Estimating effort is a very important task in any organization. Significant over or under-estimates can be very expensive for software project companies. The use of computing intelligence methods has been recently proposed for software development effort estimation. In this study, we present new models to estimate the effort required for the development of software projects. These new models were calculated using Linear Genetic Programming (PGL). The results show that the proposed models get more precise and more effective estimation for Mean Magnitude Relative error (MMRE) and Mean Magnitude of Relative Error relative to the Estimate (MMER) than using the constructive cost model (COCOMO). We performed the study based on three stages according to the type of project. The models were designed and validated by simulation with the public repository dataset COCOMO81 and NASA93. Performance of the proposed models EE_PGLa, EE_PGLb and EE_PGLc are more accurate than COCOMO.

CONTEXT: Epigenome reader domains are rapidly emerging as a new class of drug targets for a wide array of human diseases. To facilitate study of structure-activity relationship and small-molecule ligand design for these domains, we have created ChEpiMod. ChEpiMod is a free knowledgebase of chemical modulators with documented modulatory activity for epigenome reader domains. METHODS: ChEpiMod organizes information about chemical modulators and their associated binding-affinity data, as well as available structures of epigenome readers from the Protein Data Bank. The data are gathered from the literature and patents. Entries are supplemented by annotation. The current version of ChEpiMod covers six epigenome reader domain families (Bromodomain, PHD finger, Chromodomain, MBT, PWWP and Tudor). The database can be used to browse existing chemical modulators and bioactivity data, as well as, all available structures of readers and their molecular interactions. The database is updated weekly. AVAILABILITY: ChEpiMod is freely available at http://chepimod.org CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Abstract Motivation: Computational characterization of ligand-binding sites in proteins provides preliminary information for functional annotation, protein design and ligand optimization. SiteComp implements binding site analysis for comparison of binding sites, evaluation of residue contribution to binding sites and identification of sub-sites with distinct molecular interaction properties. Availability and implementation: The SiteComp server and tutorials are freely available at http://sitecomp.sanchezlab.org Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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UNLABELLED: SiteHound uses Molecular Interaction Fields (MIFs) produced by EasyMIFs to identify protein structure regions that show a high propensity for interaction with ligands. The type of binding site identified depends on the probe atom used in the MIF calculation. The input to EasyMIFs is a PDB file of a protein structure; the output MIF serves as input to SiteHound, which in turn produces a list of putative binding sites. Extensive testing of SiteHound for the detection of binding sites for drug-like molecules and phosphorylated ligands has been carried out. AVAILABILITY: EasyMIFs and SiteHound executables for Linux, Mac OS X, and MS Windows operating systems are freely available for download from http://sitehound.sanchezlab.org/download.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.