A Comparison of Computational Methods for Identifying Virulence Factors

Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan, Hubei, China.
PLoS ONE (Impact Factor: 3.23). 08/2012; 7(8):e42517. DOI: 10.1371/journal.pone.0042517
Source: PubMed


Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them.

Download full-text


Available from: Kuo-Chen Chou
  • Source
    • "Using the interaction network, they identified virulence factors based on number of neighbors and strength of interactions and compared this to a feature selection method and BLAST approaches. Their results were benchmarked against a database of validated virulence factors and the network-based method was found to out-perform the other two methods (Zheng et al., 2012). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Latent tuberculosis is a clinical syndrome that occurs after an individual has been exposed to the Mycobacterium tuberculosis (Mtb) Bacillus, the infection has been established and an immune response has been generated to control the pathogen and force it into a quiescent state. Mtb can exit this quiescent state where it is unresponsive to treatment and elusive to the immune response, and enter a rapid replicating state, hence causing infection reactivation. It remains a gray area to understand how the pathogen causes a persistent infection and it is unclear whether the organism will be in a slow replicating state or a dormant non-replicating state. The ability of the pathogen to adapt to changing host immune response mechanisms, in which it is exposed to hypoxia, low pH, nitric oxide (NO), nutrient starvation, and several other anti-microbial effectors, is associated with a high metabolic plasticity that enables it to metabolize under these different conditions. Adaptive gene regulatory mechanisms are thought to coordinate how the pathogen changes their metabolic pathways through mechanisms that sense changes in oxygen tension and other stress factors, hence stimulating the pathogen to make necessary adjustments to ensure survival. Here, we review studies that give insights into latency/dormancy regulatory mechanisms that enable infection persistence and pathogen adaptation to different stress conditions. We highlight what mathematical and computational models can do and what they should do to enhance our current understanding of TB latency.
    Full-text · Article · Aug 2013 · Frontiers in Bioengineering and Biotechnology
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Despite the tremendous progress in the field of drug designing, discovering a new drug molecule is still a challenging task. Drug discovery and development is a costly, time consuming and complex process that requires millions of dollars and 10-15 years to bring new drug molecules in the market. This huge investment and long-term process are attributed to high failure rate, complexity of the problem and strict regulatory rules, in addition to other factors. Given the availability of 'big' data with ever improving computing power, it is now possible to model systems which is expected to provide time and cost effectiveness to drug discovery process. Computer Aided Drug Designing (CADD) has emerged as a fast alternative method to bring down the cost involved in discovering a new drug. In past, numerous computer programs have been developed across the globe to assist the researchers working in the field of drug discovery. Broadly, these programs can be classified in three categories, freeware, shareware and commercial software. In this review, we have described freeware or open-source software that are commonly used for designing therapeutic molecules. Major emphasis will be on software and web services in the field of chemo- or pharmaco-informatics that includes in silico tools used for computing molecular descriptors, inhibitors designing against drug targets, building QSAR models, and ADMET properties.
    Full-text · Article · May 2013 · Current Topics in Medicinal Chemistry
  • [Show abstract] [Hide abstract]
    ABSTRACT: In drug design and enzyme engineering, the information of interactions between receptors and ligands is crucially important. In many cases, the protein structures and drug-target complex structures are determined by a delicate balance of several weak molecular interaction types. Among these interaction forces several unconventional interactions play important roles, however, less familiar for researchers. The cation-π interaction is a unique noncovalent interaction only acting between aromatic amino acids and organic cations (protonated amino acids) and inorganic cations (proton and metallic). This article reports new study results in the interaction strength, the behaviors and the structural characters of cation-π interactions between aromatic amino acids (Phe, Tyr, and Trp) and organic and inorganic cations (Lys+, Arg+, H+, H3O+, Li+, Na+, K+, Ca2+, and Zn2+) in gas phase and in solutions (water, acetonitrile, and cyclohexane). Systematical research revealed that the cation-π interactions are point-to-plane (aromatic group) interactions, distance and orientation-dependent, and the interaction energies change in a broad range. In gas phase the cation-π interaction energies between aromatic amino acids (Phe, Tyr, and Trp) and metallic cations (Li+, Na+, K+, Ca2+, and Zn2+) are in the range -12 to -160 kcal/mol, and the interaction energies of protonated amino acids (Arg+ and Lys+) are in the range from -9 to -18 kcal/mol. In solutions the cation-π energies decrease with the dielectric constant ε of solvents. However, in aqueous solution the cation-π energies of H3O+ and protonated amino acids are less affected by solvation effects. The applications of unconventional interaction forces in drug design and in protein engineering are introduced.
    No preview · Article · May 2013 · Current topics in medicinal chemistry
Show more