Homology modeling of membrane proteins: A critical assessment

Avian pathogenic Escherichia coli T6SS effector protein Hcp2a causes mitochondrial dysfunction through interaction with LETM1 protein in DF-1 cells

The type VI secretion system (T6SS) of avian pathogenic Escherichia coli (APEC) can affect the functions of eukaryotic cells by secreting or injecting effectors. Hemolysin co-regulatory protein (Hcp), one of the markers of the T6SS, is both a structural protein and an effector protein of the T6SS. According to previous studies, mitochondria in eukaryotic cells are targeted by pathogenic bacteria. However, little is known about the regulation of mitochondria in eukaryotic host cells by the T6SS effector protein Hcp of APEC. In our study, DF-1 cells co-incubated with Hcp2a protein for 6 h showed decreased mitochondrial membrane potential, increased Ca2+ concentration, and increased cellular reactive oxygen species (ROS) levels. We therefore conclude that Hcp2a protein causes dysfunction to mitochondria in DF-1 cells. To explain the mechanism that causes mitochondrial dysfunction, we reanalyzed the Hcp2a interaction protein dataset in DF-1 cells, and the Leucine zipper EF-hand-containing transmembrane protein 1 (LETM1), which is associated with mitochondria, was screened. The protein and molecular docking results showed that Hcp2a protein and LETM1 protein have better binding. Finally, subcellular localization results showed that Hcp2a was localized to mitochondria. In summary, Hcp2a effector proteins caused dysfunction to DF-1 cellular mitochondria, and we hypothesize that the interaction of Hcp2a protein with LETM1 protein induces mitochondrial dysfunction and promotes mitochondrial localization of Hcp2a in DF-1 cells.

Insights on Novel Effectors and Characterization of Metacaspase (RS107_6) as a Potential Cell Death-Inducing Protein in Rhizoctonia solani

Effectors play an important role in host–pathogen interactions. Though an economically significant disease in rice, knowledge regarding the infection strategy of Rhizoctonia solani is obscure. In this study, we performed a genome-wide identification of the effectors in R. solani based on the characteristics of previously reported effector proteins. A total of seven novel effectors (designated as RS107_1 to RS107_7) in the disease mechanism of R. solani were identified and were predicted to be non-classically secreted proteins with functionally conserved domains. The function, reactivity, and stability of these proteins were evaluated through physiochemical characterization. The target proteins involved in the regulation of rice defense mechanisms were identified. Furthermore, the effector genes were cloned and RS107_6 (metacaspase) was heterologously expressed in Escherichia coli to obtain a purified protein of ~36.5 kDa. The MALD-TOF characterization confirmed that the protein belonged to a metacaspase of the Peptidase_C14 protein family, 906 bp in size, and encoded a polypeptide of 301 amino acids. These findings suggest that the identified effectors can potentially serve as a virulence factor and can be targeted for the management of sheath blight in rice.

Effector protein Hcp2a of avian pathogenic Escherichia coli interacts with the endoplasmatic reticulum associated RPL23 protein of chicken DF-1 fibroblasts

The type VI secretion system (T6SS) is a secretion apparatus widely found in pathogenic Gram-negative bacteria and is important for competition among various bacteria and host cell pathogenesis. Hcp is a core component of functional T6SS and transports toxic effectors into target cells by assembling to form tube-like structures. Studies have shown that Hcp simultaneously acts as an effector to influence cellular physiological activities; however, the mechanism of its activity in host cells remains unclear. To investigate the target of effector protein Hcp2a in a chicken fibroblast cell line, we first detected the subcellular localization of Hcp2a in DF-1 cells by indirect immunofluorescence assay. The results showed that Hcp2a protein was localized in the endoplasmic reticulum of DF-1 cells. We also used a streptavidin-biotin affinity pull-down assay combined with LC-MS/MS to screen DF-1 cell lysates for proteins that interact with Hcp2a and analyze the cellular functional pathways affected by them. The results showed that Hcp2a interacted with 52 DF-1 cellular proteins that are involved in multiple intracellular pathways. To further explore the mechanism of Hcp2a protein targeting the endoplasmic reticulum of DF-1 cells, we screened three endoplasmic reticulum-associated proteins (RSL1D1, RPS3A, and RPL23) from 52 prey proteins of Hcp2a for protein-protein molecular docking analysis. The docking analysis showed that the effector protein Hcp2a and the RPL23 protein had good complementarity. Overall, we propose that Hcp2a has strong binding activity to the RPL23 protein in DF-1 cells and this may help Hcp2a anchor to the endoplasmic reticulum in DF-1 cells.

Genomic analysis of Paenibacillus sp. MDMC362 from the Merzouga desert leads to the identification of a potentially thermostable catalase

Microorganisms in hot deserts face heat and other environmental conditions, such as desiccation, UV radiation, or low nutrient availability. Therefore, this hostile environment harbour microorganisms with acquired characteristics related to survival in their habitat, which can be exploited in biotechnology. In this work, the genome of Paenibacillus sp. MDMC362 isolated from the Merzouga desert in Morocco was sequenced to understand its survival strategy's genetic basis; and to evaluate the thermostability of a catalase extracted from genomic annotation files using molecular dynamics. Paenibacillus sp. MDMC362 genome was rich in genetic elements involved in the fight against different stresses, notably temperature stress, UV radiations, osmotic stress, carbon starvation, and oxidative stress. Indeed, we could identify genes of the operons groES-groEL and hrcA-grpE-dnaK and those involved in the different stages of sporulation, which can help the bacteria to survive the high temperatures imposed by a desertic environment. We also observed the genetic components of the UvrABC system and additional mechanisms involved in DNA repair, which help overcome UV radiation damage. Other genes have been identified in the genome, like those coding for ectoine and proline, that aids fight osmotic stress and desiccation. Catalase thermostability investigation using molecular dynamics showed that the protein reached stability and conserved its compactness at temperatures up to 373.15 K. These results suggest a potential thermostability of the enzyme. Since the studied protein is a core protein, thermostability could be conserved among Paenibacillus sp. MDMC362 closely related strains; however, bacteria from harsh environments may have a slight advantage regarding protein stability.

Molecular Docking Study of IPBCC.08.610 Glucose Oxidase Mutant for Increasing Gluconic Acid Production

Glucose oxidase (GOD) is an oxidoreductase enzyme that catalyzes the oxidation of glucose to gluconolactone and hydrogen peroxide. Then, gluconolactone will be hydrolyzed to gluconic acid. The wide application of gluconic acid in various industries has increased production demand. However, glucose concentrations higher than 40% (w/w) inhibited the conversion of glucose to gluconic acid due to a decrease in the oxygen solubility concentration at pH 6, 30℃, and 1 bar pressure. Therefore, decreasing the value of Km is predicted to reduce saturation and enhance gluconic acid production. This study aimed to analyze the interaction between the IPBCC.08.610 GOD mutant with β-D-Glucose in improving gluconic acid production by decreasing the Km value. Mutations were performed in silico using Chimera and then docked using AutoDock Vina. The mutations resulted in distinct ligand poses in the binding pocket, different -OH conformations of the ligands, and changes in the T554M/D578P mutant’s hydrophobicity index (554 mutated from threonine to methionine, and 578 mutated from aspartate to proline), and decreased ΔG and Km values in the H559D mutant (559 mutated from histidine to aspartate), D578P and T554M/D578P. This decrease might strengthen the ligand-receptor interaction, increasing gluconic acid production. The H559D was the best mutant to increase production based on the ΔG, Km value, and stability due to the addition of hydrogen bonds.

Prediction of Epitope-Based Peptide Vaccine Against the Chikungunya Virus by Immuno-informatics Approach

BACKGROUND

Chikungunya is an arthropod-borne viral disease characterized by abrupt onset of fever frequently accompanied by joint pain, which has been identified in over 60 countries in Africa, the Americas, Asia, and Europe.

METHODS

Regardless of the availability of molecular knowledge of this virus, no definite vaccine or other remedial agents have been developed yet. In the present study, a combination of B-cell and T-cell epitope predictions, followed by molecular docking simulation approach has been carried out to design a potential epitope-based peptide vaccine, which can trigger a critical immune response against the viral infections.

RESULTS

A total of 52 sequences of E1 glycoprotein from the previously reported isolates of Chikungunya outbreaks were retrieved and examined through in silico methods to identify a potential B-cell and T-cell epitope. From the two separate epitope prediction servers, five potential B-cell epitopes were selected, among them "NTQLSEAHVEKS" was found highly conserved across strains and manifests high antigenicity with surface accessibility, flexibility, and hydrophilicity. Similarly, two highly conserved, non-allergenic, non-cytotoxic putative T-cell epitopes having maximum population coverage were screened to bind with the HLA-C 12*03 molecule. Molecular docking simulation revealed potential T-cell based epitope "KTEFASAYR" as a vaccine candidate for this virus.

CONCLUSION

A combination of these B-cell and T-cell epitope-based vaccine can open up a new skyline with broader therapeutic application against Chikungunya virus with further experimental and clinical investigation.

Biosystem Analysis of the Hypoxia Inducible Domain Family Member 2A: Implications in Cancer Biology

The expression of HIGD2A is dependent on oxygen levels, glucose concentration, and cell cycle progression. This gene encodes for protein HIG2A, found in mitochondria and the nucleus, promoting cell survival in hypoxic conditions. The genomic location of HIGD2A is in chromosome 5q35.2, where several chromosomal abnormalities are related to numerous cancers. The analysis of high definition expression profiles of HIGD2A suggests a role for HIG2A in cancer biology. Accordingly, the research objective was to perform a molecular biosystem analysis of HIGD2A aiming to discover HIG2A implications in cancer biology. For this purpose, public databases such as SWISS-MODEL protein structure homology-modelling server, Catalogue of Somatic Mutations in Cancer (COSMIC), Gene Expression Omnibus (GEO), MethHC: a database of DNA methylation and gene expression in human cancer, and microRNA-target interactions database (miRTarBase) were accessed. We also evaluated, by using Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR), the expression of Higd2a gene in healthy bone marrow-liver-spleen tissues of mice after quercetin (50 mg/kg) treatment. Thus, among the structural features of HIG2A protein that may participate in HIG2A translocation to the nucleus are an importin α-dependent nuclear localization signal (NLS), a motif of DNA binding residues and a probable SUMOylating residue. HIGD2A gene is not implicated in cancer via mutation. In addition, DNA methylation and mRNA expression of HIGD2A gene present significant alterations in several cancers; HIGD2A gene showed significant higher expression in Diffuse Large B-cell Lymphoma (DLBCL). Hypoxic tissues characterize the “bone marrow-liver-spleen” DLBCL type. The relative quantification, by using qRT-PCR, showed that Higd2a expression is higher in bone marrow than in the liver or spleen. In addition, it was observed that quercetin modulated the expression of Higd2a gene in mice. As an assembly factor of mitochondrial respirasomes, HIG2A might be unexpectedly involved in the change of cellular energetics happening in cancer. As a result, it is worth continuing to explore the role of HIGD2A in cancer biology.

Comprehensive in silico Study of GLUT10: Prediction of Possible Substrate Binding Sites and Interacting Molecules

OBJECTIVES

The Arterial Tortuosity Syndrome (ATS) is an autosomal recessive connective tissue disorder, mainly characterized by tortuosity and stenosis of the arteries with a propensity towards aneurysm formation and dissection. It is caused by mutations in the SLC2A10 gene that encodes the facilitative glucose transporter GLUT10. The molecules transported by and interacting with GLUT10 have still not been unambiguously identified. Hence, the study attempts to identify both the substrate binding site of GLUT10 and the molecules interacting with this site.

METHODS

As High-resolution X-ray crystallographic structure of GLUT10 was not available, 3D homology model of GLUT10 in open conformation was constructed. Further, molecular docking and bioinformatics investigation were employed.

RESULTS AND DISCUSSION

Blind docking of nine reported potential in vitro substrates with this 3D homology model revealed that substrate binding site is possibly made with PRO531, GLU507, GLU437, TRP432, ALA506, LEU519, LEU505, LEU433, GLN525, GLN510, LYS372, LYS373, SER520, SER124, SER533, SER504, SER436 amino acid residues. Virtual screening of all metabolites from the Human Serum Metabolome Database and muscle metabolites from Human Metabolite Database (HMDB) against the GLUT10 revealed possible substrates and interacting molecules for GLUT10, which were found to be involved directly or partially in ATS progression or different arterial disorders. Reported mutation screening revealed that a highly emergent point mutation (c. 1309G>A, p. Glu437Lys) is located in the predicted substrate binding site region.

CONCLUSION

Virtual screening expands the possibility to explore more compounds that can interact with GLUT10 and may aid in understanding the mechanisms leading to ATS.

A QSAR and molecular modelling study towards new lead finding: polypharmacological approach to Mycobacterium tuberculosis

Developing effective inhibitors against Mycobacterium tuberculosis (Mtb) is a challenging task, primarily due to the emergence of resistant strains. In this study, we have proposed and implemented an in silico guided polypharmacological approach, which is expected to be effective against resistant strains by simultaneously inhibiting several potential Mtb drug targets. A combination of pharmacophore and QSAR based virtual screening strategy taking three key targets such as InhA (enoyl-acyl-carrier-protein reductase), GlmU (N-acetyl-glucosamine-1-phosphate uridyltransferase) and DapB (dihydrodipicolinate reductase) have resulted in initial 784 hits from Asinex database of 435,000 compounds. These hits were further subjected to docking with 33 Mtb druggable targets. About 110 potential polypharmacological hits were taken by integrating the aforementioned screening protocols. Further screening was conducted by taking various parameters and properties such as cell permeability, drug-likeness, drug-induced phospholipidosisand structural alerts. A consensus analysis has yielded 59 potential hits that pass through all the filters and can be prioritized for effective drug-resistant tuberculosis. This study proposes about nine potential hits which are expected to be promising molecules, having not only drug-like properties, but also being effective against multiple Mtb targets.

Differentiating physicochemical properties between NDRIs and sNRIs clinically important for the treatment of ADHD

BACKGROUND

Drugs available for treating attention-deficit hyperactivity disorder (ADHD) are mainly selective norepinephrine (sNRIs) and dual norepinephrine-dopamine (NDRIs) reuptake inhibitors. The major problem of sNRIs lines in their delayed onset of action and partial- or non-responses, which makes NDRIs distinguished in drug efficacy. Understanding of the differential binding modes of these 2 types of drugs to their corresponding targets can give great insights into the discovery of privileged drug-like scaffolds with improved efficacy. So far, no such study has been carried out.

METHODS

A combinatorial computational strategy, integrating homology modeling, molecular docking, molecular dynamics (MD) and binding free energy calculation, was employed to analyze the binding modes of 8 clinically important ADHD drugs in their targets.

RESULTS

Binding modes of 2 types of ADHD drugs (sNRIs and NDRIs) in their targets was identified for the first time by MD simulation, and 15 hot spot residues were discovered as crucial for NDRIs' binding in hNET and hDAT. Comparing to sNRIs, a clear reduction in the hydrophobic property of NDRIs' one functional group was observed, and the depth of drugs' aromatic ring stretched into the pocket of both targets was further identified as key contributors to drugs' selectivity.

CONCLUSIONS

The hydrophobic property of NDRI ADHD drugs' one functional group contributes to their selectivity when bind hNET and hDAT.

GENERAL SIGNIFICANCE

These results provide insights into NDRI ADHD drugs' binding mechanisms, which could be utilized as structural blueprints for assessing and discovering more efficacious drugs for ADHD therapy.

Modeling the permeability of drug-like molecules through the cell wall of Mycobacterium tuberculosis: an analogue based approach

The emergence of drug resistant strains of Mycobacterium Tuberculosis (Mtb) accentuates the urgent need for the development of novel antitubercular drugs. The major causes of drug resistance are genetic mutations, the influx-efflux transporter system, and the complex cell wall system of Mtb, which can function as permeability barriers. The driving force for permeability of small molecules through a biological system depends on various physicochemical factors. To understand the permeability of small molecules and subsequent cell inhibition, we have developed predictive QSAR models based on reported enzyme-based (IC₅₀) and cell-based (MIC) Mtb inhibitory data. The compounds that are highly active in enzyme-based assays and have significant variation in cell-based assays are assumed to possess the required permeability through the Mtb cell wall. The obtained models suggest the importance of molecular connectivity, lipophilicity (log P, size, shape), electrotopology (relative atomic mass, polarizability, electronegativity, ionization potential, atomic charges, van der Waals volume, hybridization, hydrogen bond acceptors/donors, number of fused rings) and functional groups (hydroxyl groups, primary and secondary amines) of a molecule in determining both its inhibitory potency and Mtb cell permeability. The models were validated with known Mtb inhibitors (9804) collected from the ChEMBL database, Mtb drugs (27) and clinical candidates (5). Further, these validated models were used to screen and prioritize a large database of compounds, including Zinc (152 128), Asinex (435 215), DrugBank (6531) and antimicrobial compounds (1324). The results obtained from 2D-QSAR analysis could improve our understanding towards Mtb cell permeability, which may aid in the rational design of novel potent molecules for tuberculosis (TB).

Molecular characterization of alkaline protease of Bacillus amyloliquefaciens SP1 involved in biocontrol of Fusarium oxysporum

An alkaline protease gene was amplified from genomic DNA of Bacillus amyloliquefaciens SP1 which was involved in effective biocontrol of Fusarium oxysporum. We investigated the antagonistic capacity of protease of B. amyloliquifaciens SP1, under in vitro conditions. The 5.62 fold purified enzyme with specific activity of 607.69U/mg reported 24.14% growth inhibition of F. oxysporum. However, no antagonistic activity was found after addition of protease inhibitor i.e. PMSF (15mM) to purified enzyme. An 1149bp nucleotide sequence of protease gene encoded 382 amino acids of 43kDa and calculated isoelectric point of 9.29. Analysis of deduced amino acid sequence revealed high homology (86%) with subtilisin E of Bacillus subtilis. The B. amyloliquefaciens SP1 protease gene was expressed in Escherichiax coli BL21. The expressed protease was secreted into culture medium by E. coli and exhibited optimum activity at pH8.0 and 60°C. The most reliable three dimensional structure of alkaline protease was determined using Phyre 2 server which was validated on the basis of Ramachandran plot and ERRAT value. The expression and structure prediction of the enzyme offers potential value for commercial application in agriculture and industry.

Improved Homology Model of the Human all-trans Retinoic Acid Metabolizing Enzyme CYP26A1

A new CYP26A1 homology model was built based on the crystal structure of cyanobacterial CYP120A1. The model quality was examined for stereochemical accuracy, folding reliability, and absolute quality using a variety of different bioinformatics tools. Furthermore, the docking capabilities of the model were assessed by docking of the natural substrate all-trans-retinoic acid (atRA), and a group of known azole- and tetralone-based CYP26A1 inhibitors. The preferred binding pose of atRA suggests the (4S)-OH-atRA metabolite production, in agreement with recently available experimental data. The distances between the ligands and the heme group iron of the enzyme are in agreement with corresponding distances obtained for substrates and azole inhibitors for other cytochrome systems. The calculated theoretical binding energies agree with recently reported experimental data and show that the model is capable of discriminating between natural substrate, strong inhibitors (R116010 and R115866), and weak inhibitors (liarozole, fluconazole, tetralone derivatives).

Structure alignment of membrane proteins: Accuracy of available tools and a consensus strategy

Protein structure alignment methods are used for the detection of evolutionary and functionally related positions in proteins. A wide array of different methods are available, but the choice of the best method is often not apparent to the user. Several studies have assessed the alignment accuracy and consistency of structure alignment methods, but none of these explicitly considered membrane proteins, which are important targets for drug development and have distinct structural features. Here, we compared 13 widely used pairwise structural alignment methods on a test set of homologous membrane protein structures (called HOMEP3). Each pair of structures was aligned and the corresponding sequence alignment was used to construct homology models. The model accuracy compared to the known structures was assessed using scoring functions not incorporated in the tested structural alignment methods. The analysis shows that fragment-based approaches such as FR-TM-align are the most useful for aligning structures of membrane proteins. Moreover, fragment-based approaches are more suitable for comparison of protein structures that have undergone large conformational changes. Nevertheless, no method was clearly superior to all other methods. Additionally, all methods lack a measure to rate the reliability of a position within a structure alignment. To solve both of these problems, we propose a consensus-type approach, combining alignments from four different methods, namely FR-TM-align, DaliLite, MATT, and FATCAT. Agreement between the methods is used to assign confidence values to each position of the alignment. Overall, we conclude that there remains scope for the improvement of structural alignment methods for membrane proteins.

Luotonin-A based quinazolinones cause apoptosis and senescence via HDAC inhibition and activation of tumor suppressor proteins in HeLa cells

A series of novel quinazolinone hybrids were synthesized by employing click chemistry and evaluated for anti-proliferative activities against MCF-7, HeLa and K562 cell lines. Among these cell lines, HeLa cells were found to respond effectively to these quinazolinone hybrids with IC50 values ranging from 5.94 to 16.45 μM. Some of the hybrids (4q, 4r, 4e, 4k, 4t, 4w) with promising anti-cancer activity were further investigated for their effects on the cell cycle distribution. FACS analysis revealed the G1 cell cycle arrest nature of these hybrids. Further to assess the senescence inducing ability of these compounds, a senescence associated β-gal assay was performed. The senescence inducing nature of these compounds was supported by the effect of hybrid (4q) on p16 promoter activity, the marker for senescence. Moreover, cells treated with most effective compound (4q) show up-regulation of p53, p21 and down-regulation of HDAC-1, HDAC-2, HDAC-5 and EZH2 mRNA levels. Docking results suggest that, the triazole nitrogen showed Zn(+2) mediated interactions with the histidine residue of HDACs.

Evolution and Structural Analyses of Glossina morsitans (Diptera; Glossinidae) Tetraspanins

Tetraspanins are important conserved integral membrane proteins expressed in many organisms. Although there is limited knowledge about the full repertoire, evolution and structural characteristics of individual members in various organisms, data obtained so far show that tetraspanins play major roles in membrane biology, visual processing, memory, olfactory signal processing, and mechanosensory antennal inputs. Thus, these proteins are potential targets for control of insect pests. Here, we report that the genome of the tsetse fly, Glossina morsitans (Diptera: Glossinidae) encodes at least seventeen tetraspanins (GmTsps), all containing the signature features found in the tetraspanin superfamily members. Whereas six of the GmTsps have been previously reported, eleven could be classified as novel because their amino acid sequences do not map to characterized tetraspanins in the available protein data bases. We present a model of the GmTsps by using GmTsp42Ed, whose presence and expression has been recently detected by transcriptomics and proteomics analyses of G. morsitans. Phylogenetically, the identified GmTsps segregate into three major clusters. Structurally, the GmTsps are largely similar to vertebrate tetraspanins. In view of the exploitation of tetraspanins by organisms for survival, these proteins could be targeted using specific antibodies, recombinant large extracellular loop (LEL) domains, small-molecule mimetics and siRNAs as potential novel and efficacious putative targets to combat African trypanosomiasis by killing the tsetse fly vector.

Molecular docking simulations provide insights in the substrate binding sites and possible substrates of the ABCC6 transporter

The human ATP-binding cassette family C member 6 (ABCC6) gene encodes an ABC transporter protein (ABCC6), primarily expressed in liver and kidney. Mutations in the ABCC6 gene cause pseudoxanthoma elasticum (PXE), an autosomal recessive connective tissue disease characterized by ectopic mineralization of the elastic fibers. The pathophysiology underlying PXE is incompletely understood, which can at least partly be explained by the undetermined nature of the ABCC6 substrates as well as the unknown substrate recognition and binding sites. Several compounds, including anionic glutathione conjugates (N-ethylmaleimide; NEM-GS) and leukotriene C4 (LTC4) were shown to be modestly transported in vitro; conversely, vitamin K3 (VK3) was demonstrated not to be transported by ABCC6. To predict the possible substrate binding pockets of the ABCC6 transporter, we generated a 3D homology model of ABCC6 in both open and closed conformation, qualified for molecular docking and virtual screening approaches. By docking 10 reported in vitro substrates in our ABCC6 3D homology models, we were able to predict the substrate binding residues of ABCC6. Further, virtual screening of 4651 metabolites from the Human Serum Metabolome Database against our open conformation model disclosed possible substrates for ABCC6, which are mostly lipid and biliary secretion compounds, some of which are found to be involved in mineralization. Docking of these possible substrates in the closed conformation model also showed high affinity. Virtual screening expands this possibility to explore more compounds that can interact with ABCC6, and may aid in understanding the mechanisms leading to PXE.

Modeling and docking studies on novel mutants (K71L and T204V) of the ATPase domain of human heat shock 70 kDa protein 1

The purpose of exploring protein interactions between human adenovirus and heat shock protein 70 is to exploit a potentially synergistic interaction to enhance anti-tumoral efficacy and decrease toxicity in cancer treatment. However, the protein interaction of Hsp70 with E1A32 kDa of human adenovirus serotype 5 remains to be elucidated. In this study, two residues of ATPase domain of human heat shock 70 kDa protein 1 (PDB: 1 HJO) were mutated. 3D mutant models (K71L and T204V) using PyMol software were then constructed. The structures were evaluated by PROCHECK, ProQ, ERRAT, Verify 3D and ProSA modules. All evidence suggests that all protein models are acceptable and of good quality. The E1A32 kDa motif was retrieved from UniProt (P03255), as well as subjected to docking interaction with NBD, K71L and T204V, using the Autodock 4.2 program. The best lowest binding energy value of −9.09 kcal/mol was selected for novel T204V. Moreover, the protein-ligand complex structures were validated by RMSD, RMSF, hydrogen bonds and salt bridge analysis. This revealed that the T204V-E1A32 kDa motif complex was the most stable among all three complex structures. This study provides information about the interaction between Hsp70 and the E1A32 kDa motif, which emphasizes future perspectives to design rational drugs and vaccines in cancer therapy.

Structure predictions of two Bauhinia variegata lectins reveal patterns of C-terminal properties in single chain legume lectins

Bauhinia variegata lectins (BVL-I and BVL-II) are single chain lectins isolated from the plant Bauhinia variegata. Single chain lectins undergo post-translational processing on its N-terminal and C-terminal regions, which determines their physiological targeting, carbohydrate binding activity and pattern of quaternary association. These two lectins are isoforms, BVL-I being highly glycosylated, and thus far, it has not been possible to determine their structures. The present study used prediction and validation algorithms to elucidate the likely structures of BVL-I and -II. The program Bhageerath-H was chosen from among three different structure prediction programs due to its better overall reliability. In order to predict the C-terminal region cleavage sites, other lectins known to have this modification were analysed and three rules were created: (1) the first amino acid of the excised peptide is small or hydrophobic; (2) the cleavage occurs after an acid, polar, or hydrophobic residue, but not after a basic one; and (3) the cleavage spot is located 5-8 residues after a conserved Leu amino acid. These rules predicted that BVL-I and -II would have fifteen C-terminal residues cleaved, and this was confirmed experimentally by Edman degradation sequencing of BVL-I. Furthermore, the C-terminal analyses predicted that only BVL-II underwent α-helical folding in this region, similar to that seen in SBA and DBL. Conversely, BVL-I and -II contained four conserved regions of a GS-I association, providing evidence of a previously undescribed X4+unusual oligomerisation between the truncated BVL-I and the intact BVL-II. This is the first report on the structural analysis of lectins from Bauhinia spp. and therefore is important for the characterisation C-terminal cleavage and patterns of quaternary association of single chain lectins.

Homology modeling and structural comparison of leucine rich repeats of Toll like receptors 1-10 of ruminants

Toll-like receptors (TLRs) are transmembrane receptors composed of extra cellular leucine rich repeats (LRRs) that identify specific pathogen associated molecular patterns triggering a innate immune cascade. The LRR regions of TLR 1-10 proteins of goat (Capra hircus), sheep (Ovis aries), buffalo (Bubalus bubalis) and bovine (Bos taurus) were modeled using MODELLER 9v7 tool and validated. The similarities and variations of these 10 TLRs extracellular regions of each species were compared using online servers like FATCAT, SSM and SSAP. It was evident that the LRRs of TLRs like 1, 2, 3 and 6 showed structural convergence with <1 % RMSD deviation while TLRs like 5, 7, 8 and 9 had high divergence. Docking analysis showed that TLR 2, 3 and 7 of all the selected four ruminant species were able to bind with their corresponding ligands like Peptidoglycan (PGN), Poly I:C, Resiquimod (R-848) and Imiquimod. However, there were variations in the active site regions, interacting residues and the number of bonded interactions. Variations seen among TLR structures and their ligand binding characteristics is likely to be responsible for species and breed specific genetic resistance observed among species or breeds.

Modelling and enhanced molecular dynamics to steer structure-based drug discovery

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes.

The identification of novel, high affinity AQP9 inhibitors in an intracellular binding site

BACKGROUND

The involvement of aquaporin (AQP) water and small solute channels in the etiology of several diseases, including cancer, neuromyelitis optica and body fluid imbalance disorders, has been suggested previously. Furthermore, results obtained in a mouse model suggested that AQP9 function contributes to hyperglycemia in type-2 diabetes. In addition, the physiological role of several AQP family members remains poorly understood. Small molecule inhibitors of AQPs are therefore desirable to further study AQP physiological and pathophysiological functions.

METHODS

The binding of recently established AQP9 inhibitors to a homology model of AQP9 was investigated by molecular dynamics simulations and molecular docking. Putative inhibitor binding sites identified with this procedure were modified by site-directed mutagenesis. Active compounds were measured in a mammalian cell water permeability assay of mutated AQP9 isoforms and tested for changes in inhibitory effects.

CONTROLS

Three independent cell lines were established for each mutated AQP9 isoform and functionality of mutant isoforms was established.

PRINCIPAL FINDINGS

We have identified putative binding sites of recently established AQP9 inhibitors. This information facilitated successful identification of novel AQP9 inhibitors with low micromolar IC50 values in a cell based assay by in silico screening of a compound library targeting specifically this binding site.

SIGNIFICANCE

We have established a successful strategy for AQP small molecule inhibitor identification. AQP inhibitors may be relevant as experimental tools, to enhance our understanding of AQP function, and in the treatment of various diseases.

Functional analysis and structure determination of alkaline protease from Aspergillus flavus

Proteases are one of the highest value commercial enzymes as they have broad applications in food, pharmaceutical, detergent, and dairy industries and serve as vital tools in determination of structure of proteins and polypeptides. Multiple application of these enzymes stimulated interest to discover them with novel properties and considerable advancement of basic research into these enzymes. A broad understanding of the active site of the enzyme and of the mechanism of its inactivation is essential for delineating its structure-function relationship. Primary structure analysis of alkaline protease showed 42% of its content to be alpha helix making it stable for three dimensional structure modeling. Homology model of alkaline protease has been constructed using the X-ray structure (3F7O) as a template and swiss model as the workspace. The model was validated by ProSA, SAVES, PROCHECK, PROSAII and RMSD. The results showed the final refined model is reliable. It has 53% amino acid sequence identity with the template, 0.24 Å as RMSD and has -7.53 as Z-score, the Ramachandran plot analysis showed that conformations for 83.4 % of amino acid residues are within the most favored regions and only 0.4% in the disallowed regions.

The nucleotide, inhibitor, and cation binding sites of P-type II ATPases

P-type ATPases constitute a ubiquitous superfamily of cation transport enzymes, responsible for carrying out actions of paramount importance in biology such as ion transport and expulsion of toxic ions from cells. The harmonized toggling of gates in the extra- and intracellular domains explain the phenomenon of specific cation binding in selective physiological states. A quantitative understanding of the fundamental aspects of ion transport mechanism and regulation of P-type ATPases requires detailed knowledge of thermodynamical, structural, and functional properties. Computational studies have made significant contributions to our understanding of biological ion pumps. Various 3D structures of Ca(2+) -ATPase between E1 and E2 transition states have given a impetus to the theorists to work on the Na(+) K(+) - and H(+) K(+) -ATPase to address important questions about their function. The current review delineates the importance of cation, nucleotide, and inhibitor binding domains, with a focus on the therapeutic potential and biological relevance of the three P-type II ATPases. This will give an insight into the ion selectivity and their conduction across the transmembrane helices of P-type II ATPases, which may pave the way to a range of fundamental questions about the mechanism and aid in the efforts of structure- and analog-based drug design.

Docking-based virtual screening of potential human P2Y12 receptor antagonists

Platelet plays essential roles in hemostasis and its dysregulation can lead to arterial thrombosis. P2Y12 is an important platelet membrane adenosine diphosphate receptor, and its antagonists have been widely developed as anti-coagulation agents. The current P2Y12 inhibitors available in clinical practice have not fully achieved satisfactory anti-thrombotic effects, leaving room for further improvement. To identify new chemical compounds as potential anti-coagulation inhibitors, we constructed a three-dimensional structure model of human P2Y12 by homology modeling based on the recently reported G-protein coupled receptor Meleagris gallopavo β1 adrenergic receptor. Virtual screening of the modeled P2Y12 against three subsets of small molecules from the ZINC database, namely lead-like, fragment-like, and drug-like, identified a number of compounds that might have high binding affinity to P2Y12. Detailed analyses of the top three compounds from each subset with the highest scores indicated that all of these compounds beard a hydrophobic bulk supplemented with a few polar atoms which bound at the ligand binding site via largely hydrophobic interactions with the receptor. This study not only provides a structure model of P2Y12 for rational design of anti-platelet inhibitors, but also identifies some potential chemicals for further development.

Homology modeling of Ferredoxin-nitrite reductase from Arabidopsis thaliana

Nitrogen is one of the major growth-limiting nutrients for plants: The main source of nitrogen in most of the higher plants is nitrate taken up through roots. Nitrate can be reduced both in the chloroplasts (photosynthetic tissues) and in proplastes (nonphotosynthetic tissues) such as roots. Ferredoxin-nitrite reductase (NiR) catalyses the reduction of nitrite to ammonium in the second step of the nitrate- assimilation pathway. Homology model of Ferredoxin-nitrite reductase has been constructed using the X-ray structure (PDB code: 2akj) a s a template and MODELLER 9v5 software. The resulting model assessed by PROCHECK, PROSAII and RMSD that showed the final refined model is reliable: has 81% of amino acid sequence identity with template, 0.2Å as RMSD and has (-10.37) as Z-scores, the Ramachandran plot analysis showed that conformations for 99.5 % of amino acid residues are within the most favored regions. The model could prove useful in further functional characterization of this protein.

ABBREVIATIONS

PDB - Protein Data Bank, NMR - Nuclear Magnetic Resonance, NiR - Nitrite Reductase, RMSD - Root Mean Squared Deviation, Fd - ferredoxin.

Characterization of a novel β-helix antifreeze protein from the desert beetle Anatolica polita

Many ectotherms organisms produce antifreeze proteins (AFPs) which inhibit the growth of ice by binding to the surface of ice crystals. In this study, a novel antifreeze protein gene from the desert beetle Anatolica polita (named as Apafp752) was expressed in a high level in Escherichia coli strain BL21 (DE3). An approximately 30kDa fusion protein thioredoxin (Trx)-ApAFP752 was purified through Ni-NTA affinity chromatography and gel filtration chromatography. The activity of the purified fusion protein Trx-ApAFP752 was analyzed by thermal hysteresis activity (THA) and cryoprotection assay. The results suggested that Trx-ApAFP752 conferred freeze resistance on bacterium in a concentration- and time-dependent manner and the cryoprotective effect increased under alkaline conditions. Circular Dichroism (CD) spectrum analysis showed that the recombinant protein of ApAFP752 possessing β-sheet as the main structure was stable under a wide range of pH from 2.0 to 11.0 and thermal stability below 50°C. The predicted 3D structure showed that Trx-ApAFP752 could form a β-helix structure on the antifreeze protein part, which placed most of the Thr in a regular array on one side of the protein to form a putative ice-binding surface.

MEDELLER: homology-based coordinate generation for membrane proteins

MOTIVATION

Membrane proteins (MPs) are important drug targets but knowledge of their exact structure is limited to relatively few examples. Existing homology-based structure prediction methods are designed for globular, water-soluble proteins. However, we are now beginning to have enough MP structures to justify the development of a homology-based approach specifically for them.

RESULTS

We present a MP-specific homology-based coordinate generation method, MEDELLER, which is optimized to build highly reliable core models. The method outperforms the popular structure prediction programme Modeller on MPs. The comparison of the two methods was performed on 616 target-template pairs of MPs, which were classified into four test sets by their sequence identity. Across all targets, MEDELLER gave an average backbone root mean square deviation (RMSD) of 2.62 Å versus 3.16 Å for Modeller. On our 'easy' test set, MEDELLER achieves an average accuracy of 0.93 Å backbone RMSD versus 1.56 Å for Modeller.

AVAILABILITY AND IMPLEMENTATION

http://medeller.info; Implemented in Python, Bash and Perl CGI for use on Linux systems; Supplementary data are available at http://www.stats.ox.ac.uk/proteins/resources.

3D-QSAR and molecular docking studies on derivatives of MK-0457, GSK1070916 and SNS-314 as inhibitors against Aurora B kinase

Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q² = 0.605, r²_pred = 0.826), (q² = 0.52, r²_pred = 0.798) and (q² = 0.582, r²_pred = 0.971) for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors.

Amiloride docking to acid-sensing ion channel-1

Amiloride is a small molecule diuretic, which has been used to dissect sodium transport pathways in many different systems. This drug is known to interact with the epithelial sodium channel and acid-sensing ion channel proteins, as well as sodium/hydrogen antiporters and sodium/calcium exchangers. The exact structural basis for these interactions has not been elucidated as crystal structures of these proteins have been challenging to obtain, though some involved residues and domains have been mapped. This work examines the interaction of amiloride with acid-sensing ion channel-1, a protein whose structure is available using computational and experimental techniques. Using molecular docking software, amiloride and related molecules were docked to model structures of homomeric human ASIC-1 to generate potential interaction sites and predict which analogs would be more or less potent than amiloride. The predictions made were experimentally tested using whole-cell patch clamp. Drugs previously classified as NCX or NHE inhibitors are shown to also inhibit hASIC-1. Potential docking sites were re-examined against experimental data to remove spurious interaction sites. The voltage sensitivity of inhibitors was also examined. Using the aggregated data from these computational and experimental experiments, putative interaction sites for amiloride and hASIC-1 have been defined. Future work will experimentally verify these interaction sites, but at present this should allow for virtual screening of drug libraries at these putative interaction sites.

Psalmotoxin-1 docking to human acid-sensing ion channel-1

Acid-sensing ion channel-1 (ASIC-1) is a proton-gated ion channel implicated in nociception and neuronal death during ischemia. Recently the first crystal structure of a chicken ASIC was obtained. Expanding upon this work, homology models of the human ASICs were constructed and evaluated. Energy-minimized structures were tested for validity by in silico docking of the models to psalmotoxin-1, which potently inhibits ASIC-1 and not other members of the family. The data are consistent with prior radioligand binding and functional assays while also explaining the selectivity of PcTX-1 for homomeric hASIC-1a. Binding energy calculations suggest that the toxin and channel create a complex that is more stable than the channel alone. The binding is dominated by the coulombic contributions, which account for why the toxin-channel interaction is not observed at low pH. The computational data were experimentally verified with single channel and whole-cell electrophysiological studies. These validated models should allow for the rational design of specific and potent peptidomimetic compounds that may be useful for the treatment of pain or ischemic stroke.

Dopamine transporter comparative molecular modeling and binding site prediction using the LeuT(Aa) leucine transporter as a template

Pharmacological and behavioral studies indicate that binding of cocaine and the amphetamines by the dopamine transporter (DAT) protein is principally responsible for initiating the euphoria and addiction associated with these drugs. The lack of an X-ray crystal structure for the DAT or any other member of the neurotransmitter:sodium symporter (NSS) family has hindered understanding of psychostimulant recognition at the atomic level; structural information has been obtained largely from mutagenesis and biophysical studies. The recent publication of a crystal structure for the bacterial leucine transporter LeuT(Aa), a distantly related NSS family homolog, provides for the first time a template for three-dimensional comparative modeling of NSS proteins. A novel computational modeling approach using the capabilities of the Molecular Operating Environment program MOE 2005.06 in conjunction with other comparative modeling servers generated the LeuT(Aa)-directed DAT model. Probable dopamine and amphetamine binding sites were identified within the DAT model using multiple docking approaches. Binding sites for the substrate ligands (dopamine and amphetamine) overlapped substantially with the analogous region of the LeuT(Aa) crystal structure for the substrate leucine. The docking predictions implicated DAT side chains known to be critical for high affinity ligand binding and suggest novel mutagenesis targets in elucidating discrete substrate and inhibitor binding sites. The DAT model may guide DAT ligand QSAR studies, and rational design of novel DAT-binding therapeutics.

Scoring function accuracy for membrane protein structure prediction

We perform a systematic examination of the ability of several different high-resolution, atomic-detail scoring functions to discriminate native conformations of loops in membrane proteins from non-native but physically reasonable, or "decoy," conformations. Decoys constructed from changing a loop conformation while keeping the remainder of the protein fixed are a challenging test of energy function accuracy. Nevertheless, the best of the energy functions we examined recognized the native structure as lowest in energy around half the time, and consistently chose it as a low-energy structure. This suggests that the best of present energy functions, even without a representation of the lipid bilayer, are of sufficient accuracy to give reasonable confidence in predictions of membrane protein structure. We also constructed homology models for each structure, using other known structures in the same protein family as templates. Homology models were constructed using several scoring functions and modeling programs, but with a comparable sampling effort for each procedure. Our results indicate that the quality of sequence alignment is probably the most important factor in model accuracy for sequence identity from 20-40%; one can expect a reasonably accurate model for membrane proteins when sequence identity is greater than 30%, in agreement with previous studies. Most errors are localized in loop regions, which tend to be found outside the lipid bilayer. For the most discriminative energy functions, it appears that errors are most likely due to lack of sufficient sampling, although it should be stressed that present energy functions are still far from perfectly reliable.

Determining membrane protein structures: still a challenge!

Determination of structures and dynamics events of transmembrane proteins is important for the understanding of their function. Analysis of such events requires high-resolution 3D structures of the different conformations coupled with molecular dynamics analyses describing the conformational pathways. However, the solution of 3D structures of transmembrane proteins at atomic level remains a particular challenge for structural biochemists--the need for purified and functional transmembrane proteins causes a 'bottleneck'. There are various ways to obtain 3D structures: X-ray diffraction, electron microscopy, NMR and modelling; these methods are not used exclusively of each other, and the chosen combination depends on several criteria. Progress in this field will improve knowledge of ligand-induced activation and inhibition of membrane proteins in addition to aiding the design of membrane-protein-targeted drugs.

Methods for optimizing the structure alphabet sequences of proteins

Protein structure prediction based on fragment assemble has made great progress in recent years. Local protein structure prediction is receiving increased attention. One essential step of local protein structure prediction method is that the three-dimensional conformations must be compressed into one-dimensional series of letters of a structural alphabet. The traditional method assigns each structure fragment the structure alphabet that has the best local structure similarity. However, such locally optimal structure alphabet sequence does not guarantee to produce the globally optimal structure. This study presents two efficient methods trying to find the optimal structure alphabet sequence, which can model the native structures as accuracy as possible. First, a 28-letter structure alphabet is derived by clustering fragment in Cartesian space with fragment length of seven residues. The average quantization error of the 28 letters is 0.82 A in term of root mean square deviation. Then, two efficient methods are presented to encode the protein structures into series of structure alphabet letters, that is, the greedy and dynamic programming algorithm. They are tested on PDB database using the structure alphabet developed in Cartesian coordinates space (our structure alphabet) and in torsion angles space (the PB structure alphabet), respectively. The experimental results show that these two methods can find the approximately optimal structure alphabet sequences by searching a small fraction of the modeling space. The traditional local-optimization method achieves 26.27 A root mean square deviations between the reconstructed structures and the native one, while the modeling accuracy is improved to 3.28 A by the greedy algorithm. The results are helpful for local protein structure prediction.