A new technique for predicting intrinsically disordered regions based on average distance map constructed with inter-residue average distance statistics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Takumi Shimomura, Kohki Nishijima, Takeshi Kikuchi

ABSTRACT

BACKGROUND: It had long been thought that a protein exhibits its specific function through its own specific 3D-structure under physiological conditions. However, subsequent research has shown that there are many proteins without specific 3D-structures under physiological conditions, so-called intrinsically disordered proteins (IDPs). This study presents a new technique for predicting intrinsically disordered regions in a protein, based on our average distance map (ADM) technique. The ADM technique was developed to predict compact regions or structural domains in a protein. In a protein containing partially disordered regions, a domain region is likely to be ordered, thus it is unlikely that a disordered region would be part of any domain. Therefore, the ADM technique is expected to also predict a disordered region between domains. RESULTS: The results of our new technique are comparable to the top three performing techniques in the community-wide CASP10 experiment. We further discuss the case of p53, a tumor-suppressor protein, which is the most significant protein among cell cycle regulatory proteins. This protein exhibits a disordered character as a monomer but an ordered character when two p53s form a dimer. CONCLUSION: Our technique can predict the location of an intrinsically disordered region in a protein with an accuracy comparable to the best techniques proposed so far. Furthermore, it can also predict a core region of IDPs forming definite 3D structures through interactions, such as dimerization. The technique in our study may also serve as a means of predicting a disordered region which would become an ordered structure when binding to another protein. More... »

PAGES

3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12900-019-0101-3

DOI

http://dx.doi.org/10.1186/s12900-019-0101-3

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https://app.dimensions.ai/details/publication/pub.1111954525

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30727987


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