Structural and functional roles of coevolved sites in proteins

Abstract

Background: Understanding the residue covariations between multiple positions in protein families is very crucial and can be helpful for designing protein engineering experiments. These simultaneous changes or residue coevolution allow protein to maintain its overall structural-functional integrity while enabling it to acquire specific functional modifications. Despite the significant efforts in the field there is still controversy in terms of the preferable locations of coevolved residues on different regions of protein molecules, the strength of coevolutionary signal and role of coevolution in functional diversification.

Methodology: In this paper we study the scale and nature of residue coevolution in maintaining the overall functionality and structural integrity of proteins. We employed a large scale study to investigate the structural and functional aspects of coevolved residues. We found that the networks representing the coevolutionary residue connections within our dataset are in general of 'small-world' type as they have clustering coefficient values higher than random networks and also show smaller mean shortest path lengths similar and/or lower than random and regular networks. We also found that altogether 11% of functionally important sites are coevolved with any other sites. Active sites are found more frequently to coevolve with any other sites (15%) compared to protein (11%) and ligand (9%) binding sites. Metal binding and active sites are also found to be more frequently coevolved with other metal binding and active sites, respectively. Analysis of the coupling between coevolutionary processes and the spatial distribution of coevolved sites reveals that a high fraction of coevolved sites are located close to each other. Moreover, approximately 80% of charge compensatory substitutions within coevolved sites are found at very close spatial proximity (<or= 5A), pointing to the possible preservation of salt bridges in evolution.

Conclusion: Our findings show that a noticeable fraction of functionally important sites undergo coevolution and also point towards compensatory substitutions as a probable coevolutionary mechanism within spatially proximal coevolved functional sites.

Figure 1. ‘Small-world’ characteristics of coevolved networks.

Shortest paths and clustering coefficients are calculated and plotted for each family/network that has average degree (k) equal or more than 2.

Figure 2. Spatial distribution of coevolved functionally important sites.

Frequencies of coevolved functionally important site pairs are plotted versus the spatial distances between them. Bars represent frequencies of coevolved connection within each functional category.

Figure 3. Examples of coevolved functional sites.

Coevolved (panel A, marked in red spheres) and non-coevolved (panel A, marked in blue sticks) active sites are projected onto the structure of a representative member (PDB code: 1EUQ) from Glutamyl-tRNA synthetase(GluRS)/Glutaminyl-tRNA synthetase (GlnRS) catalytic domain family (CDD code: CD00418). Here an edge connects two coevolved residues (red circles). Coevolved metal binding sites (panel B, marked in red spheres) are projected onto the 3D structure of a representative member (PDB code: 1LKO) from ferritin-like diiron-carboxylate protein domain family (CDD code: CD00657). Coevolved (panel C, marked in red spheres) and non-coevolved (panel C, marked in blue sticks) protein binding sites are projected onto the structure of a representative member (PDB code: 1JD1) from YjgF_YER057c_UK114_family (CDD code: CD00448). Protein structural and network representations were created using the PyMol and Cytoscape program.

Figure 4. Charge compensatory substitutions within the coevolved sites.

Frequency of charge compensatory substitutions was calculated by counting the number of charge compensatory substitution quads (see Methods for details) for each pair of coevolved alignment columns. Frequency of charge compensatory substitutions (Y axis) is plotted against the spatial distances (X axis) between coevolved residue pairs.

Figure 5. Percentage of sites with given structural properties is shown.

Structural properties such as solvent accessibility (panel A), type of secondary structures (panel B) and hydrogen bonds (panel C) were calculated for coevolved sites. Solvent accessibility (Buried: Bur; Accessible: Acc) was measured using the PSA program from JOY package . Within the coevolved site pairs, if both residues are buried or accessible, they are shown as ‘Bur-Bur’ or ‘Acc-Acc’, respectively. Secondary structure [helix (H), strand (E) and coil (C)] distribution for coevolved residue pairs is shown in panel B. Hydrogen bonding patterns were estimated using the HBOND programs from the JOY package. ‘HBDY-HBDY’ and ‘HBDX-HBDX’ indicate cases where both coevolved residues are involved or not involved in hydrogen bonding correspondingly. ‘HBDX-HBDY/HBDY-HBDX’ indicates cases where at least one residue is involved in hydrogen bonding. Values in the parenthesis show mean and standard error of estimated from the distribution of structural property values for randomly selected non-coevolved residue pairs (5 randomizations were performed).

Figure 6. Examples of coevolved sites.

Panel A shows two examples where coevolved sites of CAP family of transcription factors (CDD code: cd00038; PDB code: 1RGS) and hedgehog/intein domains (CDD code: cd00081; PDB code: 1DQ3) are projected on their representative protein structures. Panel B shows an example of phosphoglycerate kinase family (CDD code: cd00318; PDB code: 1QPG) while panel C shows an example of coevolutionary connections from phenylalanine ammonia-lyase (PAL) and histidine ammonia-lyase (HAL) domain family (CDD code: cd00332; PDB code: 1GK2). Network representations were created using PyMol and Cytoscape program.