Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases

Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.

In Silico Design, Synthesis and Evaluation of Novel Series of Benzothiazole- Based Pyrazolidinediones as Potent Hypoglycemic Agents

Background:

The discovery of novel ligand binding domain (LBD) of peroxisome proliferator- activated receptor γ (PPARγ) has recently attracted attention to few research groups in order to develop more potent and safer antidiabetic agents.

Objective:

This study is focused on docking-based design and synthesis of novel compounds combining benzothiazole and pyrazolidinedione scaffold as potential antidiabetic agents.

Methods:

Several benzothiazole-pyrazolidinedione hybrids were synthesized and tested for their in vivo anti-hyperglycemic activity. Interactions profile of title compounds against PPARγ was examined through molecular modelling approach.

Results:

All tested compounds exhibited anti-hyperglycemic activity similar or superior to the reference drug Rosiglitazone. Introducing chlorine atom and alkyl group at position-6 and -5 respectively on benzothiazole core resulted in enhancing the anti-hyperglycemic effect. Docking study revealed that such groups demonstrated favorable hydrophobic interactions with novel LBD Ω- pocket of PPARγ protein.

Conclusion:

Among the tested compounds, N-(6-chloro-5-methylbenzo[d]thiazol-2-yl-4-(4((3,5- dioxopyrazolidin-4-ylidene)methyl)phenoxy)butanamide 5b was found to be the most potent compound and provided valuable insights to further develop novel hybrids as anti-hyperglycemic agents.

Study of combining virtual screening and antiviral treatments of the Sars-CoV-2 (Covid-19)

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 and causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. Therefore, rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. Additionally, general treatments, coronavirus-specific treatments, and antiviral treatments useful in fighting COVID-19 are addressed. This review sets out to shed light on the SARS-CoV-2 and host receptor recognition, a crucial factor for successful virus infection and taking immune-informatics approaches to identify B- and T-cell epitopes for surface glycoprotein of SARS-CoV-2. A variety of improved or new approaches also have been developed. It is anticipated that this will assist researchers and clinicians in developing better techniques for timely and effective detection of coronavirus infection. Moreover, the genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three-dimensional structure of the Main protease (Mpro) is available. The reported structure of the target Mpro was described in this review to identify potential drugs for COVID-19 using virtual high throughput screening.

An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule

OBJECTIVE

One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information.

METHODS

To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed.

RESULTS AND CONCLUSION

It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.

Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection

The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including "off-target" effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.

RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule

By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook.

Database URL: http://bioinfor.imu.edu.cn/raacbook

Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins

During its first two and a half months, the recently emerged 2019 novel coronavirus, SARS-CoV-2, has already infected over one-hundred thousand people worldwide and has taken more than four thousand lives. However, the swiftly spreading virus also caused an unprecedentedly rapid response from the research community facing the unknown health challenge of potentially enormous proportions. Unfortunately, the experimental research to understand the molecular mechanisms behind the viral infection and to design a vaccine or antivirals is costly and takes months to develop. To expedite the advancement of our knowledge, we leveraged data about the related coronaviruses that is readily available in public databases and integrated these data into a single computational pipeline. As a result, we provide comprehensive structural genomics and interactomics roadmaps of SARS-CoV-2 and use this information to infer the possible functional differences and similarities with the related SARS coronavirus. All data are made publicly available to the research community.

Progress in the Mechanism and Clinical Application of Cilostazol

Cilostazol is a unique platelet inhibitor that has been used clinically for more than 20 years. As a phosphodiesterase type III inhibitor, cilostazol is capable of reversible inhibition of platelet aggregation and vasodilation, has antiproliferative effects, and is widely used in the treatment of peripheral arterial disease, cerebrovascular disease, percutaneous coronary intervention, etc. This article briefly reviews the pharmacological mechanisms and clinical application of cilostazol.

Biological Production of (S)-acetoin: A State-of-the-Art Review

Acetoin is an important four-carbon compound that has many applications in foods, chemical synthesis, cosmetics, cigarettes, soaps, and detergents. Its stereoisomer (S)-acetoin, a high-value chiral compound, can also be used to synthesize optically active drugs, which could enhance targeting properties and reduce side effects. Recently, considerable progress has been made in the development of biotechnological routes for (S)-acetoin production. In this review, various strategies for biological (S)- acetoin production are summarized, and their constraints and possible solutions are described. Furthermore, future prospects of biological production of (S)-acetoin are discussed.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Novel antiviral drug discovery strategies to tackle drug-resistant mutants of influenza virus strains

INTRODUCTION

The emergence of drug-resistant influenza virus strains highlights the need for new antiviral therapeutics to combat future pandemic outbreaks as well as continuing seasonal cycles of influenza. Areas covered: This review summarizes the mechanisms of current FDA-approved anti-influenza drugs and patterns of resistance to those drugs. It also discusses potential novel targets for broad-spectrum antiviral drugs and recent progress in novel drug design to overcome drug resistance in influenza. Expert opinion: Using the available structural information about drug-binding pockets, research is currently underway to identify molecular interactions that can be exploited to generate new antiviral drugs. Despite continued efforts, antivirals targeting viral surface proteins like HA, NA, and M2, are all susceptible to developing resistance. Structural information on the internal viral polymerase complex (PB1, PB2, and PA) provides a new avenue for influenza drug discovery. Host factors, either at the initial step of viral infection or at the later step of nuclear trafficking of viral RNP complex, are being actively pursued to generate novel drugs with new modes of action, without resulting in drug resistance.

Exploration of synthetic multifunctional amides as new therapeutic agents for Alzheimer's disease through enzyme inhibition, chemoinformatic properties, molecular docking and dynamic simulation insights

A new series of multifunctional amides has been synthesized having moderate enzyme inhibitory potentials and mild cytotoxicity. 2-Furyl(1-piperazinyl)methanone (1) was coupled with 3,5-dichloro-2-hydroxybenzenesulfonyl chloride (2) to form {4-[(3,5-dichloro-2-hydroxyphenyl)sulfonyl]-1-piperazinyl}(2-furyl)methanone (3). Different elecrophiles were synthesized by the reaction of various un/substituted anilines (4a-o) with 2-bromoacetylbromide (5), 2‑bromo‑N-(un/substituted-phenyl)acetamides (6a-o). Further, equimolar ratios of 3 and 6a-o were allowed to react in the presence of K₂CO₃ in acetonitrile to form desired multifunctional amides (7a-o). The structural confirmation of all the synthesized compounds was carried out by their EI-MS, IR, ¹H NMR and ¹³C NMR spectral data. Enzyme inhibition activity was performed against acetyl and butyrylcholinestrase enzymes, whereby 7e showed very good activity having IC₅₀ value of 5.54 ± 0.03 and 9.15 ± 0.01 μM, respectively, relative to eserine, a reference standard. Hemolytic activity of the molecules was checked to asertain their cytotoxicity towards red blood cell membrance and it was observed that most of the compounds were not toxic up to certain range. Moreover, chemoinformatic protepties and docking simulation results also showed the significance of 7e as compared to other compounds. Based on in vitro and in silico analysis 7e could be used as a template for the development of new drugs against Alzheimer's disease.

A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs

A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure⁻toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives.

Substitution impact of highly conserved arginine residue at position 75 in GJB1 gene in association with X-linked Charcot-Marie-tooth disease: A computational study

X-linked Charcot-Marie-Tooth type 1 X (CMTX1) disease is a subtype of Charcot-Marie-Tooth (CMT), which is mainly caused by mutations in the GJB1 gene. It is also known as connexin 32 (Cx32) that leads to Schwann cell abnormalities and peripheral neuropathy. CMTX1 is considered as the second most common form of CMT disease. The aim of this study is to computationally predict the potential impact of different single amino acid substitutions at position 75 of Cx32, from arginine (R) to proline (P), glutamine (Q) and tryptophan (W). This position is known to be highly conserved among the family of connexin. To understand the structural and functional changes due to these single amino acid substitutions, we employed a homology-modeling technique to build the three-dimensional structure models for the native and mutant proteins. The protein structures were further embedded into a POPC lipid bilayer, inserted into a water box, and subjected to molecular dynamics simulation for 50 ns. Our results show that the mutants R75P, R75Q and R75W display variable structural conformation and dynamic behavior compared to the native protein. Our data proves useful in predicting the potential pathogenicity of the mutant proteins and is expected to serve as a platform for drug discovery for patients with CMT.

Screening of commercial cyclic peptide conjugated to HIV-1 Tat peptide as inhibitor of N-terminal heptad repeat glycoprotein-2 ectodomain Ebola virus through in silico analysis

Ebola Hemorrhagic Fever (EHF) is a disease caused by viruses from genus Ebolavirus. Zaire ebolavirus (EBOV) is the deadliest species which has 76% case fatality rate. Up until now, there is no U.S. Food and Drug Administration (FDA) approved drugs to treat EHF. Antiviral drug based on EBOV N-terminal heptad repeat glycoprotein-2 (NHR GP2) Ectodomain inhibitor is one kind of treatment that has not well developed. NHR GP2 Ectodomain has an important role in the process of EBOV entry into the cell through endocytosis mechanism. In this study, we used in silico methods to investigate the activity of commercial cyclic peptide conjugated to Human Immunodeficiency Virus type 1 Trans-activator of the transcription (HIV-1 Tat) peptide as ligands which act as an inhibitor of EBOV NHR GP2 Ectodomain. The commercial cyclic peptides which we used in this study were obtained from the selected chemical companies. Conjugation of the commercial cyclic peptides to HIV-1 Tat peptide was done in order to accumulate it inside the endosome. The ligands which had the best inhibition properties were screened using molecular docking and molecular dynamics simulation. Prediction of pharmacological properties of the peptides was done to choose the best drug candidate. The result of screening processes shows that Ligand 023 has the highest potency as the drug lead. The ligand needs to undergo further analysis through in vitro, in vivo, and a clinical trial to ensure that this ligand has a therapeutic ability as an antiviral drug for Ebola virus infection.

Molecular dynamics and protein interaction studies of lipopeptide (Iturin A) on α- amylase of Spodoptera litura

The small mottled willow moth (Spodoptera litura) is one of the best-known agricultural pest insects. To understand the insecticidal activity, we have selected iturin A compound produced by Bacillus amyloliquefaciens RHNK22 which showed the strongest and most common inhibitory effect on the Spodoptera litura protein. In this work we have identified the action of iturin A on α- amylase is a major digestive enzyme of Spodoptera litura using docking studies. A 3D model of α- amylase from Spodoptera litura was generated using 2HPH as a template with the help of Modeller7v7. With the aid of the molecular mechanics and molecular dynamics methods, the final model is obtained and is further checked by Procheck and Verify 3D graph programs, which showed that the final refined model is reliable. With this model, a adjustable docking study was performed with iturin A using GOLD software. The results indicated that ARG 18, THR15, LEU42 in α- amylase are important determinant residues in binding as they have strong hydrogen bonding interactions with iturin A. These hydrogen binding interactions play an important role for the stability of the complex.

3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors

Human epidermal growth factor receptor 2 (HER2) is one of the four members of the epidermal growth factor receptor (EGFR) family and is expressed to facilitate cellular proliferation across various tissue types. Therapies targeting HER2, which is a transmembrane glycoprotein with tyrosine kinase activity, offer promising prospects especially in breast and gastric/gastroesophageal cancer patients. Persistence of both primary and acquired resistance to various routine drugs/antibodies is a disappointing outcome in the treatment of many HER2 positive cancer patients and is a challenge that requires formulation of new and improved strategies to overcome the same. Identification of novel HER2 inhibitors with improved therapeutics index was performed with a highly correlating (r=0.975) ligand-based pharmacophore model (Hypo1) in this study. Hypo1 was generated from a training set of 22 compounds with HER2 inhibitory activity and this well-validated hypothesis was subsequently used as a 3D query to screen compounds in a total of four databases of which two were natural product databases. Further, these compounds were analyzed for compliance with Veber's drug-likeness rule and optimum ADMET parameters. The selected compounds were then subjected to molecular docking and Density Functional Theory (DFT) analysis to discern their molecular interactions at the active site of HER2. The findings thus presented would be an important starting point towards the development of novel HER2 inhibitors using well-validated computational techniques.

The effect of glycosylation on the transferrin structure: A molecular dynamic simulation analysis

Transferrins have been defined by the highly cooperative binding of iron and a carbonate anion to form a Fe-CO3-Tf ternary complex. As such, the layout of the binding site residues affects transferrin function significantly; In contrast to N-lobe, C-lobe binding site of the transferrin structure has been less characterized and little research which surveyed the interaction of carbonate with transferrin in the C-lobe binding site has been found. In the present work, molecular dynamic simulation was employed to gain access into the molecular level understanding of carbonate binding site and their interactions in each lobe. Residues responsible for carbonate binding of transferrin structure were pointed out. In addition, native human transferrin is a glycoprotein that two N-linked complex glycan chains located in the C-lobe. Usually, in the molecular dynamic simulation for simplifying, glycan is removed from the protein structure. Here, we explore the effect of glycosylation on the transferrin structure. Glycosylation appears to have an effect on the layout of the binding site residue and transferrin structure. On the other hand, sometimes the entire transferrin formed by separated lobes that it allows the results to be interpreted in a straightforward manner rather than more parameters required for full length protein. But, it should be noted that there are differences between the separated lobe and full length transferrin, hence, a comparative analysis by the molecular dynamic simulation was performed to investigate such structural variations. Results revealed that separation in C-lobe caused a significant structural variation in comparison to N-lobe. Consequently, the separated lobes and the full length one are different, showing the importance of the interlobe communication and the impact of the lobes on each other in the transferrin structure.

Identification of CD4+ T-cell epitope and investigation of HLA distribution for the immunogenic proteins of Burkholderia pseudomallei using in silico approaches - A key vaccine development strategy for melioidosis

Melioidosis is a serious infectious diseases affecting multi-organ system in humans with high mortality rate. The disease is caused by the bacterium, Burkholderia pseudomallei and it is intrinsically resistant to many antibiotics. Thus, there is an urgent need for protective vaccine against B. pseudomallei; which may reduce morbidity and mortality in endemic areas. The identification of peptides that bind to major histocompatibility complex II class helps in understanding the nature of immune response and identifying T-cell epitopes for the design of new vaccines. Previous studies indicate that, ompA, bipB, fliC and groEL proteins of B. pseudomallei stimulate CD4+ T-cell immune response and act as protective immunogens. However, the data for CD4+ T-cell epitopes of these immunogenic proteins are very limited. Hence, in this present study we attempted to identify CD4+ T-cell epitopes in B. pseudomallei immunogenic proteins using in silico approaches. We did population coverage analysis for these identified epitopic core sequences to identify individuals in endemic areas expected to respond to a given set of these epitopes on the basis of HLA genotype frequencies. We observed that eight epitopic core sequences, two from each immunogenic protein, were associated with the maximum number of HLA-DR binding alleles. These eight peptides are found to be immunogenic in more than 90% of population in endemic areas considered. Thus, these eight peptides containing epitopic core sequences may act as probable vaccine candidates and they may be considered for the development of epitope-based vaccines for melioidosis.

3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction

Predicting the native structure of proteins based on half-sphere exposure and contact numbers has been studied deeply within recent years. Online predictors of these vectors and secondary structures of amino acids sequences have made it possible to design a function for the folding process. By choosing variant structures and directs for each secondary structure, a random conformation can be generated, and a potential function can then be assigned. Minimizing the potential function utilizing meta-heuristic algorithms is the final step of finding the native structure of a given amino acid sequence. In this work, Imperialist Competitive algorithm was used in order to accelerate the process of minimization. Moreover, we applied an adaptive procedure to apply revolutionary changes. Finally, we considered a more accurate tool for prediction of secondary structure. The results of the computational experiments on standard benchmark show the superiority of the new algorithm over the previous methods with similar potential function.

Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations

Human Serum Albumin (HSA) has been suggested to be an alternate biomarker to the existing Hemoglobin-A1c (HbA1c) marker for glycemic monitoring. Development and usage of HSA as an alternate biomarker requires the identification of glycation sites, or equivalently, glucose-binding pockets. In this work, we combine molecular dynamics simulations of HSA and the state-of-art machine learning method Support Vector Machine (SVM) to predict glucose-binding pockets in HSA. SVM uses the three dimensional arrangement of atoms and their chemical properties to predict glucose-binding ability of a pocket. Feature selection reveals that the arrangement of atoms and their chemical properties within the first 4Å from the centroid of the pocket play an important role in the binding of glucose. With a 10-fold cross validation accuracy of 84 percent, our SVM model reveals seven new potential glucose-binding sites in HSA of which two are exposed only during the dynamics of HSA. The predictions are further corroborated using docking studies. These findings can complement studies directed towards the development of HSA as an alternate biomarker for glycemic monitoring.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.

Modelling the molecular mechanism of protein-protein interactions and their inhibition: CypD-p53 case study

Cyclophilin D (CypD) is an important regulatory protein involved in mitochondrial membrane permeability transition and cell death. Further, the mitochondrial CypD-p53 axis is an important contributor to necroptosis, a form of programmed necrosis, involved in various cardiovascular and neurological disorders. The CypD ligand, Cyclosporin A (CsA), was identified as an inhibitor of this interaction. In this study, using computational methods, we have attempted to model the CypD-p53 interaction in order to delineate their mode of binding and also to disclose the molecular mechanism, by means of which CsA interferes with this interaction. It was observed that p53 binds at the CsA-binding site of CypD. The knowledge obtained from this modelling was employed to identify novel CypD inhibitors through structure-based methods. Further, the identified compounds were tested by a similar strategy, adopted during the modelling process. This strategy could be applied to study the mechanism of protein-protein interaction (PPI) inhibition and to identify novel PPI inhibitors.

Computational assay of Zanamivir binding affinity with original and mutant influenza neuraminidase 9 using molecular docking

Based upon molecular docking, this study aimed to find notable in silico neuraminidase 9 (NA9) point mutations of the avian influenza A H7N9 virus that possess a Zanamivir resistant property and to determine the lead compound capable of inhibiting these NA9 mutations. Seven amino acids (key residues) at the binding site of neuraminidase 9 responsible for Zanamivir-NA9 direct interactions were identified and 72 commonly occurring mutant NA9 versions were created using the Sybyl-X 2.0 software. The docking scores obtained after Zanamivir was bound to all mutant molecules of NA9 revealed 3 notable mutations R292W, R118P, and R292K that could greatly reduce the binding affinity of the medicine. These 3 mutant NA9 versions were then bound to each of 154 different molecules chosen from 5 groups of compounds to determine which molecule(s) might be capable of inhibiting mutant neuraminidase 9, leading to the discovery of the lead compound of potent mutant NA9 inhibitors. This compound, together with other mutations occurring to NA9 identified in the study, would be used as data for further research regarding neuraminidase inhibitors and synthesizing new viable medications used in the fight against the virus.

Role of structural flexibility in the evolution of emerin

Emerin is a short inner nuclear membrane protein with an LEM-domain at the N-terminal end and a transmembrane domain at the C-terminal end. The middle region of human emerin contains multiple binding motifs. Since emerin is often found in evolutionarily newer species, the functional conservation of emerin becomes an interesting topic. In this study, we have demonstrated that most of the functional motifs of emerin are intrinsically disordered or highly flexible. Many post-translational modification sites and mutation sites are associated with these disordered regions. The averaged substitution rates of most functional motifs between species correlate positively with the averaged disorder scores of those functional motifs. Human emerin sequence may have acquired new functions on protein-protein interaction through the formation of hydrophobic motifs in the middle region, which is resulted from accumulated mutations during the evolution process.

Identifying key interactions stabilizing DOF zinc finger-DNA complexes using in silico approaches

DOF (DNA-binding with one finger) proteins, a family of DNA-binding transcription factors, are members of zinc fingers unique to plants. They are associated with different plant specific phenomena including germination, dormancy, light and defense responses. Until now, there is no report of experimentally solved structure for DOF proteins, making empirical investigation of DOF-DNA interaction more challenging. It has been shown that comparative modeling can be used to reliably predict the three-dimensional (3D) model of structurally unknown proteins whenever a suitable template is available. Furthermore, current molecular mechanics force fields allow prediction of interaction energies for macromolecular complexes. Therefore, the approaches considered in this work were to model the 3D structures of DOF zinc fingers (ZFs) from Arabidopsis thaliana complexed with DNA molecule, to calculate their binding energies, to identify key interactions established between ZFs and DNA, and to determine the impact of the different interactions on the binding energies. The results were used to predict the binding affinities for the novel designed ZFs and may be used in engineering DNA binding proteins.

A comparative study of structural and conformational properties of casein kinase-1 isoforms: insights from molecular dynamics and principal component analysis

Wnt signaling pathway regulates several developmental processes in human; however recently this pathway has been associated with development of different types of cancers. Casein kinase-1 (CK1) constitutes a family of serine-threonine protein kinase; various members of this family participate in Wnt signal transduction pathway and serve as molecular switch to this pathway. Among the known six isoforms of CK1, in human, at least three isoforms (viz. alpha, delta and epsilon) have been reported as oncogenic. The development of common therapeutics against these kinases is an arduous task; unless we have the detailed information of their tertiary structures and conformational properties. In the present work, the dynamical and conformational properties for each of three isoforms of CK1 are explored through molecular dynamics (MD) simulations. The conformational space distribution of backbone atoms is evaluated using principal component analysis of MD data, which are further validated on the basis of potential energy surface. Based on these analytics, it is suggested that conformational subspace shifts upon binding to ligands and guides the kinase action of CK1 isoforms. Further, this paper as a first effort to concurrently study all the three isoforms of CK1 provides structural basis for development of common anticancer therapeutics against three isoforms of CK1.

In-silico structural analysis of the influenza A subtype H7N9 neuraminidase and molecular docking with different neuraminidase inhibitors

Human infection with H7 influenza subtypes usually resulted in mild disease with a rare mortalities, however, human infection with the avian low pathogenic H7N9 influenza virus resulted in about 38.6 % human fatality. Due to the new cross-species barrier of this virus subtype, there is an urgent need to better understand the susceptibility to commercially available antivirals and their relation to the structural changes of the viral neuraminidase. Neuraminidases derived from 2013 H7N9, H5N1 and H1N1 were subjected to a structural analysis of their catalytic and framework binding sites. The modeling structure of selected neuraminidases from H7N9 and influenza A subtypes were solved and the docking studies with oseltamivir, zanamivir, laninamivir and peramivir were conducted. The active site residues that are responsible for both binding and cleavage of the terminally linked sialic acid receptors were found conserved. Docking studies with oseltamivir, zanamivir, laninamivir and peramivir revealed that the laninamivir and peramivir showed superior energy binding activities in comparison to the commonly used oseltamivir and zanamivir. The results presented in the current study provide data that are useful for the future treatment of different influenza A subtypes including the recently emerged H7N9.

QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors

In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.

Single H5N1 influenza A neuraminidase mutation develops resistance to oseltamivir due to distorted conformational and drug binding landscape: multiple molecular dynamics analyses

Clinical studies showed that a single mutation, I117V, develops severe resistance to oseltamivir, the first orally active influenza A neuraminidase inhibitor, in highly pathogenic H5N1 influenza A viruses.

Molecular basis of R294K mutation effects of H7N9 neuraminidases with drugs and cyclic peptides: an in silico and experimental study

An overview of Shanghai N9/cyclic peptide I complex structure.

Discovery of potential drugs for human-infecting H7N9 virus containing R294K mutation

Background

After the first epidemic wave from February through May 2013, the influenza A (H7N9) virus emerged and has followed a second epidemic wave since June 2013. As of June 27, 2014, the outbreak of H7N9 had caused 450 confirmed cases of human infection, with 165 deaths included. The case-fatality rate of all confirmed cases is about 36%, making the H7N9 virus a significant threat to people’s health. At present, neuraminidase inhibitors are the only licensed antiviral medications available to treat H7N9 infections in humans. Oseltamivir is the most commonly used inhibitor, and it is also a front-line drug for the threatening H7N9. Unfortunately, it has been reported that patients treated with oseltamivir can induce R294K (Arg294Lys) substitution in the H7N9 virus, which is a rare mutation and can reduce the antiviral efficacy of inhibitors. Even worse, deaths caused by such mutation after oseltamivir treatment have already been reported, indicating that the need to find substitutive neuraminidase inhibitors for currently available drugs to treat drug-resistant H7N9 is really pressing.

Materials and methods

First, the structure of H7N9 containing the R294K substitution was downloaded from the Protein Data Bank, and structural information of approved drugs was downloaded from the ZINC (ZINC Is Not Commercial) database. Taking oseltamivir carboxylate as a reference drug, we then filtered these molecules through virtual screening to find out potential inhibitors targeting the mutated H7N9 virus. For further evaluation, we carried out a 14 ns molecular dynamic simulation for each H7N9–drug complex and calculated the binding energy for each candidate drug.

Results

We found five inhibitors that could be candidate drugs for treating the mutated H7N9 virus. Docking poses showed these drugs could bind to the virus effectively, with the contribution of hydrogen bonds and hydrophobic interactions. With regard to the molecular dynamic simulations, receptor–ligand complexes formed by these candidate drugs were more stable than the one formed by oseltamivir carboxylate. The binding energy of oseltamivir carboxylate was −122.4 kJ/mol, while those for these potential inhibitors were −417.5, −404.7, −372.2, −304.3, and −289.9 kJ/mol, much better than the reference drug.

Conclusion

Given the current and future threat of the mutated H7N9 virus, it is urgent that potent drugs and effective antiviral therapeutics be found. Our study therefore is able to complement currently available drugs for influenza A infectors and helps to prevent the ongoing threat of H7N9 virus.

The small-molecule inhibitor selectivity between IKKα and IKKβ kinases in NF-κB signaling pathway

The enzyme complex IκB kinase (IKK) is an essential activator of NF-κB signaling pathway involved in propagating the cellular response to inflammation. The complex contains two functional subunits IKKα and IKKβ, which are structurally conserved kinases and selective inhibition of them would result in distinct biological effects. However, most existing IKK inhibitors show moderate or high promiscuity for the two homologous kinases. Understanding of the molecular mechanism and biological implication underlying the specific interactions in IKK-ligand recognition is thus fundamentally important for the rational design of selective IKK inhibitors. In the current work, we integrated molecular docking, quantum mechanics/molecular mechanics calculation and Poisson-Boltzmann/surface area analysis to investigate the structural basis and energetic property of the selective binding of small-molecule ligands to IKKα and IKKβ. It was found that the selectivity is primarily determined by the size and topology difference in ATP-binding pocket of IKKα and IKKβ kinase domains; bulky inhibitor molecules commonly have, respectively, low and appropriate affinities towards IKKα and IKKβ, and thus exhibit relatively high selectivity for IKKβ over IKKα, whereas small ligands can only bind weakly to both the two kinases with low selectivity. In addition, the conformation, arrangement and distribution of residues in IKK pockets are also responsible for constituting the exquisite specificity of ligand binding to KKα and IKKβ. Next, a novel quantitative structure-selectivity relationship model was developed to characterize the relative contribution of each kinase residue to inhibitor selectivity and to predict the selectivity and specificity for a number of known IKK inhibitors. Results showed that the active-site residues contribute significantly to the selectivity by directly interacting with inhibitor ligands, while those protein portions far away from the kinase active sites may also play an important role in determining the selectivity through long-range non-bonded forces and indirect allosteric effect.