Search bioRxivSearch

Biology subjects

Parisi, G.

Publications and source records attributed to Parisi, G..

2 recordsLinked to original sources

How is structural divergence related to evolutionary information?

Conservation and covariation measures, as other evolutionary analysis, require a high number of distant homologous sequences, therefore a lot of structural divergence can be expected in such divergent alignments. However, most works linking evolutionary and structural information use a single structure ignoring the structural variability inside a protein family. That common practice seems unrealistic to the light of this work.\n\nIn this work we studied how structural divergence affects conservation and covariation estimations. We uncover that, within a protein family, ~51% of multiple sequence alignment columns change their exposed/buried status between structures. Also, ~53% of residue pairs that are in contact in one structure are not in contact in another structure from the same family. We found out that residue conservation is not directly related to the relative solvent accessible surface area of a single protein structure. Using information from all the available structures rather than from a single representative structure gives more confidence in the structural interpretation of the evolutionary signals. That is particularly important for diverse multiple sequence alignments, where structures can drastically differ. High covariation scores tend to indicate residue contacts that are conserved in the family, therefore, are not suitable to find protein/conformer specific contacts.\n\nOur results suggest that structural divergence should be considered for a better understanding of protein function, to transfer annotation by homology and to model protein evolution.

bioinformatics

Homology Modeling In A Dynamical World

A key concept in template-based modeling is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity.\n\nIn this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair Template-based modelling (TMB) results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.\n\nAuthor summaryTemplate-based modelling (TBM) is the most reliable and fastest approach to obtain protein structural models. TBM relies in the high correlation between sequence and structural divergence, with the practical consequence that proteins that are similar at the sequence level will also be similar at the structural level, allowing in this way the selection of the better template to obtain the 3D model of the target sequence. However, protein native state could be described by a collection of conformers in equilibrium where their structural differences are called conformational diversity.\n\nIn this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We firstly found that the extent of conformational diversity can be as high as the maximum structural differences reached by families differing in their sequences. In these proteins with higher conformational diversity levels, the well-established correlation between sequence and structural divergence is nosier than previously suggested due to the presence of structural change without sequence variation. This lack of correlation could impair TBM results due to the uncertainty in the correct template selection. Finally, we also found that the presence of order/disorder can provide useful beforehand information for better TBM performance.

bioinformatics