DispHred : a server to predict pH-dependent order-disorder transitions in intrinsically disordered proteins
Santos Suárez, Jaime (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Iglesias, Valentin (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Pintado-Grima, Carlos (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Santos-Suárez, Juan (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Ventura, Salvador (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular

Data:	2020
Resum:	The natively unfolded nature of intrinsically disordered proteins (IDPs) relies on several physicochemical principles, of which the balance between a low sequence hydrophobicity and a high net charge appears to be critical. Under this premise, it is well-known that disordered proteins populate a defined region of the charge-hydropathy (C-H) space and that a linear boundary condition is sufficient to distinguish between folded and disordered proteins, an approach widely applied for the prediction of protein disorder. Nevertheless, it is evident that the C-H relation of a protein is not unalterable but can be modulated by factors extrinsic to its sequence. Here, we applied a C-H-based analysis to develop a computational approach that evaluates sequence disorder as a function of pH, assuming that both protein net charge and hydrophobicity are dependent on pH solution. On that basis, we developed DispHred, the first pH-dependent predictor of protein disorder. Despite its simplicity, DispHred displays very high accuracy in identifying pH-induced order/disorder protein transitions. DispHred might be useful for diverse applications, from the analysis of conditionally disordered segments to the synthetic design of disorder tags for biotechnological applications. Importantly, since many disorder predictors use hydrophobicity as an input, the here developed framework can be implemented in other state-of-the-art algorithms.
Ajuts:	Agencia Estatal de Investigación BIO2016-78310-R Ministerio de Ciencia e Innovación FPU17/01157
Nota:	Altres ajuts: ICREA Academia
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Intrinsically disordered proteins ; PH ; Bioinformatics ; Disorder prediction ; Conditional folding ; Machine learning
Publicat a:	International journal of molecular sciences, Vol. 21, Issue 16 (August 2020) , art. 5814, ISSN 1422-0067

DOI: 10.3390/ijms21165814
PMID: 32823616

12 p, 1.4 MB

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