iSulf-Cys: Prediction of S-sulfenylation Sites in Proteins with Physicochemical Properties of Amino Acids

Cysteine S-sulfenylation is an important post-translational modification (PTM) in proteins, and provides redox regulation of protein functions. Bioinformatics and structural analyses indicated that S-sulfenylation could impact many biological and functional categories and had distinct structural features. However, major limitations for identifying cysteine S-sulfenylation were expensive and low-throughout. In view of this situation, the establishment of a useful computational method and the development of an efficient predictor are highly desired. In this study, a predictor iSulf-Cys which incorporated 14 kinds of physicochemical properties of amino acids was proposed. With the 10-fold cross-validation, the value of area under the curve (AUC) was 0.7155 ± 0.0085, MCC 0.3122 ± 0.0144 on the training dataset for 20 times. iSulf-Cys also showed satisfying performance in the independent testing dataset with AUC 0.7343 and MCC 0.3315. Features which were constructed from physicochemical properties and position were carefully analyzed. Meanwhile, a user-friendly web-server for iSulf-Cys is accessible at http://app.aporc.org/iSulf-Cys/.     

Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization

One of the fundamental goals in proteomics and cell biology is to identify the functions of proteins in various cellular organelles and pathways. Information of subcellular locations of proteins can provide useful insights for revealing their functions and understanding how they interact with each other in cellular network systems. Most of the existing methods in predicting plant protein subcellular localization can only cover three or four location sites, and none of them can be used to deal with multiplex plant proteins that can simultaneously exist at two, or move between, two or more different location sites. Actually, such multiplex proteins might have special biological functions worthy of particular notice. The present study was devoted to improve the existing plant protein subcellular location predictors from the aforementioned two aspects. A new predictor called “Plant-mPLoc” is developed by integrating the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify plant proteins among the following 12 location sites: (1) cell membrane, (2) cell wall, (3) chloroplast, (4) cytoplasm, (5) endoplasmic reticulum, (6) extracellular, (7) Golgi apparatus, (8) mitochondrion, (9) nucleus, (10) peroxisome, (11) plastid, and (12) vacuole. Compared with the existing methods for predicting plant protein subcellular localization, the new predictor is much more powerful and flexible. Particularly, it also has the capacity to deal with multiple-location proteins, which is beyond the reach of any existing predictors specialized for identifying plant protein subcellular localization. As a user-friendly web-server, Plant-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results. It is anticipated that the Plant-mPLoc predictor as presented in this paper will become a very useful tool in plant science as well as all the relevant areas.     

The Overlap of Small Molecule and Protein Binding Sites within Families of Protein Structures

Protein–protein interactions are challenging targets for modulation by small molecules. Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p<0.01) overlap between ligand and protein binding positions. These “bi-functional positions”, which bind both ligands and proteins, are particularly enriched in tyrosine and tryptophan residues, similar to “energetic hotspots” described previously, and are significantly less conserved than mono-functional and solvent exposed positions. Homology transfer identifies ligands whose binding sites overlap at least 20% of the protein interface for 35% of domain–domain and 45% of domain–peptide mediated interactions. The analysis recovered known small-molecule modulators of protein interactions as well as predicted new interaction targets based on the sequence similarity of ligand binding sites. We illustrate the predictive utility of the method by suggesting structural mechanisms for the effects of sanglifehrin A on HIV virion production, bepridil on the cellular entry of anthrax edema factor, and fusicoccin on vertebrate developmental pathways. The results, available at http://pibase.janelia.org, represent a comprehensive collection of structurally characterized modulators of protein interactions, and suggest that homologous structures are a useful resource for the rational design of interaction modulators.     

GeneComps and ChemComps: a new CTD metric to identify genes and chemicals with shared toxicogenomic profiles

The Comparative Toxicogenomics Database is a public resource that promotes understanding about the effects of environmental chemicals on human health. Currently, CTD describes over 184,000 molecular interactions for more than 5,100 chemicals and 16,300 genes/proteins. We have leveraged this dataset of chemical-gene relationships to compute similarity indices following the statistical method of the Jaccard index. These scores are used to produce lists of comparable genes (“GeneComps”) or chemicals (“ChemComps”) based on shared toxicogenomic profiles. GeneComps and ChemComps are now provided for every curated gene and chemical in CTD. ChemComps are particularly significant because they provide a way to group chemicals based upon their biological effects, instead of their physical or structural properties. These metrics provide a novel way to view and classify genes and chemicals and will help advance testable hypotheses about environmental chemical-genedisease networks.

Availability

CTD is freely available at http://ctd.mdibl.org/     

Genome sequence of the Lebeckia ambigua-nodulating “Burkholderia sprentiae” strain WSM5005T

“Burkholderia sprentiae” strain WSM5005^T is an aerobic, motile, Gram-negative, non-spore-forming rod that was isolated in Australia from an effective N₂-fixing root nodule of Lebeckia ambigua collected in Klawer, Western Cape of South Africa, in October 2007. Here we describe the features of “Burkholderia sprentiae” strain WSM5005^T, together with the genome sequence and its annotation. The 7,761,063 bp high-quality-draft genome is arranged in 8 scaffolds of 236 contigs, contains 7,147 protein-coding genes and 76 RNA-only encoding genes, and is one of 20 rhizobial genomes sequenced as part of the DOE Joint Genome Institute 2010 Community Sequencing Program.     

The 2015 Bioinformatics Open Source Conference (BOSC 2015)

The Bioinformatics Open Source Conference (BOSC) is organized by the Open Bioinformatics Foundation (OBF), a nonprofit group dedicated to promoting the practice and philosophy of open source software development and open science within the biological research community. Since its inception in 2000, BOSC has provided bioinformatics developers with a forum for communicating the results of their latest efforts to the wider research community. BOSC offers a focused environment for developers and users to interact and share ideas about standards; software development practices; practical techniques for solving bioinformatics problems; and approaches that promote open science and sharing of data, results, and software. BOSC is run as a two-day special interest group (SIG) before the annual Intelligent Systems in Molecular Biology (ISMB) conference. BOSC 2015 took place in Dublin, Ireland, and was attended by over 125 people, about half of whom were first-time attendees. Session topics included “Data Science;” “Standards and Interoperability;” “Open Science and Reproducibility;” “Translational Bioinformatics;” “Visualization;” and “Bioinformatics Open Source Project Updates”. In addition to two keynote talks and dozens of shorter talks chosen from submitted abstracts, BOSC 2015 included a panel, titled “Open Source, Open Door: Increasing Diversity in the Bioinformatics Open Source Community,” that provided an opportunity for open discussion about ways to increase the diversity of participants in BOSC in particular, and in open source bioinformatics in general. The complete program of BOSC 2015 is available online at http://www.open-bio.org/wiki/BOSC_2015_Schedule.Open in a separate window     

ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions

Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classification model is in pressing need. In this study, we proposed an efficient approach, called ellipsoidFN (ellipsoid Feature Net), to model the disease complexity by ellipsoids and seek a set of heterogeneous biomarkers. Our approach achieves a non-linear classification scheme for the mixed samples by the ellipsoid concept, and at the same time uses a linear programming framework to efficiently select biomarkers from high-dimensional space. ellipsoidFN reduces the redundancy and improves the complementariness between the identified biomarkers, thus significantly enhancing the distinctiveness between cancers and normal samples, and even between cancer types. Numerical evaluation on real prostate cancer, breast cancer and leukemia gene expression datasets suggested that ellipsoidFN outperforms the state-of-the-art biomarker identification methods, and it can serve as a useful tool for cancer biomarker identification in the future. The Matlab code of ellipsoidFN is freely available from http://doc.aporc.org/wiki/EllipsoidFN.     

The DNA Damage Response and Checkpoint Adaptation in Saccharomyces cerevisiae: Distinct Roles for the Replication Protein A2 (Rfa2) N-Terminus

In response to DNA damage, two general but fundamental processes occur in the cell: (1) a DNA lesion is recognized and repaired, and (2) concomitantly, the cell halts the cell cycle to provide a window of opportunity for repair to occur. An essential factor for a proper DNA-damage response is the heterotrimeric protein complex Replication Protein A (RPA). Of particular interest is hyperphosphorylation of the 32-kDa subunit, called RPA2, on its serine/threonine-rich amino (N) terminus following DNA damage in human cells. The unstructured N-terminus is often referred to as the phosphorylation domain and is conserved among eukaryotic RPA2 subunits, including Rfa2 in Saccharomyces cerevisiae. An aspartic acid/alanine-scanning and genetic interaction approach was utilized to delineate the importance of this domain in budding yeast. It was determined that the Rfa2 N-terminus is important for a proper DNA-damage response in yeast, although its phosphorylation is not required. Subregions of the Rfa2 N-terminus important for the DNA-damage response were also identified. Finally, an Rfa2 N-terminal hyperphosphorylation-mimetic mutant behaves similarly to another Rfa1 mutant (rfa1-t11) with respect to genetic interactions, DNA-damage sensitivity, and checkpoint adaptation. Our data indicate that post-translational modification of the Rfa2 N-terminus is not required for cells to deal with “repairable” DNA damage; however, post-translational modification of this domain might influence whether cells proceed into M-phase in the continued presence of unrepaired DNA lesions as a “last-resort” mechanism for cell survival.     

The United States Culture Collection Network (USCCN): Enhancing Microbial Genomics Research through Living Microbe Culture Collections

The mission of the United States Culture Collection Network (USCCN; http://usccn.org) is “to facilitate the safe and responsible utilization of microbial resources for research, education, industry, medicine, and agriculture for the betterment of human kind.” Microbial culture collections are a key component of life science research, biotechnology, and emerging global biobased economies. Representatives and users of several microbial culture collections from the United States and Europe gathered at the University of California, Davis, to discuss how collections of microorganisms can better serve users and stakeholders and to showcase existing resources available in public culture collections.     

Molecular Pathology of Rare Bleeding Disorders (RBDs) in India: A Systematic Review

Background

Though rare in occurrence, patients with rare bleeding disorders (RBDs) are highly heterogeneous and may manifest with severe bleeding diathesis. Due to the high rate of consanguinity in many caste groups, these autosomal recessive bleeding disorders which are of rare occurrence in populations across the world, may not be as rare in India.

Objectives

To comprehensively analyze the frequency and nature of mutations in Indian patients with RBDs.

Methods

Pubmed search was used (www.pubmed.com) to explore the published literature from India on RBDs using the key words “rare bleeding disorders”, “mutations”, “India”, “fibrinogen”, “afibrinogenemia”, “factor II deficiency”, “prothrombin” “factor VII deficiency”, “factor V deficiency”, “factor X deficiency”, “factor XI deficiency”, “combined factor V and VIII deficiency”, “factor XIII deficiency”, “Bernard Soulier syndrome” and “Glanzmanns thrombasthenia” in different combinations. A total of 60 relevant articles could be retrieved. The distribution of mutations from India was compared with that of the world literature by referring to the Human Gene Mutation Database (HGMD) (www.hgmd.org).

Results

Taken together, 181 mutations in 270 patients with different RBDs have been reported from India. Though the types of mutations reported from India and their percentage distribution with respect to the world data are largely similar, yet much higher percentage of small deletions, duplication mutations, insertions, indels were observed in this analysis. Besides the identification of novel mutations and polymorphisms, several common mutations have also been reported, which will allow to develop a strategy for mutation screening in Indian patients with RBDs.

Conclusion

There is a need for a consortium of Institutions working on the molecular pathology of RBDs in India. This will facilitate a quicker and cheaper diagnosis of RBDs besides its utility in first trimester prenatal diagnosis of the affected families.     

Prion Aggregates Are Recruited to the Insoluble Protein Deposit (IPOD) via Myosin 2-Based Vesicular Transport

Aggregation of amyloidogenic proteins is associated with several neurodegenerative diseases. Sequestration of misfolded and aggregated proteins into specialized deposition sites may reduce their potentially detrimental properties. Yeast exhibits a distinct deposition site for amyloid aggregates termed “Insoluble PrOtein Deposit (IPOD)”, but nothing is known about the mechanism of substrate recruitment to this site. The IPOD is located directly adjacent to the Phagophore Assembly Site (PAS) where the cell initiates autophagy and the Cytoplasm-to-Vacuole Targeting (CVT) pathway destined for delivery of precursor peptidases to the vacuole. Recruitment of CVT substrates to the PAS was proposed to occur via vesicular transport on Atg9 vesicles and requires an intact actin cytoskeleton and “SNAP (Soluble NSF Attachment Protein) Receptor Proteins (SNARE)” protein function. It is, however, unknown how this vesicular transport machinery is linked to the actin cytoskeleton. We demonstrate that recruitment of model amyloid PrD-GFP and the CVT substrate precursor-aminopeptidase 1 (preApe1) to the IPOD or PAS, respectively, is disturbed after genetic impairment of Myo2-based actin cable transport and SNARE protein function. Rather than accumulating at the respective deposition sites, both substrates reversibly accumulated often together in the same punctate structures. Components of the CVT vesicular transport machinery including Atg8 and Atg9 as well as Myo2 partially co-localized with the joint accumulations. Thus we propose a model where vesicles, loaded with preApe1 or PrD-GFP, are recruited to tropomyosin coated actin cables via the Myo2 motor protein for delivery to the PAS and IPOD, respectively. We discuss that deposition at the IPOD is not an integrated mandatory part of the degradation pathway for amyloid aggregates, but more likely stores excess aggregates until downstream degradation pathways have the capacity to turn them over after liberation by the Hsp104 disaggregation machinery.     

Predicting Secretory Proteins of Malaria Parasite by Incorporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model

The malaria disease has become a cause of poverty and a major hindrance to economic development. The culprit of the disease is the parasite, which secretes an array of proteins within the host erythrocyte to facilitate its own survival. Accordingly, the secretory proteins of malaria parasite have become a logical target for drug design against malaria. Unfortunately, with the increasing resistance to the drugs thus developed, the situation has become more complicated. To cope with the drug resistance problem, one strategy is to timely identify the secreted proteins by malaria parasite, which can serve as potential drug targets. However, it is both expensive and time-consuming to identify the secretory proteins of malaria parasite by experiments alone. To expedite the process for developing effective drugs against malaria, a computational predictor called “iSMP-Grey” was developed that can be used to identify the secretory proteins of malaria parasite based on the protein sequence information alone. During the prediction process a protein sample was formulated with a 60D (dimensional) feature vector formed by incorporating the sequence evolution information into the general form of PseAAC (pseudo amino acid composition) via a grey system model, which is particularly useful for solving complicated problems that are lack of sufficient information or need to process uncertain information. It was observed by the jackknife test that iSMP-Grey achieved an overall success rate of 94.8%, remarkably higher than those by the existing predictors in this area. As a user-friendly web-server, iSMP-Grey is freely accessible to the public at http://www.jci-bioinfo.cn/iSMP-Grey. Moreover, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematical equations involved in this paper.     

Complete genome sequence of the Radiation-Resistant bacterium Rubrobacter radiotolerans RSPS-4

Rubrobacter radiotolerans strain RSPS-4 is a slightly thermophilic member of the phylum “Actinobacteria” isolated from a hot spring in São Pedro do Sul, Portugal. This aerobic and halotolerant bacterium is also extremely resistant to gamma and UV radiation, which are the main reasons for the interest in sequencing its genome. Here, we present the complete genome sequence of strain RSPS-4 as well as its assembly and annotation. We also compare the gene sequence of this organism with that of the type strain of the species R. radiotolerans isolated from a hot spring in Japan. The genome of strain RSPS-4 comprises one circular chromosome of 2,875,491 bp with a G+C content of 66.91%, and 3 circular plasmids of 190,889 bp, 149,806 bp and 51,047 bp, harboring 3,214 predicted protein coding genes, 46 tRNA genes and a single rRNA operon.     

Multi-state Modeling of Biomolecules

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm [9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.

This is a “Topic Page” article for PLOS Computational Biology.

     

Approximate Bayesian Computation

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

This is a “Topic Page” article for PLOS Computational Biology.

     

Fifty Ways To Inhibit Motility via Cyclic Di-GMP: the Emerging Pseudomonas aeruginosa Swarming Story

There are numerous ways by which cyclic dimeric GMP (c-di-GMP) inhibits motility. Kuchma et al. (S. L. Kuchma, N. J. Delalez, L. M. Filkins, E. A. Snavely, J. P. Armitage, and G. A. O''Toole, J. Bacteriol. 197:420–430, 2015, http://dx.doi.org/10.1128/JB.02130-14) offer a new, previously unseen way of swarming motility inhibition in Pseudomonas aeruginosa PA14. This bacterium possesses a single flagellum with one rotor and two sets of stators, only one of which can provide torque for swarming. The researchers discovered that elevated levels of c-di-GMP inhibit swarming by skewing stator selection in favor of the nonfunctional, “bad” stators.     

Non-contiguous finished genome sequence of plant-growth promoting Serratia proteamaculans S4

Serratia proteamaculans S4 (previously Serratia sp. S4), isolated from the rhizosphere of wild Equisetum sp., has the ability to stimulate plant growth and to suppress the growth of several soil-borne fungal pathogens of economically important crops. Here we present the non-contiguous, finished genome sequence of S. proteamaculans S4, which consists of a 5,324,944 bp circular chromosome and a 129,797 bp circular plasmid. The chromosome contains 5,008 predicted genes while the plasmid comprises 134 predicted genes. In total, 4,993 genes are assigned as protein-coding genes. The genome consists of 22 rRNA genes, 82 tRNA genes and 58 pseudogenes. This genome is a part of the project “Genomics of four rapeseed plant growth-promoting bacteria with antagonistic effect on plant pathogens” awarded through the 2010 DOE-JGI’s Community Sequencing Program.