Evaluating hierarchical machine learning approaches to classify biological databases.

The rate of biological data generation has increased dramatically in recent years, which has driven the importance of databases as a resource to guide innovation and the generation of biological insights. Given the complexity and scale of these databases, automatic data classification is often required. Biological data sets are often hierarchical in nature, with varying degrees of complexity, imposing different challenges to train, test and validate accurate and generalizable classification models. While some approaches to classify hierarchical data have been proposed, no guidelines regarding their utility, applicability and limitations have been explored or implemented. These include 'Local' approaches considering the hierarchy, building models per level or node, and 'Global' hierarchical classification, using a flat classification approach. To fill this gap, here we have systematically contrasted the performance of 'Local per Level' and 'Local per Node' approaches with a 'Global' approach applied to two different hierarchical datasets: BioLip and CATH. The results show how different components of hierarchical data sets, such as variation coefficient and prediction by depth, can guide the choice of appropriate classification schemes. Finally, we provide guidelines to support this process when embarking on a hierarchical classification task, which will help optimize computational resources and predictive performance.

ThermoMutDB: a thermodynamic database for missense mutations.

Proteins are intricate, dynamic structures, and small changes in their amino acid sequences can lead to large effects on their folding, stability and dynamics. To facilitate the further development and evaluation of methods to predict these changes, we have developed ThermoMutDB, a manually curated database containing >14,669 experimental data of thermodynamic parameters for wild type and mutant proteins. This represents an increase of 83% in unique mutations over previous databases and includes thermodynamic information on 204 new proteins. During manual curation we have also corrected annotation errors in previously curated entries. Associated with each entry, we have included information on the unfolding Gibbs free energy and melting temperature change, and have associated entries with available experimental structural information. ThermoMutDB supports users to contribute to new data points and programmatic access to the database via a RESTful API. ThermoMutDB is freely available at: http://biosig.unimelb.edu.au/thermomutdb.

EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening.

SUMMARY: EasyVS is a web-based platform built to simplify molecule library selection and virtual screening. With an intuitive interface, the tool allows users to go from selecting a protein target with a known structure and tailoring a purchasable molecule library to performing and visualizing docking in a few clicks. Our system also allows users to filter screening libraries based on molecule properties, cluster molecules by similarity and personalize docking parameters. AVAILABILITY AND IMPLEMENTATION: EasyVS is freely available as an easy-to-use web interface at http://biosig.unimelb.edu.au/easyvs. CONTACT: douglas.pires@unimelb.edu.au or david.ascher@unimelb.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification and expression analysis of ethylene biosynthesis and signaling genes provides insights into the early and late coffee cultivars ripening pathway.

The plant hormone ethylene is involved in the regulation of a multitude of plant processes, ranging from seed germination to organ senescence. Ethylene induces fruit ripening in climacteric fruits, such as coffee, being directly involved in fruit ripening time and synchronization. Coffee early cultivars usually show a more uniform ripening process although little is known about the genetic factors that promote the earliness of ripening. Thus, this work aimed to characterize the putative members of the coffee (Coffea arabica) ethylene biosynthesis and signaling pathways, as well as to analyze the expression patterns of these members during fruit ripening of early (Catucaí 785-15) and late (Acauã) coffee cultivars. Reverse Transcription-qPCR analysis of the four biosynthesis genes (CaACS1-like; CaACO1-like; CaACO4-like e CaACO5-like) analyzed in this study showed that CaACO1-like and CaACO4-like displayed an expression pattern typically observed in climacteric fruits, being up-regulated during ripening. CaACS1-like gene expression was also up-regulated during fruit ripening of both cultivars, although in a much lesser extent when compared to the changes in CaACO1-like and CaACO4-like gene expression. CaACO5-like was only induced in raisin fruit and may be related to senescence processes. On the other hand, members of the ethylene signaling pathway (CaETR1-like, CaETR4-like, CaCTR2-like, CaEIN2-like, CaEIN3-like, CaERF1) showed slightly higher expression levels during the initial stages of development (green and yellow-green fruits), except for the ethylene receptors CaETR1-like and CaETR4-like, which were constitutively expressed and induced in cherry fruits, respectively. The higher ethylene production levels in Catucaí 785-15 fruits, indicated by the expression analysis of CaACO1-like and CaACO4-like, suggest that it promotes an enhanced CaETR4-like degradation, leading to an increase in ethylene sensitivity and consequently to an earliness in the ripening process of this cultivar. Ethylene production in Acauã fruits may not be sufficient to inactivate the CaETR4-like levels and thus ripening changes occur in a slower pace. Thus, the expression analysis of the ethylene biosynthesis and signaling genes suggests that ethylene is directly involved in the determination of the ripening time of coffee fruits, and CaACO1-like, CaACO4-like and CaETR4-like may display essential roles during coffee fruit ripening.

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis.

Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.

A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods.

High-throughput computational techniques have become invaluable tools to help increase the overall success, process efficiency, and associated costs of drug development. By designing ligands tailored to specific protein structures in a disease of interest, an understanding of molecular interactions and ways to optimize them can be achieved prior to chemical synthesis. This understanding can help direct crucial chemical and biological experiments by maximizing available resources on higher quality leads. Moreover, predicting molecular binding affinity within specific biological contexts, as well as ligand pharmacokinetics and toxicities, can aid in filtering out redundant leads early on within the process. We describe a set of computational tools which can aid in drug discovery at different stages, from hit identification (EasyVS) to lead optimization and candidate selection (CSM-lig, mCSM-lig, Arpeggio, pkCSM). Incorporating these tools along the drug development process can help ensure that candidate leads are chemically and biologically feasible to become successful and tractable drugs.