Limits...
How structure defines affinity in protein-protein interactions.

Erijman A, Rosenthal E, Shifman JM - PLoS ONE (2014)

Bottom Line: This diversity results in PPI binding affinities that span more than nine orders of magnitude.In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations.Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.

ABSTRACT
Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.

Show MeSH

Related in: MedlinePlus

Dependence of Kd on various single biophysical features.(A) Change in the accessible interface surface area (ASA); (B) ΔASA normalized to the total interface area; (C) percent of non-polar change in the accessible surface area; (D) the total number of interfacial H bonds, (E) the number of intermolecular interfacial H bonds, (F) the number of intra-molecular H bonds; (G) Van der Waals energy; (H) volume of cavities; (I) number of hotspots; (J) electrostatic columbic energy; (K) RMSD between bound and unbound structures for interface Cαs; (L) percentage of rotamers that do not change conformation upon binding. Each point represents one PDB file in the database and the line corresponds to a linear fit to all data points in the database.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4199723&req=5

pone-0110085-g001: Dependence of Kd on various single biophysical features.(A) Change in the accessible interface surface area (ASA); (B) ΔASA normalized to the total interface area; (C) percent of non-polar change in the accessible surface area; (D) the total number of interfacial H bonds, (E) the number of intermolecular interfacial H bonds, (F) the number of intra-molecular H bonds; (G) Van der Waals energy; (H) volume of cavities; (I) number of hotspots; (J) electrostatic columbic energy; (K) RMSD between bound and unbound structures for interface Cαs; (L) percentage of rotamers that do not change conformation upon binding. Each point represents one PDB file in the database and the line corresponds to a linear fit to all data points in the database.

Mentions: ΔASA is the total area that gets buried upon the complex formation. Earlier studies reported that protein binding affinity depends on ΔASA, with high-affinity complexes burring more surface area [5], [19], [40]. We found that ΔASA exhibits some correlation with binding affinity, with an R-value of 0.32 (Figure 1A). A better correlation (R = 0.41) is obtained if we normalize ΔASA by the total area of the atoms in the binding interface, providing a measure of binding interface dehydration (Figure 1B). The moderate correlation between ΔASA and affinity is not surprising since it is known that a few point mutations can produce many-fold changes in binding affinity without significant changes in ΔASA. For example, cognate and non-cognate complexes of colicin/immune proteins exhibit affinities of 10−14 and 10−8 M respectively while showing very similar ΔASA [41]. Furthermore, a single mutation in hemagglutinin from Influenza virus reduces the affinity of this protein for an antibody from 10−9 to 10−6 M [42]. Affinity maturation experiments in many antibody-antigen complexes also argue against strong correlation between ΔASA and binding affinity [43]. Thus, PPIs with larger ΔASA do not necessarily exhibit higher affinity but have a potential for containing more productive intermolecular interactions and for achieving higher affinity through mutations.


How structure defines affinity in protein-protein interactions.

Erijman A, Rosenthal E, Shifman JM - PLoS ONE (2014)

Dependence of Kd on various single biophysical features.(A) Change in the accessible interface surface area (ASA); (B) ΔASA normalized to the total interface area; (C) percent of non-polar change in the accessible surface area; (D) the total number of interfacial H bonds, (E) the number of intermolecular interfacial H bonds, (F) the number of intra-molecular H bonds; (G) Van der Waals energy; (H) volume of cavities; (I) number of hotspots; (J) electrostatic columbic energy; (K) RMSD between bound and unbound structures for interface Cαs; (L) percentage of rotamers that do not change conformation upon binding. Each point represents one PDB file in the database and the line corresponds to a linear fit to all data points in the database.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4199723&req=5

pone-0110085-g001: Dependence of Kd on various single biophysical features.(A) Change in the accessible interface surface area (ASA); (B) ΔASA normalized to the total interface area; (C) percent of non-polar change in the accessible surface area; (D) the total number of interfacial H bonds, (E) the number of intermolecular interfacial H bonds, (F) the number of intra-molecular H bonds; (G) Van der Waals energy; (H) volume of cavities; (I) number of hotspots; (J) electrostatic columbic energy; (K) RMSD between bound and unbound structures for interface Cαs; (L) percentage of rotamers that do not change conformation upon binding. Each point represents one PDB file in the database and the line corresponds to a linear fit to all data points in the database.
Mentions: ΔASA is the total area that gets buried upon the complex formation. Earlier studies reported that protein binding affinity depends on ΔASA, with high-affinity complexes burring more surface area [5], [19], [40]. We found that ΔASA exhibits some correlation with binding affinity, with an R-value of 0.32 (Figure 1A). A better correlation (R = 0.41) is obtained if we normalize ΔASA by the total area of the atoms in the binding interface, providing a measure of binding interface dehydration (Figure 1B). The moderate correlation between ΔASA and affinity is not surprising since it is known that a few point mutations can produce many-fold changes in binding affinity without significant changes in ΔASA. For example, cognate and non-cognate complexes of colicin/immune proteins exhibit affinities of 10−14 and 10−8 M respectively while showing very similar ΔASA [41]. Furthermore, a single mutation in hemagglutinin from Influenza virus reduces the affinity of this protein for an antibody from 10−9 to 10−6 M [42]. Affinity maturation experiments in many antibody-antigen complexes also argue against strong correlation between ΔASA and binding affinity [43]. Thus, PPIs with larger ΔASA do not necessarily exhibit higher affinity but have a potential for containing more productive intermolecular interactions and for achieving higher affinity through mutations.

Bottom Line: This diversity results in PPI binding affinities that span more than nine orders of magnitude.In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations.Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.

ABSTRACT
Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.

Show MeSH
Related in: MedlinePlus