Combined protein and ligand based physicochemical aspects of molecular recognition for the discovery of CDK9 inhibitor

Screening, molecular simulation & in silico kinetics of virtually designed covid-19 main protease inhibitors

Coronavirus (covid-19) infection is considered to be deadliest ever pandemic experienced by the human being. It has very badly affected the socio-economic health of human and stuck the scientific community to think and rethink about its complete eradication. But due to no effective treatment or unavailability of vaccine the health professional could not show any significant improvement to control the pandemic. The situation needs newer molecule, vaccine or effective treatment to control covid-19 infection. Different target in viruses has been explored and proteases enzymes were found to be therapeutically effective target for the design of potential anti-covid-19 molecule as it plays the vital role in viral replication and assembly. Structure-based drug design was employed to discover the small molecule of anti-covid-19. Here we considered the small library of naturally occurring polyphenolic compounds and molecular docking, Molecular dynamics (MD) simulations, free binding energy calculation and in-silico ADME calculations to identify the newer HITs. Based upon their score the two molecules were identified as promising candidate. The docking scores were found to be −7.643 and −7.065 for the HIT1 and HIT-2 respectively. In MD simulations study the RMSD values were found to be 4.3 Å & 4.9 Å respectively. To validate these results MM-GBSA was performed and their binding free energies were computationally determined. The prime energy values of identified HITs (−13412.45 & −13441.8 kJ/mole) were found to be very close proximity to reference molecule (−13493.05 kJ/mole). Then in-silico ADME calculations were performed to calculate the drug likeliness identified HITs. BY considering all the values comparative to reference molecule and obtained in-silico pharmacokinetic properties of identified HITs we can suggest that HIT-1 and HIT-2 would be the most promising molecules that can inhibit the main protease enzyme of covid-19. These two molecules would become the potential drug candidate for the treatment of covid-19 infections.

An in-silico investigation of potential natural polyphenols for the targeting of COVID main protease inhibitor

The deadliest recent pandemic outbreak of COVID-19 disease has severely damaged the socio-economic health of the people globally. Due to unavailability of any effective vaccine or treatment the human beings are still struggling to overcome the pandemic condition. In an attempt to discover anti-COVID molecule, we used in-silico approach and reported 160 natural polyphenols to identify the most promising druggable HITs that can further used for drug discovery process. The co-crystallized structure COVID protease enzyme (PDB id 6LU7) was used. HTVS, MD simulation, binding energy calculations and in-silico ADME calculation were done and analyzed. Depending upon the scores three compounds galangin, nalsudaldain and rhamnezine were identified and the docking score were found to be -7.704, -6.51, -4.212 respectively. These docked complexes were further subjected to MD simulation runs over a 100 ns time and the RMSD and RMSF values were determined. The RMSD values of three compounds were found to be 2.9 Å, 7.6 Å & 9.5 Å respectively and the lowest RMSF values suggested the steady stability of ligand-protein complexes. The binding free energies (ΔG) of compounds with protein were found to be -49.8, -56.45, -62.87 kJ/mole. Moreover, in-silico ADME calculations indicated the drug likeliness properties of these molecules. By considering all these in-silico results the identified HITs would be the most probable anti-COVID drug molecules that can be further taken in wet lab and can act as lead for development of newer inhibitor of COVID-19 main protease enzyme.

An Evolutionary Conservation and Druggability Analysis of Enzymes Belonging to the Bacterial Shikimate Pathway

Enzymes belonging to the shikimate pathway have long been considered promising targets for antibacterial drugs because they have no counterpart in mammals and are essential for bacterial growth and virulence. However, despite decades of research, there are currently no clinically relevant antibacterial drugs targeting any of these enzymes, and there are legitimate concerns about whether they are sufficiently druggable, i.e., whether they can be adequately modulated by small and potent drug-like molecules. In the present work, in silico analyses combining evolutionary conservation and druggability are performed to determine whether these enzymes are candidates for broad-spectrum antibacterial therapy. The results presented here indicate that the substrate-binding sites of most enzymes in this pathway are suitable drug targets because of their reasonable conservation and druggability scores. An exception was the substrate-binding site of 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase, which was found to be undruggable because of its high content of charged residues and extremely high overall polarity. Although the presented study was designed from the perspective of broad-spectrum antibacterial drug development, this workflow can be readily applied to any antimicrobial target analysis, whether narrow- or broad-spectrum. Moreover, this research also contributes to a deeper understanding of these enzymes and provides valuable insights into their properties.

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

BACKGROUND

Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.

OBJECTIVE

Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.

METHODS

SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.

RESULTS

Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.

CONCLUSION

Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.

Machine Learning to Predict Binding Affinity

Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic coordinates of protein-ligand complexes. These new computational tools made application of a broad spectrum of machine-learning techniques to study protein-ligand interactions possible. The essential aspect of these machine-learning approaches is to train a new computational model by using technologies such as supervised machine-learning techniques, convolutional neural network, and random forest to mention the most commonly applied methods. In this chapter, we focus on supervised machine-learning techniques and their applications in the development of protein-targeted scoring functions for the prediction of binding affinity. We discuss the development of the program SAnDReS and its application to the creation of machine-learning models to predict inhibition of cyclin-dependent kinase and HIV-1 protease. Moreover, we describe the scoring function space, and how to use it to explain the development of targeted scoring functions.