Automatic structure classification of small proteins using random forest

<p>Abstract</p> <p><b>Background</b></p> <p>Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.</p> <p><b>Result</b>s</p> <p>Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP <it>Class, Fold, Super-family </it>or <it>Family </it>levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.</p> <p>Conclusions</p> <p>The utility of random forest in classifying domains from the place-holder classes of SCOP to the true <it>Class, Fold, Super-family </it>or <it>Family </it>levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.</p

Biosynthesis and characterization of a novel, biocompatible medium chain length polyhydroxyalkanoate by Pseudomonas mendocina CH50 using coconut oil as the carbon source

This study validated the utilization of triacylglycerides (TAGs) by Pseudomonas mendocina CH50, a wild type strain, resulting in the production of novel mcl-PHAs with unique physical properties. A PHA yield of 58% dcw was obtained using 20g/L of coconut oil. Chemical and structural characterisation confirmed that the mcl-PHA produced was a terpolymer comprising of three different repeating monomer units, 3-hydroxyoctanoate, 3-hydroxydecanoate and 3-hydroxydodecanoate or P(3HO-3HD-3HDD). Bearing in mind the potential of P(3HO-3HD-3HDD) in biomedical research, especially in neural tissue engineering, in vitro biocompatibility studies were carried out using NG108-15 (neuronal) cells. Cell viability data confirmed that P(3HO-3HD-3HDD) supported the attachment and proliferation of NG108-15 and was therefore, confirmed to be biocompatible in nature and suitable for neural regeneration

The role of flavor and fragrance chemicals in TRPA1 (transient receptor potential cation channel, member A1) activity associated with allergies

TRPA1 has been proposed to be associated with diverse sensory allergic reactions, including thermal (cold) nociception, hearing and allergic inflammatory conditions. Some naturally occurring compounds are known to activate TRPA1 by forming a Michael addition product with a cysteine residue of TRPA1 through covalent protein modification and, in consequence, to cause allergic reactions. The anti-allergic property of TRPA1 agonists may be due to the activation and subsequent desensitization of TRPA1 expressed in sensory neurons. In this review, naturally occurring TRPA1 antagonists, such as camphor, 1,8-cineole, menthol, borneol, fenchyl alcohol and 2-methylisoborneol, and TRPA1 agonists, including thymol, carvacrol, 1’S-1’- acetoxychavicol acetate, cinnamaldehyde, α-n-hexyl cinnamic aldehyde and thymoquinone as well as isothiocyanates and sulfides are discussed