DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

An in silico investigation of phytochemicals as potential inhibitors against non-structural protein 1 from dengue virus 4

Dengue fever has emerged as a big threat to human health since the last decade owing to high morbidity with considerable mortalities. The proposed study aims at the in silico investigation of the inhibitory action against DENV4-NS1 of phytochemicals from two local medicinal plants of Pakistan. Non-Structural Protein 1 of Dengue Virus 4 (DENV4-NS1) is known to be involved in the replication and maturation of viron in the host cells. A total of 129 phytochemicals (50 from Tanacetum parthenium and 79 from Silybum marianum) were selected for this study. The tertiary structure of DENV4-NS1 was predicted based on homology modelling using Modeller 9.18 and the structural stability was evaluated using molecular dynamics simulations. Absorption, distribution, metabolism, excretion and toxicity (ADMET) along with the drug-likeness was also predicted for these phytochemicals using SwissADME and PreADMET servers. The results of ADMET and drug-likeness predictions exhibited that 54 phytochemicals i.e. 25 from Tanacetum parthenium and 29 from Silybum marianum showed effective druglikeness. These phytochemicals were docked against DENV4-NS1 using AutoDock Vina and 18 most suitable phytochemicals with binding affinities ≤ -6.0 kcal/mol were selected as potential inhibitors for DENV4-NS1. Proposed study also exploits the novel inhibitory action of Jaceidin, Centaureidin, Artecanin, Secotanaparthenolide, Artematin, Schizolaenone B, Isopomiferin, 6, 8-Diprenyleriodictyol, and Anthraxin against dengue virus. It is concluded that the screened 18 phytochemicals have strong inhibition potential against Dengue Virus 4.

Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using the CANDO Platform: Implications for Drug Design.

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. The accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100⁻1000-fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma results in more drugs that could be validated in the biomedical literature compared to using those suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC.

In this study, I introduce novel global and local 0D-protein descriptors based on a statistical quantity named Total Sum of Squares (TSS). This quantity represents the sum of the squares differences of amino acid properties from the arithmetic mean property. As an extension, the amino acid-types and amino acid-groups formalisms are used for describing zones of interest in proteins. To assess the effectiveness of the proposed descriptors, a Nearest Neighbor model for predicting the major four protein structural classes was built. This model has a success rate of 98.53% on the jackknife cross-validation test; this performance being superior to other reported methods despite the simplicity of the predictor. Additionally, this predictor has an average success rate of 98.35% in different cross-validation tests performed. A value of 0.98 for the Kappa statistic clearly discriminates this model from a random predictor. The results obtained by the Nearest Neighbor model demonstrated the ability of the proposed descriptors not only to reflect relevant biochemical information related to the structural classes of proteins but also to allow appropriate interpretability. It can thus be expected that the current method may play a supplementary role to other existing approaches for protein structural class prediction and other protein attributes.

Quantiprot - a Python package for quantitative analysis of protein sequences.

BACKGROUND: The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted. RESULTS: Quantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf's law coefficient. CONCLUSIONS: We propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.

ProtPhylo: identification of protein-phenotype and protein-protein functional associations via phylogenetic profiling.

ProtPhylo is a web-based tool to identify proteins that are functionally linked to either a phenotype or a protein of interest based on co-evolution. ProtPhylo infers functional associations by comparing protein phylogenetic profiles (co-occurrence patterns of orthology relationships) for more than 9.7 million non-redundant protein sequences from all three domains of life. Users can query any of 2048 fully sequenced organisms, including 1678 bacteria, 255 eukaryotes and 115 archaea. In addition, they can tailor ProtPhylo to a particular kind of biological question by choosing among four main orthology inference methods based either on pair-wise sequence comparisons (One-way Best Hits and Best Reciprocal Hits) or clustering of orthologous proteins across multiple species (OrthoMCL and eggNOG). Next, ProtPhylo ranks phylogenetic neighbors of query proteins or phenotypic properties using the Hamming distance as a measure of similarity between pairs of phylogenetic profiles. Candidate hits can be easily and flexibly prioritized by complementary clues on subcellular localization, known protein-protein interactions, membrane spanning regions and protein domains. The resulting protein list can be quickly exported into a csv text file for further analyses. ProtPhylo is freely available at http://www.protphylo.org.

Discovering co-occurring patterns and their biological significance in protein families.

BACKGROUND: The large influx of biological sequences poses the importance of identifying and correlating conserved regions in homologous sequences to acquire valuable biological knowledge. These conserved regions contain statistically significant residue associations as sequence patterns. Thus, patterns from two conserved regions co-occurring frequently on the same sequences are inferred to have joint functionality. A method for finding conserved regions in protein families with frequent co-occurrence patterns is proposed. The biological significance of the discovered clusters of conserved regions with co-occurrences patterns can be validated by their three-dimensional closeness of amino acids and the biological functionality found in those regions as supported by published work. METHODS: Using existing algorithms, we discovered statistically significant amino acid associations as sequence patterns. We then aligned and clustered them into Aligned Pattern Clusters (APCs) corresponding to conserved regions with amino acid conservation and variation. When one APC frequently co-occurred with another APC, the two APCs have high co-occurrence. We then clustered APCs with high co-occurrence into what we refer to as Co-occurrence APC Clusters (Co-occurrence Clusters). RESULTS: Our results show that for Co-occurrence Clusters, the three-dimensional distance between their amino acids is closer than average amino acid distances. For the Co-occurrence Clusters of the ubiquitin and the cytochrome c families, we observed biological significance among the residing amino acids of the APCs within the same cluster. In ubiquitin, the residues are responsible for ubiquitination as well as conventional and unconventional ubiquitin-bindings. In cytochrome c, amino acids in the first co-occurrence cluster contribute to binding of other proteins in the electron transport chain, and amino acids in the second co-occurrence cluster contribute to the stability of the axial heme ligand. CONCLUSIONS: Thus, our co-occurrence clustering algorithm can efficiently find and rank conserved regions that contain patterns that frequently co-occurring on the same proteins. Co-occurring patterns are biologically significant due to their three-dimensional closeness and other evidences reported in literature. These results play an important role in drug discovery as biologists can quickly identify the target for drugs to conduct detailed preclinical studies.

Non-specific physiological background effects of acupuncture revealed by proteomic analysis in normal rats.

BACKGROUND: The total effects of adequate real acupuncture treatment consist of pathologic-specific and non-specific physiological effects. The latter may be the fundamental component of the therapeutic effects of acupuncture. This study investigated the physiological background effects of acupuncture in normal rats treated with acupuncture. METHODS: Manual acupuncture was performed on normal rats at experienced acupoints, GV14 (Dazhui), BL12 (Fengmen) and BL13 (Feishu), once every other day for two weeks. The proteomic profile of rat lung tissue was examined using 2-DE/MS-based proteomic techniques. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed for differentially expressed proteins using the WebGestalt toolkit. RESULTS: In total, 25 differentially expressed protein spots were detected in the 2-DE gels. Among these spots, 24 corresponded to 20 unique proteins that were successfully identified using mass spectrometry. Subsequent GO and KEGG pathway analyses demonstrated that these altered proteins were mainly involved in biological processes, such as 'protein stabilization', 'glycolysis/gluconeogenesis' and 'response to stimulus'. CONCLUSIONS: Our study indicated the non-specific background effects of acupuncture at acupoints GV14, BL12 and BL13 likely maintained internal homeostasis via regulation of the local stimulus response, energy metabolism, and biomolecule function balance, which may be important contributors to the therapeutic effects of acupuncture.

Effects of size and surface of zinc oxide and aluminum-doped zinc oxide nanoparticles on cell viability inferred by proteomic analyses.

Although the health effects of zinc oxide nanoparticles (ZnONPs) on the respiratory system have been reported, the fate, potential toxicity, and mechanisms in biological cells of these particles, as related to particle size and surface characteristics, have not been well elucidated. To determine the physicochemical properties of ZnONPs that govern cytotoxicity, we investigated the effects of size, electronic properties, zinc concentration, and pH on cell viability using human alveolar-basal epithelial A549 cells as a model. We observed that a 2-hour or longer exposure to ZnONPs induced changes in cell viability. The alteration in cell viability was associated with the zeta potentials and pH values of the ZnONPs. Proteomic profiling of A549 exposed to ZnONPs for 2 and 4 hours was used to determine the biological mechanisms of ZnONP toxicity. p53-pathway activation was the core mechanism regulating cell viability in response to particle size. Activation of the Wnt and TGFß signaling pathways was also important in the cellular response to ZnONPs of different sizes. The cadherin and Wnt signaling pathways were important cellular mechanisms triggered by surface differences. These results suggested that the size and surface characteristics of ZnONPs might play an important role in their observed cytotoxicity. This approach facilitates the design of more comprehensive systems for the evaluation of nanoparticles.

Comprehensive proteomic analysis of a Chinese 2-herb formula (Astragali Radix and Rehmanniae Radix) on mature endothelial cells.

Endothelial cells are crucially involved in wound healing angiogenesis, restoring blood flow to wound tissues. Our previous study demonstrated that the Chinese 2-herb formula (NF3) possesses significant wound healing effect in diabetic foot ulcer rats with promising in vitro proangiogenic effects on human umbilical vein endothelial cells (HUVEC). Here, we present the comparative global proteome analysis of NF3-treated HUVEC in static or scratch conditions, screening the comprehensive molecular targets in governing the proangiogenic response in wound healing. Our results suggest plasminogen activator inhibitor-1, specifically down-regulated in static condition and Annexin A1 and Annexin A2, up-regulated in scratch condition, as principal proteins responsible for the proangiogenesis in wound healing. We also identified a panel of cytoskeleton regulatory proteins in static and scratch condition, mediating the migratory behavior of NF3-treated HUVEC. The key proteins in static state include myosin regulatory light polypeptide 9, SPAST, tropomyosin (TPM)2, and Vimentin while that in scratch state contained prelamin-A/C, TPM1, TPM2, and Vimentin. In addition, NF3 was shown to regulate transcription and translation, cell-cell interaction, and ROS defense in HUVEC. Proliferation and migration assays further confirmed the identified principal proteins plasminogen activator inhibitor-1 and Annexin A2 which are responsible for NF3-induced proangiogenesis of HUVEC in wound healing. This is the first study on the global proteome expression of NF3-treated HUVEC with the identification of the differences at the molecular level, between static and scratch conditions involved in wound healing angiogenesis.

canSAR: updated cancer research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is a public integrative cancer-focused knowledgebase for the support of cancer translational research and drug discovery. Through the integration of biological, pharmacological, chemical, structural biology and protein network data, it provides a single information portal to answer complex multidisciplinary questions including--among many others--what is known about a protein, in which cancers is it expressed or mutated, and what chemical tools and cell line models can be used to experimentally probe its activity? What is known about a drug, its cellular sensitivity profile and what proteins is it known to bind that may explain unusual bioactivity? Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities and new target, cancer cell line, protein family and 3D structure summaries and tools.

Mouse model phenotypes provide information about human drug targets.

MOTIVATION: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. RESULTS: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. AVAILABILITY AND IMPLEMENTATION: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com.

Effects of kiwifruit extracts on colonic gene and protein expression levels in IL-10 gene-deficient mice.

Inflammatory bowel disease (IBD) is a collective term for conditions characterised by chronic inflammation of the gastrointestinal tract involving an inappropriate immune response to commensal micro-organisms in a genetically susceptible host. Previously, aqueous and ethyl acetate extracts of gold kiwifruit (Actinidia chinensis) or green kiwifruit (A. deliciosa) have demonstrated anti-inflammatory activity using in vitro models of IBD. The present study examined whether these kiwifruit extracts (KFE) had immune-modulating effects in vivo against inflammatory processes that are known to be increased in patients with IBD. KFE were used as a dietary intervention in IL-10-gene-deficient (Il10(-/-)) mice (an in vivo model of IBD) and the C57BL/6J background strain in a 3 × 2 factorial design. While all Il10(-/-) mice developed significant colonic inflammation compared with C57BL/6J mice, this was not affected by the inclusion of KFE in the diet. These findings are in direct contrast to our previous study where KFE reduced inflammatory signalling in primary cells isolated from Il10(-/-) and C57BL/6J mice. Whole-genome gene and protein expression level profiling indicated that KFE influenced immune signalling pathways and metabolic processes within the colonic tissue; however, the effects were subtle. In particular, expression levels across gene sets related to adaptive immune pathways were significantly reduced using three of the four KFE in C57BL/6J mice. The present study highlights the importance of investigating food components identified by cell-based assays with appropriate in vivo models before making dietary recommendations, as a food that looks promising in vitro may not be effective in vivo.

Prediction of protein structural class for low-similarity sequences using support vector machine and PSI-BLAST profile.

Knowledge of structural class plays an important role in understanding protein folding patterns. In this study, a simple and powerful computational method, which combines support vector machine with PSI-BLAST profile, is proposed to predict protein structural class for low-similarity sequences. The evolution information encoding in the PSI-BLAST profiles is converted into a series of fixed-length feature vectors by extracting amino acid composition and dipeptide composition from the profiles. The resulting vectors are then fed to a support vector machine classifier for the prediction of protein structural class. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence similarity lower than 40% and 25%, respectively. The overall accuracies attain 70.7% and 72.9% for 1189 and 25PDB datasets, respectively. Comparison of our results with other methods shows that our method is very promising to predict protein structural class particularly for low-similarity datasets and may at least play an important complementary role to existing methods.

Comparative assessment of scoring functions on a diverse test set.

Scoring functions are widely applied to the evaluation of protein-ligand binding in structure-based drug design. We have conducted a comparative assessment of 16 popular scoring functions implemented in main-stream commercial software or released by academic research groups. A set of 195 diverse protein-ligand complexes with high-resolution crystal structures and reliable binding constants were selected through a systematic nonredundant sampling of the PDBbind database and used as the primary test set in our study. All scoring functions were evaluated in three aspects, that is, "docking power", "ranking power", and "scoring power", and all evaluations were independent from the context of molecular docking or virtual screening. As for "docking power", six scoring functions, including GOLD::ASP, DS::PLP1, DrugScore(PDB), GlideScore-SP, DS::LigScore, and GOLD::ChemScore, achieved success rates over 70% when the acceptance cutoff was root-mean-square deviation < 2.0 A. Combining these scoring functions into consensus scoring schemes improved the success rates to 80% or even higher. As for "ranking power" and "scoring power", the top four scoring functions on the primary test set were X-Score, DrugScore(CSD), DS::PLP, and SYBYL::ChemScore. They were able to correctly rank the protein-ligand complexes containing the same type of protein with success rates around 50%. Correlation coefficients between the experimental binding constants and the binding scores computed by these scoring functions ranged from 0.545 to 0.644. Besides the primary test set, each scoring function was also tested on four additional test sets, each consisting of a certain number of protein-ligand complexes containing one particular type of protein. Our study serves as an updated benchmark for evaluating the general performance of today's scoring functions. Our results indicate that no single scoring function consistently outperforms others in all three aspects. Thus, it is important in practice to choose the appropriate scoring functions for different purposes.

Proteomic analysis of Latrodectus tredecimguttatus venom for uncovering potential latrodectism-related proteins.

Black widow spider is one of the most poisonous spiders in the world. Up to now, there have been few systematic analyses of the spider venom components, and the mechanism of action of the venom has not been completely understood. In this work, we employed combinative proteomic strategy to analyze the venom collected from living adult spider Latrodectus tredecimguttatus by electrical stimulation. The experiments demonstrated that the venom is primarily composed of high molecular weight proteins and has high abundance proteins around 100 kDa. The content of peptides and proteins with low molecular weight is low. A total of 75 nonredundant venom proteins with distinct function were unambiguously identified. Besides the known black widow spider venom proteins including latrotoxins, a variety of hydrolases and other proteins with special activity were found in the venom, such as proteinase, phospholipase, phosphatase, nuclease, fucolectin, venom allergen antigen 5-like protein and trypsin inhibitor, and so on. Their possible biological actions and relationship with latrodectism were discussed. The results help to understand the complexity and action mechanism of L. tredecimguttatus venom.

Metaproteomics provides functional insight into activated sludge wastewater treatment.

BACKGROUND: Through identification of highly expressed proteins from a mixed culture activated sludge system this study provides functional evidence of microbial transformations important for enhanced biological phosphorus removal (EBPR). METHODOLOGY/PRINCIPAL FINDINGS: A laboratory-scale sequencing batch reactor was successfully operated for different levels of EBPR, removing around 25, 40 and 55 mg/l P. The microbial communities were dominated by the uncultured polyphosphate-accumulating organism "Candidatus Accumulibacter phosphatis". When EBPR failed, the sludge was dominated by tetrad-forming alpha-Proteobacteria. Representative and reproducible 2D gel protein separations were obtained for all sludge samples. 638 protein spots were matched across gels generated from the phosphate removing sludges. 111 of these were excised and 46 proteins were identified using recently available sludge metagenomic sequences. Many of these closely match proteins from "Candidatus Accumulibacter phosphatis" and could be directly linked to the EBPR process. They included enzymes involved in energy generation, polyhydroxyalkanoate synthesis, glycolysis, gluconeogenesis, glycogen synthesis, glyoxylate/TCA cycle, fatty acid beta oxidation, fatty acid synthesis and phosphate transport. Several proteins involved in cellular stress response were detected. CONCLUSIONS/SIGNIFICANCE: Importantly, this study provides direct evidence linking the metabolic activities of "Accumulibacter" to the chemical transformations observed in EBPR. Finally, the results are discussed in relation to current EBPR metabolic models.

Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components.

The proteins structure can be mainly classified into four classes: all-alpha, all-beta, alpha/beta, and alpha + beta protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification.

Influence of temperature on the ontogenetic expression of neural development-related genes from developing tilapia brain expressed sequence tags.

The developing central neural circuits in teleosts are genetically controlled and temperature-initiated. We compiled a list of transcripts expressed in the developing tilapia (Oreochromis mossambicus) brain using expressed sequence tags derived from the developing brain, and investigated genes with thermosensitive ontogenetic expression. Of 1084 clones, 893 were unique genes, 445 of which were known. Fourteen of the latter were neural development-related, and the ontogenetic expression of nine was temperature-influenced. Discs large homolog 5, myelin expression factor 2, plasticity-related protein-2, tsc2 gene product-related genes, and an inhibitor of differentiation protein 2 (Id2) were differentially temperature-influenced according to their developmental stages. Endothelial differentiation-related factor 1, midkine-related growth factor b, and mitogen-activated protein kinase 14b were specifically influenced by elevated temperature, and beta-catenin-like isoform 1 by lower temperature. Neural development-related genes, particularly those with thermosensitive ontogenetic expression, might be important for developing central neural circuits in teleosts.

Using pseudo-amino acid composition and support vector machine to predict protein structural class.

As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor is developed for predicting protein structural class. It is featured by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), which was introduced to, to some extent, take into account the sequence-order effects to represent protein samples. As a demonstration, the jackknife cross-validation test was performed on a working dataset that contains 204 non-homologous proteins. The predicted results are very encouraging, indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.