Software for structure-based drug design

Advancement of Computational Design Drug Delivery System in COVID-19: Current Updates and Future Crosstalk- A Critical update

Positive strides have been achieved in developing vaccines to combat the coronavirus-2019 infection (COVID-19) pandemic. Still, the outline of variations, particularly the most current delta divergent, has posed significant health encounters for people. Therefore, developing strong treatment strategies, such as an anti-COVID-19 medicine plan, may help deal with the pandemic more effectively. During the COVID-19 pandemic, some drug design techniques were effectively used to develop and substantiate relevant critical medications. Extensive research, both experimental and computational, has been dedicated to comprehending and characterizing the devastating COVID-19 disease. The urgency of the situation has led to the publication of over 130,000 COVID-19-related research papers in peer-reviewed journals and preprint servers. A significant focus of these efforts has been the identification of novel drug candidates and the repurposing of existing drugs to combat the virus. Many projects have utilized computational or computer-aided approaches to facilitate their studies. In this overview, we will explore the key computational methods and their applications in the discovery of small-molecule therapeutics for COVID-19, as reported in the research literature. We believe that the true effectiveness of computational tools lies in their ability to provide actionable and experimentally testable hypotheses, which in turn facilitate the discovery of new drugs and combinations thereof. Additionally, we recognize that open science and the rapid sharing of research findings are vital in expediting the development of much-needed therapeutics for COVID-19.

Computational anti-COVID-19 drug design: progress and challenges

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal

We report the discovery of a novel small-molecule inhibitor of the dengue virus (DENV) protease (NS2B-NS3pro) using a newly constructed Web-based portal (DrugDiscovery@TACC) for structure-based virtual screening. Our drug discovery portal, an extension of virtual screening studies performed using IBM's World Community Grid, facilitated access to supercomputer resources managed by the Texas Advanced Computing Center (TACC) and enabled druglike commercially available small-molecule libraries to be rapidly screened against several high-resolution DENV NS2B-NS3pro crystallographic structures. Detailed analysis of virtual screening docking scores and hydrogen-bonding interactions between each docked ligand and the NS2B-NS3pro Ser135 side chain were used to select molecules for experimental validation. Compounds were ordered from established chemical companies, and compounds with established aqueous solubility were tested for their ability to inhibit DENV NS2B-NS3pro cleavage of a model substrate in kinetic studies. As a proof-of-concept, we validated a small-molecule dihydronaphthalenone hit as a single-digit-micromolar mixed noncompetitive inhibitor of the DENV protease. Since the dihydronaphthalenone was predicted to interact with NS2B-NS3pro residues that are largely conserved between DENV and the related West Nile virus (WNV), we tested this inhibitor against WNV NS2B-NS3pro and observed a similar mixed noncompetitive inhibition mechanism. However, the inhibition constants were ∼10-fold larger against the WNV protease relative to the DENV protease. This novel validated lead had no chemical features or pharmacophores associated with adverse toxicity, carcinogenicity, or mutagenicity risks and thus is attractive for additional characterization and optimization.

Folded conformation of an immunostimulating tetrapeptide rigin: high temperature molecular dynamics simulation study

Employing high temperature quenched molecular dynamics (QMD) stimulations the conformational energy space of an immunostimulating tetrapeptide rigin: H-Gly341-Gln-Pro-Arg344-OH, is explored. Using distance dependent dielectric (epsilon =r(ij)) 31 different low energy starting structures with identical sequence were computed for their conformational preferences. According to the hypothesis of O'Connors et al. [J. Med. Chem. 35 (1992), 2870], 83 low-energy conformers resulted from unrestrained molecular dynamics (MD) simulations, could be classified into two energy minimized families: A and B, comprised of 64 (Pro C(gamma)-endo orientation) and 19 (Pro C(gamma)-exo orientation) structures, respectively. An examination of these families revealed the existence of a remarkably similar folded backbone conformation: torsion angles being phi(i+1) approximately -65 degrees, psi(i+1) approximately -65 degrees, phi(i+2) approximately -65 degrees, psi(i+2) approximately -60 degrees, characterizing a distorted type III beta-turn structure across the central Gln-Pro segment. The folded conformation of rigin is devoid of a classical 1 <-- 4 intra-molecular hydrogen bond nevertheless, the conformation is stabilized by an effective 'salt-bridge', i.e., Gly H(3)N(+)...C(alpha)OO(-) Arg interaction. Surprisingly, in both the families the unusual folded side-chain dispositions of the Gln residue favor the formation of a unique intra-residue 'main-chain to side-chain' H-bond, i.e., N(alpha)-H...N(epsilon) interaction, encompassing a seven-membered ring motif. The conformational attributes may be valuable in de novo construction of structure-based drug candidates having sufficient stimulating activity.

Computational methods for biomolecular docking

With the rapidly increasing amount of molecular biological data available, the computer-based analysis of molecular interactions becomes more and more feasible. Methods for computer-aided molecular docking have to include a reasonably accurate model of energy and must be able to deal with the combinatorial complexity incurred by the molecular flexibility of the docking partners. In both respects, recent years have seen substantial progress.

Molecular docking towards drug discovery

Fueled by advances in molecular structure determination, tools for structure-based drug design are proliferating rapidly. Lead discovery through searching of ligand databases with molecular docking techniques represents an attractive alternative to high-throughout random screening. The size of commercial databases imposes severe computational constraints on molecular docking, compromising the level of calculational detail permitted for each putative ligand. We describe alternative philosophies for docking which effectively address this challenge. With respect to the dynamic aspects of molecular recognition, these strategies lie along a spectrum of models bounded by the Lock-and-Key and Induced-Fit theories for ligand binding. We explore the potential of a rigid model in exploiting species specificity and of a tolerant model in predicting absolute ligand binding affinity. Current molecular docking methods are limited primarily by their ability to rank docked complexes; we therefore place particular emphasis on this aspect of the problem throughout our validation of docking strategies.

A preference-based free-energy parameterization of enzyme-inhibitor binding. Applications to HIV-1-protease inhibitor design

The interface between protein receptor-ligand complexes has been studied with respect to their binary interatomic interactions. Crystal structure data have been used to catalogue surfaces buried by atoms from each member of a bound complex and determine a statistical preference for pairs of amino-acid atoms. A simple free energy model of the receptor-ligand system is constructed from these atom-atom preferences and used to assess the energetic importance of interfacial interactions. The free energy approximation of binding strength in this model has a reliability of about +/- 1.5 kcal/mol, despite limited knowledge of the unbound states. The main utility of such a scheme lies in the identification of important stabilizing atomic interactions across the receptor-ligand interface. Thus, apart from an overall hydrophobic attraction (Young L, Jernigan RL, Covell DG, 1994, Protein Sci 3:717-729), a rich variety of specific interactions is observed. An analysis of 10 HIV-1 protease inhibitor complexes is presented that reveals a common binding motif comprised of energetically important contacts with a rather limited set of atoms. Design improvements to existing HIV-1 protease inhibitors are explored based on a detailed analysis of this binding motif.

Ligand-protein docking and rational drug design

Over the past year there have been some interesting and significant advances in computer-based ligand-protein docking techniques and related rational drug-design tools, including flexible ligand docking and better estimation of binding free energies and solvation energies. As a result, the successful use of computational tools to help generate interesting new guide (lead) compounds for targeted receptors is becoming more commonplace.