Aman Chandra Kaushik

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections.

Nano-particle mediated inhibition of Parkinson’s disease using computational biology approach

Parkinson’s disease (PD) arises as neurodegenerative disorder and characterized by progressive deterioration of motor functions due to forfeiture of dopamine-releasing neurons. During PD, neurons at stake loss their functionality that results into cognition impairment and forgetfulness, commonly called as dementia. Recently, nanoparticles (NPs) have been reported for easy drug delivery through blood-brain barrier (BBB) into the central nervous system (CNS) against the conventional drug delivery systems. However, present study attempted to elucidate the α-synuclein activity, a major factor casing PD, in presence of its inhibitor cerium oxide (CeO2) nanoparticle via computational biology approach. A computational analysis was also conducted for the α-synuclein activity with biocompatible metal NPs such as GOLD NPs and SPIONs to scrutinize the efficacy and degree of inhibition induced by the CeO2 NP. The obtained results concluded that CeO2 NP fit best in the active site of α-synuclein with good contacts and interaction, and potentially inhibited the PD against L-DOPA drug selected as positive control in the designed PD biochemical pathway. Hence, CeO2 NP has been purposed as potential inhibitor of α-synuclein and can be employed as nano-drug against the PD.

Three-Dimensional (3D) Pharmacophore Modelling-Based Drug Designing by Computational Technique

Three-dimensional (3D) pharmacophore modelling is a modern approach used to elucidate the intermolecular interaction of ligands with the target of interest. In the past few years, pharmacophore models have been developed with chemical features and are intuitively understandable and broadly employed successfully in computational drug discovery by the researchers. The concert and utility of pharmacophore modelling are demarcated by the two major factors; (i) definition and placement of pharmacophoric features and (ii) the arrangement approaches used to overlay the 3D pharmacophore models and small molecules. This chapter provides a brief account of the recent technologies and developed model used in pharmacophores-based drug design.

Molecular Dynamics Simulation Approach to Investigate Dynamic Behaviour of System Through the Application of Newtonian Mechanics

Molecular dynamics simulations have been successfully incorporated and evolved into a mature technique within a variety of pharmaceutical research programs to study the complex biological and chemical systems. Broadly used in modern drug design, molecular docking methods can be used effectively to understand the macromolecular structure-to-function relationships and ligand conformations adopted within the binding sites of macromolecular targets. Information gathered about the dynamic properties of ligand–receptor binding such as free energy by evaluating critical phenomena involved in the intermolecular recognition process. These results can be employed to shift the usual paradigm of structural bioinformatics from studying single structures to analyse conformational ensembles. Today, as a variety of docking algorithms are available, an understanding of advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this chapter is to examine the current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advancements in the field and role played by integration of structure-and ligand-based methods.

Ligand-Based Approach for In-silico Drug Designing

In this chapter, a brief introduction to ligand-based methodologies employed for designing of drug has been described. Generally, ligand-based approach for drug designing (LB-CADD) technique is employed when biological target structure is not known and hence, this technique is considered as an ancillary approach for the drug designing. The theoretical basis of ligand-based approach involves quantitative structure–activity relationships (QSAR) and biomolecular docking studies. Like molecular descriptors, molecular fingerprint, similarity searches, similarity networks and off-target predictions. Finally, a brief description of the present work is given.

Structure-Based Approach for In-silico Drug Designing

In recent years, research area of structure-based drug design is a rising field that has been used to achieve many successes. Structure-based computer-aided drug design (SB-CADD) depends on the ability to determine and analyse the 3D structures of the target of interest. In other words, a prerequisite for the SB-CADD approach can be defined based on molecule’s ability to interrelate with a specific ligand, that can be a chemical species or biomolecule such as protein, and a desired biological activity based on its ability to favourably interact at a binding site on the selected target. This purposed that the molecules sharing those favourable interactions will reflect the similar biological effects. Therefore, novel ligands can be predicted and concluded by careful analysis of a protein’s binding site. Also, structure-based approach for drug designing allows a rapid selection of potential ligands from different and large compound libraries that can be later validated through modelling/simulation and visualization techniques.

Receptor Thermodynamics of Ligand–Receptor or Ligand–Enzyme Association

Experimental techniques that directly assess the thermodynamics of ligand–receptor or ligand–enzyme association, such as isothermal titration calorimetry, have been improved in recent years and can provide thermodynamic details of the binding process. Parallel to the continuous increase in computational power, several classes of computational methods have been developed that can be used to get a more detail insight into the mode and affinity of compounds (drug) to their target (off). Such methods are affiliated with a qualitative and/or quantitative assessment of binding free energies, and differently trade off speed versus physical accuracy. With the current wealth of available three-dimensional structures of proteins and their complexes with ligands, structure-based drug design studies can be used to identify the key ligand interactions and free energy calculations, and can quantify the thermodynamics of binding between ligand and the target of interest.

Genomics and Proteomics Using Computational Biology

Current functional genomics relies on known and characterised genes, but despite significant efforts in the field of genome annotation, accurate identification and elucidation of protein coding gene structures remains challenging. Methods are limited to computational predictions and transcript-level experimental evidence; hence translation cannot be verified. Proteomic mass spectrometry is a method that enables sequencing of gene product fragments, enabling the validation and refinement of existing gene annotation as well as the elucidation of novel protein coding regions. However, the application of proteomics data to genome annotation is hindered by the lack of suitable tools and methods to achieve automatic data processing and genome mapping at high accuracy and throughput.

Thermodynamic Cycles and Their Application in Protein Targets

A key part of drug design and development is the optimization of molecular interactions between an engineered drug candidate and its binding target. Thermodynamic characterization provides information about the balance of energetic forces driving binding interactions and is essential for understanding and optimizing molecular interactions. Comprehensive thermodynamic evaluation is vital in the drug development process to speed drug development towards an optimal energetic interaction profile while retaining good pharmacological properties. Practical thermodynamic approaches, such as enthalpic optimization, thermodynamic optimization plots and the enthalpic efficiency index, have now been developed to provide proven utility in design process. Improved throughput in calorimetric methods remains essential for even greater integration of thermodynamics into drug design.

Allosteric ligands for the pharmacologically important Flavivirus target (NS5) from ZINC database based on pharmacophoric points, free energy calculations and dynamics correlation

Dengue virus belongs to a group of human pathogens, which causes different diseases, dengue hemorrhagic fever and dengue shock syndrome in humans. It possesses RNA as a genetic material and is replicated with the aid of NS5 protein. RNA-dependent RNA polymerase (RdRp) is an important domain of NS5 in the replication of that virus. The catalytic process activity of RdRp is making it an important target for antiviral chemical therapy. To date, No FDA drug has been approved and marketed for the treatment of diseases caused by Dengue virus. So, there is a dire need to advance an area of active antiviral inhibitors that is safe, less expensive and widely available. An experimentally validated complex of Dengue NS5 and compound 27 (6LS) were used as pharmacophoric input and hits were identified. We also used Molecular dynamics (MD) simulations alongside free energy and dynamics of the internal residues of the apo and holo systems to understand the binding mechanism. Our analysis resulted that the three inhibitors (ZINC72070002, ZINC6551486, and ZINC39588257) greatly affected the interior dynamics and residual signaling to dysfunction the replicative role of NS5. The interaction of these inhibitors caused the loss of the correlated motion of NS5 near to the N terminus and helped the stability of the binding complex. This investigation provided a methodological route to discover allosteric inhibitors against the epidemics of this Flaviviruses. Allosteric inhibitors are important and major assets in considering the development of the competitive and robust antiviral agents such as against Dengue viral infection.