Abstract
The protein tertiary structure prediction is not a simple task but the assessment of this information becomes essential for functional annotation. Computer protein structure prediction is an important tool in structural biology helping to construct large quantity of interaction model of protein complexes or used to obtain three-dimensional structure and functional information of non-crystalizing proteins. However, the complexity of modeling softwares and a hard-to-use user interface makes it difficult the use for non-expert scientists. On this context, semi-automatic client-server software for protein homology modeling was developed, the AutoModel. The main goal of AutoModel is to provide a graphical, intuitive, interactive and practical interface to perform modeling experiments in a distributed architecture, with the possibility of importing water and ligand structural information from pdb templates, intended for easy modeling of different protein-ligand complexes. Our system facilitates the new users interaction with the modeling pipeline as it follows: 1. Searching structural templates; 2. Sequences Alignment; 3. Protein modelling; 4. Model refinement and 5. Loops refinement. In AutoModel 0.5 development we evaluated the use of different alignment tools in order to increase the quality of generated models, reduce the computational cost, and evaluate the impact of these changes in modeling quality and the experimentation speed. Our data suggest that using Muscle as alignment tool in the pipeline increases the quality of obtained models if compared to the other tested releases with significantly lower computational costs, which is always interesting in a distributed system running on a central server as AutoModel. “AutoModel Server” and “AutoModel Client” packages are available for Linux users through pypi package index. AutoModel is also freely available for academic community “as is” in http://biocomp.uenf.br.
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Acknowledgements
This research was supported by E-26/110.216/2011 FAPERJ grant for J.H.F. and FAPERJ master degree grant for J.de A.F.
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de A. Filho, J.L., del Real Tamariz, A., Fernandez, J.H. (2018). AutoModel: A Client-Server Tool for Intuitive and Interactive Homology Modeling of Protein-Ligand Complexes. In: Alves, R. (eds) Advances in Bioinformatics and Computational Biology. BSB 2018. Lecture Notes in Computer Science(), vol 11228. Springer, Cham. https://doi.org/10.1007/978-3-030-01722-4_8
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