Density Functional Theory and Molecular Simulation Studies for Prioritizing Anaplastic Lymphoma Kinase Inhibitors

A multitier virtual screening of antagonists targeting PD-1/PD-L1 interface for the management of triple-negative breast cancer

Immunotherapies are promising therapeutic options for the management of triple-negative breast cancer because of its high mutation rate and genomic instability. Of note, the blockade of the immune checkpoint protein PD-1 and its ligand PD-L1 has been proven to be an efficient and potent strategy to combat triple-negative breast cancer. To date, various anti-PD-1/anti-PD-L1 antibodies have been approved. However, the intrinsic constraints of these therapeutic antibodies significantly limit their application, making small molecules a potentially significant option for PD-1/PD-L1 inhibition. In light of this, the current study aims to use a high-throughput virtual screening technique to identify potential repurposed candidates as PD-L1 inhibitors. Thus, the present study explored binding efficiency of 2509 FDA-approved compounds retrieved from the drug bank database against PD-L1 protein. The binding affinity of the compounds was determined using the glide XP docking programme. Furthermore, prime-MM/GBSA, DFT calculations, and RF score were used to precisely re-score the binding free energy of the docked complexes. In addition, the ADME and toxicity profiles for the lead compounds were also examined to address PK/PD characteristics. Altogether, the screening process identified three molecules, namely DB01238, DB06016 and DB01167 as potential therapeutics for the PD-L1 protein. To conclude, a molecular dynamic simulation of 100 ns was run to characterise the stability and inhibitory action of the three lead compounds. The results from the simulation study confirm the robust structural and thermodynamic stability of DB01238 than other investigated molecules. Thus, our findings hypothesize that DB01238 could serve as potential PD-L1 inhibitor in the near future for triple-negative breast cancer patients.

Identification of novel inhibitors for Prp protein of Mycobacterium tuberculosis by structure based drug design, and molecular dynamics simulations

In this study, we assess the effective inhibition of a series of thiazolidine derivatives (1a-1q) were adopting structure-based drug design. Thiazolidine is a five-membered ring structure with thioether and amino groups at positions 1 and 3. Although, thiazolidine may bind to a wide range of protein targets, it is a major heterocyclic core in medicinal chemistry. Different scoring utilities including AutoDock Vina, Glide, and MM/GBSA analysis were performed to commensurate the improvement of screening progress. The evaluated binding affinities were validated by molecular dynamics simulations over a period of 20 ns for the interactions between the Mycobacterium tuberculosis protein PrpR with three novel scaffolds (1b, 1j, and 1k). All the scaffolds exhibited distinct stable interactions with the significant residues like Arg169, Arg197, Tyr248, Arg308, and Gly311 respectively. Further, the inhibitory activities of scaffolds were predicted and evaluated by machine learning based algorithm to rank the above proposed compounds. This study reveals the potential of 1k and 1j as effective inhibitor candidates for the treatment of tuberculosis.

Exploration of potential inhibitors for tuberculosis via structure-based drug design, molecular docking, and molecular dynamics simulation studies

Drug resistance in tuberculosis is major threat to human population. In the present investigation, we aimed to identify novel and potent benzimidazole molecules to overcome the resistance management. A series of 20 benzimidazole derivatives were examined for its activity as selective antitubercular agents. Initially, AutodockVina algorithm was performed to assess the efficacy of the molecules. The results are further enriched by redocking by means of Glide algorithm. The binding free energies of the compounds were then calculated by MM-generalized-born surface area method. Molecular docking studies elucidated that benzimidazole derivatives has revealed formation of hydrogen bond and strong binding affinity in the active site of Mycobacterium tuberculosis protein. Note that ARG308, GLY189, VAL312, LEU403, and LEU190 amino acid residues of Mycobacterium tuberculosis protein PrpR are involved in binding with ligands of benzimidazoles. Interestingly, the ligands exhibited same binding potential to the active site of protein complex PrpR in both the docking programs. In essence, the result portrays that benzimidazole derivatives such as 1p, 1q, and 1 t could be potent and selective antitubercular agents than the standard drug isoniazid. These compounds were then subjected to molecular dynamics simulation to validate the dynamics activity of the compounds against PrpR. Finally, the inhibitory behavior of compounds was predicted using a machine learning algorithm trained on a data collection of 15,000 compounds utilizing graph-based signatures. Overall, the study concludes that designed benzimidazoles can be employed as antitubercular agents. Indeed, the results are helpful for the experimental biologists to develop safe and non-toxic drugs against tuberculosis.

Newly designed compounds from scaffolds of known actives as inhibitors of survivin: computational analysis from the perspective of fragment-based drug design

Survivin is an apoptosis suppressing protein linked to different forms of cancer. As it stands, there are no approved drugs for the inhibition of survivin in cancer cells despite a number of promising compounds in clinical trials. This study designed a new set of compounds from fragments of active survivin inhibitors to potentiate their binding with survivin at BIR domain. Three hundred and five (305) fragments made from eight potent inhibitors of survivin were reconstructed to form a new set of compounds. The compounds were optimized using R group enumeration and bioisostere replacement after extensive docking analysis. The optimised compounds were filtered by a validated pharmacophore model to reveal how well they are aligned to the pharmacophore sites. Molecular docking of the well aligned compounds revealed the top-scoring compounds; and these compounds were compared with the eight inhibitors used as template for fragment-based design on the basis of binding affinity (rigid and flexible docking), predicted pIC50 and intermolecular interactions. The electronic behaviours (global descriptors, HOMO/LUMO, molecular electrostatic potential and Fukui functions) of newly designed compounds were calculated to investigate their reactivity and atomic sites prone to neutrophilic/electrophilic attack. The nine newly designed compounds had better rigid and flexible docking scores, free energy of binding and intermolecular interactions with survivin at BIR domain than the eight active inhibitors. Based on frontier molecular orbitals, OPE-3 was found to be the most reactive and less stable compound (0.13194 eV), followed by OPE-4 and OPE-9. The global descriptive parameters showed that OPE-3 had highest softness value (7.5245 eV) while OPE-8 recorded the maximum hardness value (0.08486 eV). The well-validated QSAR model also showed that OPE-3, OPE-7 and OPE-8 had the most significant bioactivity of all the inhibitors. This study thus provides new insight into the design of compounds capable of modulating the activity of survivin.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00108-8.

Multiscale Simulation of Adsorption Based Microcantilever Biosensors for Radiation Exposure Effects

Background

This article is focused on biological measurements based on molecular interactions. The specific biomarker implemented for radiation biosensor is FLT3, which bears changes in the body regarding radiation exposure. Experimental results of sensing vancomycin verify the overall results of two steps of numerical methods for different scales.

Objectives

The aim is to provide adequate modeling procedures to predict sensory data. Multiscale modeling is implemented to simulate molecular interaction and its consequent micro mechanical effects. The method is implemented to calculate surface traction of microcantilever biosensor.

Materials and Methods

The method consists of molecular dynamics simulation of adsorption process by implementing classical mechanics theory to calculate the final response of the sensor as tip deflection. The sequential information transaction is assumed between the physical parameters of two governing scales. The numerical method consists of the location of particles providing for a nano-metric periodic boundary conditioned functionalized surface implemented, and the numerical thermodynamic formula is, in turn, use energy parameters to acquire macro-mechanical deflection of a specific microcantilever. Also, novel sensitivity analysis of the results as the adsorption process moves toward more saturated substrate provided.

Results

Verification of the simulation method for Vancomycin sensing results enjoys less than 20 percent of deviation regarding the experimental data. The standard deviation of 0.054 in the final expected response of the sensor is calculated as the accuracy of the radiation biosensor based on FLT3.

Conclusions

The method is still to reach a correlation between the concentration of target molecules in solution and the number of adsorbed molecules per area of the sensor. A scaled correlation between sensor's response and the amount of biomarker is found using tip deflection of a sample designed microcantilever. Around one micrometer deflection that can be read out using various conventional methods was observed at saturation of adsorption surface. The analyses provide adequate data to design a sensor capable of measuring the effect of cosmic radiation to the human body.

Structural dynamic studies on identification of EGCG analogues for the inhibition of Human Papillomavirus E7

High risk human papillomaviruses are highly associated with the cervical carcinoma and the other genital tumors. Development of cervical cancer passes through the multistep process initiated from benign cyst to increasingly severe premalignant dysplastic lesions in an epithelium. Replication of this virus occurs in the fatal differentiating epithelium and involves in the activation of cellular DNA replication proteins. The oncoprotein E7 of human papillomavirus expressed in the lower epithelial layers constrains the cells into S-phase constructing an environment favorable for genome replication and cell proliferation. To date, no suitable drug molecules exist to treat HPV infection whereas anticipation of novel anti-HPV chemotherapies with distinctive mode of actions and identification of potential drugs are crucial to a greater extent. Hence, our present study focused on identification of compounds analogue to EGCG, a green tea molecule which is considered to be safe to use for mammalian systems towards treatment of cancer. A three dimensional similarity search on the small molecule library from natural product database using EGCG identified 11 potential small molecules based on their structural similarity. The docking strategies were implemented with acquired small molecules and identification of the key interactions between protein and compounds were carried out through binding free energy calculations. The conformational changes between the apoprotein and complexes were analyzed through simulation performed thrice demonstrating the dynamical and structural effects of the protein induced by the compounds signifying the domination. The analysis of the conformational stability provoked us to describe the features of the best identified small molecules through electronic structure calculations. Overall, our study provides the basis for structural insights of the identified potential identified small molecules and EGCG. Hence, the identified analogue of EGCG can be potent inhibitors against the HPV 16 E7 oncoprotein.

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

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.