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Protein-protein docking with dynamic residue protonation states.

Kilambi KP, Reddy K, Gray JJ - PLoS Comput. Biol. (2014)

Bottom Line: On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock.Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc-FcRn complex, suggesting that it can be exploited to improve affinity predictions.The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.

ABSTRACT
Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc-FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.

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Summary of pHDock performance.Correlation plot comparing discrimination scores of pHDock and RosettaDock docking predictions for each target in the complete benchmark dataset. Complexes docked at acidic pH (pH≤7.0) and basic pH (pH>7.0) are represented as circles and squares, respectively. The discrimination score cutoffs for a successful prediction (D<0) are marked using broken lines. Corner numbers indicate the total predictions in each plot section (edges defined by the broken lines and the solid line at 45°).
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pcbi-1004018-g003: Summary of pHDock performance.Correlation plot comparing discrimination scores of pHDock and RosettaDock docking predictions for each target in the complete benchmark dataset. Complexes docked at acidic pH (pH≤7.0) and basic pH (pH>7.0) are represented as circles and squares, respectively. The discrimination score cutoffs for a successful prediction (D<0) are marked using broken lines. Corner numbers indicate the total predictions in each plot section (edges defined by the broken lines and the solid line at 45°).

Mentions: For a large-scale docking performance analysis, we tested pHDock over a dataset of diverse protein–protein complexes from the curated Docking Benchmark 4.0 [29]. On average, 25% of the interface residues in the dataset complexes are ionizable (Asp, Glu, His, Tyr, Lys) (S1 Figure). Fig. 3 compares the discrimination scores of the docking funnels generated using pHDock and RosettaDock. pHDock produces successful docking funnels (discrimination score ≤0) in approximately half (79/161) the structures from the dataset, including 19 cases where RosettaDock fails to produce a successful prediction. Based on the discrimination score, pHDock outperforms RosettaDock in approximately 60% of the targets (94/161) (Table S2), and the improvements are statistically significant (paired t-test, p = 0.039). Additionally, since models are generated stochastically, we performed bootstrap case resampling [36] to quantify the variation of the discrimination scores. The bootstrap mean discrimination scores µ(D) (S2 Figure) again show that pHDock produces successful funnels [µ(D) ≤0] in half the targets (79/161) including 17 cases where RosettaDock fails. Hence the results are robust to the stochastic sampling noise. The average standard deviation of the discrimination scores [σ(D): 0.07] is approximately 4% of the total observed µ (D) range.


Protein-protein docking with dynamic residue protonation states.

Kilambi KP, Reddy K, Gray JJ - PLoS Comput. Biol. (2014)

Summary of pHDock performance.Correlation plot comparing discrimination scores of pHDock and RosettaDock docking predictions for each target in the complete benchmark dataset. Complexes docked at acidic pH (pH≤7.0) and basic pH (pH>7.0) are represented as circles and squares, respectively. The discrimination score cutoffs for a successful prediction (D<0) are marked using broken lines. Corner numbers indicate the total predictions in each plot section (edges defined by the broken lines and the solid line at 45°).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4263365&req=5

pcbi-1004018-g003: Summary of pHDock performance.Correlation plot comparing discrimination scores of pHDock and RosettaDock docking predictions for each target in the complete benchmark dataset. Complexes docked at acidic pH (pH≤7.0) and basic pH (pH>7.0) are represented as circles and squares, respectively. The discrimination score cutoffs for a successful prediction (D<0) are marked using broken lines. Corner numbers indicate the total predictions in each plot section (edges defined by the broken lines and the solid line at 45°).
Mentions: For a large-scale docking performance analysis, we tested pHDock over a dataset of diverse protein–protein complexes from the curated Docking Benchmark 4.0 [29]. On average, 25% of the interface residues in the dataset complexes are ionizable (Asp, Glu, His, Tyr, Lys) (S1 Figure). Fig. 3 compares the discrimination scores of the docking funnels generated using pHDock and RosettaDock. pHDock produces successful docking funnels (discrimination score ≤0) in approximately half (79/161) the structures from the dataset, including 19 cases where RosettaDock fails to produce a successful prediction. Based on the discrimination score, pHDock outperforms RosettaDock in approximately 60% of the targets (94/161) (Table S2), and the improvements are statistically significant (paired t-test, p = 0.039). Additionally, since models are generated stochastically, we performed bootstrap case resampling [36] to quantify the variation of the discrimination scores. The bootstrap mean discrimination scores µ(D) (S2 Figure) again show that pHDock produces successful funnels [µ(D) ≤0] in half the targets (79/161) including 17 cases where RosettaDock fails. Hence the results are robust to the stochastic sampling noise. The average standard deviation of the discrimination scores [σ(D): 0.07] is approximately 4% of the total observed µ (D) range.

Bottom Line: On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock.Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc-FcRn complex, suggesting that it can be exploited to improve affinity predictions.The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.

ABSTRACT
Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc-FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.

Show MeSH