Subtle structural differences among homologous proteins may be responsible of the modulation of their functional properties. Therefore, we are exploring novel and strengthened methods to investigate in deep protein structure, and to analyze conformational features, in order to highlight relationships to functional properties. We selected some protein families based on their different structural class from CATH database, and studied in detail many structural parameters for these proteins. Some valuable results from Pearson’s correlation matrix have been validated with a Student’s t‐distribution test at a significance level of 5% (p‐value). We investigated in detail the best relationships among parameters, by using partial correlation. Moreover, PCA technique has been used for both single family and all families, in order to demonstrate how to find outliers for a family and extract new combined features. The correctness of this approach was borne out by the agreement of our results with geometric and structural properties, known or expected. In addition, we found unknown relationships, which will be object of further studies, in order to consider them as putative markers related to the peculiar structure‐function relationships for each family.

Statistical analysis of protein structural features: relationships and PCA grouping

MARABOTTI, ANNA;FACCHIANO, Angelo
2015

Abstract

Subtle structural differences among homologous proteins may be responsible of the modulation of their functional properties. Therefore, we are exploring novel and strengthened methods to investigate in deep protein structure, and to analyze conformational features, in order to highlight relationships to functional properties. We selected some protein families based on their different structural class from CATH database, and studied in detail many structural parameters for these proteins. Some valuable results from Pearson’s correlation matrix have been validated with a Student’s t‐distribution test at a significance level of 5% (p‐value). We investigated in detail the best relationships among parameters, by using partial correlation. Moreover, PCA technique has been used for both single family and all families, in order to demonstrate how to find outliers for a family and extract new combined features. The correctness of this approach was borne out by the agreement of our results with geometric and structural properties, known or expected. In addition, we found unknown relationships, which will be object of further studies, in order to consider them as putative markers related to the peculiar structure‐function relationships for each family.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4651301
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