IIBBA   05544
INSTITUTO DE INVESTIGACIONES BIOQUIMICAS DE BUENOS AIRES
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification.
Autor/es:
ELIN TEPPA; DI DOMENICO TOMAS; MORTEN NIELSEN; CRISTINA MARINO BUSLE
Lugar:
Universidad Nacional de Quilmes
Reunión:
Congreso; Primer congreso de la Asociación Argentina de Bioinformética y Biología Computacional (A2B2C); 2010
Institución organizadora:
Asociación Argentina de Bioinformática y Biología Computacional (A2B2C)
Resumen:
<!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:ES-AR;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 3.0cm 70.85pt 3.0cm; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> Identification of catalytic residue is essential for the characterization of enzyme function. Catalytic residues (CR) are in general conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of catalytic residues challenging. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to differentiate functional from other non-functional conserved residue. Using a data set of 434 Pfam families included in the CSA database, we tested this hypothesis and demonstrated that MI can complement conventional amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measure was shown to significantly outperform both the Shannon entropy and maximal frequency measures.  Sequence weighting and low-count correction was found not improve the predictive performance for any of the conservation-based methods. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR thus confirming the proposed hypothesis. A catalytic likelihood score (Cls) combining the KL and pMI measures was shown to lead to significantly improved prediction accuracy (figure1).  At a specificity threshold of 0.90, the KL, pMI and Cls methods were found to have sensitivity of 0.716, 0.560 and 0.802, respectively. In summary, we first demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversity of the structural environment during the course of evolution. Secondly, we seek to integrate this mutual information signature to create a method to identify catalytic residues to guide the identification of functional sites in proteins.