Homology modelling
Homology modeling
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Contents
Further reading and resources
| Bramucci et al. (2012) PyMod: sequence similarity searches, multiple sequence-structure alignments, and homology modeling within PyMOL. BMC Bioinformatics 13 Suppl 4:S2. (pmid: 22536966) |
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[ PubMed ] [ DOI ] BACKGROUND: In recent years, an exponential growing number of tools for protein sequence analysis, editing and modeling tasks have been put at the disposal of the scientific community. Despite the vast majority of these tools have been released as open source software, their deep learning curves often discourages even the most experienced users. RESULTS: A simple and intuitive interface, PyMod, between the popular molecular graphics system PyMOL and several other tools (i.e., [PSI-]BLAST, ClustalW, MUSCLE, CEalign and MODELLER) has been developed, to show how the integration of the individual steps required for homology modeling and sequence/structure analysis within the PyMOL framework can hugely simplify these tasks. Sequence similarity searches, multiple sequence and structural alignments generation and editing, and even the possibility to merge sequence and structure alignments have been implemented in PyMod, with the aim of creating a simple, yet powerful tool for sequence and structure analysis and building of homology models. CONCLUSIONS: PyMod represents a new tool for the analysis and the manipulation of protein sequences and structures. The ease of use, integration with many sequence retrieving and alignment tools and PyMOL, one of the most used molecular visualization system, are the key features of this tool.Source code, installation instructions, video tutorials and a user's guide are freely available at the URL http://schubert.bio.uniroma1.it/pymod/index.html. |
| McGuffin (2008) Aligning sequences to structures. Methods Mol Biol 413:61-90. (pmid: 18075162) |
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[ PubMed ] [ DOI ] Most newly sequenced proteins are likely to adopt a similar structure to one which has already been experimentally determined. For this reason, the most successful approaches to protein structure prediction have been template-based methods. Such prediction methods attempt to identify and model the folds of unknown structures by aligning the target sequences to a set of representative template structures within a fold library. In this chapter, I discuss the development of template-based approaches to fold prediction, from the traditional techniques to the recent state-of-the-art methods. I also discuss the recent development of structural annotation databases, which contain models built by aligning the sequences from entire proteomes against known structures. Finally, I run through a practical step-by-step guide for aligning target sequences to known structures and contemplate the future direction of template-based structure prediction. |
