Drug Design Strategies Research Articles

Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.

Abnormal protein–membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein–membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein–membrane interface would shed light into the accessibility of membrane–protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein–membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.

Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions.

Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.

In silico discovery of novel inhibitors from Northern African natural products database against main protease (Mpro) of SARS-CoV-2

The recent outbreak of COVID-19 (Coronavirus Disease 2019), caused by a novel SARS-CoV-2 virus, has led to public health emergencies worldwide where time is as important as equipment to save lives. Antimalarial drugs such as hydroxychloroquine and chloroquine derivatives are used in emergencies but they are not suitable for patients with high blood pressure, diabetes and heart problems. Since there are no approved drugs for this disease, science is challenged to find vaccines and new drugs. Therefore, as part of our Silico drug design strategy, we identified drug-like compounds that inhibit replication of the main protease (Mpro) of SARS-CoV-2 based on receptor-based virtual database screening, molecular docking, molecular dynamics, and drug-similarity profiling from the NANPDB natural products database available at North African. The two resulting hit compounds named 5- Chloro-Omega-hydroxy-1-O-methylemodin and cystodion E showed the highest binding energy with Mpro of SARS-CoV-2 and strong inhibitory activity compared with the previously published N3 inhibitor. The complexes of these two compounds were validated by molecular dynamics analysis (RMSD, RMSF, Rg, total number of hydrogen bonds and secondary structure fractions of the protein in the complex) as the best method to evaluate the biological stability of the system. Therefore, these molecules deserve more attention in drug development compared to COVID-19. Highlights A large database of natural compounds was screened against nCoV-2's Mpro. Molecular docking and Molecular dynamics were used as powerful methods. Two compounds were found are very attractive to inhibit Mpro of nCoV-2. ADME-Tox profiling is evaluated the active compounds are not cancerogenic. Communicated by Ramaswamy H. Sarma

Molecular Docking: A Review Paper

Molecular docking basically explains about the orientation of a molecule preferred to the other molecule in a manner as in when they attach to each other in order to get a steady complex. By using scoring functions further the power of alliance or the irrevocable affinity of the different molecules is predicted by the preferred orientation of the two molecules. These interactions, which are achieved by molecular docking between important biological components such as proteins, peptides, nucleic acid, carbohydrates, and lipids, are crucial in signal transduction. Further, these interactions between two molecules which are being performed by the help of molecular docking also helps in predicting the type of signals produced. Therefore, molecular docking is helpful in predicting both strength and type of signals produced by the molecules. Because of its ability to predict the coupling compliance of small particle ligands to the appropriate target restriction site, molecular docking is among the most often used strategies in structure-based drug design. Characterization of the coupling conduct assumes a significant job in discerning structure of medications just as to clarify crucial biochemical procedures.

Challenges and Perspectives in the Discovery of Dengue Virus Entry Inhibitors.

Dengue virus (DENV) disease has become one of the major challenges in public health. Currently, there is no antiviral treatment for this infection. Since human transmission occurs via mosquitoes of the Aedes genus, most efforts have been focused on the control of this vector. However, these control strategies have not been totally successful, as reflected in the increasing number of DENV infections per year, becoming an endemic disease in more than 100 countries worldwide. Consequently, the development of a safe antiviral agent is urgently needed. In this sense, rational design approaches have been applied in the development of antiviral compounds that inhibit one or more steps in the viral replication cycle. The entry of viruses into host cells is an early and specific stage of infection. Targeting either viral components or cellular protein targets are an affordable and effective strategy for therapeutic intervention of viral infections. This review provides an extensive overview of the small organic molecules, peptides, and inorganic moieties that have been tested so far as DENV entry direct-acting antiviral agents. The latest advances based on computer-aided drug design (CADD) strategies and traditional medicinal chemistry approaches in the design and evaluation of DENV virus entry inhibitors will be discussed. Furthermore, physicochemical drug properties, such as solubility, lipophilicity, stability, and current results of pre-clinical and clinical studies will also be discussed in detail.

A covalent strategy to target intrinsically disordered proteins: Discovery of novel tau aggregation inhibitors

Intrinsically disordered proteins (IDPs) play important roles in disease pathologies; however, their lack of defined stable 3D structures make traditional drug design strategies typically less effective against these targets. Based on promising results of targeted covalent inhibitors (TCIs) on challenging targets, we have developed a covalent design strategy targeting IDPs. As a model system we chose tau, an endogenous IDP of the central nervous system that is associated with severe neurodegenerative diseases via its aggregation. First, we mapped the tractability of available cysteines in tau and prioritized suitable warheads. Next, we introduced the selected vinylsulfone warhead to the non-covalent scaffolds of potential tau aggregation inhibitors. The designed covalent tau binders were synthesized and tested in aggregation models, and inhibited tau aggregation effectively. Our results revealed the usefulness of the covalent design strategy against therapeutically relevant IDP targets and provided promising candidates for the treatment of tauopathies.

Aspirin damages the cell wall of Saccharomyces cerevisiae by inhibiting the expression and activity of dolichol-phosphate mannose synthase 1.

Aspirin is a commonly used anti-inflammatory, analgesic and antithrombotic drug. It has attracted attention due to its potential antifungal therapeutic effect; however, the molecular mechanism is poorly understood. Here, the effects of aspirin on the cell wall of Saccharomyces cerevisiae were explored. We observed by scanning electron microscopy that aspirin could damage the cell wall ultrastructure. Meanwhile, a cellular surface hydrophobicity (CSH) assay showed that aspirin increased the hydrophobicity of the yeast cell surface. A drug sensitivity assay indicated that the overexpression of dolichol phosphate mannose synthase 1 (DPM1) reversed the cell wall damage and decreased the CSH induced by aspirin. Importantly, aspirin decreased the expression and enzyme activity of DPM1 in S. cerevisiae. Molecular docking results demonstrated that aspirin could directly bind to the Ser141 site of DPM1. Similarly, we found that aspirin damaged the cell wall and inhibited the expression of DPM1 in Candida albicans. These findings improve the current understanding of the action mode of aspirin and provide new strategies for antifungal drug design.

Drug discovery of histone lysine demethylases (KDMs) inhibitors (progress from 2018 to present)

Post-translational modifications (PTMs) of histone by histone demethylases (KDMs) play an important role in the regulation of gene expression, which implicates the development of various human cancers and other diseases. Discovering and developing inhibitors targeting KDMs have become an active and fast-growing research area over the past decades. In this review, the latest emerging small-molecule inhibitors of KDMs were surveyed with the emphasis on the literature since 2018, including lysine specific demethylases (LSD or KDM1) inhibitors and JmjC family N-methyl lysine demethylases (JmjC KDMs, i.e. KDM2-7) inhibitors. The drug design strategy, the structure-activity relationships (SARs), the analysis and insight of co-crystal structures, and the mechanisms of action (MOA) were also discussed.

Fascinating Transformation of SAM-Competitive Protein Methyltransferase Inhibitors from Nucleoside Analogues to Non-Nucleoside Analogues.

The abnormal expression of protein methyltransferase (PMT) has been linked with many diseases such as diabetes, neurological disorders, and cancer. S-Adenyl-l-methionine (SAM) is a universal methyl donor and gets converted to S-adenyl-l-homocysteine (SAH), an endogenous competitive inhibitor of SAM. Initially developed SAM/SAH mimetic nucleoside analogues were pan methyltransferase inhibitors. The gradual understanding achieved through ligand-receptor interaction paved the way for various rational approaches of drug design leading to potent and selective nucleoside inhibitors. The present perspective is based on the systematic evolution of selective SAM-competitive heterocyclic non-nucleoside inhibitors from nucleoside inhibitors. This fascinating transition has resolved several issues inherent to nucleoside analogues such as poor pharmacokinetics leading to poor in vivo efficacy. The perspective has brought together various concepts and strategies of drug design that contributed to this rational transition. We firmly believe that the strategies described herein will serve as a template for the future development of drugs in general.

A systematic review of RdRp of SARS-CoV-2 through artificial intelligence and machine learning utilizing structure-based drug design strategy.

Since the coronavirus disease has been declared a global pandemic, it had posed a challenge among researchers and raised common awareness and collaborative efforts towards finding the solution. Caused by severe acute respiratory coronavirus syndrome-2 (SARS-CoV-2), coronavirus drug design strategy needs to be optimized. It is understandable that cognizance of the pathobiology of COVID-19 can help scientists in the development and discovery of therapeutically effective antiviral drugs by elucidating the unknown viral pathways and structures. Considering the role of artificial intelligence and machine learning with its advancements in the field of science, it is rational to use these methods which can aid in the discovery of new potent candidates in silico. Our review utilizes similar methodologies and focuses on RNA-dependent RNA polymerase (RdRp), based on its importance as an essential element for virus replication and also a promising target for COVID-19 therapeutics. Artificial neural network technique was used to shortlist articles with the support of PRISMA, from different research platforms including Scopus, PubMed, PubChem, and Web of Science, through a combination of keywords. "English language", from the year "2000" and "published articles in journals" were selected to carry out this research. We summarized that structural details of the RdRp reviewed in this analysis will have the potential to be taken into consideration when developing therapeutic solutions and if further multidisciplinary efforts are taken in this domain then potential clinical candidates for RdRp of SARS-CoV-2 could be successfully delivered for experimental validations.

DISCOVERY OF TRIKETONE-QUINOXALINE HYBRIDS AS POTENT HPPD INHIBITORS USING STRUCTURE-BASED DRUG DESIGN

<List> <ListItem><ItemContent> • HPPD is one of the most promising targets for new herbicides. </ItemContent></ListItem> <ListItem><ItemContent> • A family of novel HPPD inhibitors based on the triketone-quinoxaline scaffold was designed and synthesized. </ItemContent></ListItem> <ListItem><ItemContent> • One particular product (7d) gave the highest inhibition of HPPD of the newly synthesized derivatives. </ItemContent></ListItem> <ListItem><ItemContent> • Triketone-quinoxaline derivatives provide a useful molecular scaffold for the discovery of novel HPPD-inhibiting herbicides. </ItemContent></ListItem></List> <i>p</i>-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) belongs to the family of Fe(II)-dependent non-heme oxygenases that occur in the majority of aerobic organisms. HPPD has proved to be a promising target in herbicide research and development. A battery of novel triketone-quinoxaline compounds has been designed using a structure-based drug design strategy and then prepared. Enzyme inhibition assays show that these synthesized derivatives possess favorable inhibition capability against <i>Arabidopsis thaliana</i> HPPD with IC₅₀ values ranging from 0.317 to 0.891 μmol·L⁻¹. Subsequently, the molecular docking results indicate that two adjacent carbonyls of the triketone moiety of the representative compound 2-(2,3-dimethyl-8-(o-tolyl)quinoxaline-6-carbonyl)-3-hydroxycyclohex-2-en-1-one (7d) engage in chelation with the ferrous ion of <i>A. thaliana</i> HPPD in a bidentate pose, and its quinoxaline scaffold forms two sets of parallel π-stacking interaction between two phenylalanine residues (Phe424 and Phe381). In addition, the extended phenyl group also interacts with Phe392 in a π-π stacking way. This study indicates that triketone-quinoxaline is a promising scaffold for discovering HPPD inhibitors with substantially increased potency, providing insight into the molecular design of new herbicides.

Extended-ensemble docking to probe dynamic variation of ligand binding sites during large-scale structural changes of proteins.

Proteins can sample a broad landscape as they undergo conformational transition between different functional states. At the same time, as key players in almost all cellular processes, proteins are important drug targets. Considering the different conformational states of a protein is therefore central for a successful drug-design strategy. Here we introduce a novel docking protocol, termed extended-ensemble docking, pertaining to proteins that undergo large-scale (global) conformational changes during their function. In its application to multidrug ABC-transporter P-glycoprotein (Pgp), extensive non-equilibrium molecular dynamics simulations employing system-specific collective variables are first used to describe the transition cycle of the transporter. An extended set of conformations (extended ensemble) representing the full transition cycle between the inward- and the outward-facing states is then used to seed high-throughput docking calculations of known substrates, non-substrates, and modulators of the transporter. Large differences are predicted in the binding affinities to different conformations, with compounds showing stronger binding affinities to intermediate conformations compared to the starting crystal structure. Hierarchical clustering of the binding modes shows all ligands preferably bind to the large central cavity of the protein, formed at the apex of the transmembrane domain (TMD), whereas only small binding populations are observed in the previously described R and H sites present within the individual TMD leaflets. Based on the results, the central cavity is further divided into two major subsites, first preferably binding smaller substrates and high-affinity inhibitors, whereas the second one shows preference for larger substrates and low-affinity modulators. These central subsites along with the low-affinity interaction sites present within the individual TMD leaflets may respectively correspond to the proposed high- and low-affinity binding sites in Pgp. We propose further an optimization strategy for developing more potent inhibitors of Pgp, based on increasing its specificity to the extended ensemble of the protein, instead of using a single protein structure, as well as its selectivity for the high-affinity binding site. In contrast to earlier in silico studies using single static structures of Pgp, our results show better agreement with experimental studies, pointing to the importance of incorporating the global conformational flexibility of proteins in future drug-discovery endeavors.

Design and synthesis of the novel, selective WZ4002 analogue as EGFR-L858R/T790M tyrosine kinase inhibitors for targeted drug therapy in non-small-cell lung cancer (NSCLC)

To conquer the drug-resistance of first-generation EGFR (epidermal growth factor receptor) kinase inhibitors and second-generation inhibitors’ non-selective toxicities in Non-Small Cell Lung Cancer (NSCLC) patients, a series of WZ4002 derivatives (6–46) were discovered as novel double mutant EGFR-L858R/T790M TK inhibitors. This objective was attained by employing structure-based drug design and traditional optimization strategies based on the WZ4002 scaffold. Among the synthesized compounds, representative compounds 8 and 38 displayed significant anti-proliferative activity on the Gefitinib-resistant cell line NCI-H1975, with an IC50 value of 0.179 μM and 0.173 μM, respectively. Also, these compounds exhibited moderate anti-proliferative activity against the A549 cell, with an IC50 of 0.550 μM and 0.528 μM respectively, suggesting their improved selectivity over the mutant EGFR-L858R/T790M. Excitingly, both these compounds showed significant inhibition of the double mutant EGFR-L858R/T790M TK with an IC50 value of 0.0063 μM and 0.0060 μM, respectively. The IC50 values of both the promising compounds against the HepG2 cell line were more than 1 μM, indicating safety for normal cells. Covalent docking and MD simulation further confirm their irreversible binding mode with the target protein. These results demonstrate that compounds 8 and 38 would be promising lead compound-targeting double mutant EGFR-L858R/T790M TK.

Aromatic Sulfonamides including a Sulfonic Acid Tail: New Membrane Impermeant Carbonic Anhydrase Inhibitors for Targeting Selectively the Cancer-Associated Isoforms.

We report here a new drug design strategy for producing membrane-impermeant carbonic anhydrase (CA; EC 4.2.1.1) inhibitors selectively targeting the tumor-associated, membrane-bound human CAs IX and XII over off-target cytosolic isoforms. To date, this approach has only been pursued by including permanent positively charged pyridinium type or highly hydrophilic glycosidic moieties into the structure of aromatic sulfonamide CA inhibitors (CAIs). Aliphatic (propyl and butyl) sulfonic acid tails, deprotonated at physiological pH, were thus incorporated onto a benzenesulfonamide scaffold by a common 1,2,3-triazole linker and different types of spacers. Twenty such derivatives were synthesized and tested for their inhibition of target (hCAs IV, IX, and XII) and off-target CAs (hCAs I and II). Most sulfonate CAIs induced a potent inhibition of hCAs II, IX, and XII up to a low nanomolar KI range (0.9–459.4 nM) with a limited target/off-target CA selectivity of action. According to the drug design schedule, a subset of representative derivatives was assessed for their cell membrane permeability using Caco-2 cells and a developed FIA-MS/MS method. The complete membrane impermeability of the sulfonate tailed CAIs (≥98%) validated these negatively charged moieties as being suitable for achieving, in vivo, the selective targeting of the tumor-associated CAs over off-target ones.

Development of Computational Approaches with a Fragment-Based Drug Design Strategy: In Silico Hsp90 Inhibitors Discovery.

A semi-exhaustive approach and a heuristic search algorithm use a fragment-based drug design (FBDD) strategy for designing new inhibitors in an in silico process. A deconstruction reconstruction process uses a set of known Hsp90 ligands for generating new ones. The deconstruction process consists of cutting off a known ligand in fragments. The reconstruction process consists of coupling fragments to develop a new set of ligands. For evaluating the approaches, we compare the binding energy of the new ligands with the known ligands.

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.

Structure-Function Characteristics of SARS-CoV-2 Proteases and Their Potential Inhibitors from Microbial Sources.

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is considered the greatest challenge to the global health community of the century as it continues to expand. This has prompted immediate urgency to discover promising drug targets for the treatment of COVID-19. The SARS-CoV-2 viral proteases, 3-chymotrypsin-like protease (3CLpro) and papain-like cysteine protease (PLpro), have become the promising target to study due to their essential functions in spreading the virus by RNA transcription, translation, protein synthesis, processing and modification, virus replication, and infection of the host. As such, understanding of the structure and function of these two proteases is unavoidable as platforms for the development of inhibitors targeting this protein which further arrest the infection and spread of the virus. While the abundance of reports on the screening of natural compounds such as SARS-CoV-2 proteases inhibitors are available, the microorganisms-based compounds (peptides and non-peptides) remain less studied. Indeed, microorganisms-based compounds are also one of the potent antiviral candidates against COVID-19. Microbes, especially bacteria and fungi, are other resources to produce new drugs as well as nucleosides, nucleotides, and nucleic acids. Thus, we have compiled various reported literature in detail on the structures, functions of the SARS-CoV-2 proteases, and potential inhibitors from microbial sources as assistance to other researchers working with COVID-19. The compounds are also compared to HIV protease inhibitors which suggested the microorganisms-based compounds are advantageous as SARS-CoV2 proteases inhibitors. The information should serve as a platform for further development of COVID-19 drug design strategies.

From natural products to HDAC inhibitors: An overview of drug discovery and design strategy

Histone deacetylases (HDACs) play a key role in the homeostasis of protein acetylation in histones and have recently emerged as a therapeutic target for numerous diseases. The inhibition of HDACs may block angiogenesis, arrest cell growth, and lead to differentiation and apoptosis in tumour cells. Thus, HDAC inhibitors (HDACi) have received increasing attention and many of which are developed from natural sources. In the past few decades, naturally occurring HDACi have been identified to have potent anticancer activities, some of which have demonstrated promising therapeutic effects on haematological malignancies. In this review, we summarized the discovery and modification of HDAC inhibitors from natural sources, novel drug design that uses natural products as parent nuclei, and dual target design strategies that combine HDAC with non-HDAC targets.

Structure, Function, and Thermodynamics of Lactate Dehydrogenases from Humans and the Malaria Parasite P. falciparum.

Temperature adaptation is ubiquitous among all living organisms, yet the molecular basis for this process remains poorly understood. It can be assumed that for parasite-host systems, the same enzymes found in both organisms respond to the same selection factor (human body temperature) with similar structural changes. Herein, we report the existence of a reversible temperature-dependent structural transition for the glycolytic enzyme lactate dehydrogenase (LDH) from the malaria parasite Plasmodium falciparum (pfLDH) and human heart (hhLDH) occurring in the temperature range of human fever. This transition is observed for LDHs from psychrophiles, mesophiles, and moderate thermophiles in their operating temperature range. Thermodynamic analysis reveals unique thermodynamic signatures of the LDH-substrate complexes defining a specific temperature range to which human LDH is adapted and parasite LDH is not, despite their common mesophilic nature. The results of spectroscopic analysis combined with the available crystallographic data reveal the existence of an active center within pfLDH that imparts psychrophilic structural properties to the enzyme. This center consists of two pockets, one formed by the five amino acids (5AA insert) within the substrate specificity loop and the other by the active site, that mutually regulate one another in response to temperature and induce structural and functional changes in the Michaelis complex. Our findings pave the way toward a new strategy for malaria treatments and drug design using therapeutic agents that inactivate malarial LDH selectively at a specific temperature range of the cyclic malaria paroxysm.

Finding the First Potential Inhibitors of Shikimate Kinase from Methicillin Resistant Staphylococcus aureus through Computer-Assisted Drug Design.

Methicillin-resistant Staphylococcus aureus (MRSA) is an important threat as it causes serious hospital and community acquired infections with deathly outcomes oftentimes, therefore, development of new treatments against this bacterium is a priority. Shikimate kinase, an enzyme in the shikimate pathway, is considered a good target for developing antimicrobial drugs; this is given because of its pathway, which is essential in bacteria whereas it is absent in mammals. In this work, a computer-assisted drug design strategy was used to report the first potentials inhibitors for Shikimate kinase from methicillin-resistant Staphylococcus aureus (SaSK), employing approximately 5 million compounds from ZINC15 database. Diverse filtering criteria, related to druglike characteristics and virtual docking screening in the shikimate binding site, were performed to select structurally diverse potential inhibitors from SaSK. Molecular dynamics simulations were performed to elucidate the dynamic behavior of each SaSK–ligand complex. The potential inhibitors formed important interactions with residues that are crucial for enzyme catalysis, such as Asp37, Arg61, Gly82, and Arg138. Therefore, the compounds reported provide valuable information and can be seen as the first step toward developing SaSK inhibitors in the search of new drugs against MRSA.