“ESTUDIO DE LA CORRELACIÓN ENTRE LAS SECUENCIAS DE PROTEÍNAS CON LA TEMPERATURA ÓPTIMA DE CRECIMIENTO DEL ORGANISMO DEL CUAL PROVIENEN”

HERNANDEZ BERTHET, AYELEN; ARIEL APTEKMANN; NOGUERA, MARTÍN E.; SÁNCHEZ, IGNACIO E.; ERNESTO A. ROMAN

Simposio; VI Simposio Argentino de Bioinformatica; 2021

Structural and functional information of proteins are codified in their amino acid sequences. Within a protein family, there are proteins that come from different kinds of organisms: some growth at high temperatures (termophiles), some at middle (mesophiles) and others at low temperatures (psychrophiles). Usually occurs that optimal protein function temperature (OPFT) is close to the optimal growth temperature (OGT) of the organisms that come from. These behaviour is reflected in the sequence differences between orthologs that could affect their function, stability and conformational flexibility. But which of these are more associated to the OGTs? Is there any region more coupled than others? In this work we seek to study the sequences of an Archaea’s database to compare the differences between these sequences and the OGT. To this end, we aligned the sequences of Adenylate kinase, SPT5 elongation factor and Copper-exporting P-type ATPase protein families. Then we compared each possible pair of sequences and quantify their differences, using different substitution matrices. We made comparisons in windows of different size throughout the alignment, or taking positions near in space (using a representative structure of the family as a reference) or taking combinations of random positions of the alignment. Then we made a linear fit of the data and analyzed the correlation’s coefficient ( R ) and Pearson’s significance (p). We obtained values of R for each family and analyze those that outliers of more than two or three standard deviations from the average. We obtained positions of significant correlation for each family, and compared the correlation of these positions with bibliographic data. We found that in the studied families are positions or combinations that have positive correlation with the organisms OGT. Our preliminary results suggest that upon refinement, this method may be used to predict the OGT of a given organism only from sequence data.