Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment

Predicted Cold Shock Proteins from the Extremophilic Bacterium Deinococcus maricopensis and Related Deinococcus Species

While many studies have examined the mechanisms by which extremophilic Deinococci survive exposure to ionizing radiation, very few publications have characterized the cold shock adaptations of this group, despite many species being found in persistent cold environments and environments prone to significant daily temperature fluctuations. Bacterial cold shock proteins (Csps) are a family of conserved, RNA chaperone proteins that commonly play a role in cold temperature adaptation, including a downward shift in temperature (i.e., cold shock). The primary aim of this study was to test whether a representative, desert-dwelling Deinococcus, Deinococcus maricopensis, encodes Csps as part of its genome. Bioinformatic approaches were used to identify a Csp from D. maricopensis LB-34. The Csp, termed Dm-Csp1, contains sequence features of Csps including a conserved cold shock domain and nucleic acid binding motifs. A tertiary model of Dm-Csp1 revealed an anticipated Csp structure containing five anti-parallel beta-strands, and ligand prediction experiments identified N-terminally located residues capable of binding single-stranded nucleic acids. Putative Csps were identified from 100% of (27 of 27) Deinococci species for which genome information is available; and the Deinococci-encoded Csps identified contain a C-terminally located region that appears to be limited to members of the class Deinococci.

Structural insights into the Middle East respiratory syndrome coronavirus 4a protein and its dsRNA binding mechanism

Middle East respiratory syndrome coronavirus (MERS-CoV) has evolved to navigate through the sophisticated network of a host's immune system. The immune evasion mechanism including type 1 interferon and protein kinase R-mediated antiviral stress responses has been recently attributed to the involvement of MERS-CoV protein 4a (p4a) that masks the viral dsRNA. However, the structural mechanism of how p4a recognizes and establishes contacts with dsRNA is not well explained. In this study, we report a dynamic mechanism deployed by p4a to engage the viral dsRNA and make it unavailable to the host immune system. Multiple variants of p4a-dsRNA were created and investigated through extensive molecular dynamics procedures to highlight crucial interfacial residues that may be used as potential pharmacophores for future drug development. The structural analysis revealed that p4a exhibits a typical αβββα fold structure, as found in other dsRNA-binding proteins. The α1 helix and the β1-β2 loop play a crucial role in recognizing and establishing contacts with the minor grooves of dsRNA. Further, mutational and binding free energy analyses suggested that in addition to K63 and K67, two other residues, K27 and W45, might also be crucial for p4a-dsRNA stability.

Novel Organelles with Elements of Bacterial and Eukaryotic Secretion Systems Weaponize Parasites of Drosophila

The evolutionary success of parasitoid wasps, a highly diverse group of insects widely used in biocontrol, depends on a variety of life history strategies in conflict with those of their hosts [1]. Drosophila melanogaster is a natural host of parasitic wasps of the genus Leptopilina. Attack by L. boulardi (Lb), a specialist wasp to flies of the melanogaster group, activates NF-κB-mediated humoral and cellular immunity. Inflammatory blood cells mobilize and encapsulate Lb eggs and embryos [2-5]. L. heterotoma (Lh), a generalist wasp, kills larval blood cells and actively suppresses immune responses. Spiked virus-like particles (VLPs) in wasp venom have clearly been linked to wasps' successful parasitism of Drosophila [6], but the composition of VLPs and their biotic nature have remained mysterious. Our proteomics studies reveal that VLPs lack viral coat proteins but possess a pharmacopoeia of (1) the eukaryotic vesicular transport system, (2) immunity, and (3) previously unknown proteins. These novel proteins distinguish Lh from Lb VLPs; notably, some proteins specific to Lh VLPs possess sequence similarities with bacterial secretion system proteins. Structure-informed analyses of an abundant Lh VLP surface and spike-tip protein, p40, reveal similarities to the needle-tip invasin proteins SipD and IpaD of Gram-negative bacterial type-3 secretion systems that breach immune barriers and deliver virulence factors into mammalian cells. Our studies suggest that Lh VLPs represent a new class of extracellular organelles and share pathways for protein delivery with both eukaryotic microvesicles and bacterial surface secretion systems. Given their mixed prokaryotic and eukaryotic properties, we propose the term mixed-strategy extracellular vesicle (MSEV) to replace VLP.

Prediction of interface residue based on the features of residue interaction network

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model.

Identification of two natural compound inhibitors of Leishmania donovani Spermidine Synthase (SpdS) through molecular docking and dynamic studies

Visceral leishmaniasis caused by the protozoan Leishmania donovani is the most severe form of leishmaniasis and it is potentially lethal if untreated. Despite the availability of drugs for treating the disease, the current drug regime suffers from drawbacks like antibiotic resistance and toxicity. New drugs have to be discovered in order to overcome these limitations. Our aim is to identify natural compounds from plant sources as putative inhibitors considering the occurrence of structural diversity in plant sources. Spermidine Synthase (SpdS) was chosen as the target enzyme as it plays a vital role in growth, survival, and due to its contribution in virulence. Our initial investigation started with a literature survey in identifying natural compounds that showed antileishmanial activity. Subsequently, we identified two monoterpenoid compounds, namely Geraniol and Linalool, that were structurally analogous to one of the substrates (putrescine) of SpdS. In the present study, homology model of L. donovani SpdS was generated and the binding affinity of the identified compounds was analyzed and also compared with the putrescine through molecular docking and dynamic studies. The pharmacokinetic properties of the identified compounds were validated and the binding efficiency of these ligands over the original substrate has been demonstrated. Based on these studies, Geraniol and Linalool can be considered as lead molecules for future investigations targeting SpdS. This study further emphasizes the choice of natural compounds as a good source of therapeutic agents.

Expression, Docking, and Molecular Dynamics of Endo-β-1,4-xylanase I Gene of Trichoderma virens in Pichia stipitis

It is essential that major carbohydrate polymers in the lignocellulosic biomass are converted into fermentable sugars for the economical production of energy. Xylan, the major component of hemicelluloses, is the second most naturally abundant carbohydrate polymer comprising 20-40% of the total biomass. Endoxylanase (EXN) hydrolyzes xylan into mixtures of xylooligosaccharides. The objective of this study was to genetically modify Pichia stipitis, a pentose sugar fermenting yeast species, to hydrolyze xylan into xylooligosaccharides via cloning and heterologous extracellular expression of EXNI gene from locally isolated Trichoderma virens species. Pichia stipitis was engineered to carry the EXNI gene of T. virens using pGAPZα expression vector. The open reading frame encodes 191 amino acids and SDS-PAGE analysis revealed a 24 kDA recombinant protein. The EXNI activity expressed by recombinant P. stipitis clone under standard conditions using 1% beechwood xylan was 31.7 U/ml. Molecular docking and molecular dynamics simulations were performed to investigate EXNI-xylan interactions. Free EXNI and xylan bound EXNI exhibited similar stabilities and structural behavior in aqueous medium. Furthermore, this in silico work opens avenues for the development of newer generation EXN proteins that can perform better and have enhanced catalytic activity.

In silico analyses of conservational, functional and phylogenetic distribution of the LuxI and LuxR homologs in Gram-positive bacteria

LuxI and LuxR are key factors that drive quorum sensing (QS) in bacteria through secretion and perception of the signaling molecules e.g. N-Acyl homoserine lactones (AHLs). The role of these proteins is well established in Gram-negative bacteria for intercellular communication but remain under-explored in Gram-positive bacteria where QS peptides are majorly responsible for cell-to-cell communication. Therefore, in the present study, we explored conservation, potential function, topological arrangements and evolutionarily aspects of these proteins in Gram-positive bacteria. Putative LuxI/LuxR containing proteins were retrieved using the domain-based strategy from InterPro v62.0 meta-database. Conservational analyses via multiple sequence alignment and domain showed that these are well conserved in Gram-positive bacteria and possess relatedness with Gram-negative bacteria. Further, Gene ontology and ligand-based functional annotation explain their active involvement in signal transduction mechanism via QS signaling molecules. Moreover, Phylogenetic analyses (LuxI, LuxR, LuxI + LuxR and 16s rRNA) revealed horizontal gene transfer events with significant statistical support among Gram-positive and Gram-negative bacteria. This in-silico study offers a detailed overview of potential LuxI/LuxR distribution in Gram-positive bacteria (mainly Firmicutes and Actinobacteria) and their functional role in QS. It would further help in understanding the extent of interspecies communications between Gram-positive and Gram-negative bacteria through QS signaling molecules.

Evaluation of Sofosbuvir (β-D-2'-deoxy-2'-α-fluoro-2'-β-C-methyluridine) as an inhibitor of Dengue virus replication<sup/>

We evaluated Sofosbuvir (SOF), the anti-hepatitis C virus prodrug of β-d-2'-deoxy-2'-α-fluoro-2'-β-C-methyluridine-5'-monophosphate, for potential inhibitory activity against DENV replication. Both cell-based and biochemical assays, based on use of purified DENV full-length NS5 enzyme, were studied. Cytopathic effect protection and virus yield reduction assays confirmed that SOF possessed anti-DENV activity in cell culture with a 50% effective concentration (EC₅₀) of 4.9 µM and 1.4 µM respectively. Real-time RT-PCR verified that SOF inhibits generation of viral RNA with an EC₅₀ of 9.9 µM. Purified DENV NS5 incorporated the active triphosphate form (SOF-TP) into nascent RNA, causing chain-termination. Relative to the natural UTP, the incorporation efficiency of SOF-TP was low (discrimination value = 327.5). In a primer extension assay, SOF-TP was active against DENV NS5 wild-type polymerase activity with an IC₅₀ of 14.7 ± 2.5 µM. The S600T substitution in the B Motif of DENV polymerase conferred 4.3-fold resistance to SOF-TP; this was due to decreased incorporation efficiency rather than enhanced excision of the incorporated SOF nucleotide. SOF has antiviral activity against DENV replication. The high discrimination value in favor of UTP in enzyme assays may not necessarily preclude antiviral activity in cells. SOF may be worthy of evaluation against severe DENV infections in humans.

Evolutionarily distinct carotenoid cleavage dioxygenases are responsible for crocetin production in Buddleja davidii

Crocetin, one of the few colored apocarotenoids known in nature, is present in flowers and fruits and has long been used medicinally and as a colorant. Saffron is the main source of crocetin, although a few other plants produce lower amounts of this apocarotenoid. Notably, Buddleja davidii accumulates crocetin in its flowers. Recently, a carotenoid dioxygenase cleavage enzyme, CCD2, has been characterized as responsible for crocetin production in Crocus species. We searched for CCD2 homologues in B. davidii and identified several CCD enzymes from the CCD1 and CCD4 subfamilies. Unexpectedly, two out of the three CCD4 enzymes, namely BdCCD4.1 and BdCCD4.3, showed 7,8;7',8' activity in vitro and in vivo over zeaxanthin. In silico analyses of these enzymes and CCD2 allowed the determination of key residues for this activity. Both BdCCD4 genes are highly expressed during flower development and transcripts levels parallel the accumulation of crocins in the petals. Phylogenetic analysis showed that BdCCD4.2 grouped with almost all the characterized CCD4 enzymes, while BdCCD4.1 and BdCCD4.3 form a new sub-cluster together with CCD4 enzymes from certain Lamiales species. The present study indicates that convergent evolution led to the acquisition of 7,8;7',8' apocarotenoid cleavage activity in two separate CCD enzyme families.

Computational tools help improve protein stability but with a solubility tradeoff

Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only ∼2% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against ∼600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.

3D-QSAR and Molecular Docking Studies on the TcPMCA1-Mediated Detoxification of Scopoletin and Coumarin Derivatives

The carmine spider mite, Tetranychus cinnabarinus (Boisduval), is an economically important agricultural pest that is difficult to prevent and control. Scopoletin is a botanical coumarin derivative that targets Ca2+-ATPase to exert a strong acaricidal effect on carmine spider mites. In this study, the full-length cDNA sequence of a plasma membrane Ca2+-ATPase 1 gene (TcPMCA1) was cloned. The sequence contains an open reading frame of 3750 bp and encodes a putative protein of 1249 amino acids. The effects of scopoletin on TcPMCA1 expression were investigated. TcPMCA1 was significantly upregulated after it was exposed to 10%, 30%, and 50% of the lethal concentration of scopoletin. Homology modeling, molecular docking, and three-dimensional quantitative structure-activity relationships were then studied to explore the relationship between scopoletin structure and TcPMCA1-inhibiting activity of scopoletin and other 30 coumarin derivatives. Results showed that scopoletin inserts into the binding cavity and interacts with amino acid residues at the binding site of the TcPMCA1 protein through the driving forces of hydrogen bonds. Furthermore, CoMFA (comparative molecular field analysis)- and CoMSIA (comparative molecular similarity index analysis)-derived models showed that the steric and H-bond fields of these compounds exert important influences on the activities of the coumarin compounds.Notably, the C3, C6, and C7 positions in the skeletal structure of the coumarins are the most suitable active sites. This work provides insights into the mechanism underlying the interaction of scopoletin with TcPMCA1. The present results can improve the understanding on plasma membrane Ca2+-ATPase-mediated (PMCA-mediated) detoxification of scopoletin and coumarin derivatives in T. cinnabarinus, as well as provide valuable information for the design of novel PMCA-inhibiting acaricides.

Trichoderma virens β-glucosidase I (BGLI) gene; expression in Saccharomyces cerevisiae including docking and molecular dynamics studies

BACKGROUND

Cellulose, a linear polymer of β 1-4, linked glucose, is the most abundant renewable fraction of plant biomass (lignocellulose). It is synergistically converted to glucose by endoglucanase (EG) cellobiohydrolase (CBH) and β-glucosidase (BGL) of the cellulase complex. BGL plays a major role in the conversion of randomly cleaved cellooligosaccharides into glucose. As it is well known, Saccharomyces cerevisiae can efficiently convert glucose into ethanol under anaerobic conditions. Therefore, S.cerevisiae was genetically modified with the objective of heterologous extracellular expression of the BGLI gene of Trichoderma virens making it capable of utilizing cellobiose to produce ethanol.

RESULTS

The cDNA and a genomic sequence of the BGLI gene of Trichoderma virens was cloned in the yeast expression vector pGAPZα and separately transformed to Saccharomyces cerevisiae. The size of the BGLI cDNA clone was 1363 bp and the genomic DNA clone contained an additional 76 bp single intron following the first exon. The gene was 90% similar to the DNA sequence and 99% similar to the deduced amino acid sequence of 1,4-β-D-glucosidase of T. atroviride (AC237343.1). The BGLI activity expressed by the recombinant genomic clone was 3.4 times greater (1.7 x 10-3 IU ml-1) than that observed for the cDNA clone (5 x 10-4 IU ml-1). Furthermore, the activity was similar to the activity of locally isolated Trichoderma virens (1.5 x 10-3 IU ml-1). The estimated size of the protein was 52 kDA. In fermentation studies, the maximum ethanol production by the genomic and the cDNA clones were 0.36 g and 0.06 g /g of cellobiose respectively. Molecular docking results indicated that the bare protein and cellobiose-protein complex behave in a similar manner with considerable stability in aqueous medium. The deduced binding site and the binding affinity of the constructed homology model appeared to be reasonable. Moreover, it was identified that the five hydrogen bonds formed between the amino acid residues of BGLI and cellobiose are mainly involved in the integrity of enzyme-substrate association.

CONCLUSIONS

The BGLI activity was remarkably higher in the genomic DNA clone compared to the cDNA clone. Cellobiose was successfully fermented into ethanol by the recombinant S.cerevisiae genomic DNA clone. It has the potential to be used in the industrial production of ethanol as it is capable of simultaneous saccharification and fermentation of cellobiose. Homology modeling, docking studies and molecular dynamics simulation studies will provide a realistic model for further studies in the modification of active site residues which could be followed by mutation studies to improve the catalytic action of BGLI.

SARS-unique fold in the Rousettus bat coronavirus HKU9

The coronavirus nonstructural protein 3 (nsp3) is a multifunctional protein that comprises multiple structural domains. This protein assists viral polyprotein cleavage, host immune interference, and may play other roles in genome replication or transcription. Here, we report the solution NMR structure of a protein from the “SARS‐unique region” of the bat coronavirus HKU9. The protein contains a frataxin fold or double‐wing motif, which is an α + β fold that is associated with protein/protein interactions, DNA binding, and metal ion binding. High structural similarity to the human severe acute respiratory syndrome (SARS) coronavirus nsp3 is present. A possible functional site that is conserved among some betacoronaviruses has been identified using bioinformatics and biochemical analyses. This structure provides strong experimental support for the recent proposal advanced by us and others that the “SARS‐unique” region is not unique to the human SARS virus, but is conserved among several different phylogenetic groups of coronaviruses and provides essential functions.

PDB Code(s): 5UTV

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

Data imbalance is frequently encountered in biomedical applications. Resampling techniques can be used in binary classification to tackle this issue. However such solutions are not desired when the number of samples in the small class is limited. Moreover the use of inadequate performance metrics, such as accuracy, lead to poor generalization results because the classifiers tend to predict the largest size class. One of the good approaches to deal with this issue is to optimize performance metrics that are designed to handle data imbalance. Matthews Correlation Coefficient (MCC) is widely used in Bioinformatics as a performance metric. We are interested in developing a new classifier based on the MCC metric to handle imbalanced data. We derive an optimal Bayes classifier for the MCC metric using an approach based on Frechet derivative. We show that the proposed algorithm has the nice theoretical property of consistency. Using simulated data, we verify the correctness of our optimality result by searching in the space of all possible binary classifiers. The proposed classifier is evaluated on 64 datasets from a wide range data imbalance. We compare both classification performance and CPU efficiency for three classifiers: 1) the proposed algorithm (MCC-classifier), the Bayes classifier with a default threshold (MCC-base) and imbalanced SVM (SVM-imba). The experimental evaluation shows that MCC-classifier has a close performance to SVM-imba while being simpler and more efficient.

Species-Specificity in Myoglobin Oxygenation and Reduction Potential Properties

The objective was to compare oxygenation and reduction potential properties of bovine and porcine myoglobins in-vitro. Cyclic voltammetry and homology-based myoglobin modeling were used to determine the species-specific effects on myoglobin reduction potential and oxygenation properties at pH 5.6, 6.4, and 7.4. At all pHs, porcine myoglobin had greater (P = 0.04) oxygen affinity than bovine myoglobin. For both species, oxygen affinity was higher at pH 6.4 > pH 7.4 > 5.6 (P = 0.0002). Myoglobin reduction potential for both species was affected by pH (P < 0.0001). The redox potentials became more negative as pH increased, indicating a proton-coupled electron transfer. There were no differences (P = 0.51) between species in reduction potential properties of heme. Homology-based myoglobin modeling indicated that the porcine myoglobin has a shorter distance between the distal histidine and heme than does bovine myoglobin. The variation in amino acid composition between bovine and porcine myoglobin could be partially responsible for differences in oxygen affinity.

Computational Exploration of Putative LuxR Solos in Archaea and Their Functional Implications in Quorum Sensing

LuxR solos are unexplored in Archaea, despite their vital role in the bacterial regulatory network. They assist bacteria in perceiving acyl homoserine lactones (AHLs) and/or non-AHLs signaling molecules for establishing intraspecies, interspecies, and interkingdom communication. In this study, we explored the potential LuxR solos of Archaea from InterPro v62.0 meta-database employing taxonomic, probable function, distribution, and evolutionary aspects to decipher their role in quorum sensing (QS). Our bioinformatics analyses showed that putative LuxR solos of Archaea shared few conserved domains with bacterial LuxR despite having less similarity within proteins. Functional characterization revealed their ability to bind various AHLs and/or non-AHLs signaling molecules that involve in QS cascades alike bacteria. Further, the phylogenetic study indicates that Archaeal LuxR solos (with less substitution per site) evolved divergently from bacteria and share distant homology along with instances of horizontal gene transfer. Moreover, Archaea possessing putative LuxR solos, exhibit the correlation between taxonomy and ecological niche despite being the inhabitant of diverse habitats like halophilic, thermophilic, barophilic, methanogenic, and chemolithotrophic. Therefore, this study would shed light in deciphering the role of the putative LuxR solos of Archaea to adapt varied habitats via multilevel communication with other organisms using QS.

Virtual Screening for Potential Inhibitors of CTX-M-15 Protein of Klebsiella pneumoniae

The Gram-negative bacterium Klebsiella pneumoniae, responsible for a wide variety of nosocomial infections in immuno-deficient patients, involves the respiratory, urinary and gastrointestinal tract infections and septicemia. Extended spectrum β-lactamases (ESBL) belong to β-lactamases capable of conferring antibiotic resistance in Gram-negative bacteria. CTX-M-15, a prevalent ESBL reported from Enterobacteriaceae including K. pneumoniae, was selected as a potent anti-bacterial target. To identify the novel drug-like compounds, structure-based screening procedure was employed against downloaded drug-like compounds from ZINC database. An acronym for "ZINC" is not commercial. The docking free energy values were investigated and compared to the known inhibitor Avibactam. Six best novel drug-like compounds were selected and their hydrogen bindings with the receptor were determined. Based on the binding efficiency mode, three among these six identified most potential inhibitors, ZINC21811621, ZINC93091917 and ZINC19488569, were predicted as potential competitive inhibitors against CTX-M-15 compared to Avibactam. These three inhibitors may provide a framework for the experimental studies to develop anti-Klebsiella novel drug candidates targeting CTX-M-15.

Molecular and Structural Characterization of the Tegumental 20.6-kDa Protein in Clonorchis sinensis as a Potential Druggable Target

The tegument, representing the membrane-bound outer surface of platyhelminth parasites, plays an important role for the regulation of the host immune response and parasite survival. A comprehensive understanding of tegumental proteins can provide drug candidates for use against helminth-associated diseases, such as clonorchiasis caused by the liver fluke Clonorchis sinensis. However, little is known regarding the physicochemical properties of C. sinensis teguments. In this study, a novel 20.6-kDa tegumental protein of the C. sinensis adult worm (CsTegu20.6) was identified and characterized by molecular and in silico methods. The complete coding sequence of 525 bp was derived from cDNA clones and encodes a protein of 175 amino acids. Homology search using BLASTX showed CsTegu20.6 identity ranging from 29% to 39% with previously-known tegumental proteins in C. sinensis. Domain analysis indicated the presence of a calcium-binding EF-hand domain containing a basic helix-loop-helix structure and a dynein light chain domain exhibiting a ferredoxin fold. We used a modified method to obtain the accurate tertiary structure of the CsTegu20.6 protein because of the unavailability of appropriate templates. The CsTegu20.6 protein sequence was split into two domains based on the disordered region, and then, the structure of each domain was modeled using I-TASSER. A final full-length structure was obtained by combining two structures and refining the whole structure. A refined CsTegu20.6 structure was used to identify a potential CsTegu20.6 inhibitor based on protein structure-compound interaction analysis. The recombinant proteins were expressed in Escherichia coli and purified by nickel-nitrilotriacetic acid affinity chromatography. In C. sinensis, CsTegu20.6 mRNAs were abundant in adult and metacercariae, but not in the egg. Immunohistochemistry revealed that CsTegu20.6 localized to the surface of the tegument in the adult fluke. Collectively, our results contribute to a better understanding of the structural and functional characteristics of CsTegu20.6 and homologs of flukes. One compound is proposed as a putative inhibitor of CsTegu20.6 to facilitate further studies for anthelmintics.

Purification, Biochemical Characterization, and Amino Acid Sequence of a Novel Type of Lectin from Aplysia dactylomela Eggs with Antibacterial/Antibiofilm Potential

A new lectin from Aplysia dactylomela eggs (ADEL) was isolated by affinity chromatography on HCl-activated Sepharose™ media. Hemagglutination caused by ADEL was inhibited by several galactosides, mainly galacturonic acid (Ka = 6.05 × 10⁶ M^-1). The primary structure of ADEL consists of 217 residues, including 11 half-cystines involved in five intrachain and one interchain disulfide bond, resulting in a molecular mass of 57,228 ± 2 Da, as determined by matrix-assisted laser desorption/ionization time of flight mass spectrometry. ADEL showed high similarity with lectins isolated from Aplysia eggs, but not with other known lectins, indicating that these lectins could be grouped into a new family of animal lectins. Three glycosylation sites were found in its polypeptide backbone. Data from peptide-N-glycosidase F digestion and MS suggest that all oligosaccharides attached to ADEL are high in mannose. The secondary structure of ADEL is predominantly β-sheet, and its tertiary structure is sensitive to the presence of ligands, as observed by CD. A 3D structure model of ADEL was created and shows two domains connected by a short loop. Domain A is composed of a flat three-stranded and a curved five-stranded β-sheet, while domain B presents a flat three-stranded and a curved four-stranded β-sheet. Molecular docking revealed favorable binding energies for interactions between lectin and galacturonic acid, lactose, galactosamine, and galactose. Moreover, ADEL was able to agglutinate and inhibit biofilm formation of Staphylococcus aureus, suggesting that this lectin may be a potential alternative to conventional use of antimicrobial agents in the treatment of infections caused by Staphylococcal biofilms.

Mutation-Structure-Function Relationship Based Integrated Strategy Reveals the Potential Impact of Deleterious Missense Mutations in Autophagy Related Proteins on Hepatocellular Carcinoma (HCC): A Comprehensive Informatics Approach

Autophagy, an evolutionary conserved multifaceted lysosome-mediated bulk degradation system, plays a vital role in liver pathologies including hepatocellular carcinoma (HCC). Post-translational modifications (PTMs) and genetic variations in autophagy components have emerged as significant determinants of autophagy related proteins. Identification of a comprehensive spectrum of genetic variations and PTMs of autophagy related proteins and their impact at molecular level will greatly expand our understanding of autophagy based regulation. In this study, we attempted to identify high risk missense mutations that are highly damaging to the structure as well as function of autophagy related proteins including LC3A, LC3B, BECN1 and SCD1. Number of putative structural and functional residues, including several sites that undergo PTMs were also identified. In total, 16 high-risk SNPs in LC3A, 18 in LC3B, 40 in BECN1 and 43 in SCD1 were prioritized. Out of these, 2 in LC3A (K49A, K51A), 1 in LC3B (S92C), 6 in BECN1 (S113R, R292C, R292H, Y338C, S346Y, Y352H) and 6 in SCD1 (Y41C, Y55D, R131W, R135Q, R135W, Y151C) coincide with potential PTM sites. Our integrated analysis found LC3B Y113C, BECN1 I403T, SCD1 R126S and SCD1 Y218C as highly deleterious HCC-associated mutations. This study is the first extensive in silico mutational analysis of the LC3A, LC3B, BECN1 and SCD1 proteins. We hope that the observed results will be a valuable resource for in-depth mechanistic insight into future investigations of pathological missense SNPs using an integrated computational platform.

Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates

Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.

Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks

Tens of thousands of splice isoforms of proteins have been catalogued as predicted sequences from transcripts in humans and other species. Relatively few have been characterized biochemically or structurally. With the extensive development of protein bioinformatics, the characterization and modeling of isoform features, isoform functions, and isoform-level networks have advanced notably. Here we present applications of the I-TASSER family of algorithms for folding and functional predictions and the IsoFunc, MIsoMine, and Hisonet data resources for isoform-level analyses of network and pathway-based functional predictions and protein-protein interactions. Hopefully, predictions and insights from protein bioinformatics will stimulate many experimental validation studies.

In Silico Identification of Mimicking Molecules as Defense Inducers Triggering Jasmonic Acid Mediated Immunity against Alternaria Blight Disease in Brassica Species

Alternaria brassicae and Alternaria brassicicola are two major phytopathogenic fungi which cause Alternaria blight, a recalcitrant disease on Brassica crops throughout the world, which is highly destructive and responsible for significant yield losses. Since no resistant source is available against Alternaria blight, therefore, efforts have been made in the present study to identify defense inducer molecules which can induce jasmonic acid (JA) mediated defense against the disease. It is believed that JA triggered defense response will prevent necrotrophic mode of colonization of Alternaria brassicae fungus. The JA receptor, COI1 is one of the potential targets for triggering JA mediated immunity through interaction with JA signal. In the present study, few mimicking compounds more efficient than naturally occurring JA in terms of interaction with COI1 were identified through virtual screening and molecular dynamics simulation studies. A high quality structural model of COI1 was developed using the protein sequence of Brassica rapa. This was followed by virtual screening of 767 analogs of JA from ZINC database for interaction with COI1. Two analogs viz. ZINC27640214 and ZINC43772052 showed more binding affinity with COI1 as compared to naturally occurring JA. Molecular dynamics simulation of COI1 and COI1-JA complex, as well as best screened interacting structural analogs of JA with COI1 was done for 50 ns to validate the stability of system. It was found that ZINC27640214 possesses efficient, stable, and good cell permeability properties. Based on the obtained results and its physicochemical properties, it is capable of mimicking JA signaling and may be used as defense inducers for triggering JA mediated resistance against Alternaria blight, only after further validation through field trials.

Mining the Proteome of Fusobacterium nucleatum subsp. nucleatum ATCC 25586 for Potential Therapeutics Discovery: An In Silico Approach

The plethora of genome sequence information of bacteria in recent times has ushered in many novel strategies for antibacterial drug discovery and facilitated medical science to take up the challenge of the increasing resistance of pathogenic bacteria to current antibiotics. In this study, we adopted subtractive genomics approach to analyze the whole genome sequence of the Fusobacterium nucleatum, a human oral pathogen having association with colorectal cancer. Our study divulged 1,499 proteins of F. nucleatum, which have no homolog's in human genome. These proteins were subjected to screening further by using the Database of Essential Genes (DEG) that resulted in the identification of 32 vitally important proteins for the bacterium. Subsequent analysis of the identified pivotal proteins, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) Automated Annotation Server (KAAS) resulted in sorting 3 key enzymes of F. nucleatum that may be good candidates as potential drug targets, since they are unique for the bacterium and absent in humans. In addition, we have demonstrated the three dimensional structure of these three proteins. Finally, determination of ligand binding sites of the 2 key proteins as well as screening for functional inhibitors that best fitted with the ligands sites were conducted to discover effective novel therapeutic compounds against F. nucleatum.

MIB: Metal Ion-Binding Site Prediction and Docking Server

The structure of a protein determines its biological function(s) and its interactions with other factors; the binding regions tend to be conserved in sequence and structure, and the interacting residues involved are usually in close 3D space. The Protein Data Bank currently contains more than 110 000 protein structures, approximately one-third of which contain metal ions. Identifying and characterizing metal ion-binding sites is thus essential for investigating a protein's function(s) and interactions. However, experimental approaches are time-consuming and costly. The web server reported here was built to predict metal ion-binding residues and to generate the predicted metal ion-bound 3D structure. Binding templates have been constructed for regions that bind 12 types of metal ion-binding residues have been used to construct binding templates. The templates include residues within 3.5 Å of the metal ion, and the fragment transformation method was used for structural comparison between query proteins and templates without any data training. Through the adjustment of scoring functions, which are based on the similarity of structure and binding residues. Twelve kinds of metal ions (Ca²⁺, Cu²⁺, Fe³⁺, Mg²⁺, Mn²⁺, Zn²⁺, Cd²⁺, Fe²⁺, Ni²⁺, Hg²⁺, Co²⁺, and Cu⁺) binding residues prediction are supported. MIB also provides the metal ions docking after prediction. The MIB server is available at http://bioinfo.cmu.edu.tw/MIB/ .

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.

Understand protein functions by comparing the similarity of local structural environments

The three-dimensional structures of proteins play an essential role in regulating binding between proteins and their partners, offering a direct relationship between structures and functions of proteins. It is widely accepted that the function of a protein can be determined if its structure is similar to other proteins whose functions are known. However, it is also observed that proteins with similar global structures do not necessarily correspond to the same function, while proteins with very different folds can share similar functions. This indicates that function similarity is originated from the local structural information of proteins instead of their global shapes. We assume that proteins with similar local environments prefer binding to similar types of molecular targets. In order to testify this assumption, we designed a new structural indicator to define the similarity of local environment between residues in different proteins. This indicator was further used to calculate the probability that a given residue binds to a specific type of structural neighbors, including DNA, RNA, small molecules and proteins. After applying the method to a large-scale non-redundant database of proteins, we show that the positive signal of binding probability calculated from the local structural indicator is statistically meaningful. In summary, our studies suggested that the local environment of residues in a protein is a good indicator to recognize specific binding partners of the protein. The new method could be a potential addition to a suite of existing template-based approaches for protein function prediction.

Protein ligand-specific binding residue predictions by an ensemble classifier

BACKGROUND

Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands.

RESULTS

In this study, we present ligand-specific methods to predict the binding sites of protein-ligand interactions. First, a sequence-based method is proposed that only extracts features from protein sequence information, including evolutionary conservation scores and predicted structure properties. An improved AdaBoost algorithm is applied to address the serious imbalance problem between the binding and non-binding residues. Then, a combined method is proposed that combines the current template-free method and four other well-established template-based methods. The above two methods predict the ligand binding sites along the sequences using a ligand-specific strategy that contains metal ions, acid radical ions, nucleotides and ferroheme. Testing on a well-established dataset showed that the proposed sequence-based method outperformed the profile-based method by 4-19% in terms of the Matthews correlation coefficient on different ligands. The combined method outperformed each of the individual methods, with an improvement in the average Matthews correlation coefficients of 5.55% over all ligands. The results also show that the ligand-specific methods significantly outperform the general-purpose methods, which confirms the necessity of developing elaborate ligand-specific methods for ligand binding site prediction.

CONCLUSIONS

Two efficient ligand-specific binding site predictors are presented. The standalone package is freely available for academic usage at http://dase.ecnu.edu.cn/qwdong/TargetCom/TargetCom_standalone.tar.gz  or request upon the corresponding author.

Connecting common genetic polymorphisms to protein function: A modular project sequence for lecture or lab

Single nucleotide polymorphisms (SNPs) in DNA can result in phenotypes where the biochemical basis may not be clear due to the lack of protein structures. With the growing number of modeling and simulation software available on the internet, students can now participate in determining how small changes in genetic information impact cellular protein structure and function. We have developed a modular series of activities to engage lab or lecture students in examining the basis for common phenotypes. The activities range from basic phenotype testing/observation to DNA sequencing and simulation of protein structure and dynamics. We provide as an example study of the bitterness receptor TAS2R38 and PTC tasting, however these activities are applicable to other SNPs or genomic variants with a direct connection to an observable phenotype. These activities are modular and can be mixed to meet the student capabilities and infrastructure availability. The complete sequence of activities will demonstrate the direct connection between gene structure, protein structure and organism function. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(6):526-536, 2016.

Computational analysis, structural modeling and ligand binding site prediction of Plasmodium falciparum 1-deoxy-d-xylulose-5-phosphate synthase

Malaria remains one of the most serious infectious diseases in the world. There are five human species of the Plasmodium genus, of which Plasmodium falciparum is the most virulent and responsible for the vast majority of malaria related deaths. The unique biochemical processes that exist in Plasmodium falciparum provide a useful way to develop novel inhibitors. One such biochemical pathway is the methyl erythritol phosphate pathway (MEP), required to synthesize isoprenoid precursors. In the present study, a detailed computational analysis has been performed for 1-deoxy-d-xylulose-5-phosphate synthase, a key enzyme in MEP. The protein is found to be stable and residues from 825 to 971 are highly conserved across species. The homology model of the enzyme is developed using three web-based servers and Modeller software. It has twelve disordered regions indicating its druggability. Virtual screening of ZINC database identifies ten potential compounds in thiamine diphosphate binding region of the enzyme.

Design of novel lead molecules against RhoG protein as cancer target - a computational study

Cancer is a class of diseases characterized by uncontrolled cell growth. Every year more than 2 million people are affected by the disease. Rho family proteins are actively involved in cytoskeleton regulation. Over-expression of Rho family proteins show oncogenic activity and promote cancer progression. In the present work RhoG protein is considered as novel target of cancer. It is a member of Rho family and Rac subfamily protein, which plays pivotal role in regulation of microtubule formation, cell migration and contributes in cancer progression. In order to understand the binding interaction between RhoG protein and the DH domain of Ephexin-4 protein, the 3D structure of RhoG was evaluated and Molecular Dynamic Simulations was performed to stabilize the structure. The 3D structure of RhoG protein was validated and active site identified using standard computational protocols. Protein-protein docking of RhoG with Ephexin-4 was done to understand binding interactions and the active site structure. Virtual screening was carried out with ligand databases against the active site of RhoG protein. The efficiency of virtual screening is analysed with enrichment factor and area under curve values. The binding free energy of docked complexes was calculated using prime MM-GBSA module. The SASA, FOSA, FISA, PISA and PSA values of ligands were carried out. New ligands with high docking score, glide energy and acceptable ADME properties were prioritized as potential inhibitors of RhoG protein.

A novel approach using C. elegans DNA damage-induced apoptosis to characterize the dynamics of uptake transporters for therapeutic drug discoveries

Organic cation transporter (OCT) function is critical for cellular homeostasis. C. elegans lacking OCT-1 displays a shortened lifespan and increased susceptibility to oxidative stress. We show that these phenotypes can be rescued by downregulating the OCT-1 paralogue, OCT-2. Herein, we delineate a biochemical pathway in C. elegans where uptake of genotoxic chemotherapeutics such as doxorubicin and cisplatin, and subsequent DNA damage-induced apoptosis of germ cells, are dependent exclusively upon OCT-2. We characterized OCT-2 as the main uptake transporter for doxorubicin, as well as a number of other therapeutic agents and chemical compounds, some identified through ligand-protein docking analyses. We provide insights into the conserved features of the structure and function and gene regulation of oct-1 and oct-2 in distinct tissues of C. elegans. Importantly, our innovative approach of exploiting C. elegans uptake transporters in combination with defective DNA repair pathways will have broad applications in medicinal chemistry.

Rational design of nitrofuran derivatives: Synthesis and valuation as inhibitors of Trypanosoma cruzi trypanothione reductase

The rational design and synthesis of a series of 5-nitro-2-furoic acid analogues are presented. The trypanocidal activity against epimastigote forms of Trypanosoma cruzi and the toxic effects on human HeLa cells were tested. Between all synthetic compounds, three of thirteen had an IC₅₀ value in the range of Nfx, but compound 13 exhibited an improved effect with an IC₅₀ of 1.0 ± 0.1 μM and a selective index of 70 in its toxicity against HeLa cells. We analyzed the activity of compounds 8, 12 and 13 to interfere in the central redox metabolic pathway in trypanosomatids, which is dependent of reduced trypanothione as the major pivotal thiol. The three compounds behaved as better inhibitors of trypanothione reductase than Nfx (Ki values of 118 μM, 61 μM and 68 μM for 8, 12 and 13, respectively, compared with 245 μM for Nfx), all following an uncompetitive enzyme inhibition pattern. Docking analysis predicted a binding of inhibitors to the enzyme-substrate complex with binding energy calculated in-silico that supports such molecular interaction.

Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota

Methanogenesis is the primary biogenic source of methane in the atmosphere and a key contributor to climate change. The long-standing dogma that methanogenesis originated within the Euryarchaeota was recently challenged by the discovery of putative methane-metabolizing genes in members of the Bathyarchaeota, suggesting that methanogenesis may be more phylogenetically widespread than currently appreciated. Here, we present the discovery of divergent methyl-coenzyme M reductase genes in population genomes recovered from anoxic environments with high methane flux that belong to a new archaeal phylum, the Verstraetearchaeota. These archaea encode the genes required for methylotrophic methanogenesis, and may conserve energy using a mechanism similar to that proposed for the obligate H₂-dependent methylotrophic Methanomassiliicoccales and recently described Candidatus 'Methanofastidiosa'. Our findings indicate that we are only beginning to understand methanogen diversity and support an ancient origin for methane metabolism in the Archaea, which is changing our understanding of the global carbon cycle.

Designing New Kinase Inhibitor Derivatives as Therapeutics Against Common Complex Diseases: Structural Basis of Microtubule Affinity-Regulating Kinase 4 (MARK4) Inhibition

Drug development for common complex diseases is in need of new molecular entities and actionable drug targets. MAP/microtubule affinity-regulating kinase 4 (MARK4) is associated with numerous diseases such as neurodegenerative disorders, obesity, cancer, and type 2 diabetes. Understanding the structural basis of ligands' (inhibitors) and substrates' binding to MARK4 is crucial to design new kinase inhibitors for therapeutic purposes. This study reports new observations on docking three well-known kinase inhibitors in the kinase domain of MARK4 variants and the calculated binding affinity. These variants of MARK4 are named as MARK4-F1 (59 N-terminal residues along with kinase domain) and MARK4-F2 (kinase domain of MARK4). We additionally performed molecular dynamics (MD) simulation and fluorescence binding studies to calculate the actual binding affinity of kinase inhibitors, BX-912, BX-795, and OTSSP167 (hydrochloride) for the MARK4. Docking analyses revealed that ligands bind in the large hydrophobic cavity of the kinase domain of MARK4 through several hydrophobic and hydrogen-bonded interactions. Simulations suggested that OTSSP167 (hydrochloride) is forming a stable complex, and hence the best inhibitor of MARK4. Intrinsic fluorescence of MARK4 was significantly quenched by addition of ligands, indicating their potential binding to MARK4. A lower KD value of MARK4 with OTSSP167 (hydrochloride) suggested that it is a better interacting partner than BX-912 and BX-795. These data form a basis for designing novel and potent OTSSP167 (hydrochloride) derivatives as therapeutic candidates against common complex diseases. The inhibitors designed as such might possibly suppress the growth of tumor-forming cells and be potentially applied for treatment of a wide range of human cancers as well.

Computational Analysis of the Ligand Binding Site of the Extracellular ATP Receptor, DORN1

DORN1 (also known as P2K1) is a plant receptor for extracellular ATP, which belongs to a large gene family of legume-type (L-type) lectin receptor kinases. Extracellular ATP binds to DORN1 with strong affinity through its lectin domain, and the binding triggers a variety of intracellular activities in response to biotic and abiotic stresses. However, information on the tertiary structure of the ligand binding site of DORN1is lacking, which hampers efforts to fully elucidate the mechanism of receptor action. Available data of the crystal structures from more than 50 L-type lectins enable us to perform an in silico study of molecular interaction between DORN1 and ATP. In this study, we employed a computational approach to develop a tertiary structure model of the DORN1 lectin domain. A blind docking analysis demonstrated that ATP binds to a cavity made by four loops (defined as loops A B, C and D) of the DORN1 lectin domain with high affinity. In silico target docking of ATP to the DORN1 binding site predicted interaction with 12 residues, located on the four loops, via hydrogen bonds and hydrophobic interactions. The ATP binding pocket is structurally similar in location to the carbohydrate binding pocket of the canonical L-type lectins. However, four of the residues predicted to interact with ATP are not conserved between DORN1 and the other carbohydrate-binding lectins, suggesting that diversifying selection acting on these key residues may have led to the ATP binding activity of DORN1. The in silico model was validated by in vitro ATP binding assays using the purified extracellular lectin domain of wild-type DORN1, as well as mutated DORN1 lacking key ATP binding residues.

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites.

bSiteFinder, an improved protein-binding sites prediction server based on structural alignment: more accurate and less time-consuming

MOTIVATION

Protein-binding sites prediction lays a foundation for functional annotation of protein and structure-based drug design. As the number of available protein structures increases, structural alignment based algorithm becomes the dominant approach for protein-binding sites prediction. However, the present algorithms underutilize the ever increasing numbers of three-dimensional protein-ligand complex structures (bound protein), and it could be improved on the process of alignment, selection of templates and clustering of template. Herein, we built so far the largest database of bound templates with stringent quality control. And on this basis, bSiteFinder as a protein-binding sites prediction server was developed.

RESULTS

By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server has been significantly improved. Further, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset. For 210 bound dataset, bSiteFinder achieved high accuracies up to 94.8 % (MCC 0.95). For another 48 bound/unbound dataset, bSiteFinder achieved high accuracies up to 93.8 % for bound proteins (MCC 0.95) and 85.4 % for unbound proteins (MCC 0.72). Our bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/, and the source code is provided at the methods page.

CONCLUSION

An online bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/. Our work lays a foundation for functional annotation of protein and structure-based drug design. With ever increasing numbers of three-dimensional protein-ligand complex structures, our server should be more accurate and less time-consuming.Graphical Abstract bSiteFinder (http://binfo.shmtu.edu.cn/bsitefinder/) as a protein-binding sites prediction server was developed based on the largest database of bound templates so far with stringent quality control. By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server have been significantly improved. What's more, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset.

Characterization of the structure and catalytic activity of Legionella pneumophila VipF

The pathogenic bacteria Legionella pneumophila is known to cause Legionnaires' Disease, a severe pneumonia that can be fatal to immunocompromised individuals and the elderly. Shohdy et al. identified the L. pneumophila vacuole sorting inhibitory protein VipF as a putative N-acetyltransferase based on sequence homology. We have characterized the basic structural and functional properties of VipF to confirm this original functional assignment. Sequence conservation analysis indicates two putative CoA-binding regions within VipF. Homology modeling and small angle X-ray scattering suggest a monomeric, dual-domain structure joined by a flexible linker. Each domain contains the characteristic beta-splay motif found in many acetyltransferases, suggesting that VipF may contain two active sites. Docking experiments suggest reasonable acetyl-CoA binding locations within each beta-splay motif. Broad substrate screening indicated that VipF is capable of acetylating chloramphenicol and both domains are catalytically active. Given that chloramphenicol is not known to be N-acetylated, this is a surprising finding suggesting that VipF is capable of O-acetyltransferase activity. Proteins 2016; 84:1422-1430. © 2016 Wiley Periodicals, Inc.

Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals

MOTIVATION

More than half of proteins require binding of metal and acid radical ions for their structure and function. Identification of the ion-binding locations is important for understanding the biological functions of proteins. Due to the small size and high versatility of the metal and acid radical ions, however, computational prediction of their binding sites remains difficult.

RESULTS

We proposed a new ligand-specific approach devoted to the binding site prediction of 13 metal ions (Zn²⁺, Cu²⁺, Fe²⁺, Fe³⁺, Ca²⁺, Mg²⁺, Mn²⁺, Na⁺, K⁺) and acid radical ion ligands (CO3^2-, NO2^-, SO4^2-, PO4^3-) that are most frequently seen in protein databases. A sequence-based ab initio model is first trained on sequence profiles, where a modified AdaBoost algorithm is extended to balance binding and non-binding residue samples. A composite method IonCom is then developed to combine the ab initio model with multiple threading alignments for further improving the robustness of the binding site predictions. The pipeline was tested using 5-fold cross validations on a comprehensive set of 2,100 non-redundant proteins bound with 3,075 small ion ligands. Significant advantage was demonstrated compared with the state of the art ligand-binding methods including COACH and TargetS for high-accuracy ion-binding site identification. Detailed data analyses show that the major advantage of IonCom lies at the integration of complementary ab initio and template-based components. Ion-specific feature design and binding library selection also contribute to the improvement of small ion ligand binding predictions.

AVAILABILITY AND IMPLEMENTATION

http://zhanglab.ccmb.med.umich.edu/IonCom CONTACT: hxz@imut.edu.cn or zhng@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online.