Summary: We present AbDesigner3D, a new tool for identification of optimal immunizing peptides for antibody production using a peptide-based strategy. AbDesigner3D integrates 3D structural data from the Protein Data Bank (PDB) with UniProt data, which includes basic sequence data, post-translational modification sites, SNP occurrences and more. Other features, such as uniqueness and conservation scores, are calculated based on sequences from UniProt. The 3D visualization capabilities allow an intuitive interface, while an abundance of quantitative output simplifies the process of comparing immunogen peptides. Important quantitative features added in this tool include calculation and display of accessible surface area (ASA) and protein-protein interacting residues (PPIR). The specialized data visualization features of AbDesigner3D will greatly assist users to optimize their choice of immunizing peptides. Availability and implementation: AbDesigner3D is freely available at http://sysbio.chula.ac.th/AbDesigner3D or https://hpcwebapps.cit.nih.gov/AbDesigner3D/. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: One very important functional domain of proteins is the protein-protein interacting region (PPIR), which forms the binding interface between interacting polypeptide chains. Post-translational modifications (PTMs) that occur in the PPIR can either interfere with or facilitate the interaction between proteins. The ability to predict whether sites of protein modifications are inside or outside of PPIRs would be useful in further elucidating the regulatory mechanisms by which modifications of specific proteins regulate their cellular functions. RESULTS: Using two of the comprehensive databases for protein-protein interaction and protein modification site data (PDB and PhosphoSitePlus, respectively), we created new databases that map PTMs to their locations inside or outside of PPIRs. The mapped PTMs represented only 5 % of all known PTMs. Thus, in order to predict localization within or outside of PPIRs for the vast majority of PTMs, a machine learning strategy was used to generate predictive models from these mapped databases. For the three mapped PTM databases which had sufficient numbers of modification sites for generating models (acetylation, phosphorylation, and ubiquitylation), the resulting models yielded high overall predictive performance as judged by a combined performance score (CPS). Among the multiple properties of amino acids that were used in the classification tasks, hydrophobicity was found to contribute substantially to the performance of the final predictive models. Compared to the other classifiers we also evaluated, the SVM provided the best performance overall. CONCLUSIONS: These models are the first to predict whether PTMs are located inside or outside of PPIRs, as demonstrated by their high predictive performance. The models and data presented here should be useful in prioritizing both known and newly identified PTMs for further studies to determine the functional relationship between specific PTMs and protein-protein interactions. The implemented R package is available online ( http://sysbio.chula.ac.th/PtmPPIR ).