ReviewComputational and structural biotechnology journal2013
Recent advances in functional region prediction by using structural and evolutionary information - Remaining problems and future extensions.
Review in Computational and structural biotechnology journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.Journal of computer-aided molecular design · 2019Article
- Bioinformatics calls the school: Use of smartphones to introduce Python for bioinformatics in high schools.PLoS computational biology · 2019Article
- Optimization of sequence alignments according to the number of sequences vs. number of sites trade-off.BMC bioinformatics · 2015Article
- YAC tripeptide of epidermal growth factor promotes the proliferation of HaCaT keratinocytes through activation of EGFR.BMB reports · 2014Article
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3 authors.
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Abstract
Structural genomics projects have solved many new structures with unknown functions. One strategy to investigate the function of a structure is to computationally find the functionally important residues or regions on it. Therefore, the development of functional region prediction methods has become an important research subject. An effective approach is to use a method employing structural and evolutionary information, such as the evolutionary trace (ET) method. ET ranks the residues of a protein structure by calculating the scores for relative evolutionary importance, and locates functionally important sites by identifying spatial clusters of highly ranked residues. After ET was developed, numerous ET-like methods were subsequently reported, and many of them are in practical use, although they require certain conditions. In this mini review, we first introduce the remaining problems and the recent improvements in the methods using structural and evolutionary information. We then summarize the recent developments of the methods. Finally, we conclude by describing possible extensions of the evolution- and structure-based methods.
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