Inhibitors of HIV-1 protease: a major success of structure-assisted drug design

Tandem ketone reduction in pepstatin biosynthesis reveals an F420H2-dependent statine pathway

Pepstatins are potent inhibitors of aspartic proteases, featuring two statine residues crucial for target binding. However, the biosynthesis of pepstatins, especially their statine substructure, remains elusive. Here, we discover and characterize an unconventional gene cluster responsible for pepstatin biosynthesis, comprising discrete nonribosomal peptide synthetase and polyketide synthase genes, highlighting its trans-acting and iterative nature. Central to this pathway is PepI, an F420H2-dependent oxidoreductase. The biochemical characterization of PepI reveals its role in the tandem reduction of β-keto pepstatin intermediates. PepI first catalyzes the formation of the central statine, then produces the C-terminal statine moiety. The post-assembly-line formation of statine by PepI contrasts with the previously hypothesized biosynthesis involving polyketide synthase ketoreductase domains. Structural studies, site-directed mutagenesis, and deuterium-labeled enzyme assays probe the mechanism of F420H2-dependent oxidoreductases and identify critical residues. Our findings uncover a unique statine biosynthetic pathway employing the only known iterative F420H2-dependent oxidoreductase to date.

Structural Virology: The Key Determinants in Development of Antiviral Therapeutics

Structural virology has emerged as the foundation for the development of effective antiviral therapeutics. It is pivotal in providing crucial insights into the three-dimensional frame of viruses and viral proteins at atomic-level or near-atomic-level resolution. Structure-based assessment of viral components, including capsids, envelope proteins, replication machinery, and host interaction interfaces, is instrumental in unraveling the multiplex mechanisms of viral infection, replication, and pathogenesis. The structural elucidation of viral enzymes, including proteases, polymerases, and integrases, has been essential in combating viruses like HIV-1 and HIV-2, SARS-CoV-2, and influenza. Techniques including X-ray crystallography, Nuclear Magnetic Resonance spectroscopy, Cryo-electron Microscopy, and Cryo-electron Tomography have revolutionized the field of virology and significantly aided in the discovery of antiviral therapeutics. The ubiquity of chronic viral infections, along with the emergence and reemergence of new viral threats necessitate the development of novel antiviral strategies and agents, while the extensive structural diversity of viruses and their high mutation rates further underscore the critical need for structural analysis of viral proteins to aid antiviral development. This review highlights the significance of structure-based investigations for bridging the gap between structure and function, thus facilitating the development of effective antiviral therapeutics, vaccines, and antibodies for tackling emerging viral threats.

Flexibility-Induced Collective Behavior Drives Symmetry Breaking in Discrimination of Undesired Ions

Structure flexibility is essential for the biological function of proteins. At the same time, many proteins need to discriminate ligands with subtle differences, with one example being ion selectivity. Investigating the mechanisms by which flexible proteins achieve such precise discrimination is crucial for advancing our understanding of their functions. In this work, we study transporter KCC4, which undergoes continuous conformation changes during ion transport and can realize K+ over Na+ selectivity. Our findings reveal that the center of the binding site no longer represents a stable equilibrium for the undesired Na+, and its binding mode exhibits bifurcation. Interestingly, protein conformation fluctuation can induce collective behavior throughout the entire binding region, which contributes to this bifurcation. Thus, the symmetry of the binding mode decreases from the inherent T d symmetry to a C2v symmetry, and the binding stability of Na+ is largely reduced. A similar phenomenon is observed in a GPCR, β2-AR, where a less favored ligand forms a biased binding mode with reduced stability. The mechanism underlying the selectivity in such flexible regions could be interpreted as spontaneous symmetry breaking, which may represent a general mechanism by which flexible proteins achieve efficient ligand discrimination.

Contrasting the effect of hinge region insertions and non-active site mutations on HIV protease-inhibitor interactions: Insights from altered flap dynamics

HIV-1 protease (PR) enzyme is a viable antiretroviral drug target due to its crucial role in HIV maturation. Over many decades, the HIV-1 PR enzyme has exhibited mutations brought on by drug pressure and error-prone nature of HIV-1 reverse transcriptase. Non-active site mutations have played a pivotal role in drug resistance; however, their mechanism of action has not been fully elucidated. We investigated how non-active site mutations affect the conformational stability and drug binding ability of HIV-1 PR. In light of this, we studied a novel HIV-1 subtype C protease variant containing an insertion of valine (↑V) in the hinge region. We analysed the mutations in the presence and absence of ten background mutations. Molecular dynamics simulations revealed that both with and without the background mutations, the PR exhibited increased flexibility of hinge, flaps and fulcrum regions. This allowed the PR to adopt a wider flap conformation when in complex with several inhibitors. Additionally, the simulations revealed that the protease inhibitors (PIs) could not bring the mutated variant proteases into a stable, closed conformation, resulting in increased solvent exposure of the inhibitors. Together, these results suggest that the mutations decrease the favourability of binding by altering the dynamics of the flap regions. Notably, the insertion mutation increased PR hinge flexibility and the introduction of background mutations compensated for this by stabilising the cantilever and hinge regions. Together, these findings provide insight into how non-active site mutations affect PR conformational dynamics in critical areas of the PR thus impacting on drug binding capacity and potentially contributing to drug resistance.

Multidisciplinary structural optimization of polysaccharides preventing alcohol-induced liver disease with computer-aided molecular design

Here, we optimized the active units of polysaccharides and investigated the conformational relationship between the polysaccharides and alcoholic liver disease (ALD) at the molecular level. We used data mining to screen polysaccharide structural parameters for ALD (PSP-ALD). Most ALD-resistant polysaccharides against ALD comprised glucose (Glc), mannose (Man), galactose (Gal), arabinose (Ara), and rhamnose (Rha). Additionally, (1 → 6)-, (1 → 3)-, and (1 → 4)- glycosidic linkages were mainly contained. Polysaccharides against ALD have a wide molecular weight distribution (2.1 × 103 Da - 9.6 × 107 Da). Based on the PSP-ALD analysis, six commercially available oligosaccharides were selected and their structures were built. After molecular docking, the binding affinities between stachyose and the key ALD targets were stronger, indicating that stachyose may be a polysaccharide-active unit against ALD (PAU-ALD). Furthermore, histological examination of liver tissue combined with serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and triglycerides (TG) showed that stachyose had a significant protective effect against ALD in mice. In summary, we optimized a PAU-ALD and developed a method for studying the structure-activity relationship between polysaccharides and ALD at the molecular level, which provides a new research direction for the development and utilization of polysaccharides and their clinical applications in ALD.

In silico fragment-based design and pharmacophore modelling of therapeutics against dengue virus envelope protein

Dengue virus, an arbovirus of genus Flavivirus, is an infectious disease causing organisms in the tropical environment leading to numerous deaths every year. No therapeutic is available against the virus till date with only symptomatic relief available. Here, we have tried to design therapeutic compounds from scratch by fragment based method followed by pharmacophore based modelling to find suitable similar structure molecules and validated the same by MD simulation, followed by binding energy calculations and ADMET analysis. The receptor binding region of the dengue envelope protein was considered as the target for prevention of viral host cell entry and thus infection. This resulted in the final selection of kanamycin as a stable binding molecule against the Dengue virus envelope protein receptor binding domain. This study results in selection of a single molecule having high binding energy and prominent stable interactions as determined by post simulation analyses. This study aims to provide a direction for development of small molecule therapeutics against the dengue virus in order to control infection. This study may open a new avenue in the arena of structure based and fragment based therapeutic design to obtain novel molecules with therapeutic potential.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00262-9.

An Enzymatic Route to the Synthesis of Tricyclic Fused Hexahydrofuranofuran P2-Ligand for a Series of Highly Potent HIV-1 Protease Inhibitors

We describe a stereoselective synthesis of an optically active (1R, 3aS, 5R, 6S, 7aR)-octahydro-1,6-epoxy-isobenzo-furan-5-ol derivative. This stereochemically defined heterocycle serves as a high-affinity ligand for a variety of HIV-1 protease inhibitors. The key synthetic steps involve a highly enantioselective enzymatic desymmetrization of meso-1,2(dihydroxymethyl)cyclohex-4-ene and conversion of the resulting optically active alcohol to a methoxy hexahydroisobenzofuran derivative. A substrate controlled stereoselective dihydroxylation afforded syn-1,2-diols. Oxidation of diol provided the substituted 1,2-diketone and L-Selectride reduction provided the corresponding inverted syn-1,2-diols. Acid catalyzed cyclization furnished the ligand alcohol in optically active form.

Natural Polymorphisms D60E and I62V Stabilize a Closed Conformation in HIV-1 Protease in the Absence of an Inhibitor or Substrate

HIV infection remains a global health issue plagued by drug resistance and virological failure. Natural polymorphisms (NPs) contained within several African and Brazilian protease (PR) variants have been shown to induce a conformational landscape of more closed conformations compared to the sequence of subtype B prevalent in North America and Western Europe. Here we demonstrate through experimental pulsed EPR distance measurements and molecular dynamic (MD) simulations that the two common NPs D60E and I62V found within subtypes F and H can induce a closed conformation when introduced into HIV-1PR subtype B. Specifically, D60E alters the conformation in subtype B through the formation of a salt bridge with residue K43 contained within the nexus between the flap and hinge region of the HIV-1 PR fold. On the other hand, I62V modulates the packing of the hydrophobic cluster of the cantilever and fulcrum, also resulting in a more closed conformation.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Identification of a novel inhibitor of SARS-CoV-2 main protease: an in silico, biochemical, and cell-based approach

The recurrent nature of coronavirus outbreaks, severity of the COVID-19 pandemic, rapid emergence of novel variants, and concerns over the effectiveness of existing vaccines against novel variants have highlighted the need to develop therapeutic interventions. Targeted efforts to identify inhibitors of crucial viral proteins are the preferred strategy. In this study, we screened FDA-approved and natural product libraries using in silico approach for potential hits against the SARS-CoV-2 main protease (Mpro) and experimentally validated their potency using in vitro biochemical and cell-based assays. Seven potential hits were identified through in silico screening and were subsequently evaluated in SARS-CoV-2-based cell-free assays, followed by testing in the HCoV-229E-based culture system. Of the tested compounds, 4-(3,4-dihydroxyphenyl)-6,7-dihydroxy-1-isopropyl-1H-benzofuro[3,2-b]pyrazolo[4,3-e]pyridin-3(2H)-one (PubChem CID:71755304, hereafter referred to as STL522228) exhibited significant antiviral activity. Subsequently, its potential as a novel COVID therapeutic molecule was validated in the SARS-CoV-2-culture system, where STL522228 demonstrated superior antiviral activity (EC50  = 0.44 μm) compared to Remdesivir (EC50  = 0.62 μm). Based on these findings, we report the strong anti-coronavirus activity of STL522228, and propose that it as a promising pan-coronavirus Mpro inhibitor for further experimental and preclinical validation.

How Robust Is the Ligand Binding Transition State?

For many drug targets, it has been shown that the kinetics of drug binding (e.g., on rate and off rate) is more predictive of drug efficacy than thermodynamic quantities alone. This motivates the development of predictive computational models that can be used to optimize compounds on the basis of their kinetics. The structural details underpinning these computational models are found not only in the bound state but also in the short-lived ligand binding transition states. Although transition states cannot be directly observed experimentally due to their extremely short lifetimes, recent successes have demonstrated that modeling the ligand binding transition state is possible with the help of enhanced sampling molecular dynamics methods. Previously, we generated unbinding paths for an inhibitor of soluble epoxide hydrolase (sEH) with a residence time of 11 min. Here, we computationally modeled unbinding events with the weighted ensemble method REVO (resampling of ensembles by variation optimization) for five additional inhibitors of sEH with residence times ranging from 14.25 to 31.75 min, with average prediction accuracy within an order of magnitude. The unbinding ensembles are analyzed in detail, focusing on features of the ligand binding transition state ensembles (TSEs). We find that ligands with similar bound poses can show significant differences in their ligand binding TSEs, in terms of their spatial distribution and protein-ligand interactions. However, we also find similarities across the TSEs when examining more general features such as ligand degrees of freedom. Together these findings show significant challenges for rational, kinetics-based drug design.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Structural analysis of novel drug targets for mitigation of Pseudomonas aeruginosa biofilms

Pseudomonas aeruginosa is an opportunistic human pathogen responsible for acute and chronic, hard to treat infections. Persistence of P. aeruginosa is due to its ability to develop into biofilms, which are sessile bacterial communities adhered to substratum and encapsulated in layers of self-produced exopolysaccharides. These biofilms provide enhanced protection from the host immune system and resilience towards antibiotics, which poses a challenge for treatment. Various strategies have been expended for combating biofilms, which involve inhibiting biofilm formation or promoting their dispersal. The current remediation approaches offer some hope for clinical usage, however, treatment and eradication of preformed biofilms is still a challenge. Thus, identifying novel targets and understanding the detailed mechanism of biofilm regulation becomes imperative. Structure-based drug discovery (SBDD) provides a powerful tool that exploits the knowledge of atomic resolution details of the targets to search for high affinity ligands. This review describes the available structural information on the putative target protein structures that can be utilized for high throughput in silico drug discovery against P. aeruginosa biofilms. Integrating available structural information on the target proteins in readily accessible format will accelerate the process of drug discovery.

Dynamozones are the most obvious sign of the evolution of conformational dynamics in HIV-1 protease

Proteins are not static but are flexible molecules that can adopt many different conformations. The HIV-1 protease is an important target for the development of therapies to treat AIDS, due to its critical role in the viral life cycle. We investigated several dynamics studies on the HIV-1 protease families to illustrate the significance of examining the dynamic behaviors and molecular motions for an entire understanding of their dynamics-structure-function relationships. Using computer simulations and principal component analysis approaches, the dynamics data obtained revealed that: (i) The flap regions are the most obvious sign of the evolution of conformational dynamics in HIV-1 protease; (ii) There are dynamic structural regions in some proteins that contribute to the biological function and allostery of proteins via appropriate flexibility. These regions are a clear sign of the evolution of conformational dynamics of proteins, which we call dynamozones. The flap regions are one of the most important dynamozones members that are critical for HIV-1 protease function. Due to the existence of other members of dynamozones in different proteins, we propose to consider dynamozones as a footprint of the evolution of the conformational dynamics of proteins.

Exploration of imatinib and nilotinib-derived templates as the P2-Ligand for HIV-1 protease inhibitors: Design, synthesis, protein X-ray structural studies, and biological evaluation

Structure-based design, synthesis, X-ray structural studies, and biological evaluation of a new series of potent HIV-1 protease inhibitors are described. These inhibitors contain various pyridyl-pyrimidine, aryl thiazole or alkylthiazole derivatives as the P2 ligands in combination with darunavir-like hydroxyethylamine sulfonamide isosteres. These heterocyclic ligands are inherent to kinase inhibitor drugs, such as nilotinib and imatinib. These ligands are designed to make hydrogen bonding interactions with the backbone atoms in the S2 subsite of HIV-1 protease. Various benzoic acid derivatives have been synthesized and incorporation of these ligands provided potent inhibitors that exhibited subnanomolar level protease inhibitory activity and low nanomolar level antiviral activity. Two high resolution X-ray structures of inhibitor-bound HIV-1 protease were determined. These structures provided important ligand-binding site interactions for further optimization of this class of protease inhibitors.

A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery

Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.

AI-Aided Search for New HIV-1 Protease Ligands

The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. The search for new potential ligands for proteins involved in the life cycle of pathogens is, therefore, an extremely important research field today. In this work, we have considered the HIV-1 protease, one of the main targets for AIDS therapy. Several drugs are used today in clinical practice whose mechanism of action is based on the inhibition of this enzyme, but after years of use, even these molecules are beginning to be interested by resistance phenomena. We used a simple artificial intelligence system for the initial screening of a data set of potential ligands. These results were validated by docking and molecular dynamics, leading to the identification of a potential new ligand of the enzyme which does not belong to any known class of HIV-1 protease inhibitors. The computational protocol used in this work is simple and does not require large computational power. Furthermore, the availability of a large number of structural information on viral proteins and the presence of numerous experimental data on their ligands, with which it is possible to compare the results obtained with computational methods, make this research field the ideal terrain for the application of these new computational techniques.

Comprehending the Structure, Dynamics, and Mechanism of Action of Drug-Resistant HIV Protease

Since the emergence of the Human Immunodeficiency Virus (HIV) in the 1980s, strategies to combat HIV-AIDS are continuously evolving. Among the many tested targets to tackle this virus, its protease enzyme (PR) was proven to be an attractive option that brought about numerous research publications and ten FDA-approved drugs to inhibit the PR activity. However, the drug-induced mutations in the enzyme made these small molecule inhibitors ineffective with prolonged usage. The research on HIV PR, therefore, remains a thrust area even today. Through this review, we reiterate the importance of understanding the various structural and functional components of HIV PR in redesigning the structure-based small molecule inhibitors. We also discuss at length the currently available FDA-approved drugs and how these drug molecules induced mutations in the enzyme structure. We then recapitulate the reported mechanisms on how these drug-resistant variants remain sufficiently active to cleave the natural substrates. We end with the future scope covering the recently proposed strategies that show promise to deal with the mutations.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Revealing the drug resistance mechanism of saquinavir due to G48V and V82F mutations in subtype CRF01_AE HIV-1 protease: molecular dynamics simulation and binding free energy calculations

Human immunodeficiency virus-1 (HIV-1) protease is one of the important targets in AIDS therapy. The majority of HIV infections are caused due to non-B subtypes in developing countries. The co-occurrence of mutations along with naturally occurring polymorphisms in HIV-1 protease cause resistance to the FDA approved drugs, thereby posing a major challenge in the treatment of antiretroviral therapy. In this work, the resistance mechanism against SQV due to active site mutations G48V and V82F in CRF01_AE (AE) protease was explored. The binding free energy calculations showed that the direct substitution of valine at position 48 introduces a bulkier side chain, directly impairing the interaction with SQV in the binding pocket. Also, the intramolecular hydrogen bonding network of the neighboring residues is altered, indirectly affecting the binding of SQV. Interestingly, the substitution of phenylalanine at position 82 induces conformational changes in the 80's loop and the flap region, thereby favoring the binding of SQV. The V82F mutant structure also maintains similar intramolecular hydrogen bond interactions as observed in AE-WT.Communicated by Ramaswamy H. Sarma.

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

Combination of structure-based virtual screening, molecular docking and molecular dynamics approaches for the discovery of anti-prion fibril flavonoids

Prion diseases are a group of rare neurodegenerative diseases caused by the structural conversion of cellular prion into Scrapie prion resulting aggregated fibrils. Therapy of prion diseases has been developed for several decades, especially drug designs based on the structure of prion monomers. Unfortunately, none of the designed anti-prion drugs function well clinically. To fight against prion fibrils, a drug design based on the precise structure of mammalian prion fibrils is highly required. Fortunately, based on the advantage of newly advanced cryo-electron microscopy (cryo-EM) in the deconvolution of large complexes, three prion fibril structures were resolved in the last 2 years. Based on the cryo-EM solved prion fibril structures, we are able to find some molecules fighting against prion fibrils. Quercetin, one flavonoid molecule in the polyphenol group, has been found to disaggregate the prion fibrils in vitro. In this study, we performed the molecular docking and molecular dynamics simulation on quercetin-like molecules possessing pharmacological properties to evaluate the anti-prion ability of tested molecules. As a result, four quercetin-like molecules interact with prion fibril and decrease the β-strand content by converting some β-strands into loop and helical structures to disintegrate the existing fibril structure. The results of this study are significant in the treatment of prion diseases, and the approaches used in this study are applicable to other amyloid diseases.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Molecular basis for reduced cleavage activity and drug resistance in D30N HIV-1 protease

Nelfinavir is one of the FDA-approved HIV-1 protease inhibitors and a part of highly active anti-retroviral therapy (HAART) for the treatment of HIV-AIDS. Nelfinavir was the first HIV-1 protease inhibitor to be approved as a paediatric formulation. The application of HAART had resulted in significant improvement in the lives of AIDS patients. However, the emergence of drug resistance in HIV-1 protease has limited the use of many of these drugs including nelfinavir. A unique mutation observed frequently in patients treated with nelfinavir is D30N as it is selected exclusively by nelfinavir. The D30N mutation imparts very high resistance to nelfinavir but unlike other primary mutations does not give cross-resistance to the majority of other drugs. D30N mutation also significantly reduces cleavage activity of HIV-1 protease and affects viral fitness. Here, we have determined crystal structures of D30N HIV-1 protease in unliganded form and in complex with nelfinavir. These structures provide the rationale for reduced cleavage activity and the molecular basis of drug resistance induced by D30N mutation. The loss of coulombic interaction part of a crucial hydrogen bond between the drug and the protease is likely to play a major role in reduced affinity and resistance towards nelfinavir. The decreased catalytic activity of D30N HIV-1 protease due to altered interaction with the substrates and reduced stability of folding core may be the reason for the reduced replicative capacity of the virus harboring mutant HIV-1 protease.Communicated by Ramaswamy H. Sarma.

pH Regulates Ligand Binding to an Enzyme Active Site by Modulating Intermediate Populations

Understanding the mechanism of ligands binding to their protein targets and the influence of various factors governing the binding thermodynamics is essential for rational drug design. The solution pH is one of the critical factors that can influence ligand binding to a protein cavity, especially in enzymes whose function is sensitive to the pH. Using computer simulations, we studied the pH effect on the binding of a guanidinium ion (Gdm⁺) to the active site of hen egg-white lysozyme (HEWL). HEWL serves as a model system for enzymes with two acidic residues in the active site and ligands with Gdm⁺ moieties, which can bind to the active sites of such enzymes and are present in several approved drugs treating various disorders. The computed free energy surface (FES) shows that Gdm⁺ binds to the HEWL active site using two dominant binding pathways populating multiple intermediates. We show that the residues close to the active site that can anchor the ligand could play a critical role in ligand binding. Using a Markov state model, we quantified the lifetimes and kinetic pathways connecting the different states in the FES. The protonation and deprotonation of the acidic residues in the active site in response to the pH change strongly influence the Gdm⁺ binding. There is a sharp jump in the ligand-binding rate constant when the pH approaches the largest pK_a of the acidic residue present in the active site. The simulations reveal that, at most, three Gdm⁺ can bind at the active site, with the Gdm⁺ bound in the cavity of the active site acting as a scaffold for the other two Gdm⁺ ions binding. These results can aid in providing greater insights into designing novel molecules containing Gdm⁺ moieties that can have high binding affinities to inhibit the function of enzymes with acidic residues in their active site.

Structural similarities between SARS-CoV2 3CLpro and other viral proteases suggest potential lead molecules for developing broad spectrum antivirals

Considering the significant impact of the recent COVID-19 outbreak, development of broad-spectrum antivirals is a high priority goal to prevent future global pandemics. Antiviral development processes generally emphasize targeting a specific protein from a particular virus. However, some antiviral agents developed for specific viral protein targets may exhibit broad spectrum antiviral activity, or at least provide useful lead molecules for broad spectrum drug development. There is significant potential for repurposing a wide range of existing viral protease inhibitors to inhibit the SARS-CoV2 3C-like protease (3CLpro). If effective even as relatively weak inhibitors of 3CLpro, these molecules can provide a diverse and novel set of scaffolds for new drug discovery campaigns. In this study, we compared the sequence- and structure-based similarity of SARS-CoV2 3CLpro with proteases from other viruses, and identified 22 proteases with similar active-site structures. This structural similarity, characterized by secondary-structure topology diagrams, is evolutionarily divergent within taxonomically related viruses, but appears to result from evolutionary convergence of protease enzymes between virus families. Inhibitors of these proteases that are structurally similar to the SARS-CoV2 3CLpro protease were identified and assessed as potential inhibitors of SARS-CoV2 3CLpro protease by virtual docking. Several of these molecules have docking scores that are significantly better than known SARS-CoV2 3CLpro inhibitors, suggesting that these molecules are also potential inhibitors of the SARS-CoV2 3CLpro protease. Some have been previously reported to inhibit SARS-CoV2 3CLpro. The results also suggest that established inhibitors of SARS-CoV2 3CLpro may be considered as potential inhibitors of other viral 3C-like proteases.

Decoding drug resistant mechanism of V32I, I50V and I84V mutations of HIV-1 protease on amprenavir binding by using molecular dynamics simulations and MM-GBSA calculations

Mutations V32I, I50V and I84V in the HIV-1 protease (PR) induce drug resistance towards drug amprenavir (APV). Multiple short molecular dynamics (MSMD) simulations and molecular mechanics generalized Born surface area (MM-GBSA) method were utilized to investigate drug-resistant mechanism of V32I, I50V and I84V towards APV. Dynamic information arising from MSMD simulations suggest that V32I, I50V and I84V highly affect structural flexibility, motion modes and conformational behaviours of two flaps in the PR. Binding free energies calculated by MM-GBSA method suggest that the decrease in binding enthalpy and the increase in binding entropy induced by mutations V32I, I50V and I84V are responsible for drug resistance of the mutated PRs on APV. The energetic contributions of separate residues on binding of APV to the PR show that V32I, I50V and I84V highly disturb the interactions of two flaps with APV and mostly drive the decrease in binding ability of APV to the PR. Thus, the conformational changes of two flaps in the PR caused by V32I, I50V and I84V play key roles in drug resistance of three mutated PR towards APV. This study can provide useful dynamics information for the design of potent inhibitors relieving drug resistance.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Protein-ligand binding affinity prediction of cyclin-dependent kinase-2 inhibitors by dynamically averaged fragment molecular orbital-based interaction energy

Fragment molecular orbital (FMO) method is a powerful computational tool for structure-based drug design, in which protein-ligand interactions can be described by the inter-fragment interaction energy (IFIE) and its pair interaction energy decomposition analysis (PIEDA). Here, we introduced a dynamically averaged (DA) FMO-based approach in which molecular dynamics simulations were used to generate multiple protein-ligand complex structures for FMO calculations. To assess this approach, we examined the correlation between the experimental binding free energies and DA-IFIEs of six CDK2 inhibitors whose net charges are zero. The correlation between the experimental binding free energies and snapshot IFIEs for X-ray crystal structures was R²  = 0.75. Using the DA-IFIEs, the correlation significantly improved to 0.99. When an additional CDK2 inhibitor with net charge of -1 was added, the DA FMO-based scheme with the dispersion energies still achieved R²  = 0.99, whereas R² decreased to 0.32 employing all the energy terms of PIEDA.

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

Protein-Ligand Docking in the Machine-Learning Era

Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.

Design, Synthesis and X-Ray Structural Studies of Potent HIV-1 Protease Inhibitors Containing C-4 Substituted Tricyclic Hexahydro-Furofuran Derivatives as P2 Ligands

The design, synthesis, X-ray structural, and biological evaluation of a series of highly potent HIV-1 protease inhibitors are reported herein. These inhibitors incorporate novel cyclohexane-fused tricyclic bis-tetrahydrofuran as P2 ligands in combination with a variety of P1 and P2' ligands. The inhibitor with a difluoromethylphenyl P1 ligand and a cyclopropylaminobenzothiazole P2' ligand exhibited the most potent antiviral activity. Also, it maintained potent antiviral activity against a panel of highly multidrug-resistant HIV-1 variants. The corresponding inhibitor with an enantiomeric ligand was significantly less potent in these antiviral assays. The new P2 ligands were synthesized in optically active form using enzymatic desymmetrization of meso-diols as the key step. To obtain molecular insight, two high-resolution X-ray structures of inhibitor-bound HIV-1 protease were determined and structural analyses have been highlighted.

Pocket-to-Lead: Structure-Based De Novo Design of Novel Non-peptidic HIV-1 Protease Inhibitors Using the Ligand Binding Pocket as a Template

A novel strategy for lead identification that we have dubbed the "Pocket-to-Lead" strategy is demonstrated using HIV-1 protease as a model target. Sometimes, it is difficult to obtain hit compounds because of the difficulties in satisfying the complex pharmacophoric features. In this study, a virtual fragment hit which does not match all of the pharmacophore features but has key interactions and vectors that could grow into remaining pharmacophore features was optimized in silico. The designed compound 9 demonstrated weak but evident inhibitory activity (IC₅₀ = 54 μM), and the design concept was proven by the co-crystal structure. Then, structure-based drug design promptly gave compound 14 (IC₅₀ = 0.0071 μM, EC₅₀ = 0.86 μM), an almost 10,000-fold improvement in activity from 9. The structure of the designed molecules proved to be novel with high synthetic feasibility, indicating the usefulness of this strategy to tackle tough targets with complex pharmacophore.

Data Mining Meets Machine Learning: A Novel ANN-based Multi-Body Interaction Docking Scoring Function (MBI-Score) based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-Ligand Complexes

Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. It is a simple machine-learning-based statistical function that employs frequent geometric and chemical patterns of interacting atoms at protein-ligand interfaces. The patterns are derived by mining interfaces of "native" protein-ligand complexes. Each interface is represented by a graph where nodes are atoms and edges connect protein-ligand interfacial atoms located within certain cutoff distance of each other. Applying frequent subgraph mining to these interfaces provides "native" frequent patterns of interacting atoms. Subsequently, given a pose for a protein-ligand complex of interest, the pose-scoring function (the information-processing unit or neuron) calculates the degree of matching between the interaction patterns present at the pose's interface and the native frequent patterns. The pose-scoring function takes into account the frequency of occurrence of the matching native patterns, the size of the match, and the degree of geometrical similarity between pose-specific and matching native frequent patterns. This novel "multi-body interaction" pose-scoring function (MBI-Score) was validated using two databases, PDBbind and Astex-85, and it outperformed seven commonly used commercial scoring functions. MBI-Score is available at www.khashanlab.org/mbi-score.

Structure and function of retroviral integrase

A hallmark of retroviral replication is establishment of the proviral state, wherein a DNA copy of the viral RNA genome is stably incorporated into a host cell chromosome. Integrase is the viral enzyme responsible for the catalytic steps involved in this process, and integrase strand transfer inhibitors are widely used to treat people living with HIV. Over the past decade, a series of X-ray crystallography and cryogenic electron microscopy studies have revealed the structural basis of retroviral DNA integration. A variable number of integrase molecules congregate on viral DNA ends to assemble a conserved intasome core machine that facilitates integration. The structures additionally informed on the modes of integrase inhibitor action and the means by which HIV acquires drug resistance. Recent years have witnessed the development of allosteric integrase inhibitors, a highly promising class of small molecules that antagonize viral morphogenesis. In this Review, we explore recent insights into the organization and mechanism of the retroviral integration machinery and highlight open questions as well as new directions in the field.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Precursors of Viral Proteases as Distinct Drug Targets

Viral proteases are indispensable for successful virion maturation, thus making them a prominent drug target. Their enzyme activity is tightly spatiotemporally regulated by expression in the precursor form with little or no activity, followed by activation via autoprocessing. These cleavage events are frequently triggered upon transportation to a specific compartment inside the host cell. Typically, precursor oligomerization or the presence of a co-factor is needed for activation. A detailed understanding of these mechanisms will allow ligands with non-canonical mechanisms of action to be designed, which would specifically modulate the initial irreversible steps of viral protease autoactivation. Binding sites exclusive to the precursor, including binding sites beyond the protease domain, can be exploited. Both inhibition and up-regulation of the proteolytic activity of viral proteases can be detrimental for the virus. All these possibilities are discussed using examples of medically relevant viruses including herpesviruses, adenoviruses, retroviruses, picornaviruses, caliciviruses, togaviruses, flaviviruses, and coronaviruses.

Structural determinants for subnanomolar inhibition of the secreted aspartic protease Sapp1p from Candida parapsilosis

Pathogenic Candida albicans yeasts frequently cause infections in hospitals. Antifungal drugs lose effectiveness due to other Candida species and resistance. New medications are thus required. Secreted aspartic protease of C. parapsilosis (Sapp1p) is a promising target. We have thus solved the crystal structures of Sapp1p complexed to four peptidomimetic inhibitors. Three potent inhibitors (K_i: 0.1, 0.4, 6.6 nM) resembled pepstatin A (K_i: 0.3 nM), a general aspartic protease inhibitor, in terms of their interactions with Sapp1p. However, the weaker inhibitor (K_i: 14.6 nM) formed fewer nonpolar contacts with Sapp1p, similarly to the smaller HIV protease inhibitor ritonavir (K_i: 1.9 µM), which, moreover, formed fewer H-bonds. The analyses have revealed the structural determinants of the subnanomolar inhibition of C. parapsilosis aspartic protease. Because of the high similarity between Saps from different Candida species, these results can further be used for the design of potent and specific Sap inhibitor-based antimycotic drugs.

Insights into effect of the Asp25/Asp25' protonation states on binding of inhibitors Amprenavir and MKP97 to HIV-1 protease using molecular dynamics simulations and MM-GBSA calculations

The protonation states of two aspartic acids in the catalytic strands of HIV-1 protease (PR) remarkably affect bindings of inhibitors to PR. It is requisite for the design of potent inhibitors towards PR to investigate the influences of Asp25/Asp25' protonated states on dynamics behaviour of PR and binding mechanism of inhibitors to PR. In this work, molecular dynamics (MD) simulations, MM-GBSA method and principal component (PC) analysis were coupled to explore the effect of Asp25/Asp25' protonation states on conformational changes of PR and bindings of Amprenavir and MKP97 to PR. The results show that the Asp25/Asp25' protonation states exert different impacts on structural fluctuations, flexibility and motion modes of PR. Dynamics analysis verifies that Asp25/Asp25' protonated states highly affect conformational dynamics of two flaps in PR. The binding free energy calculations results suggest that the Asp25/Asp25' protonated states obviously strengthen bindings of inhibitors to PR compared to the non-protonation state. Calculations of residue-based free energy decomposition indicate that the Asp25/Asp25' protonation not only disturbs the interaction network of inhibitors with PR but also stabilizes bindings of inhibitors to PR by cancelling the electrostatic repulsive interaction. Therefore, special attentions should be paid to the Asp25/Asp25' protonation in the design of potent inhibitors towards PR.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

A structure-based approach for the discovery of inhibitors against methylcitrate synthase of Paracoccidioides lutzii

Paracoccidioidomycosis (PCM) is a systemic mycosis, endemic in Latin America, caused by fungi of the genus Paracoccidioides. The treatment of PCM is complex, requiring a long treatment period, which often results in serious side effects. The aim of this study was to screen for inhibitors of a specific target of the fungus that is absent in humans. Methylcitrate synthase (MCS) is a unique enzyme of microorganisms and is responsible for the synthesis of methylcitrate at the beginning of the propionate degradation pathway. This pathway is essential for several microorganisms, since the accumulation of propionyl-CoA can impair virulence and prevent the development of the pathogen. We performed the modeling and molecular dynamics of the structure of Paracoccidioides lutzii MCS (PlMCS) and performed a virtual screening on 89,415 compounds against the active site of the enzyme. The compounds were selected according to the affinity and efficiency criteria of in vitro tests. Six compounds were able to inhibit the enzymatic activity of recombinant PlMCS but only the compound ZINC08964784 showed fungistatic and fungicidal activity against Paracoccidioides spp. cells. The analysis of the interaction profile of this compound with PlMCS showed its effectiveness in terms of specificity and stability when compared to the substrate (propionyl-CoA) of the enzyme. In addition, this compound did not show cytotoxicity in mammalian cells, with an excellent selectivity index. Our results suggest that the compound ZINC08964784 may become a promising alternative antifungal against Paracoccidioides spp. Communicated by Ramaswamy H. Sarma.

Remodelling structure-based drug design using machine learning

To keep up with the pace of rapid discoveries in biomedicine, a plethora of research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Drug Design (SBDD). In the past few decades, SBDD played a stupendous role in identification of novel drug-like molecules that are capable of altering the structures and/or functions of the target macromolecules involved in different disease pathways and networks. Unfortunately, post-delivery drug failures due to adverse drug interactions have constrained the use of SBDD in biomedical applications. However, recent technological advancements, along with parallel surge in clinical research have led to the concomitant establishment of other powerful computational techniques such as Artificial Intelligence (AI) and Machine Learning (ML). These leading-edge tools with the ability to successfully predict side-effects of a wide range of drugs have eventually taken over the field of drug design. ML, a subset of AI, is a robust computational tool that is capable of data analysis and analytical model building with minimal human intervention. It is based on powerful algorithms that use huge sets of 'training data' as inputs to predict new output values, which improve iteratively through experience. In this review, along with a brief discussion on the evolution of the drug discovery process, we have focused on the methodologies pertaining to the technological advancements of machine learning. This review, with specific examples, also emphasises the tremendous contributions of ML in the field of biomedicine, while exploring possibilities for future developments.

HIV Protease: Historical Perspective and Current Research

The retroviral protease of human immunodeficiency virus (HIV) is an excellent target for antiviral inhibitors for treating HIV/AIDS. Despite the efficacy of therapy, current efforts to control the disease are undermined by the growing threat posed by drug resistance. This review covers the historical background of studies on the structure and function of HIV protease, the subsequent development of antiviral inhibitors, and recent studies on drug-resistant protease variants. We highlight the important contributions of Dr. Stephen Oroszlan to fundamental knowledge about the function of the HIV protease and other retroviral proteases. These studies, along with those of his colleagues, laid the foundations for the design of clinical inhibitors of HIV protease. The drug-resistant protease variants also provide an excellent model for investigating the molecular mechanisms and evolution of resistance.

Structure-based drug design of an inhibitor of the SARS-CoV-2 (COVID-19) main protease using free software: A tutorial for students and scientists

This paper describes the structure-based design of a preliminary drug candidate against COVID-19 using free software and publicly available X-ray crystallographic structures. The goal of this tutorial is to disseminate skills in structure-based drug design and to allow others to unleash their own creativity to design new drugs to fight the current pandemic. The tutorial begins with the X-ray crystallographic structure of the main protease (M^pro) of the SARS coronavirus (SARS-CoV) bound to a peptide substrate and then uses the UCSF Chimera software to modify the substrate to create a cyclic peptide inhibitor within the M^pro active site. Finally, the tutorial uses the molecular docking software AutoDock Vina to show the interaction of the cyclic peptide inhibitor with both SARS-CoV M^pro and the highly homologous SARS-CoV-2 M^pro. The supporting information provides an illustrated step-by-step protocol, as well as a video showing the inhibitor design process, to help readers design their own drug candidates for COVID-19 and the coronaviruses that will cause future pandemics. An accompanying preprint in bioRxiv [https://doi.org/10.1101/2020.08.03.234872] describes the synthesis of the cyclic peptide and the experimental validation as an inhibitor of SARS-CoV-2 M^pro.