Why similar protein sequences encode similar three-dimensional structures?

The Characteristics and Expression Analysis of the Tomato KWL Gene Family Under Biotic Stress

BACKGROUND

Tomatoes are renowned for their popularity and nutritional value across the globe, yet their production and quality face significant challenges from various biotic stresses in their growing environments. Kiwellin (KWL) has been implicated in plant disease resistance. However, our comprehension of this gene family in plants is still remarkably insufficient.

METHODS

We conducted a comprehensive genomic analysis of the KWL gene family in tomatoes. The tertiary structures of SlKWLs were predicted by AlphaFold2. EMBOSS was used for codon analysis. RNA-seq and RT-qPCR analysis were performed to explore the expression profile of SlKWLs.

RESULTS

Our findings identified 12 distinct SlKWL members distributed across four chromosomes within the tomato genome. By examining their gene structure, conserved motifs, functional domains, and phylogenetic relationships, we elucidated the complex evolutionary relationships and potential functions of these genes. Notably, we identified numerous cis-regulatory elements within the promoter regions of the SlKWL genes which are associated with responses to both abiotic and biotic stresses, as well as hormone signaling pathways. This finding strongly implies that SlKWLs are integral to plant growth and adaptation to diverse stress conditions. Furthermore, RNA-seq and RT-qPCR analysis revealed an upregulation of five SlKWLs expressed subsequent to Phytophthora infestans infection. Particularly, SlKWL2 and SlKWL3 exhibited substantially elevated expression levels, underscoring their active involvement in biotic stress responses.

CONCLUSIONS

Collectively, these findings advance our comprehension of the SlKWL gene family and provide a robust foundation for future investigations into the roles of SlKWL genes in tomato stress responses.

Virtual screening of natural products to enhance melanogenosis

Natural products have long been an important source of inspiration for medicinal chemistry and drug discovery. In the cosmetic field, they remain the major elements of the composition and serve as marketing asset. Recent research showed the implication of salt-inducible kinases on the melanin production in skin via MITF regulation. Finding new potent modulators on such target could open the way to several cosmetic applications to attenuate visible signs of photoaging and improve the tan without sun. Since virtual screening can be a powerful tool for detecting hit compounds in the early stages of a drug discovery process, we applied this method on salt-inducible kinase 2 to discover potential interesting compounds. Here, we present the different steps from the construction of a database of natural products, to the validation of a docking protocol and the results of the virtual screening. Hits from the screening were tested in vitro to confirm their efficiency and results are discussed.

Molecular Modeling Techniques and In-Silico Drug Discovery

Molecular modeling is the technique to determine the overall structure of an unknown molecule, be it a small one or a macromolecule. The technique encompasses the method of screening ligand libraries for the development of new candidate drug molecules. All these aspects have become an essential topic of research. This field is truly interdisciplinary and finds its applications in almost all fields of life science research. In this chapter, an overview of the protocol associated with molecular modeling techniques will be discussed.

'Synthesis, antiviral activity, molecular docking, and molecular dynamics studies of ethoxy phthalimide pyrazole derivatives against Cytomegalovirus and Varicella-Zoster virus: potential consequences and strategies for developing new antiviral treatments'

Substituted ethoxy phthalimide pyrazole derivatives (6a-e) have been produced using a one-pot synthesis technique. Spectral analysis was used to establish the molecular structure of the synthesized compounds, and they were examined in silico and in vitro for their ability to bind to and inhibit replication of the AD-169 strain, the Davis strain of CMV, the OKA strain and the 07/1 strain of Varicella-Zoster virus (VZV). Molecular Docking was used to estimate the binding mechanism and energy of compounds 4, 6a-e to their respective target proteins, thymidine kinase (TK), Varicella-Zoster protease (VZP) of VZV and tegument protein pp71 (TPpp71) of Cytomegalovirus (CMV). The MIC50 and EC50 were utilized to evaluate the antiviral and cytotoxic activities of test compounds in human embryonic lung (HEL) cells against the two reference medicines, Ganciclovir and Acyclovir. The chemicals studied showed a high affinity for binding sites and near binding sites of target proteins by generating H-bonds, carbon-hydrogen bonds, π-anion, π-sulfur, π-sigma, alkyl and π-alkyl interactions. All of the test compounds (6a-e) had higher binding energy than the standard medications. The ADME/T data suggests that these potential inhibitors are less toxic. Drug-protein complexes are structurally compact and demonstrate minimal conformational change in molecular dynamics (MDs) simulations, indicating stability and stiffness. MM-PBSA and post-simulation analysis can predict lead compound active cavity binding stability. By inhibiting multitargeted proteins, these synthetic compounds may improve antiviral therapy. Our research suggests that these unique synthesized chemicals may be useful and accessible adjuvant antiviral therapy for Varicella Zoster and CMV. HighlightsTwo components synthesis of substituted ethoxy phthalimide pyrazole derivatives (6a-e).Tested compounds (6a-e) have antiviral and cytotoxicity activity against CMV and Varicella-Zoster virus (VZV) in HEL cells.Compounds bind to TK, Varicella-Zoster protease (VZP) of VZV, and modeled TPpp71 of Cytomegalovirus (CMV).In comparison to reference drugs, compounds have strong binding free energy and interactions with VZV and CMV protein complexes.The RMSD, RMSF, Rg, residual correlative motion (RCM), No. of hydrogen bonds, protein secondary structure content, per-residue protein secondary structure and MM/PBSA energy calculated for the selected compound with thymidine kinase (TK), VZP of VZV, and modeled tegument protein pp71 (TPpp71) of CMV through MD simulation studies for 50 ns.In comparison to the two reference drugs, ligands/compounds were found to meet the Lipinski rule of five and to have strong biological activity.Communicated by Ramaswamy H. Sarma.

Modeling of Transmembrane Domain and Full-Length TLRs in Membrane Models

Toll-like receptors (TLRs), classified as pattern recognition receptors, have a primordial role in the activation of the innate immunity. In particular, TLR4 binds to lipopolysaccharides (LPS), a membrane constituent of Gram-negative bacteria, and, together with Myeloid Differentiation factor 2 (MD-2) protein, forms a heterodimeric complex which leads to the activation of the innate immune system response. Identification of TLRs has sparked great interest in the therapeutic manipulation of the innate immune system. In particular, TLR4 antagonists may be useful for the treatment of septic shock, certain autoimmune diseases, noninfectious inflammatory disorders, and neuropathic pain, and TLR4 agonists are under development as vaccine adjuvants in antitumoral treatments. Therefore, TLR4 has risen as a promising therapeutic target, and its modulation constitutes a highly relevant and active research area. Deep structural understanding of TLR4 signaling may help in the design and discovery of TLR4-modulating molecules with desirable therapeutic properties.Computational studies of the different independent domains composing the TLR4 were undertaken, to understand the differential domain organization of TLR4 in aqueous and membrane environments, including Liquid-disordered (Ld) and Liquid-ordered (Lo) membrane models, to account for the TLR4 recruitment in lipid rafts over activation. We modeled, by means of all-atom Molecular Dynamics (MD) simulations, the structural assembly of plausible full-length TLR4 models embedded into a realistic plasma membrane, accounting for the active (agonist) state of the TLR4, thus providing an analysis at both atomic/molecular and thermodynamic levels of the TLR4 assembly and biological activity. Our results unveil relevant molecular aspects involved in the mechanism of receptor activation, and adaptor recruitment in the innate immune pathways, and will promote the discovery of new TLR4 modulators and probes.

Quality Estimates for 3D Protein Models

Protein structure modeling is one of the most advanced and complex processes in computational biology. One of the major problems for the protein structure prediction field has been how to estimate the accuracy of the predicted 3D models, on both a local and global level, in the absence of known structures. We must be able to accurately measure the confidence that we have in the quality predicted 3D models of proteins for them to become widely adopted by the general bioscience community. To address this major issue, it was necessary to develop new model quality assessment (MQA) methods and integrate them into our pipelines for building 3D protein models. Our MQA method, called ModFOLD, has been ranked as one of the most accurate MQA tools in independent blind evaluations. This chapter discusses model quality assessment in the protein modeling field, demonstrating both its strengths and limitations. We also present some of the best methods according to independent benchmarking data, which has been gathered in recent years.

Peroxisome Proliferator-Activated Receptors as Superior Targets for Treating Diabetic Disease, Design Strategies - Review Article

Thiazolidinedione (TZD), a class of drugs that are mainly used to control type 2 diabetes mellitus (T2DM), acts fundamentally as a ligand of peroxisome proliferator-activated receptors (PPARs). Besides activating pathways responsible for glycemic control by enhancing insulin sensitivity and lipid homeostasis, activating PPARs leads to exciting other pathways related to bone formation, inflammation, and cell proliferation. Unfortunately, this diverse effect of activating several pathways may show in some studies adverse health outcomes as osteological, hepatic, cardiovascular, and carcinogenic effects. Thus, a silver demand is present to find and develop new active and potent antiglycemic drugs for treating T2DM. To achieve this goal, the structure of TZD for research is considered a leading structure domain. This review will guide future research in the design of novel TZD derivatives by highlighting the general modifications conducted on the structure component of TZD scaffold affecting their potency, binding efficacy, and selectivity for the control of T2DM.

In Silico Inhibition of BACE-1 by Selective Phytochemicals as Novel Potential Inhibitors: Molecular Docking and DFT Studies

BACKGROUND

Alzheimer's Disease (AD) has become the most common age-dependent disease of dementia. The trademark pathologies of AD are the presence of amyloid aggregates in neurofibrils. Recently phytochemicals being considered as potential inhibitors against various neurodegenerative, antifungal, antibacterial and antiviral diseases in human beings.

OBJECTIVE

This study targets the inhibition of BACE-1 by phytochemicals using in silico drug discovery analysis.

METHODS

A total of 3150 phytochemicals were collected from almost 25 different plants through literature assessment. The ADMET studies, molecular docking and density functional theory (DFT) based analysis were performed to analyze the potential inhibitory properties of these phytochemicals.

RESULTS

The ADMET and docking results exposed seven compounds that have high potential as an inhibitory agent against BACE-1 and show binding affinity >8.0 kcal/mol against BACE-1. They show binding affinity greater than those of various previously reported inhibitors of BACE-1. Furthermore, DFT based analysis has shown high reactivity for these seven phytochemicals in the binding pocket of BACE- 1, based on ELUMO, EHOMO and Kohn-Sham energy gap. All seven phytochemicals were testified (as compared to experimental ones) as novel inhibitors against BACE-1.

CONCLUSION

Out of seven phytochemicals, four were obtained from plant Glycyrrhiza glabra i.e. Shinflavanone, Glabrolide, Glabrol and PrenyllicoflavoneA, one from Huperzia serrate i.e. Macleanine, one from Uncaria rhynchophylla i.e. 3a-dihydro-cadambine and another one was from VolvalerelactoneB from plant Valeriana-officinalis. It is concluded that these phytochemicals are suitable candidates for drug/inhibitor against BACE-1, and can be administered to humans after experimental validation through in vitro and in vivo trials.

Characterization of the SARS-CoV-2 coronavirus X4-like accessory protein

Background

The novel coronavirus SARS-CoV-2 is currently a global threat to health and economies. Therapeutics and vaccines are in rapid development; however, none of these therapeutics are considered as absolute cure, and the potential to mutate makes it necessary to find therapeutics that target a highly conserved regions of the viral structure.

Results

In this study, we characterized an essential but poorly understood coronavirus accessory X4 protein, a core and stable component of the SARS-CoV family. Sequence analysis shows a conserved ~ 90% identity between the SARS-CoV-2 and previously characterized X4 protein in the database. QMEAN Z score of the model protein shows a value of around 0.5, within the acceptable range 0-1. A MolProbity score of 2.96 was obtained for the model protein and indicates a good quality model. The model has Ramachandran values of φ = - 57o and ψ = - 47o for α-helices and values of φ = - 130o and ψ = + 140o for twisted sheets.

Conclusions

The protein data obtained from this study provides robust information for further in vitro and in vivo experiment, targeted at devising therapeutics against the virus. Phylogenetic analysis further supports previous evidence that the SARS-CoV-2 is positioned with the SL-CoVZC45, BtRs-BetaCoV/YN2018B and the RS4231 Bat SARS-like corona viruses.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

The Journey of Thiazolidinediones as Modulators of PPARs for the Management of Diabetes: A Current Perspective

Peroxisome Proliferator-Activated Receptors (PPARs) also known as glitazone receptors are a family of receptors that regulate the expression of genes and have an essential role in carbohydrate, lipid and protein metabolism apart from other functions. PPARs come in 3 sub-types: PPAR-α, PPAR-β/δ and PPAR-γ - with PPAR-γ having 2 isoforms - γ1 and γ2. Upon activation, the PPARs regulate the transcription of various genes involved in lipid and glucose metabolism, adipocyte differentiation, increasing insulin sensitivity, prevention of oxidative stress and to a certain extent, modulation of immune responses via macrophages that have been implicated in the pathogenesis of insulin resistance. Hence, PPARs are an attractive molecular target for designing new anti-diabetic drugs. This has led to a boost in the research efforts directed towards designing of PPAR ligands - particularly ones that can selectively and specifically activate one or more of the PPAR subtypes. Though, PPAR- γ full agonists such as Thiazolidinediones (TZDs) are well established agents for dyslipidemia and type 2 diabetes mellitus (T2D), the side effect profile associated with TZDs has potentiated an imminent need to come up with newer agents that act through this pathway. Several newer derivatives having TZD scaffold have been designed using structure based drug designing technique and computational tools and tested for their PPAR binding affinity and efficacy in combating T2D and some have shown promising activities. This review would focus on the role of PPARs in the management of T2D; recently reported TZD derivatives which acted as agonists of PPAR- γ and its subtypes and are potentially useful in the new drug discovery for the disease.

Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science

Organic anion and cation transporting proteins (OATs, OATPs, and OCTs), as well as the Multidrug and Toxin Extrusion (MATE) transporters of the Solute Carrier (SLC) family are playing a pivotal role in the discovery and development of new drugs due to their involvement in drug disposition, drug-drug interactions, adverse drug effects and related toxicity. Computational methods to understand and predict clinically relevant transporter interactions can provide useful guidance at early stages in drug discovery and design, especially if they include contemporary data science approaches. In this review, we summarize the current state-of-the-art of computational approaches for exploring ligand interactions and selectivity for these drug (uptake) transporters. The computational methods discussed here by highlighting interesting examples from the current literature are ranging from semiautomatic data mining and integration, to ligand-based methods (such as quantitative structure-activity relationships, and combinatorial pharmacophore modeling), and finally structure-based methods (such as comparative modeling, molecular docking, and molecular dynamics simulations). We are focusing on promising computational techniques such as fold-recognition methods, proteochemometric modeling or techniques for enhanced sampling of protein conformations used in the context of these ADMET-relevant SLC transporters with a special focus on methods useful for studying ligand selectivity.

Homology modeling of human histone deacetylase 10 and design of potential selective inhibitors

Histone deacetylases (HDACs) are implicated in the pathology of various cancers, and their pharmacological blockade has proven to be promising in reversing the malignant phenotypes. However, lack of crystal structures of some of the human HDAC isoforms (e.g., HDAC10) hinders the design of the isoform-selective inhibitor. Here, the recently solved X-ray crystal structure of Danio rerio (zebrafish) HDAC10 (Protein Data Bank (PDB) ID; 5TD7, released on 24 May 2017) was retrieved from the PDB and used as a template structure to model the three-dimensional structure of human HDAC10. The overall quality of the best model (M0017) was assessed by computing its z-score-a measure of the deviation of the total energy of the structure with respect to an energy distribution derived from random conformations and by docking of known HDAC10 inhibitors to its catalytic cavity. Furthermore, to identify potential HDAC10-selective inhibitor ligand-based virtual screening was carried out against the ZINC database. The free modeled structure of HDAC10 and its complexes with quisinostat and the highest-ranked compound ZINC19749069 were submitted to molecular dynamics simulation. The comparative analysis of root-mean-squared deviation, root-mean-squared fluctuation, radius of gyration (Rg), and potential energy of these systems showed that HDAC10-ZINC19749069 complex remained the most stable over time. Thus, M0017 could be potentially used for structure-based inhibitor against HDAC10, and ZINC19749069 may provide a scaffold for further optimization. Communicated by Ramaswamy H. Sarma.

The 2.1 Å structure of protein F9 and its comparison to L1, two components of the conserved poxvirus entry-fusion complex

The poxvirus F9 protein is a component of the vaccinia virus entry fusion complex (EFC) which consists of 11 proteins. The EFC forms a unique apparatus among viral fusion proteins and complexes. We solved the atomic structure of the F9 ectodomain at 2.10 Å. A structural comparison to the ectodomain of the EFC protein L1 indicated a similar fold and organization, in which a bundle of five α-helices is packed against two pairs of β-strands. However, instead of the L1 myristoylation site and hydrophobic cavity, F9 possesses a protruding loop between α-helices α3 and α4 starting at Gly90. Gly90 is conserved in all poxviruses except Salmon gill poxvirus (SGPV) and Diachasmimorpha longicaudata entomopoxvirus. Phylogenetic sequence analysis of all Poxviridae F9 and L1 orthologs revealed the SGPV genome to contain the most distantly related F9 and L1 sequences compared to the vaccinia proteins studied here. The structural differences between F9 and L1 suggest functional adaptations during evolution from a common precursor that underlie the present requirement for each protein.

Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?

Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences.

Three unrelated and unexpected amino acids determine the susceptibility of the interface cysteine to a sulfhydryl reagent in the triosephosphate isomerases of two trypanosomes

Proteins with great sequence similarity usually have similar structure, function and other physicochemical properties. But in many cases, one or more of the physicochemical or functional characteristics differ, sometimes very considerably, among these homologous proteins. To better understand how critical amino acids determine quantitative properties of function in proteins, the responsible residues must be located and identified. This can be difficult to achieve, particularly in cases where multiple amino acids are involved. In this work, two triosephosphate isomerases with very high similarity from two related human parasites were used to address one such problem. We demonstrate that a seventy-fold difference in the reactivity of an interface cysteine to the sulfhydryl reagent methylmethane sulfonate in these two enzymes depends on three amino acids located far away from this critical residue and which could not have been predicted using other current methods. Starting from previous observations with chimeric proteins involving these two triosephosphate isomerases, we developed a strategy involving additive mutant enzymes and selected site directed mutants to locate and identify the three amino acids. These three residues seem to induce changes in the interface cysteine in reactivity by increasing (or decreasing) its apparent pKa. Some enzymes with four to seven mutations also exhibited altered reactivity. This study completes a strategy for identifying key residues in the sequences of proteins that can have applications in future protein structure-function studies.

Protein secondary structure prediction based on the fuzzy support vector machine with the hyperplane optimization

The prediction of the protein secondary structure is a crucial point in bioinformatics and related fields. In the last years, machine learning methods have become a valuable tool, achieving satisfactory results. However, the prediction accuracy needs to be further ameliorated. This paper proposes a new method based on an improved fuzzy support vector machine (FSVM) for the prediction of the secondary structure of proteins. Unlike traditional methods to set the membership function, it firstly constructs an approximate optimal separating hyperplane by iterating the class centers in the feature space. Then sample points close to this hyperplane are assigned with large membership values, while outliers with small membership values according to the K-nearest neighbor. And some sample points with low membership values are removed, reducing the training time and improving the prediction accuracy. To optimize the prediction results, our method also exploits information on sequence-based structural similarity. We used three databases (e.g. RS126, CB513 and data1199) to test this method, showing the achievement of 94.2%, 93.1%, 96.7% Q₃ accuracy and 91.7%, 89.7%, 94.1% SOV values for the three datasets, respectively. Overall, our method results are comparable to or often better than commonly used methods (Magnan & Baldi, 2014; Sheng et al., 2016) for secondary structure prediction.

Analysis of aquaporins from the euryhaline barnacle Balanus improvisus reveals differential expression in response to changes in salinity

Barnacles are sessile macro-invertebrates, found along rocky shores in coastal areas worldwide. The euryhaline bay barnacle Balanus improvisus (Darwin, 1854) (= Amphibalanus improvisus) can tolerate a wide range of salinities, but the molecular mechanisms underlying the osmoregulatory capacity of this truly brackish species are not well understood. Aquaporins are pore-forming integral membrane proteins that facilitate transport of water, small solutes and ions through cellular membranes, and that have been shown to be important for osmoregulation in many organisms. The knowledge of the function of aquaporins in crustaceans is, however, limited and nothing is known about them in barnacles. We here present the repertoire of aquaporins from a thecostracan crustacean, the barnacle B. improvisus, based on genome and transcriptome sequencing. Our analyses reveal that B. improvisus contains eight genes for aquaporins. Phylogenetic analysis showed that they represented members of the classical water aquaporins (Aqp1, Aqp2), the aquaglyceroporins (Glp1, Glp2), the unorthodox aquaporin (Aqp12) and the arthropod-specific big brain aquaporin (Bib). Interestingly, we also found two big brain-like proteins (BibL1 and BibL2) constituting a new group of aquaporins not yet described in arthropods. In addition, we found that the two water-specific aquaporins were expressed as C-terminal splice variants. Heterologous expression of some of the aquaporins followed by functional characterization showed that Aqp1 transported water and Glp2 water and glycerol, agreeing with the predictions of substrate specificity based on 3D modeling and phylogeny. To investigate a possible role for the B. improvisus aquaporins in osmoregulation, mRNA expression changes in adult barnacles were analysed after long-term acclimation to different salinities. The most pronounced expression difference was seen for AQP1 with a substantial (>100-fold) decrease in the mantle tissue in low salinity (3 PSU) compared to high salinity (33 PSU). Our study provides a base for future mechanistic studies on the role of aquaporins in osmoregulation.

Targeting Peroxisome Proliferator-Activated Receptors Using Thiazolidinediones: Strategy for Design of Novel Antidiabetic Drugs

Thiazolidinediones are a class of well-established antidiabetic drugs, also named as glitazones. Thiazolidinedione structure has been an important structural domain of research, involving design and development of new drugs for the treatment of type 2 diabetes. Extensive research on the mechanism of action and the structural requirements has revealed that the intended antidiabetic activity in type 2 diabetes is due to their agonistic effect on peroxisome proliferator-activated receptor (PPAR) belonging to the nuclear receptor super family. Glitazones have specific affinity to PPARγ, one of the subtypes of PPARs. Certain compounds under development have dual PPARα/γ agonistic activity which might be beneficial in obesity and diabetic cardiomyopathy. Interesting array of hybrid compounds of thiazolidinedione PPARγ agonists exhibited therapeutic potential beyond antidiabetic activity. Pharmacology and chemistry of thiazolidinediones as PPARγ agonists and the potential of newer analogues as dual agonists of PPARs and other emerging targets for the therapy of type 2 diabetes are presented. This review highlights the possible modifications of the structural components in the general frame work of thiazolidinediones with respect to their binding efficacy, potency, and selectivity which would guide the future research in design of novel thiazolidinedione derivatives for the management of type 2 diabetes.

Characterization of Bile Salt Hydrolase from Lactobacillus gasseri FR4 and Demonstration of Its Substrate Specificity and Inhibitory Mechanism Using Molecular Docking Analysis

Probiotic bacteria are beneficial to the health of poultry animals, thus are used as alternative candidates for antibiotics used as growth promoters (AGPs). However, they also reduce the body weight gain due to innate bile salt hydrolase (BSH) activity. Hence, the addition of a suitable BSH inhibitor along with the probiotic feed can decrease the BSH activity. In this study, a BSH gene (981 bp) encoding 326-amino acids was identified from the genome of Lactobacillus gasseri FR4 (LgBSH). The LgBSH-encoding gene was cloned and purified using an Escherichia coli BL21 (DE3) expression system, and its molecular weight (37 kDa) was confirmed by SDS-PAGE and a Western blot analysis. LgBSH exhibited greater hydrolysis toward glyco-conjugated bile salts compared to tauro-conjugated bile salts. LgBSH displayed optimal activity at 52°C at a pH of 5.5, and activity was further increased by several reducing agents (DTT), surfactants (Triton X-100 and Tween 80), and organic solvents (isopropanol, butanol, and acetone). Riboflavin and penicillin V, respectively, inhibited LgBSH activity by 98.31 and 97.84%. A homology model of LgBSH was predicted using EfBSH (4WL3) as a template. Molecular docking analysis revealed that the glycocholic acid had lowest binding energy of -8.46 kcal/mol; on the other hand, inhibitors, i.e., riboflavin and penicillin V, had relatively higher binding energies of -6.25 and -7.38 kcal/mol, respectively. Our results suggest that L. gasseri FR4 along with riboflavin might be a potential alternative to AGPs for poultry animals.

Structures, Properties, and Dynamics of Intermediates in eEF2-Diphthamide Biosynthesis

The eukaryotic translation Elongation Factor 2 (eEF2) is an essential enzyme in protein synthesis. eEF2 contains a unique modification of a histidine (His699 in yeast; HIS) into diphthamide (DTA), obtained via 3-amino-3-carboxypropyl (ACP) and diphthine (DTI) intermediates in the biosynthetic pathway. This essential and unique modification is also vulnerable, in that it can be efficiently targeted by NAD(+)-dependent ADP-ribosylase toxins, such as diphtheria toxin (DT). However, none of the intermediates in the biosynthesis path is equally vulnerable against the toxins. This study aims to address the different susceptibility of DTA and its precursors against bacterial toxins. We have herein undertaken a detailed in silico study of the structural features and dynamic motion of different His699 intermediates along the diphthamide synthesis pathway (HIS, ACP, DTI, DTA). The study points out that DTA forms a strong hydrogen bond with an asparagine which might explain the ADP-ribosylation mechanism caused by the diphtheria toxin (DT). Finally, in silico mutagenesis studies were performed on the DTA modified protein, in order to hamper the formation of such a hydrogen bond. The results indicate that the mutant structure might in fact be less susceptible to attack by DT and thereby behave similarly to DTI in this respect.

Homology modeling and molecular dynamics study on Schwanniomyces occidentalis alpha-amylase

With consumers growing increasingly aware of environmental issues, industries find enzymes as a reasonable alternative over physical conditions and chemical catalysts. Amylases are important hydrolase enzymes, which have been widely used in variety of industrial process such as pharmaceutical, food, and fermentation industries. Among amylases α-Amylase is in maximum demand due to its wide range of applications. The homology modeling study on Schwanniomyces occidentalis amylase (AMY1, UniProt identifier number: P19269) was performed by Modeller using Aspergillus oryzae (6TAA) as the template. The resulting structure was analyzed for validity and subjected to 14 ns of molecular dynamics (MD) simulation trough GROMACS. The validity of obtained model may represent that utilized OPLS force field is suitable for calcium-containing enzymes. DSSP secondary structure and contact map analysis represent the conservation of domain A TIM barrel feature together with calcium ion coordination sphere. Investigating the covariance matrix followed by principle component analyses for the first five eigenvectors of both trajectories indicate a little more flexibility for AMY1 structure. The electrostatic calculation for the final structures shows similar isoelectric point and superimposed buffering zone in the 5-8 pH range.

Insight into the selective inhibition of JNK family members through structure-based drug design

The c-Jun N-terminal kinase (JNK) family, which comprises JNK1, JNK2 and JNK3, belongs to the mitogen-activated protein kinase (MAPK) superfamily, whose members regulate myriad biological processes, including those implicated in tumorigenesis and neurodegenerative disorders.

Transcriptome Analysis of the Central and Peripheral Nervous Systems of the Spider Cupiennius salei Reveals Multiple Putative Cys-Loop Ligand Gated Ion Channel Subunits and an Acetylcholine Binding Protein

Invertebrates possess a diverse collection of pentameric Cys-loop ligand gated ion channel (LGIC) receptors whose molecular structures, evolution and relationships to mammalian counterparts have been intensely investigated in several clinically and agriculturally important species. These receptors are targets for a variety of control agents that may also harm beneficial species. However, little is known about Cys-loop receptors in spiders, which are important natural predators of insects. We assembled de novo transcriptomes from the central and peripheral nervous systems of the Central American wandering spider Cupiennius salei, a model species for neurophysiological, behavioral and developmental studies. We found 15 Cys-loop receptor subunits that are expected to form anion or cation permeable channels, plus a putative acetylcholine binding protein (AChBP) that has only previously been reported in molluscs and one annelid. We used phylogenetic and sequence analysis to compare the spider subunits to homologous receptors in other species and predicted the 3D structures of each protein using the I-Tasser server. The quality of homology models improved with increasing sequence identity to the available high-resolution templates. We found that C. salei has orthologous γ-aminobutyric acid (GABA), GluCl, pHCl, HisCl and nAChα LGIC subunits to other arthropods, but some subgroups are specific to arachnids, or only to spiders. C. salei sequences were phylogenetically closest to gene fragments from the social spider, Stegodyphus mimosarum, indicating high conservation within the Araneomorphae suborder of spiders. C. salei sequences had similar ligand binding and transmembrane regions to other invertebrate and vertebrate LGICs. They also had motifs associated with high sensitivity to insecticides and antiparasitic agents such as fipronil, dieldrin and ivermectin. Development of truly selective control agents for pest species will require information about the molecular structure and pharmacology of Cys-loop receptors in beneficial species.

Construction of a genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum provides new strategies for bactericide discovery

We reconstructed the first genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum subsp. carotovorum PC1 based on its genomic sequence, annotation, and physiological data. Metabolic characteristics were analyzed using flux balance analysis (FBA), and the results were afterwards validated by phenotype microarray (PM) experiments. The reconstructed genome-scale metabolic model, iPC1209, contains 2235 reactions, 1113 metabolites and 1209 genes. We identified 19 potential bactericide targets through a comprehensive in silico gene-deletion study. Next, we performed virtual screening to identify candidate inhibitors for an important potential drug target, alkaline phosphatase, and experimentally verified that three lead compounds were able to inhibit both bacterial cell viability and the activity of alkaline phosphatase in vitro. This study illustrates a new strategy for the discovery of agricultural bactericides.

Diversification and distinctive structural features of S-RNase alleles in the genus Solanum

The multigenic and multiallelic S-locus in plants is responsible for the gametophytic self-incompatibility system, which is important to prevent the detrimental effects of self-fertilization and inbreeding depression. Several studies have discussed the importance of punctual mutations, recombination, and natural selection in the generation of allelic diversity in the S-locus. However, there has been no wide-ranging study correlating the molecular evolution and structural aspects of the corresponding proteins in Solanum. Therefore, we evaluated the molecular evolution of one gene in this locus and generated a statistically well-supported phylogenetic tree, as well as evidence of positive selection, helping us to understand the diversification of S alleles in Solanum. The three-dimensional structures of some of the proteins corresponding to the major clusters of the phylogenetic tree were constructed and subsequently submitted to molecular dynamics to stabilize the folding and obtain the native structure. The positively selected amino acid residues were predominantly located in the hyper variable regions and on the surface of the protein, which appears to be fundamental for allele specificity. One of the positively selected residues was identified adjacent to a conserved strand that is crucial for enzymatic catalysis. Additionally, we have shown significant differences in the electrostatic potential among the predicted molecular surfaces in S-RNases. The structural results indicate that local changes in the three-dimensional structure are present in some regions of the molecule, although the general structure seems to be conserved. No previous study has described such structural variations in S-RNases.

Niche Genetic Algorithms are better than traditional Genetic Algorithms for de novo Protein Folding

Here we demonstrate that Niche Genetic Algorithms (NGA) are better at computing de novo protein folding than traditional Genetic Algorithms (GA). Previous research has shown that proteins can fold into their active forms in a limited number of ways; however, predicting how a set of amino acids will fold starting from the primary structure is still a mystery.  GAs have a unique ability to solve these types of scientific problems because of their computational efficiency. Unfortunately, GAs are generally quite poor at solving problems with multiple optima.  However, there is a special group of GAs called Niche Genetic Algorithms (NGA) that are quite good at solving problems with multiple optima.  In this study, we use a specific NGA: the Dynamic-radius Species-conserving Genetic Algorithm (DSGA), and show that DSGA is very adept at predicting the folded state of proteins, and that DSGA is better than a traditional GA in deriving the correct folding pattern of a protein.

SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity

MOTIVATION

Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and function and as a component of protein 3D structure prediction pipelines. Most predictors use a combination of machine learning and profiles, and thus must be retrained and assessed periodically as the number of available protein sequences and structures continues to grow.

RESULTS

We present newly trained modular versions of the SSpro and ACCpro predictors of secondary structure and relative solvent accessibility together with their multi-class variants SSpro8 and ACCpro20. We introduce a sharp distinction between the use of sequence similarity alone, typically in the form of sequence profiles at the input level, and the additional use of sequence-based structural similarity, which uses similarity to sequences in the Protein Data Bank to infer annotations at the output level, and study their relative contributions to modern predictors. Using sequence similarity alone, SSpro's accuracy is between 79 and 80% (79% for ACCpro) and no other predictor seems to exceed 82%. However, when sequence-based structural similarity is added, the accuracy of SSpro rises to 92.9% (90% for ACCpro). Thus, by combining both approaches, these problems appear now to be essentially solved, as an accuracy of 100% cannot be expected for several well-known reasons. These results point also to several open technical challenges, including (i) achieving on the order of ≥ 80% accuracy, without using any similarity with known proteins and (ii) achieving on the order of ≥ 85% accuracy, using sequence similarity alone.

AVAILABILITY AND IMPLEMENTATION

SSpro, SSpro8, ACCpro and ACCpro20 programs, data and web servers are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu.

Homology modeling and molecular dynamics studies of Wilms' tumor gene 1 frameshift mutations in exon 7

As a transcription factor, the Wilms' tumor 1 (WT1) gene plays an important role in leukemogenesis. The impact of WT1 gene mutations has been reported in acute myeloid leukemia (AML). However, the number of available studies on the spatial configuration changes following WT1 mutation is limited. In this study, we sequenced the mutation in exon 7 of the WT1 gene in 60 children with newly diagnosed AML and the spatial configuration of WT1 with frameshift mutations in exon 7 was evaluated using the software for homology modeling and optimization of molecular dynamics. Three cases with frameshift mutations in exon 7 were identified (3/60; mutation rate, 5%). One case had a mutation that had been previously described, whereas the remaining two mutations were first described in our study. Of the three cases, one case presented with antecedent myelodysplastic syndrome (MDS) and the remaining two cases exhibited primary resistance to induction chemotherapy. The spatial configuration analysis demonstrated that the three mutations affected the spatial structure of exon 7 and even affected exon 8 compared to its wild-type. This study demonstrated that the frameshift mutation in exon 7 of the WT1 gene is a poor prognostic factor for children with AML, partly through the spatial configuration changes following frameshift mutations of WT1, which highlights the structure-based function analysis and may facilitate the elucidation of the pathogenesis underlying WT1 gene mutations.

Identification and functional characterization of silicon transporters in soybean using comparative genomics of major intrinsic proteins in Arabidopsis and rice

Silicon (Si) confers several benefits to many plant species when absorbed as silicic acid through nodulin 26-like intrinsic proteins (NIPs). The NIPs belong to major intrinsic protein (MIP) family, members of which form channels with high selectivity to control transport of water and different solutes. Here, comparative genomic analysis of the MIPs was performed to investigate the presence of Si transporter MIPs in soybean. Thorough analysis of phylogeny, gene organization, transcriptome profiling and protein modeling was performed to characterize MIPs in rice, Arabidopsis and soybean. Based on several attributes, two putative Si transporter genes, GmNIP2-1 and GmNIP2-2, were identified, characterized and cloned from soybean. Expression of both genes was detected in shoot and root tissues, and decreased as Si increased. The protein encoded by GmNIP2-2 showed functionality for Si transport when expressed in Xenopus oocytes, thus confirming the genetic capability of soybean to absorb the element. Comparative analysis of MIPs in plants provides opportunities to decipher gene evolution, functionality and selectivity of nutrient uptake mechanisms. Exploitation of this strategy has helped to uncover unique features of MIPs in soybean. The identification and functional characterization of Si transporters can be exploited to optimize the benefits that plants can derive from Si absorption.

The eradication of leprosy: molecular modeling techniques for novel drug discovery

INTRODUCTION

Leprosy is a slowly progressing bacterial infection caused by Mycobacterium leprae. The World Health Organization recommended multidrug therapy (MDT) which is extremely effective and halts the progress of the disease. Even though the objective of eliminating leprosy as a public health problem has been achieved successfully, leprosy is not yet eradicated. Furthermore, the long-term use of MDT results in single- and multidrug resistance. Therefore, there is still a need for new drug discovery for leprosy.

AREAS COVERED

The authors explain the importance of discovery of new drug to leprosy and the significance of homology modeling to drug discovery. This review highlights the principle steps, applications, and the resources of homology modeling. Finally, the authors emphasize the application of different structure-based drug design (SBDD) approaches to design novel therapeutics for leprosy.

EXPERT OPINION

MDT has proved to be effective in controlling infection, with prevalence of leprosy now predominantly isolated to the developing countries. The emergence of single- and multidrug-resistant strains of M. leprae has, however, provided some concern with the need for newer antibacterial agents. Drug resistance can be overcome by multi-targeted therapy. SBDD approaches, which reported many successful drugs, depend predominantly on the three-dimensional (3D) structure of drug targets. As of 2013, only very few experimental structures are available for M. leprae proteins. Hence, SBDD, in leprosy research, relies heavily on homology modeling to predict the 3D structure of drug targets and to design better therapeutics.

Computational identification and analysis of arsenate reductase protein in Cronobacter sakazakii ATCC BAA-894 suggests potential microorganism for reducing arsenate

This study focuses a bioinformatics-based prediction of arsC gene product arsenate reductase (ArsC) protein in Cronobacter sakazakii BAA-894 strain. A protein structure-based study encloses three-dimensional structural modeling of target ArsC protein, was carried out by homology modeling method. Ultimately, the detection of active binding regions was carried out for characterization of functional sites in protein. The ten probable ligand binding sites were predicted for target protein structure and highlighted the common binding residues between target and template protein. It has been first time identified that modeled ArsC protein structure in C. sakazakii was structurally and functionally similar to well-characterized ArsC protein of Escherichia coli because of having same structural motifs and fold with similar protein topology and function. Investigation revealed that ArsC from C. sakazakii can play significant role during arsenic resistance and potential microorganism for bioremediation of arsenic toxicity.

Molecular dynamics simulation of the complex PBP-2x with drug cefuroxime to explore the drug resistance mechanism of Streptococcus suis R61

Drug resistance of Streptococcus suis strains is a worldwide problem for both humans and pigs. Previous studies have noted that penicillin-binding protein (PBPs) mutation is one important cause of β-lactam antibiotic resistance. In this study, we used the molecular dynamics (MD) method to study the interaction differences between cefuroxime (CES) and PBP2x within two newly sequenced Streptococcus suis: drug-sensitive strain A7, and drug-resistant strain R61. The MM-PBSA results proved that the drug bound much more tightly to PBP2x in A7 (PBP2x-A7) than to PBP2x in R61 (PBP2x-R61). This is consistent with the evidently different resistances of the two strains to cefuroxime. Hydrogen bond analysis indicated that PBP2x-A7 preferred to bind to cefuroxime rather than to PBP2x-R61. Three stable hydrogen bonds were formed by the drug and PBP2x-A7, while only one unstable bond existed between the drug and PBP2x-R61. Further, we found that the Gln569, Tyr594, and Gly596 residues were the key mutant residues contributing directly to the different binding by pair wise energy decomposition comparison. By investigating the binding mode of the drug, we found that mutant residues Ala320, Gln553, and Thr595 indirectly affected the final phenomenon by topological conformation alteration. Above all, our results revealed some details about the specific interaction between the two PBP2x proteins and the drug cefuroxime. To some degree, this explained the drug resistance mechanism of Streptococcus suis and as a result could be helpful for further drug design or improvement.

Structural position correlation analysis (SPCA) for protein family

Background

The proteins in a family, which perform the similar biological functions, may have very different amino acid composition, but they must share the similar 3D structures, and keep a stable central region. In the conservative structure region similar biological functions are performed by two or three catalytic residues with the collaboration of several functional residues at key positions. Communication signals are conducted in a position network, adjusting the biological functions in the protein family.

Methodology

A computational approach, namely structural position correlation analysis (SPCA), is developed to analyze the correlation relationship between structural segments (or positions). The basic hypothesis of SPCA is that in a protein family the structural conservation is more important than the sequence conservation, and the local structural changes may contain information of biology functional evolution. A standard protein P⁽⁰⁾ is defined in a protein family, which consists of the most-frequent amino acids and takes the average structure of the protein family. The foundational variables of SPCA is the structural position displacements between the standard protein P⁽⁰⁾ and individual proteins P_i of the family. The structural positions are organized as segments, which are the stable units in structural displacements of the protein family. The biological function differences of protein members are determined by the position structural displacements of individual protein P_i to the standard protein P⁽⁰⁾. Correlation analysis is used to analyze the communication network among segments.

Conclusions

The structural position correlation analysis (SPCA) is able to find the correlation relationship among the structural segments (or positions) in a protein family, which cannot be detected by the amino acid sequence and frequency-based methods. The functional communication network among the structural segments (or positions) in protein family, revealed by SPCA approach, well illustrate the distantly allosteric interactions, and contains valuable information for protein engineering study.

Prediction of peptide reactivity with human IVIg through a knowledge-based approach

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far.We developed a logistic regression model to predict the peptide reactivity, by facing the challenge as a machine learning problem. The initial features have been generated on the basis of the available knowledge and the information reported in the dataset. Our predictive model had the second best performance of the challenge. We also developed a method, based on a clustering approach, able to "in-silico" generate a list of positive and negative new peptide sequences, as requested by the DREAM5 "bonus round" additional challenge.The paper describes the developed model and its results in terms of reactivity prediction, and highlights some open issues concerning the propensity of a peptide to react with human antibodies.

Correlation analysis for protein evolutionary family based on amino acid position mutations and application in PDZ domain

BACKGROUND

It has been widely recognized that the mutations at specific directions are caused by the functional constraints in protein family and the directional mutations at certain positions control the evolutionary direction of the protein family. The mutations at different positions, even distantly separated, are mutually coupled and form an evolutionary network. Finding the controlling mutative positions and the mutative network among residues are firstly important for protein rational design and enzyme engineering.

METHODOLOGY

A computational approach, namely amino acid position conservation-mutation correlation analysis (CMCA), is developed to predict mutually mutative positions and find the evolutionary network in protein family. The amino acid position mutative function, which is the foundational equation of CMCA measuring the mutation of a residue at a position, is derived from the MSA (multiple structure alignment) database of protein evolutionary family. Then the position conservation correlation matrix and position mutation correlation matrix is constructed from the amino acid position mutative equation. Unlike traditional SCA (statistical coupling analysis) approach, which is based on the statistical analysis of position conservations, the CMCA focuses on the correlation analysis of position mutations.

CONCLUSIONS

As an example the CMCA approach is used to study the PDZ domain of protein family, and the results well illustrate the distantly allosteric mechanism in PDZ protein family, and find the functional mutative network among residues. We expect that the CMCA approach may find applications in protein engineering study, and suggest new strategy to improve bioactivities and physicochemical properties of enzymes.