An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease

The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic, and the SARS-CoV-2 main protease 3CLpro is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue toward therapeutic intervention. In this work, a computational docking approach was developed to identify potential small-molecule inhibitors for SARS-CoV-2 3CLpro. Totally 288 potential hits were identified from a half-million bioactive chemicals via a protein-ligand docking protocol. To further evaluate the docking results, a quantitative structure activity relationship (QSAR) model of 3CLpro inhibitors was developed based on existing small molecule inhibitors of the 3CLproSARS– CoV– 1 and their corresponding IC50 data. The QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLproSARS– CoV– 2. Seventy-one potential inhibitors of 3CLpro were selected through these computational approaches and further evaluated via an enzyme activity assay. The results show that two chemicals, i.e., 5-((1-([1,1′-biphenyl]-4-yl)-2,5-dimethyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione and N-(4-((3-(4-chlorophenylsulfonamido)quinoxalin-2-yl)amino)phenyl)acetamide, effectively inhibited 3CLpro SARS-CoV-2 with IC50’s of 19 ± 3 μM and 38 ± 3 μM, respectively. The compounds contain two basic structures, pyrimidinetrione and quinoxaline, which were newly found in 3CLpro inhibitor structures and are of high interest for lead optimization. The findings from this work, such as 3CLpro inhibitor candidates and the QSAR model, will be helpful to accelerate the discovery of inhibitors for related coronaviruses that may carry proteases with similar structures to SARS-CoV-2 3CLpro

Novel Inhibitor Design for Hemagglutinin against H1N1 Influenza Virus by Core Hopping Method

The worldwide spread of H1N1 avian influenza and the increasing reports about its resistance to the current drugs have made a high priority for developing new anti-influenza drugs. Owing to its unique function in assisting viruses to bind the cellular surface, a key step for them to subsequently penetrate into the infected cell, hemagglutinin (HA) has become one of the main targets for drug design against influenza virus. To develop potent HA inhibitors, the ZINC fragment database was searched for finding the optimal compound with the core hopping technique. As a result, the Neo6 compound was obtained. It has been shown through the subsequent molecular docking studies and molecular dynamic simulations that Neo6 not only assumes more favorable conformation at the binding pocket of HA but also has stronger binding interaction with its receptor. Accordingly, Neo6 may become a promising candidate for developing new and more powerful drugs for treating influenza. Or at the very least, the findings reported here may provide useful insights to stimulate new strategy in this area

Use of Structure-And Ligand-Based Drug Design Tools for the Discovery of Small Molecule Inhibitors of Cysteine Proteases for the Treatment of Malaria and Sars Infection

A wide array of molecular modeling tools were utilized to design and develop inhibitors against cysteine protease of P. Falciparum Malaria and Severe Acute Respiratory Syndrome (SARS). A number of potent inhibitors were developed against cysteine protease and hemoglobinase of P. falciparum , referred as Falcipains (FPs), by the structure-based virtual screening of the focused libraries enriched in soft-electrophiles containing compounds. Twenty one diverse, non-peptidic, low micromolar hits were identified. A combined data mining and combinatorial library synthesis approach was performed to discover analogs of virtual screening hits and establish the structure-activity relationships (SAR). However, the resulting SAR of the identified hits was unusually steep in some cases and could not be explained by a traditional analysis of the interactions (electrostatics, van der Waals or H-bond). To gain insights, a statistical thermodynamic analysis of explicit solvent in the ligand binding domain of FP-2 and FP-3 was performed that explained some of the complex trends in the SAR. Furthermore, the moderate potency of a subset of FP-2 hits was elucidated using quantum mechanics calculations that shoreduced reactivity of the electrophilic center of these hits. In addition, solvent thermodynamics and reactivity analysis also helped to elucidate the complex trends in SAR of peptidomimetic inhibitors of FP-2 and FP-3 synthesized in our laboratory. Multi nanosecond explicit solvent molecular dynamics simulations were carried out using the docking poses of the known inhibitors in the binding site of SARS-3CLpro, a cysteine protease important for replication of SARS virus, to study the overall stability of the binding site interactions as well as identify important changes in the interaction profile that were not apparent from the docking study. Analysis of the simulation studies led to the identification of certain protein-ligand interaction patterns which would be useful in further structure based design efforts against cysteine protease (3CLpro) of SARS

3D QSAR Pharmacophore Modeling, in Silico Screening, and Density Functional Theory (DFT) Approaches for Identification of Human Chymase Inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating “Hypo1”, it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC50) data thus successfully validating “Hypo1” by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors

Design Novel Dual Agonists for Treating Type-2 Diabetes by Targeting Peroxisome Proliferator-Activated Receptors with Core Hopping Approach

Owing to their unique functions in regulating glucose, lipid and cholesterol metabolism, PPARs (peroxisome proliferator-activated receptors) have drawn special attention for developing drugs to treat type-2 diabetes. By combining the lipid benefit of PPAR-alpha agonists (such as fibrates) with the glycemic advantages of the PPAR-gamma agonists (such as thiazolidinediones), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of the powerful “core hopping” and “glide docking” techniques, a novel class of PPAR dual agonists was discovered based on the compound GW409544, a well-known dual agonist for both PPAR-alpha and PPAR-gamma modified from the farglitazar structure. It was observed by molecular dynamics simulations that these novel agonists not only possessed the same function as GW409544 did in activating PPAR-alpha and PPAR-gamma, but also had more favorable conformation for binding to the two receptors. It was further validated by the outcomes of their ADME (absorption, distribution, metabolism, and excretion) predictions that the new agonists hold high potential to become drug candidates. Or at the very least, the findings reported here may stimulate new strategy or provide useful insights for discovering more effective dual agonists for treating type-2 diabetes. Since the “core hopping” technique allows for rapidly screening novel cores to help overcome unwanted properties by generating new lead compounds with improved core properties, it has not escaped our notice that the current strategy along with the corresponding computational procedures can also be utilized to find novel and more effective drugs for treating other illnesses

DDESC: Dragon database for exploration of sodium channels in human

<p>Abstract</p> <p>Background</p> <p>Sodium channels are heteromultimeric, integral membrane proteins that belong to a superfamily of ion channels. The mutations in genes encoding for sodium channel proteins have been linked with several inherited genetic disorders such as febrile epilepsy, Brugada syndrome, ventricular fibrillation, long QT syndrome, or channelopathy associated insensitivity to pain. In spite of these significant effects that sodium channel proteins/genes could have on human health, there is no publicly available resource focused on sodium channels that would support exploration of the sodium channel related information.</p> <p>Results</p> <p>We report here Dragon Database for Exploration of Sodium Channels in Human (DDESC), which provides comprehensive information related to sodium channels regarding different entities, such as "genes and proteins", "metabolites and enzymes", "toxins", "chemicals with pharmacological effects", "disease concepts", "human anatomy", "pathways and pathway reactions" and their potential links. DDESC is compiled based on text- and data-mining. It allows users to explore potential associations between different entities related to sodium channels in human, as well as to automatically generate novel hypotheses.</p> <p>Conclusion</p> <p>DDESC is first publicly available resource where the information related to sodium channels in human can be explored at different levels. This database is freely accessible for academic and non-profit users via the worldwide web <url>http://apps.sanbi.ac.za/ddesc</url>.</p

Protein Domain Boundary Predictions: A Structural Biology Perspective

One of the important fields to apply computational tools for domain boundaries prediction is structural biology. They can be used to design protein constructs that must be expressed in a stable and functional form and must produce diffraction-quality crystals. However, prediction of protein domain boundaries on the basis of amino acid sequences is still very problematical. In present study the performance of several computational approaches are compared. It is observed that the statistical significance of most of the predictions is rather poor. Nevertheless, when the right number of domains is correctly predicted, domain boundaries are predicted within very few residues from their real location. It can be concluded that prediction methods cannot be used yet as routine tools in structural biology, though some of them are rather promising

Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions

Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions

Application of Signal Processing and Soft Computing To Genomics

A major challenge for genomic research is to establish a relationship among sequences,structures and function of genes. In addition processing and analyzing this information are of prime importance. Basically genes are repositories for protein coding information and proteins in turn are responsible for most of the important biological functions in all cells. These in turn gives rise to analysis of DNA sequences in proteins, designing of various drugs for genetic diseases. This thesis deals with the applications of signal processing and soft computing algorithms to the field of genomics and proteinomics. Diseases like SARS and Migraine have been modeled using these tools and potential druggable compounds have been proposed which are better than the previous available drugs. Protein structural classes have been identified more accurately based on Genetic Algorithm and Particle Swarm Optimization.Better and efficient methods like Sliding-DFT and Adaptive AR Modeling were proposed to identify Protein coding regions in genes. The proposed methods showed better results as compared to existing methods

Likelihood of protein structure determination

Strukturelle Genomanalyse (SG) beinhaltet die, mit hohem datendurchsatz verbundene bestimmung der dreidimensionalen struktur von makromolekülen durch experimentelle Methoden wie röntgenstrahlen-kristallographie und NMR spektroskopie. Eines der ziele von SG ist es, zeit und kosten der bestimmung von dreidimensionalen proteinstrukturen zu reduzieren, für die homologe strukturen noch nicht gelöst worden sind. Mehrere faktoren wie unregelmäßige conformationen, unzulässige selektion von domängrenzen und löslichkeit können die produktion von proteinkonstrukten für die strukturbiologie erschweren. Zuverlässige, auf aminosäuresequenz basierende prädiktoren zur berechnung von proteinkristallisation sind folglich von nöten. Die vorhersage von unregelmäßigen konformationen ist essentiell, da diese schwierigkeiten in der kristallisation verursachen können. In dieser arbeit wird eine neue methode präsentiert, die es erlaubt, ungeordnete residuen auf basis der aminosäuresequenz mit hoher genauigkeit vorherzusagen, indem verschiedene, auf einer konsensusmethode basierende vorhersagemittel verwendet werden. Die Leistung dieser neuen methode ist signifikant besser als von jedem einzelnen, bisher erwähnten Prädiktor. Zusätzlich ist es wichtig, die voraussetzungen für den quartärstatus eines proteins auf basis seiner sequenz vorherzusagen. Eine Proteinkette kann aus einem monomeren protein bestehen, oder kann, zusammen mit anderen ketten, oligomere komplexe formen, die entweder aus homo-oligomeren oder hetero-oligomeren bestehen können. Im letzten fall muss vermieden werden, die dreidimensionale struktur eines einzelnen protomers zu bestimmen, weil es nicht funktionell ist und auch extrem schwer in löslicher form zu exprimieren ist. Es ist daher erstrebenswert, ein berechnungsmittel zu nützen, das vorherzusagen erlaubt, ob ein potentielles genprodukt teil eines permanenten und obligaten hetero-oligomeren komplexes ist. Hier wird eine neue, auf der aminosäuresequenz basierende methode präsentiert, um hetero-oligomere von monomer und homo-oligomeren proteinen und auch um monomere von homo-oligomeren mit hoher genauigkeit zu unterscheiden. Das erfordernis von metallionen ist im design von strukturbiologischen experimenten ebenso wichtig. Metalloproteine bilden etwa ein drittel der proteoms. Die vorhersage von metalloproteinen hilft kristallographen, geeignetes wachstumsmedium für überexpressionsstudien auszuwählen und auch die wahrscheinlichkeit zu erhöhen, ein korrekt gefaltetes und funktionelles molekül zu erhalten. Hier wird gezeigt, dass die aufnahme von metallionen von proteinen auf basis der aminosäurenzusammensetzung und durch verwenden von lernfähigen analyseprogrammen mit hoher genauigkeit vorhergesagt werden kann. Die ergebnisse in der vorliegenden Doktorarbeit stellen die basis für das sorgfältige design von Proteinkonstrukten dar. Diese computer basierenden selektionsmethoden sind hilfreich, um die auswahl von unmöglichen Zielen zu vermeiden – ein Muss in Strukturbiologie und Proteomics.Structural Genomics (SG) involves the high-throughput determination of threedimensional structures of macromolecules by experimental methods such as X-ray crystallography and NMR spectroscopy. One of the aims of SG is to reduce the time and cost in the determination of three-dimensional protein structures for which a homologous structure had not yet been solved. Several factors such as conformational disorder, improper selection of domain boundaries and solubility can hamper the production of protein constructs for structural biology. Reliable computational protein crystallization propensity predictors, based on amino acid sequences, are consequently required. Prediction of protein conformational disorder is important since it can cause difficulty in crystallization. In this work, a new procedure is presented that allows one to predict disordered residues with high accuracy on the basis of amino acid sequences, by using a consensus method based on various prediction tools. The performance of this new procedure is significantly better than that of each individual predictor previously reported. Furthermore, it is important to be able to predict the quaternary status requirements of a protein on the basis of its sequence. A protein chain can be a monomeric protein or it can form, together with other chains, oligomeric assemblies, which can be either homooligomers or hetero-oligomers. In the later case, it must be avoided to determine the three-dimensional structure of a single protomer, since it will not be functional and it will also be extremely difficult to express in a soluble form. It is thus desirable to have a computational tool that allows one to predict if a potential gene product is a part of permanent and obligate hetero-oligomeric assembly. A new method is presented for discriminating hetero-oligomers from monomeric and homo-oligomeric proteins and also between monomers and homo-oliogmers with high accuracy on the basis of amino acid sequences. Metal ion requirements are also important in designing structural biology experiments. Metalloproteins constitute about one-third of the proteome. Prediction of metalloprotein helps crystallographers to select the proper growth medium for over-expression studies and also to increase the probability of obtaining a properly folded and functional molecule. Here it is shown that the uptake of metal ions by proteins can be predicted with high accuracy on the basis of the amino acid composition and by using machine learning methods. The results described in the present Thesis provide a basis for the careful design of protein constructs. These computational screening methods are helpful to avoid the selection of 'impossible' targets- a must in structural biology and proteomics