Roles of Virtual Screening and Molecular Dynamics Simulations in Discovering and Understanding Antimalarial Drugs.

Malaria continues to be a global health threat, with approximately 247 million cases worldwide. Despite therapeutic interventions being available, patient compliance is a problem due to the length of treatment. Moreover, drug-resistant strains have emerged over the years, necessitating urgent identification of novel and more potent treatments. Given that traditional drug discovery often requires a great deal of time and resources, most drug discovery efforts now use computational methods. In silico techniques such as quantitative structure-activity relationship (QSAR), docking, and molecular dynamics (MD) can be used to study protein-ligand interactions and determine the potency and safety profile of a set of candidate compounds to help prioritize those tested using assays and animal models. This paper provides an overview of antimalarial drug discovery and the application of computational methods in identifying candidate inhibitors and elucidating their potential mechanisms of action. We conclude with the continued challenges and future perspectives in the field of antimalarial drug discovery.

In silico evaluation of Philippine Natural Products against SARS-CoV-2 Main Protease.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is a novel strain of coronavirus first reported in December 2019 which rapidly spread throughout the world and was subsequently declared a pandemic by the World Health Organization (WHO) in March 2020. Although vaccines, as well as treatments, have been rapidly developed and deployed, these are still spread thin, especially in the developing world. There is also a continuing threat of the emergence of mutated variants which may not be as responsive to available vaccines and drugs. Accessible and affordable sources of antiviral drugs against SARS-CoV-2 offer wider options for the clinical treatment of populations at risk for severe COVID-19. Using in silico methods, this study identified potential inhibitors against the SARS-CoV-2 main protease (Mpro), the protease directly responsible for the activation of the viral replication enzyme, from a consolidated database of 1516 Philippine natural products. Molecular docking experiments, along with in silico ADME predictions, determined top ligands from this database with the highest potential inhibitory effects against Mpro. Molecular dynamic trajectories of the apo and diosmetin-7-O-b-D-glucopyranoside (DG) in complex with the protein predicted potential mechanisms of action for the ligand-by separating the Cys145-His41 catalytic dyad and by influencing the protein network through key intra-signaling residues within the Mpro binding site. These findings show the inhibitory potential of DG against the SARS-CoV-2 Mpro, and further validation is recommended through in vitro or in vivo experimentation.

N-terminus-independent activation of c-Src via binding to a tetraspan(in) TM4SF5 in hepatocellular carcinoma is abolished by the TM4SF5 C-terminal peptide application.

Active c-Src non-receptor tyrosine kinase localizes to the plasma membrane via N-terminal lipid modification. Membranous c-Src causes cancer initiation and progression. Even though transmembrane 4 L six family member 5 (TM4SF5), a tetraspan(in), can be involved in this mechanism, the molecular and structural influence of TM4SF5 on c-Src remains unknown. Methods: Here, we investigated molecular and structural details by which TM4SF5 regulated c-Src devoid of its N-terminus and how cell-penetrating peptides were able to interrupt c-Src activation via interference of c-Src-TM4SF5 interaction in hepatocellular carcinoma models. Results: The TM4SF5 C-terminus efficiently bound the c-Src SH1 kinase domain, efficiently to the inactively-closed form. The complex involved protein tyrosine phosphatase 1B able to dephosphorylate Tyr530. The c-Src SH1 domain alone, even in a closed form, bound TM4SF5 to cause c-Src Tyr419 and FAK Y861 phosphorylation. Homology modeling and molecular dynamics simulation studies predicted the directly interfacing residues, which were further validated by mutational studies. Cell penetration of TM4SF5 C-terminal peptides blocked the interaction of TM4SF5 with c-Src and prevented c-Src-dependent tumor initiation and progression in vivo. Conclusions: Collectively, these data demonstrate that binding of the TM4SF5 C-terminus to the kinase domain of inactive c-Src leads to its activation. Because this binding can be abolished by cell-penetrating peptides containing the TM4SF5 C-terminus, targeting this direct interaction may be an effective strategy for developing therapeutics that block the development and progression of hepatocellular carcinoma.

Structure-based discovery of small molecule APC-Asef interaction inhibitors: In silico approaches and molecular dynamics simulations.

Colorectal cancer, which is considered one of the leading causes of mortality worldwide, develops through the formation of benign polyps on the inner colon or rectum wall. Truncations in adenomatous polyposis coli (APC) gene lead to the spread of the disease in the entire colon region when combined with the guanine nucleotide exchange factor (GEF) Asef. A series of peptidomimetic agents were previously discovered as protein-protein interaction inhibitors that can target the APC-Asef interface. Structure-based virtual screening (SBVS), using a set of docking methods combined with molecular dynamics simulations, was carried out to identify new small drug-like agents. After the initial screening process, compounds with diverse chemical scaffolds and direct interaction with Arg549 and other active site residues were chosen and subjected to induce fit. The amide functional group found in the ligand hit structures showed strong interactions with Arg549, leading to observable conformational changes that allow suitable positioning within the peptide binding site. Furthermore, the pH-specific MD simulations of the top hit 838 within the APC-Asef binding site depicted significant interactions required for biochemical recognition in changing microenvironment. Predicted inhibitory constant (Ki) values and binding free energies of hits further described the significance of the amide group over the other chemical scaffolds. This combination of in silico approaches provides key insights for colorectal drug discovery programs targeting the APC-Asef interaction. Graphical abstract The common active site residues involved in interaction with ligands.

In Silico Strategies in Tuberculosis Drug Discovery.

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Discovery of Conformationally Restricted Human Glutaminyl Cyclase Inhibitors as Potent Anti-Alzheimer's Agents by Structure-Based Design.

Alzheimer's disease (AD) is an incurable, progressive neurodegenerative disease whose pathogenesis cannot be defined by one single element but consists of various factors; thus, there is a call for alternative approaches to tackle the multifaceted aspects of AD. Among the potential alternative targets, we aim to focus on glutaminyl cyclase (QC), which reduces the toxic pyroform of ß-amyloid in the brains of AD patients. On the basis of a putative active conformation of the prototype inhibitor 1, a series of N-substituted thiourea, urea, and α-substituted amide derivatives were developed. The structure-activity relationship analyses indicated that conformationally restrained inhibitors demonstrated much improved QC inhibition in vitro compared to nonrestricted analogues, and several selected compounds demonstrated desirable therapeutic activity in an AD mouse model. The conformational analysis of a representative inhibitor indicated that the inhibitor appeared to maintain the Z-E conformation at the active site, as it is critical for its potent activity.

Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs).

Demand for novel GPCR modulators is increasing as the association between the GPCR signaling pathway and numerous diseases such as cancers, psychological and metabolic disorders continues to be established. In silico structure-based drug design (SBDD) offers an outlet where researchers could exploit the accumulating structural information of GPCR to expedite the process of drug discovery. The coupling of structure-based approaches such as virtual screening and molecular docking with molecular dynamics and/or Monte Carlo simulation aids in reflecting the dynamics of proteins in nature into previously static docking studies, thus enhancing the accuracy of rationally designed ligands. This review will highlight recent computational strategies that incorporate protein flexibility into SBDD of GPCR-targeted ligands.

Transmembrane 4 L Six Family Member 5 Senses Arginine for mTORC1 Signaling.

The mechanistic target of rapamycin complex (mTORC1) is a signaling hub on the lysosome surface, responding to lysosomal amino acids. Although arginine is metabolically important, the physiological arginine sensor that activates mTOR remains unclear. Here, we show that transmembrane 4 L six family member 5 (TM4SF5) translocates from plasma membrane to lysosome upon arginine sufficiency and senses arginine, culminating in mTORC1/S6K1 activation. TM4SF5 bound active mTOR upon arginine sufficiency and constitutively bound amino acid transporter SLC38A9. TM4SF5 binding to the cytosolic arginine sensor Castor1 decreased upon arginine sufficiency, thus allowing TM4SF5-mediated sensing of metabolic amino acids. TM4SF5 directly bound free L-arginine via its extracellular loop possibly for the efflux, being supported by mutant study and homology and molecular docking modeling. Therefore, we propose that lysosomal TM4SF5 senses and enables arginine efflux for mTORC1/S6K1 activation, and arginine-auxotroph in hepatocellular carcinoma may be targeted by blocking the arginine sensing using anti-TM4SF5 reagents.

Structural and Biochemical Characterization of the Curcumin-Reducing Activity of CurA from Vibrio vulnificus.

Curcumin is a yellow-colored ingredient in dietary spice turmeric ( Curcuma longa Linn). This nontoxic polyphenol has antitumor, anti-inflammatory, apoptotic, and antioxidant activities. The ingested curcumin is reduced to multihydrated forms with more potent therapeutic potentials by the curcumin reductase (CurA) from commensal Escherichia coli. In this study, we demonstrated that Vibrio vulnificus CurA ( VvCurA) with 87% sequence similarity to the E. coli CurA exhibits the curcumin-reducing activity through spectrophotometric detection of NADPH oxidation and high performance liquid chromatographic analysis of curcumin consumption and product generation. Afterward, we determined the crystal structures of VvCurA and the VvCurA/NADPH complex, and made the in silico model of the VvCurA/NADPH/curcumin ternary complex through induced fit docking. Based on structural information, active site residues that play critical roles in catalysis have been identified and characterized by mutational and kinetic studies, leading us to propose the reaction mechanism of CurA.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

In silico discovery and in vitro activity of inhibitors against Mycobacterium tuberculosis 7,8-diaminopelargonic acid synthase (Mtb BioA).

Computer-aided drug discovery and development approaches such as virtual screening, molecular docking, and in silico drug property calculations have been utilized in this effort to discover new lead compounds against tuberculosis. The enzyme 7,8-diaminopelargonic acid aminotransferase (BioA) in Mycobacterium tuberculosis (Mtb), primarily involved in the lipid biosynthesis pathway, was chosen as the drug target due to the fact that humans are not capable of synthesizing biotin endogenously. The computational screening of 4.5 million compounds from the Enamine REAL database has ultimately yielded 45 high-scoring, high-affinity compounds with desirable in silico absorption, distribution, metabolism, excretion, and toxicity properties. Seventeen of the 45 compounds were subjected to bioactivity validation using the resazurin microtiter assay. Among the 4 actives, compound 7 ((Z)-N-(2-isopropoxyphenyl)-2-oxo-2-((3-(trifluoromethyl)cyclohexyl)amino)acetimidic acid) displayed inhibitory activity up to 83% at 10 µg/mL concentration against the growth of the Mtb H37Ra strain.

Toward antituberculosis drugs: in silico screening of synthetic compounds against Mycobacterium tuberculosisl,d-transpeptidase 2.

Mycobacterium tuberculosis (Mtb) the main causative agent of tuberculosis, is the main reason why this disease continues to be a global public health threat. It is therefore imperative to find a novel antitubercular drug target that is unique to the structural machinery or is essential to the growth and survival of the bacterium. One such target is the enzyme l,d-transpeptidase 2, also known as LdtMt2, a protein primarily responsible for the catalysis of 3→3 cross-linkages that make up the mycolyl-arabinogalactan-peptidoglycan complex of Mtb. In this study, structure-based pharmacophore screening, molecular docking, and in silico toxicity evaluations were employed in screening compounds from a database of synthetic compounds. Out of the 4.5 million database compounds, 18 structures were identified as high-scoring, high-binding hits with very satisfactory absorption, distribution, metabolism, excretion, and toxicity properties. Two out of the 18 compounds were further subjected to in vitro bioactivity assays, with one exhibiting a good inhibitory activity against the Mtb H37Ra strain.

Role of computer-aided drug design in modern drug discovery.

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.