Fully automated antibody structure prediction using BIOVIA tools: Validation study

• Published in: PloS one 2017-05, Vol.12 (5), p.e0177923-e0177923
• Main Authors: Kemmish, Helen, Fasnacht, Marc, Yan, Lisa
• Format: Article
• Language: English
• Subjects: Agglomeration; Amino acid sequence; Analysis; Antibodies; Antibodies - chemistry; Antigen-antibody interactions; Antigenicity; Arthritis; Automation; Bioinformatics; Biology; Biology and Life Sciences; Cancer; Care and treatment; Complementarity; Computational Biology - methods; Computer applications; Computer graphics; Computer programs; Computer simulation; Construction; Construction materials; Construction methods; Crystallography; Data processing; Databases, Protein; Design; Design engineering; Elbow; Engineering; Environment models; Fab; Grain; Graphical user interface; Homology; Immunoglobulins; Immunology; Inflammation; Intervention; Light; Markov chains; Medical research; Methods; Models, Molecular; Molecular biology; Molecular structure; Nucleic acids; Orientation; Permutations; Physical Sciences; Practice guidelines (Medicine); Predictions; Protein engineering; Protein structure; Proteins; Protocol (computers); Research and Analysis Methods; Rheumatoid arthritis; Solubility; Sweet taste; Technology application; Three dimensional models; Viscosity
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0177923

We describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data set (157 unique antibody Fv domains versus 11 in the previous study). In the current study, we explore the effect of varying several parameter settings in order to better understand their influence on the resulting model quality. Specifically, we investigated the dependence on different methods of framework model construction, antibody numbering schemes (Chothia, IMGT, Honegger and Kabat), the influence of compatibility of loop templates using canonical type filtering, wider exploration of model solution space, and others. Our results show that our recently introduced Top5 framework modeling method results in a small but significant improvement in model quality whereas the effect of other parameters is not significant. Our analysis provides improved guidelines of best practices for using our protocol to build antibody structures. We also identify some limitations of the current computational model which will enhance proper evaluation of model quality by users and suggests possible future enhancements.