Homology modeling a fast tool for drug discovery: current perspectives

G protein-coupled estrogen receptor-1: homology modeling approaches and application in screening new GPER-1 modulators

G protein-coupled receptors (GPCRs) belong to the largest family of protein targets comprising over 800 members in which at least 500 members are the therapeutic targets. Among the GPCRs, G protein-coupled estrogen receptor-1 (GPER-1) has shown to have the ability in estrogen signaling. As GPER-1 plays a critical role in several physiological responses, GPER-1 has been considered as a potential therapeutic target to treat estrogen-based cancers and other non-communicable diseases. However, the progress in the understanding of GPER-1 structure and function is relatively slow due to the availability of a only a few selective GPER-1 modulators. As with many GPCRs, the X-ray crystal structure of GPER-1 is yet to be resolved and thus has led the researchers to search for new GPER-1 modulators using homology models of GPER-1. In this review, we aim to summarize various approaches used in the generation of GPER-1 homology model and their applications that have resulted in new GPER-1 ligands.

Comparative Proteome Analysis of Mycobacterium Tuberculosis Strains - H37Ra, H37Rv, CCDC5180, and CAS/NITR204: A Step Forward to Identify Novel Drug Targets

Background:

Mycobacterium tuberculosis is a causative agent of tuberculosis. It is a non-motile, acid-fast, obligatory aerobic bacterium. Finding novel drug targets in Mycobacterium tuberculosis has become extremely important as the bacterium is evolving into a more dangerous multi-drug resistant pathogen. The predominant strains in India belong to the Central-Asian, East- African Indian, and Beijing clad. For the same reason, the whole proteomes of a non-virulent strain (H37Ra), a virulent (H37Rv) and two clinical strains, a Central-Asian clad (CAS/NITR204) and a Beijing clad (CCDC5180) have been selected for comparative study. Selecting a phylogenetically close and majorly studied non-virulent strain is helpful in removing the common and undesired proteins from the study.

Objective:

The study compares the whole proteome of non-virulent strain with the other three virulent strains to find a unique protein responsible for virulence in virulent strains. It is expected that the drugs developed against identified targets will be specific to the virulent strains. Additionally, to assure minimal toxicity to the host, we also screened the human proteome.

Methods:

Comparative proteome analysis was used for target identification and in silico validation of identified target protein Rv2466c, identification of the respective ligand of the identified target protein and binding interaction study using Molecular docking and Molecular Dynamic Simulation study were used in this study.

Results and Discussion:

Finally, eleven proteins were found to be unique in virulent strain only and out of which, Rv2466c (PDB-ID: 4ZIL) was found to be an essential protein and identified as a putative drug target protein for further study. The compound glutathione was found to be a suitable inhibitor for Rv2466c. In this study, we used a comparative proteomics approach to identify novel target proteins.

Conclusion:

This study is unique as we are assured that the study will move forward the research in a new direction to cure the deadly disease (tuberculosis) caused by Mycobacterium tuberculosis. Rv2466c was identified as a novel drug target and glutathione as a respective ligand of Rv2466c. Discovery of the novel drug target as well as the drug will provide a solution to drug resistance as well as the infection caused by Mycobacterium tuberculosis.

Potential neuroinvasive and neurotrophic properties of SARS-CoV-2 in pediatric patients: comparison of SARS-CoV-2 with non-segmented RNA viruses

The emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing global health crises. Children can be infected, but are less likely to develop severe neurological abnormalities compared with adults. However, whether SARS-CoV-2 can directly cause neurological impairments in pediatric patients is not known. The possible evolutionary and molecular relationship between SARS-CoV-2 and non-segmented RNA viruses were examined with reference to neurological disorders in pediatric patients. SARS-CoV-2 shares similar functional domains with neuroinvasive and neurotropic RNA viruses. The Spike 1 (S1) receptor binding domain and the cleavage sites at S1/S2 boundary are less conserved compared with the S2 among coronaviruses.

Protease Inhibitory Effect of Natural Polyphenolic Compounds on SARS-CoV-2: An In Silico Study

The current pandemic, caused by SARS-CoV-2 virus, is a severe challenge for human health and the world economy. There is an urgent need for development of drugs that can manage this pandemic, as it has already infected 19 million people and led to the death of around 711,277 people worldwide. At this time, in-silico studies are providing lots of preliminary data about potential drugs, which can be a great help in further in-vitro and in-vivo studies. Here, we have selected three polyphenolic compounds, mangiferin, glucogallin, and phlorizin. These compounds are isolated from different natural sources but share structural similarities and have been reported for their antiviral activity. The objective of this study is to analyze and predict the anti-protease activity of these compounds on SARS-CoV-2main protease (Mpro) and TMPRSS2 protein. Both the viral protein and the host protein play an important role in the viral life cycle, such as post-translational modification and viral spike protein priming. This study has been performed by molecular docking of the compounds using PyRx with AutoDock Vina on the two aforementioned targets chosen for this study, i.e., SARS-CoV-2 Mpro and TMPRSS2. The compounds showed good binding affinity and are further analyzed by (Molecular dynamic) MD and Molecular Mechanics Poisson-Boltzmann Surface Area MM-PBSA study. The MD-simulation study has predicted that these natural compounds will have a great impact on the stabilization of the binding cavity of the Mpro of SARS-CoV-2. The predicted pharmacokinetic parameters also show that these compounds are expected to have good solubility and absorption properties. Further predictions for these compounds also showed no involvement in drug-drug interaction and no toxicity.

The Universal Soldier: Enzymatic and Non-Enzymatic Antioxidant Functions of Serum Albumin

As a carrier of many biologically active compounds, blood is exposed to oxidants to a greater extent than the intracellular environment. Serum albumin plays a key role in antioxidant defence under both normal and oxidative stress conditions. This review evaluates data published in the literature and from our own research on the mechanisms of the enzymatic and non-enzymatic activities of albumin that determine its participation in redox modulation of plasma and intercellular fluid. For the first time, the results of numerous clinical, biochemical, spectroscopic and computational experiments devoted to the study of allosteric modulation of the functional properties of the protein associated with its participation in antioxidant defence are analysed. It has been concluded that it is fundamentally possible to regulate the antioxidant properties of albumin with various ligands, and the binding and/or enzymatic features of the protein by changing its redox status. The perspectives for using the antioxidant properties of albumin in practice are discussed.

Dissecting the conformation of glycans and their interactions with proteins

The use of in silico strategies to develop the structural basis for a rational optimization of glycan-protein interactions remains a great challenge. This problem derives, in part, from the lack of technologies to quantitatively and qualitatively assess the complex assembling between a glycan and the targeted protein molecule. Since there is an unmet need for developing new sugar-targeted therapeutics, many investigators are searching for technology platforms to elucidate various types of molecular interactions within glycan-protein complexes and aid in the development of glycan-targeted therapies. Here we discuss three important technology platforms commonly used in the assessment of the complex assembly of glycosylated biomolecules, such as glycoproteins or glycosphingolipids: Biacore analysis, molecular docking, and molecular dynamics simulations. We will also discuss the structural investigation of glycosylated biomolecules, including conformational changes of glycans and their impact on molecular interactions within the glycan-protein complex. For glycoproteins, secreted protein acidic and rich in cysteine (SPARC), which is associated with various lung disorders, such as chronic obstructive pulmonary disease (COPD) and lung cancer, will be taken as an example showing that the core fucosylation of N-glycan in SPARC regulates protein-binding affinity with extracellular matrix collagen. For glycosphingolipids (GSLs), Globo H ceramide, an important tumor-associated GSL which is being actively investigated as a target for new cancer immunotherapies, will be used to demonstrate how glycan structure plays a significant role in enhancing angiogenesis in tumor microenvironments.

Pharmacoinformatics approaches to identify potential hits against tetraacyldisaccharide 4'-kinase (LpxK) of Pseudomonas aeruginosa

Pseudomonas aeruginosa infection can cause pneumonia and urinary tract infection and the management of Pseudomonas aeruginosa infection is critical in multidrug resistance, hospital-acquired bacteremia and ventilator-associated pneumonia. The key enzymes of lipid A biosynthesis in Pseudomonas aeruginosa are promising drug targets. However, the enzyme tetraacyldisaccharide 4'-kinase (LpxK) has not been explored as a drug target so far. Several pharmacoinformatics tools such as comparative metabolic pathway analysis (Metacyc), data mining from a database of essential genes (DEG), homology modeling, molecular docking, pharmacophore based virtual screening, ADMET prediction and molecular dynamics simulation were used in identifying novel lead compounds against this target. The top virtual hits STOCK6S-33288, 43621, 39892, 37164 and 35740 may serve as the templates for the design and synthesis of potent LpxK inhibitors in the management of serious Pseudomonas aeruginosa infection.

Homology Modeling and Probable Active Site Cavity Prediction of Uncharacterized Arsenate Reductase in Bacterial spp

The arsC gene-encoded arsenate reductase is a vital catalytic enzyme for remediation of environmental arsenic (As). Microorganisms containing the arsC gene can convert pentavalent arsenate (As[V]) to trivalent arsenite (As[III]) to be either retained in the bacterial cell or released into the air. The molecular mechanism governing this process is unknown. Here we present an in silico model of the enzyme to describe their probable active site cavities using SCFBio servers. We retrieved the amino acid sequence of bacterial arsenate reductase enzymes in FASTA format from the NCBI database. Enzyme structure was predicted using the I-TASSER server and visualized using PyMOL tools. The ProSA and the PROCHECK servers were used to evaluate the overall significance of the predicted model. Accordingly, arsenate reductase from Streptococcus pyogenes, Oligotropha carboxidovorans OM5, Rhodopirellula baltica SH 1, and Serratia ureilytica had the highest quality scores with statistical significance. The plausible cavities of the active site were identified in our examined arsenate reductase enzymes which were abundant in glutamate and lysine residues with 6 to 16 amino acids. This in silico experiment may contribute greatly to the remediation of arsenic pollution through the utilization of microbial species.

Current Computational Approaches for the Development of Anti-HIV Inhibitors: An Overview

BACKGROUND

Today, HIV-1 infection has become an extensive problem to public health and a greater challenge to all working researchers throughout the world. Since the beginning of HIV-1 virus, several antiviral therapeutic agents have been developed at various stages to combat HIV-1 infection. But, many of antiviral drugs are on the platform of drug resistance and toxicology issues, needs an urgent constructive investigation for the development of productive and protective therapeutics to make an improvement of individual life suffering with viral infection. As developing a novel agent is very costly, challenging and time taking route in the recent times.

METHODS

The review summarized about the modern approaches of computational aided drug discovery to developing a novel inhibitor within a short period of time and less cost.

RESULTS

The outcome suggests on the premise of reported information that the computational drug discovery is a powerful technology to design a defensive and fruitful therapeutic agents to combat HIV-1 infection and recover the lifespan of suffering one.

CONCLUSION

Based on survey of the reported information, we concluded that the current computational approaches is highly supportive in the progress of drug discovery and controlling the viral infection.

The Se…S/N interactions as a possible mechanism of δ-aminolevulinic acid dehydratase enzyme inhibition by organoselenium compounds: A computational study

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DPDS and PSA interacts with cysteine residues from AlaD active site.

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The Se…S interactions could be involved in the δ-AlaD inhibition.

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δ-AlaD from Cucumis sativus does not present cysteine residues in the active site.

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Se…N interactions could be involved in the organoselenium action.

Organoselenium compounds present many pharmacological properties and are promising drugs. However, toxicological effects associated with inhibition of thiol-containing enzymes, such as the δ-aminolevulinic acid dehydratase (δ-AlaD), have been described. The molecular mechanism(s) by which they inhibit thiol-containing enzymes at the atomic level, is still not well known. The use of computational methods to understand the physical–chemical properties and biological activity of chemicals is essential to the rational design of new drugs. In this work, we propose an in silico study to understand the δ-AlaD inhibition mechanism by diphenyl diselenide (DPDS) and its putative metabolite, phenylseleninic acid (PSA), using δ-AlaD enzymes from Homo sapiens (Hsδ-AlaD), Drosophila melanogaster (Dmδ-AlaD) and Cucumis sativus (Csδ-AlaD). Protein modeling homology, molecular docking, and DFT calculations are combined in this study. According to the molecular docking, DPDS and PSA might bind in the Hsδ-AlaD and Dmδ-AlaD active sites interacting with the cysteine residues by Se^…S interactions. On the other hand, the DPDS does not access the active site of the Csδ-AlaD (a non-thiol protein), while the PSA interacts with the amino acids residues from the active site, such as the Lys291. These interactions might lead to the formation of a covalent bond, and consequently, to the enzyme inhibition. In fact, DFT calculations (mPW1PW91/def2TZVP) demonstrated that the selenylamide bond formation is energetically favored. The in silico data showed here are in accordance with previous experimental studies, and help us to understand the reactivity and biological activity of organoselenium compounds.

Drug repurposing for coronavirus (COVID-19): in silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes

In December 2019, COVID-19 epidemic was described in Wuhan, China, and the infection has spread widely affecting hundreds of thousands. Herein, an effort was made to identify commercially available drugs in order to repurpose them against coronavirus by the means of structure-based virtual screening. In addition, ZINC15 library was used to identify novel leads against main proteases. Human TMPRSS2 3D structure was first generated using homology modeling approach. Our molecular docking study showed four potential inhibitors against Mpro enzyme, two available drugs (Talampicillin and Lurasidone) and two novel drug-like compounds (ZINC000000702323 and ZINC000012481889). Moreover, four promising inhibitors were identified against TMPRSS2; Rubitecan and Loprazolam drugs, and compounds ZINC000015988935 and ZINC000103558522. ADMET profile showed that the hits from our study are safe and drug-like compounds. Furthermore, molecular dynamic (MD) simulation and binding free energy calculation using the MM-PBSA method was performed to calculate the interaction energy of the top-ranked drugs.Communicated by Ramaswamy H. Sarma.

Ten quick tips for homology modeling of high-resolution protein 3D structures

The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

Potential Therapeutic Approaches to Alzheimer's Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

BACKGROUND

Alzheimer's disease (AD) is considered a severe, irreversible and progressive neurodegenerative disorder. Currently, the pharmacological management of AD is based on a few clinically approved acethylcholinesterase (AChE) and N-methyl-D-aspartate (NMDA) receptor ligands, with unclear molecular mechanisms and severe side effects.

METHODS

Here, we reviewed the most recent bioinformatics, cheminformatics (SAR, drug design, molecular docking, friendly databases, ADME-Tox) and experimental data on relevant structurebiological activity relationships and molecular mechanisms of some natural and synthetic compounds with possible anti-AD effects (inhibitors of AChE, NMDA receptors, beta-secretase, amyloid beta (Aβ), redox metals) or acting on multiple AD targets at once. We considered: (i) in silico supported by experimental studies regarding the pharmacological potential of natural compounds as resveratrol, natural alkaloids, flavonoids isolated from various plants and donepezil, galantamine, rivastagmine and memantine derivatives, (ii) the most important pharmacokinetic descriptors of natural compounds in comparison with donepezil, memantine and galantamine.

RESULTS

In silico and experimental methods applied to synthetic compounds led to the identification of new AChE inhibitors, NMDA antagonists, multipotent hybrids targeting different AD processes and metal-organic compounds acting as Aβ inhibitors. Natural compounds appear as multipotent agents, acting on several AD pathways: cholinesterases, NMDA receptors, secretases or Aβ, but their efficiency in vivo and their correct dosage should be determined.

CONCLUSION

Bioinformatics, cheminformatics and ADME-Tox methods can be very helpful in the quest for an effective anti-AD treatment, allowing the identification of novel drugs, enhancing the druggability of molecular targets and providing a deeper understanding of AD pathological mechanisms.

Theoretical and Experimental Approaches Aimed at Drug Design Targeting Neurodegenerative Diseases

In recent years, green chemistry has been strengthening, showing how basic and applied sciences advance globally, protecting the environment and human health. A clear example of this evolution is the synergy that now exists between theoretical and computational methods to design new drugs in the most efficient possible way, using the minimum of reagents and obtaining the maximum yield. The development of compounds with potential therapeutic activity against multiple targets associated with neurodegenerative diseases/disorders (NDD) such as Alzheimer’s disease is a hot topic in medical chemistry, where different scientists from various disciplines collaborate to find safe, active, and effective drugs. NDD are a public health problem, affecting mainly the population over 60 years old. To generate significant progress in the pharmacological treatment of NDD, it is necessary to employ different experimental strategies of green chemistry, medical chemistry, and molecular biology, coupled with computational and theoretical approaches such as molecular simulations and chemoinformatics, all framed in the rational drug design targeting NDD. Here, we review how green chemistry and computational approaches have been used to develop new compounds with the potential application against NDD, as well as the challenges and new directions of the drug development multidisciplinary process.

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.

Combined approach of homology modeling, molecular dynamics, and docking: computer-aided drug discovery

With the continuous development in software, algorithms, and increase in computer speed, the field of computer-aided drug design has been witnessing reduction in the time and cost of the drug designing process. Structure based drug design (SBDD), which is based on the 3D structure of the enzyme, is helping in proposing novel inhibitors. Although a number of crystal structures are available in various repositories, there are various proteins whose experimental crystallization is difficult. In such cases, homology modeling, along with the combined application of MD and docking, helps in establishing a reliable 3D structure that can be used for SBDD. In this review, we have reported recent works, which have employed these three techniques for generating structures and further proposing novel inhibitors, for cytoplasmic proteins, membrane proteins, and metal containing proteins. Also, we have discussed these techniques in brief in terms of the theory involved and the various software employed. Hence, this review can give a brief idea about using these tools specifically for a particular problem.

Rigorous sampling of docking poses unveils binding hypothesis for the halogenated ligands of L-type Amino acid Transporter 1 (LAT1)

L-type Amino acid Transporter 1 (LAT1) plays a significant role in the growth and propagation of cancer cells by facilitating the cross-membrane transport of essential nutrients, and is an attractive drug target. Several halogen-containing L-phenylalanine-based ligands display high affinity and high selectivity for LAT1; nonetheless, their molecular mechanism of binding remains unclear. In this study, a combined in silico strategy consisting of homology modeling, molecular docking, and Quantum Mechanics-Molecular Mechanics (QM-MM) simulation was applied to elucidate the molecular basis of ligand binding in LAT1. First, a homology model of LAT1 based on the atomic structure of a prokaryotic homolog was constructed. Docking studies using a set of halogenated ligands allowed for deriving a binding hypothesis. Selected docking poses were subjected to QM-MM calculations to investigate the halogen interactions. Collectively, the results highlight the dual nature of the ligand-protein binding mode characterized by backbone hydrogen bond interactions of the amino acid moiety of the ligands and residues I63, S66, G67, F252, G255, as well as hydrophobic interactions of the ligand’s side chains with residues I139, I140, F252, G255, F402, W405. QM-MM optimizations indicated that the electrostatic interactions involving halogens contribute to the binding free energy. Importantly, our results are in good agreement with the recently unraveled cryo-Electron Microscopy structures of LAT1.

Structure-based identification of potential novel inhibitors targeting FAM3B (PANDER) causing type 2 diabetes mellitus through virtual screening

Type 2 diabetes mellitus is a metabolic disorder that requires potent therapeutic approaches. The FAM3B is a cytokine-like protein also referred to as PANcreatic-DERrived factor (PANDER) which mainly exists in pancreatic islets. In the process of identifying potential inhibitors with the aid of structure-based method PANDER protein is identified as a novel therapeutic target against type 2 diabetes mellitus as it involved in the development of type 2 diabetes by negatively regulating the pancreatic β-cell function and insulin sensitivity in the liver. In the present study, the 3d model of target protein FAM3B was generated by homology modeling technique using the MODELLER9.9 program. The assessment of the structural stability of the 3d model was established by energy minimization technique. The structural quality was evaluated with standard validating protocols. Binding regions of the target protein has been determined by literature and SiteMap tool. In the current study of research, the FAM3B model was subjected to molecular screening with the Asinex-elite database of 14849 output molecules using the Glide virtual screening module in the Schrodinger suite. The final XP descriptor output of 14 molecules was analyzed and prioritized based on molecular interactions at the FAM3B active site. The docking score, binding free energies (Prime MM/GBSA) and bioavailability were undertaken into the consideration to identify lead inhibitors. The identified lead compounds were checked for ADME properties all falling within the permeable ranges. The analysis of results gave the insight to develop the novel therapeutic strategies against type 2 diabetes mellitus.

Computational Approaches for Elucidating Protein-Protein Interactions in Cation Channel Signaling

BACKGROUND

The lipid bilayer of the plasma membrane is impermeable to ions, yet changes in the flux of ions across the cell membrane are critical regulatory events in cells. Because of their regulatory roles in a range of physiological processes, such as electrical signaling in muscles and neurons, to name a few, these proteins are one of the most important drug targets.

OBJECTIVE

This review mainly focused on the computational approaches for elucidating proteinprotein interactions in cation channel signaling.

DISCUSSION

Due to continuously advanced facilities and technologies in computer sciences, the physical contacts of macromolecules of channel structures have been virtually visualized. Indeed, techniques like protein-protein docking, homology modeling, and molecular dynamics simulation are valuable tools for predicting the protein complex and refining channels with unreleased structures. Undoubtedly, these approaches will greatly expand the cation channel signaling research, thereby speeding up structure-based drug design and discovery.

CONCLUSION

We introduced a series of valuable computational tools for elucidating protein-protein interactions in cation channel signaling, including molecular graphics, protein-protein docking, homology modeling, and molecular dynamics simulation.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

In-silico studies on conformational stability of flagellin-receptor complexes

Flagellin is a protein, responsible for virulent activities of bacteria. The host cell surface receptor protein TLR5 is known to interact with flagellin in order to activate immune response. However, the underlying microscopic details of this immune response are still elusive. In this study, we report on conformational stability of flagellin of two different organisms known as fliC and flaD in bilayer with reference to water. We find that both the flagellin is conformationally more stable in bilayer than in water. We also observe that fliC-TLR5 and flaD-TLR5 complexes are conformationally stable when the extracellular domain of the protein binds to conserved D1 domain of both fliC and flaD, although the binding interface between fliC-TLR5 and flaD-TLR5 is not identical. Our studies suggest that this might lead to differences in coreceptor bindings involved in immune response and thus have potential application in pharmaceutical developments. AbbreviationsA2Aadenosine receptorDPPCdipalmitoyl phosphatidylcholineecdextracellular domainecl2extracellular loop 2eLRRextracellular Leucine rich repeat domainflaDflagellin of Vibrio choleraefliCflagellin of Salmonella typhimuriumHPVhyper-variableMDmolecular dynamicsRMSDroot means squared deviationTIRtoll-interleukin receptorTLR5toll like receptor 5VPAC1vasoactive intestinal peptide receptorCommunicated by Ramaswamy H. Sarma.

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.

Review on chemogenomic approaches towards hepatitis C viral targets

Hepatitis C virus (HCV) is the most prevalent viral pathogen that infects more than 185 million people worldwide. HCV infection leads to chronic liver diseases such as liver cirrhosis and hepatocellular carcinoma. Direct-acting antivirals (DAAs) are the recent combination therapy for HCV infection with reduced side effects than prior therapies. Sustained virological response (SVR) acts as a gold standard marker to monitor the success of antiviral treatment. Older treatment therapies attain 50-55% of SVR compared with DAAs which attain around 90-95%. The current review emphasizes the recent chemogenomic updates that have been unfolded through structure-based drug design of HCV drug target proteins (NS3/4A, NS5A, and NS5B) and ligand-based drug design of DAAs in achieving a stable HCV viral treatment strategies.

Newly Synthesized Oxygenated Xanthones as Potential P-Glycoprotein Activators: In Vitro, Ex Vivo, and In Silico Studies

P-glycoprotein (P-gp) plays a crucial role in the protection of susceptible organs, by significantly decreasing the absorption/distribution of harmful xenobiotics and, consequently, their toxicity. Therefore, P-gp has been proposed as a potential antidotal pathway, when activated and/or induced. Knowing that xanthones are known to interact with P-gp, the main goal was to study P-gp induction or/and activation by six new oxygenated xanthones (OX 1-6). Furthermore, the potential protection of Caco-2 cells against paraquat cytotoxicity was also assessed. The most promising compound was further tested for its ability to increase P-gp activity ex vivo, using everted intestinal sacs from adult Wistar-Han rats. The oxygenated xanthones interacted with P-gp in vitro, increasing P-gp expression and/or activity 24 h after exposure. Additionally, after a short-incubation period, several xanthones were identified as P-gp activators, as they immediately increased P-gp activity. Moreover, some xanthones decreased PQ cytotoxicity towards Caco-2 cells, an effect prevented under P-gp inhibition. Ex vivo, a significant increase in P-gp activity was observed in the presence of OX6, which was selectively blocked by a model P-gp inhibitor, zosuquidar, confirming the in vitro results. Docking simulations between a validated P-gp model and the tested xanthones predicted these interactions, and these compounds also fitted onto previously described P-gp induction and activation pharmacophores. In conclusion, the in vitro, ex vivo, and in silico results suggest the potential of some of the oxygenated xanthones in the modulation of P-gp, disclosing new perspectives in the therapeutics of intoxications by P-gp substrates.

Simple high-resolution NMR spectroscopy as a tool in molecular biology

NMR is one of the major techniques for investigating the structure, dynamics and interactions between biomolecules. However, non-experts often experience NMR experimentation and data analysis as intimidating. We discuss a simple yet powerful NMR technique, the so-called chemical shift perturbation (CSP) analysis, as a tool to elucidate macromolecular interactions in small- and medium-sized complexes, including protein-protein, protein-drug, and protein-DNA/RNA interactions. We discuss current software packages for NMR data analysis and present a new interactive graphical tool implemented in CcpNmr AnalysisAssign version-3, which can drastically reduce the time required for the CSP analysis. Lastly, we illustrate the usefulness of a protein three-dimensional structure for interpretation of the CSP data.

Approaches to the Structure-Based Design of Antivirulence Drugs: Therapeutics for the Post-Antibiotic Era

The alarming rise of multidrug-resistant bacterial strains, coupled with decades of stagnation in the field of antibiotic development, necessitates exploration of new therapeutic approaches to treat bacterial infections. Targeting bacterial virulence is an attractive alternative to traditional antibiotics in that this approach disarms pathogens that cause human diseases, without placing immediate selective pressure on the target bacterium or harming commensal species. The growing number of validated virulence protein targets for which structural information has been obtained, along with advances in computational power and screening algorithms, make the rational design of antivirulence drugs a promising avenue to explore. Here, we review the principles of structure-based drug design and the exciting opportunities this technique presents for antivirulence drug discovery.

Comparative Modeling and Evaluation of Leukotriene B4 Receptors for Selective Drug Discovery Towards the Treatment of Inflammatory Diseases

Leukotriene B4 (LTB4) exerts its biological effects through stimulation of specific G protein-coupled receptors (GPCRs)-namely BLT1 and BLT2. Due to the absence of human BLT1 and BLT2 crystal structures, the current study was set to predict the 3D structures of these two receptors for structure-based anti-inflammatory drug discovery. Homology modeling of the BLT1 receptor was first constructed, based on various X-ray and NMR GPCR templates, followed by molecular dynamics (MD) refinement. Using a single-template approach, nine well-established alignment methods and ten secondary structure prediction methods during the backbone generation were implemented and assessed. The binding sites of the BLT1 receptor were then mapped using fifteen chemical probes with the help of FTMAP and AutoDock Vina 4.2 software. Model validation was performed through the docking of eight specific antagonists that have experimental inhibition constants (k_i) towards BLT1. The antagonists-BLT1 docked structures were then subjected to AMBER-based molecular mechanical minimization and the corresponding binding energies were calculated using molecular mechanics-generalized Born surface area (MM/GBSA) approach. According to the results, the most energetically stable models were constructed using SAlign method for the alignment process and PSIPRED for secondary structure prediction. In comparison, the refined BLT1 model built on 2KS9 as an NMR template has the lowest DOPE energy compared to those built on 4EA3 and 4XT1 as X-ray templates. According to the mapping results, two main binding sites were identified: one was among TMs II, III and VII and the other was among TMs III, IV and V. For the antagonists, correlation between binding energies and experimental data was in a good agreement, with a correlation coefficient (R² value) of 0.91. Due to the great amino acid sequence similarity between BLT1 and BLT2 receptors (calculated as 45.2%), BLT2 model was constructed based on the predicted BLT1 model.

Mass Spectrometry- and Computational Structural Biology-Based Investigation of Proteins and Peptides

Recent developments of mass spectrometry (MS) allow us to identify, estimate, and characterize proteins and protein complexes. At the same time, structural biology helps to determine the protein structure and its structure-function relationship. Together, they aid to understand the protein structure, property, function, protein-complex assembly, protein-protein interaction, and dynamics. The present chapter is organized with illustrative results to demonstrate how experimental mass spectrometry can be combined with computational structural biology for detailed studies of protein's structures. We have used tumor differentiation factor protein/peptide as ligand and Hsp70/Hsp90 as receptor protein as examples to study ligand-protein interaction. To investigate possible protein conformation, we will describe two proteins-lysozyme and myoglobin. As an application of MS-based assignment of disulfide bridges, the case of the spider venom polypeptide Phα1β will also be discussed.

Molecular docking, pharmacophore based virtual screening and molecular dynamics studies towards the identification of potential leads for the management of H. pylori

The enzyme pantothenate synthetase panC is one of the potential new antimicrobial drug targets, but it is poorly characterized in H. pylori. H. pylori infection can cause gastric cancer and the management of H. pylori infection is crucial in various gastric ulcers and gastric cancer. The current study describes the use of innovative drug discovery and design approaches like comparative metabolic pathway analysis (Metacyc), exploration of database of essential genes (DEG), homology modelling, pharmacophore based virtual screening, ADMET studies and molecular dynamics simulations in identifying potential lead compounds for the H. pylori specific panC. The top ranked virtual hits STOCK1N-60270, STOCK1N-63040, STOCK1N-44424 and STOCK1N-63231 can act as templates for synthesis of new H. pylori inhibitors and they hold a promise in the management of gastric cancers caused by H. pylori.

Computational approaches such as pharmacophore modeling, virtual screening and MD simulations were explored to find the potential hits as H. pylori specific panC inhibitors for the management of gastric ulcers and gastric cancers.

Toward a better understanding of the interaction between somatostatin receptor 2 and its ligands: a structural characterization study using molecular dynamics and conceptual density functional theory

This study is a part of the extensive research intending to provide the structural insights on somatostatin and its receptor. Herein, we have studied the structural complexity involved in the binding of somatostatin receptor 2 (SSTR2) with its agonists and antagonist. A 3D QSAR study based on comparative molecular field analysis and comparative molecular similarity analysis (CoMSIA) discerned that a SSTR2 ligand with electronegative, less-bulkier, and hydrogen atom donating/accepting substitutions is important for their biological activity. A conceptual density functional theory (DFT) study was followed to study the chemical behavior of the ligands based on the molecular descriptors derived using the Fukui's molecular orbital theory. We have performed molecular dynamics simulations of receptor-ligand complexes for 100 ns to analyze the dynamic stability of the backbone Cα atoms of the receptor and strength and approachability of the receptor-ligand complex. The findings of this study could be efficacious in the further studies understanding intricate structural features of the somatostatin receptors and in discovering novel subtype-specific ligands with higher affinity. Communicated by Ramaswamy H. Sarma.

GPCR homology model template selection benchmarking: Global versus local similarity measures

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development. GPCR ligand interaction studies often have a starting point with either crystal structures or comparative models. The majority of GPCR do not have experimentally-characterized 3-dimensional structures, so comparative modeling, also called homology modeling, is a good structure-based starting point. Comparative modeling is a widely used method for generating models of proteins with unknown structures by analogy to crystallized proteins that are expected to exhibit structural conservation. Traditionally, comparative modeling template selection is based on global sequence identity and shared function. However high sequence identity localized to the ligand binding pocket may produce better models to examine protein-ligand interactions. This in silico benchmark study examined the performance of a global versus local similarity measure applied to comparative modeling template selection for 6 previously crystallized, class A GCPR (CXCR4, FFAR1, NOP, P2Y12, OPRK, and M1) with the long-term goal of optimizing GPCR ligand identification efforts. Comparative models were generated from templates selected using both global and local similarity measures. Similarity to reference crystal structures was reflected in RMSD values between atom positions throughout the structure or localized to the ligand binding pocket. Overall, models deviated from the reference crystal structure to a similar degree regardless of whether the template was selected using a global or local similarity measure. Ligand docking simulations were performed to assess relative performance in predicting protein-ligand complex structures and interaction networks. Calculated RMSD values between ligand poses from docking simulations and crystal structures indicate that models based on locally selected templates give docked poses that better mimic crystallographic ligand positions than those based on globally-selected templates in five of the six benchmark cases. However, protein model refinement strategies in advance of ligand docking applications are clearly essential as the average RMSD between crystallographic poses and poses docked into local template models was 9.7 Å and typically less than half of the ligand interaction sites are shared between the docked and crystallographic poses. These data support the utilization of local similarity measures to guide template selection in protocols using comparative models to investigate ligand-receptor interactions.

A Structure- and Ligand-Based Virtual Screening of a Database of "Small" Marine Natural Products for the Identification of "Blue" Sigma-2 Receptor Ligands

Sigma receptors are a fascinating receptor protein class whose ligands are actually under clinical evaluation for the modulation of opioid analgesia and their use as positron emission tomography radiotracers. In particular, peculiar biological and therapeutic functions are associated with the sigma-2 (σ₂) receptor. The σ₂ receptor ligands determine tumor cell death through apoptotic and non-apoptotic pathways, and the overexpression of σ₂ receptors in several tumor cell lines has been well documented, with significantly higher levels in proliferating tumor cells compared to quiescent ones. This acknowledged feature has found practical application in the development of cancer cell tracers and for ligand-targeting therapy. In this context, the development of new ligands that target the σ₂ receptors is beneficial for those diseases in which this protein is involved. In this paper, we conducted a search of new potential σ₂ receptor ligands among a database of 1517 “small” marine natural products constructed by the union of the Seaweed Metabolite and the Chemical Entities of Biological Interest (ChEBI) Databases. The structures were passed through two filters that were constituted by our developed two-dimensional (2D) and three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) statistical models, and successively docked upon a σ₂ receptor homology model that we built according to the FASTA sequence of the σ₂/TMEM97 (SGMR2_HUMAN) receptor.

Homology modeling in drug discovery: Overview, current applications, and future perspectives

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Computer Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders

Background

Neurodegenerative disorders (NDs) are diverse group of disorders characterized by escalating loss of neurons (structural and functional). The development of potential therapeutics for NDs presents an important challenge, as traditional treatments are inefficient and usually are unable to stop or retard the process of neurodegeneration. Computer-Aided Drug Design (CADD) has emerged as an efficient means of developing candidate drugs for the treatment of many disease types. Applications of CADD approach to drug discovery are progressing day by day. The recent tendency in drug design is to rationally design potent therapeutics with multi-targeting effects, higher efficacies, and fewer side effects, especially in terms of toxicity.

Methods

A wide literature search was performed for writing this review. An updated view on different types of NDs, their effect on human population and a brief introduction to CADD, various approaches involved in this technique, ranging from structural-based to ligand-based drug design has been discussed. The successful application of CADD approaches for the treatment of neurodegenerative disorders is also included in this review.

Results

In this review, we have briefly described about CADD and its use in the development of the therapeutic drug candidates against NDs. The successful applications, limitations and future prospects of this approach have also been discussed.

Conclusion

CADD can assist researchers studying interactions between drugs and receptors. We believe this review will be helpful for better understanding of CADD and its applications towards the discovery of new drug candidates against various fatal NDs.

Novel investigation of the performance of continuous packed bed bioreactor (CPBBR) by isolated Bacillus sp. M4 and proteomic study

The present study reveals the benzene degrading potential of bacterial species isolated from petroleum contaminated soil. Genomic analysis suggests that Bacillus sp. M4 was found to be dominating species. The process parameters were optimized and found to be pH 7.0 ± 0.2, temperature 32 ± 5 °C, immobilization time (20 days) and benzene concentration 400 mg/L. The maximum removal efficiency of benzene was calculated and found to be 93.13% at elimination capacity156 (mg/L/day) and inlet loading rate 192 (mg/L/day) achieved in 54th days of operation. In the study, the residual metabolites were analyzed by GC/MS analysis and identified as benzene-1,2-diol. In order to identify the responsible protein involved in the process of benzene biodegradation The proteomic study was performed and proteins were identified by MALDI-TOF analysis. The molecular docking was confirmed by the benzene biodegradation.

SAR-Guided Scoring Function and Mutational Validation Reveal the Binding Mode of CGS-8216 at the α1+/γ2- Benzodiazepine Site

The structural resolution of a bound ligand-receptor complex is a key asset to efficiently drive lead optimization in drug design. However, structural resolution of many drug targets still remains a challenging endeavor. In the absence of structural knowledge, scientists resort to structure-activity relationships (SARs) to promote compound development. In this study, we incorporated ligand-based knowledge to formulate a docking scoring function that evaluates binding poses for their agreement with a known SAR. We showcased this protocol by identifying the binding mode of the pyrazoloquinolinone (PQ) CGS-8216 at the benzodiazepine binding site of the GABA_A receptor. Further evaluation of the final pose by molecular dynamics and free energy simulations revealed a close proximity between the pendent phenyl ring of the PQ and γ2D56, congruent with the low potency of carboxyphenyl analogues. Ultimately, we introduced the γ2D56A mutation and in fact observed a 10-fold potency increase in the carboxyphenyl analogue, providing experimental evidence in favor of our binding hypothesis.

Comparative Study of Carborane- and Phenyl-Modified Adenosine Derivatives as Ligands for the A2A and A3 Adenosine Receptors Based on a Rigid in Silico Docking and Radioligand Replacement Assay

Adenosine receptors are involved in many physiological processes and pathological conditions and are therefore attractive therapeutic targets. To identify new types of effective ligands for these receptors, a library of adenosine derivatives bearing a boron cluster or phenyl group in the same position was designed. The ligands were screened in silico to determine their calculated affinities for the A2A and A3 adenosine receptors. An virtual screening protocol based on the PatchDock web server was developed. In the first screening phase, the effects of the functional group (organic or inorganic modulator) on the adenosine ligand affinity for the receptors were determined. Then, the lead compounds were identified for each receptor in the second virtual screening phase. Two pairs of the most promising ligands, compounds 3 and 4, and two ligands with lower affinity scores (compounds 11 and 12, one with a boron cluster and one with a phenyl group) were synthesized and tested in a radioligand replacement assay for affinity to the A2A and A3 receptors. A reasonable correlation of in silico and biological assay results was observed. In addition, the effects of a phenyl group and boron cluster, which is new adenosine modifiers, on the adenosine ligand binding were compared.

Structural Characterization and Disulfide Assignment of Spider Peptide Phα1β by Mass Spectrometry

Native Phα1β is a peptide purified from the venom of the armed spider Phoneutria nigriventer that has been shown to have an extensive analgesic effect with fewer side effects than ω-conotoxin MVIIA. Recombinant Phα1β mimics the effects of the native Phα1β. Because of this, it has been suggested that Phα1β may have potential to be used as a therapeutic for controlling persistent pathological pain. The amino acid sequence of Phα1β is known; however, the exact structure and disulfide arrangement has yet to be determined. Determination of the disulfide linkages and exact structure could greatly assist in pharmacological analysis and determination of why this peptide is such an effective analgesic. Here, we used biochemical and mass spectrometry approaches to determine the disulfide linkages present in the recombinant Phα1β peptide. Using a combination of MALDI-MS, direct infusion ESI-MS, and nanoLC-MS/MS analysis of the undigested recombinant Phα1β peptide and digested with AspN, trypsin, or AspN/trypsin, we were able to identify and confirm all six disulfide linkages present in the peptide as Cys1-2, Cys3-4, Cys5-6, Cys7-8, Cys9-10, and Cys11-12. These results were also partially confirmed in the native Phα1β peptide. These experiments provide essential structural information about Phα1β and may assist in providing insight into the peptide's analgesic effect with very low side effects. Graphical Abstract ᅟ.