HBX and NFκB Protein to Study the Activation of NFκB in the Regulatory Mechanism of Liver Cancer

Molecular docking is an efficient way to study protein-protein and protein-ligand interactions in virtual mode, this provides structural annotations of molecular interactions, required in the drug discovery process. The Cartesian FFT approach in ‘Hex’ spherical polar Fourier (SPF) uses rotational correlations, this method is used here to study protein-protein interactions. Hepatitis B virus (HBV) X protein (HBx) is essential for virus infection and has been used in the development of therapeutics for liver cancer. It can interact with many cellular proteins. It interferes with cell viability and stimulates HBV replication. The von Hippel-Lindau binding protein 1(VBP1) has an important role in HBx-mediated nuclear factor kappa B (NFkB) stimulation. VBP1 and HBx function as coactivators in the activation of NFκB binding. Docking results revealed that HBx and NFkB bind with VBP1 at the common site on amino acids positions Arg 161, Glu 92, and Arg 82, which may have a role in HBx-mediated NFκB activation. Lowest energy complex VBP1- NFkB1 was obtained at -883.70 Kcal/mol. The amino acids involved in interaction among HBx, VBP1, and NFκB proteins, may be involved in transcriptional regulation and has significance in normal and abnormal regulation. These amino acid interactions may be associated with the manifestation of Liver cancer.

Mechanistic Validation of an Ergogenic T. arjuna Standardized Extract (Oxyjun®) Using a Molecular Docking Approach

The bark of the tree Terminalia arjuna commonly referred as Arjuna is widely used in Ayurveda as a therapeutic agent for heart disease. More recently, a proprietary botanical extract of T. arjuna with tradename, Oxyjun®, demonstrated cardiotonic and ergogenic benefits for the first time in a younger and healthier population. However, the mechanism of action and biological actives of this novel sports ingredient were not clear. A molecular docking approach was adopted to understand the protein-ligand interactions and establish the most probable mechanism(s) of cardio vascular actions of the phytoconstituents of the T. arjuna standardized extract (TASE). Twenty-one phytochemicals (ligands) were chosen from Arjuna and their binding affinities against eight proteins serving cardiovascular functions (target proteins) were investigated. Autodock Vina was used to carry out the molecular docking studies. Potential efficacy in humans was assessed on the basis of ADMET properties and Lipinski’s Rule of 5. We found that arjunic acid, arjungenin, arjunetin, arjunglucoside1, chrysin, kaempferol, luteolin, rhamnetin and taxifolin demonstrated good docking scores and bioactivity.

Identification of Novel Potential Anti-Diabetic Candidates targeting Human Pancreatic ɑ-Amylase and Human ɑ-Glycosidase: An Exhaustive Structure-based Screening

Diabetes is a major health issue that half a billion people affected worldwide. It is a serious, long-term medical condition majorly impacting the lives and well-being of individuals, families, and societies at large. It is amongst the top 10 diseases responsible for the death amongst adults with an expected rise to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045. The carbohydrates absorbed into the body are hydrolyzed by pancreatic α-amylase and other enzymes, human α-glucosidase. The α-amylase and α-glucosidase are validated therapeutic targets in the treatment of Type II diabetes (T2DM) as they play a vital role in modulating the blood glucose post meal. Herein, we report novel and diverse molecules as potential candidates, with predicted affinity for α-amylase and α-glucosidase. These molecules have been identified via hierarchical multistep docking of small molecules database with the estimated binding free energies. A Glide XP Score cutoff −8.00 kcal/mol was implemented to filter out non potential molecules. Four molecules viz. amb22034702, amb18105639, amb17153304, and amb9760832 have been identified after an exhaustive computational study involving, evaluation of binding interactions and assessment of the pharmacokinetics and toxicity profiles. The in-depth analysis of protein– ligand interactions was performed using a 100ns molecular dynamics (MD) simulation to establish the dynamic stability. Furthermore MM-GBSA based binding free energies were computed for 1000 trajectory snapshots to ascertain the strong binding affinity of these molecules for α-amylase and αglucosidase. The identified molecules can be considered as promising candidates for further drug development through necessary experimental assessments.

Inverse PI by NMR: Analysis of Ligand 1H-Chemical Shifts in the Protein-Bound State

The study of protein-ligand interactions via protein-based NMR generally relies on the detection of chemical-shift changes induced by ligand binding. However, the chemical shift of the ligand when bound to the protein is rarely discussed, since it is not readily detectable. In this work we use protein deuteration in combination with [1H-1H]-NOESY NMR to extract 1H chemical shift values of the ligand in the bound state. The chemical shift perturbations (CSPs) experienced by the proton ligand resonances (free vs bound) are an extremely rich source of information on protein-ligand complexes. Besides allowing for the detection of intermolecular CH-π interactions and elucidation of the protein-bound ligand conformation, the CSP information can be used to analyse (de)solvation effects in a site-specific manner. In conjunction with crystal structure information, it is possible to distinguish protons whose desolvation penalty is compensated for upon protein-binding, from those that are not. Combined with the previously reported PI by NMR technique for the protein-based detection of intermolecular CH-π interactions, this method represents another important step towards the ultimate goal of Interaction-Based Drug Discovery.

Processing binding data using an open-source workflow

The thermal shift assay (TSA)—also known as differential scanning fluorimetry (DSF), thermofluor, and Tm shift—is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (FBLD) to detect protein–ligand interactions. By comparing the thermal stability of a target protein in the presence and absence of a ligand, potential binders can be identified. The technique is easy to set up, has low protein consumption, and can be run on most real-time polymerase chain reaction (PCR) instruments. While data analysis is straightforward in principle, it becomes cumbersome and time-consuming when the screens involve multiple 96- or 384-well plates. There are several approaches that aim to streamline this process, but most involve proprietary software, programming knowledge, or are designed for specific instrument output files. We therefore developed an analysis workflow implemented in the Konstanz Information Miner (KNIME), a free and open-source data analytics platform, which greatly streamlined our data processing timeline for 384-well plates. The implementation is code-free and freely available to the community for improvement and customization to accommodate a wide range of instrument input files and workflows. Graphical Abstract

Molecular Dynamics Simulation of Bioactive Compounds Against Six Protein Target of Sars-Cov-2 As Covid-19 Antivirus Candidates

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the virus that causes Coronavirus 2019 (COVID-19). To date, there has been no proven effective drug for the treatment or prevention of COVID-19. A study on developing inhibitors for this virus was performed using molecular dynamics simulation. 3CL-Pro, PL-Pro, Helicase, N, E, and M protein were used as protein targets. This study aimed to determine the stability of the selected protein-ligand complex through molecular dynamics simulation by Amber20 to propose bioactive compounds from natural products that have potential as a drug for COVID-19. Based on our previous study, the best value of free binding energy and protein-ligand interactions of the candidate compounds are obtained for each target protein through molecular docking. Corilagin (-14.42 kcal/mol), Scutellarein 7-rutinoside (-13.2 kcal/mol), Genistein 7-O-glucuronide (-10.52 kcal/mol), Biflavonoid-flavone base + 3O (-11.88 and -9.61 kcal/mol), and Enoxolone (-6.96 kcal/mol) has the best free energy value at each protein target. In molecular dynamics simulation, the 3CL-Pro-Corilagin complex was the most stable compared to other complexes, so that it was the most recommended compound. Further research is needed to test the selected ligand activity, which has the lowest free energy value of the six target proteins.