Correlating disease-related mutations to their effect on protein stability: A large-scale analysis of the human proteome
Single residue mutations in proteins are known to affect protein stability and function. As a consequence, they can be disease associated. Available computational methods starting from protein sequence/structure can predict whether a mutated residue is or not disease associated and whether it is pro...
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Published in: | Human mutation Vol. 32; no. 10; pp. 1161 - 1170 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01-10-2011
Hindawi Limited |
Subjects: | |
Online Access: | Get full text |
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Summary: | Single residue mutations in proteins are known to affect protein stability and function. As a consequence, they can be disease associated. Available computational methods starting from protein sequence/structure can predict whether a mutated residue is or not disease associated and whether it is promoting instability of the protein‐folded structure. However, the relationship among stability changes in proteins and their involvement in human diseases still needs to be fully exploited. Here, we try to rationalize in a nutshell the complexity of the question by generalizing over information already stored in public databases. For each single aminoacid polymorphysm (SAP) type, we derive the probability of being disease‐related (Pd) and compute from thermodynamic data three indexes indicating the probability of decreasing (P−), increasing (P+), and perturbing the protein structure stability (Pp). Statistically validated analysis of the different P/Pd correlations indicate that Pd best correlates with Pp. Pp/Pd correlation values are as high as 0.49, and increase up to 0.67 when data variability is taken into consideration. This is indicative of a medium/good correlation among Pd and Pp and corroborates the assumption that protein stability changes can also be disease associated at the proteome level. Hum Mutat 32:1161–1170, 2011. ©2011 Wiley‐Liss, Inc. |
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Bibliography: | Communicated by Mauno Vihinen MIUR-FIRB 2003/LIBI-International Laboratory for Bioinformatics - No. RBLA039M7M istex:98820D0023FACA151EF15FF2ADA56A1D35ADFE5C ArticleID:HUMU21555 ark:/67375/WNG-7HWF7V1H-D ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 1059-7794 1098-1004 1098-1004 |
DOI: | 10.1002/humu.21555 |