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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
Amino acid interface propensities.(A) Amino acid propensities to be in an interface compared to protein surface calculated according to [5] (B) Amino acid propensities for high-affinity (black) and low-affinity (grey) complexes.
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pone-0110085-g002: Amino acid interface propensities.(A) Amino acid propensities to be in an interface compared to protein surface calculated according to [5] (B) Amino acid propensities for high-affinity (black) and low-affinity (grey) complexes.

Mentions: Several recent studies concluded that certain amino acids such as Tyr, Trp, Phe, Met, Val, Cys and Ile appear more frequently in PPI binding interfaces [5]. Other studies however, showed no appreciable difference in the composition of interfaces of the whole genomes [59], [60]. To our knowledge, no study attempted to correlate amino acid composition to binding affinity. We hence examined whether this feature plays a role in determining binding affinity by calculating amino acid propensity to be in an interface [5], first for all complexes in our database and then separately for low- and high-affinity complexes (Figures 2A and B). For all complexes in the database, we observe that Trp, Tyr, Phe and Met are the most frequent interfacial amino acids, in agreement with previous studies. We also observe that Tyr, Trp, His, and Cys have a higher propensity to be found in high-affinity interfaces compared to low-affinity interfaces. However, due to a relatively small number of data points, these results are not statistically significant, pointing to the necessity of enlarging the dataset under study. Interestingly, Ala propensity shows an anti-correlation with affinity that is highly significant, indicating that this amino acid, within an interface, cannot provide favorable interactions. Lys also shows higher propensity to be found in low-affinity complexes compared to high-affinity complexes, in agreement with a previous study [61].


How structure defines affinity in protein-protein interactions.

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

Amino acid interface propensities.(A) Amino acid propensities to be in an interface compared to protein surface calculated according to [5] (B) Amino acid propensities for high-affinity (black) and low-affinity (grey) complexes.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110085-g002: Amino acid interface propensities.(A) Amino acid propensities to be in an interface compared to protein surface calculated according to [5] (B) Amino acid propensities for high-affinity (black) and low-affinity (grey) complexes.
Mentions: Several recent studies concluded that certain amino acids such as Tyr, Trp, Phe, Met, Val, Cys and Ile appear more frequently in PPI binding interfaces [5]. Other studies however, showed no appreciable difference in the composition of interfaces of the whole genomes [59], [60]. To our knowledge, no study attempted to correlate amino acid composition to binding affinity. We hence examined whether this feature plays a role in determining binding affinity by calculating amino acid propensity to be in an interface [5], first for all complexes in our database and then separately for low- and high-affinity complexes (Figures 2A and B). For all complexes in the database, we observe that Trp, Tyr, Phe and Met are the most frequent interfacial amino acids, in agreement with previous studies. We also observe that Tyr, Trp, His, and Cys have a higher propensity to be found in high-affinity interfaces compared to low-affinity interfaces. However, due to a relatively small number of data points, these results are not statistically significant, pointing to the necessity of enlarging the dataset under study. Interestingly, Ala propensity shows an anti-correlation with affinity that is highly significant, indicating that this amino acid, within an interface, cannot provide favorable interactions. Lys also shows higher propensity to be found in low-affinity complexes compared to high-affinity complexes, in agreement with a previous study [61].

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