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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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Distribution curves of interface RMSDs (Irmsd) and fraction of recovered native contacts (fnat) for the docking models.(A) Irmsd distribution curve of the lowest-Irmsd models generated using pHDock (orange) and RosettaDock (grey). (B, C) Irmsd and fnat distribution curve for the top-ranked models according to interface scores (Isc) for each protein complex. The distribution curves are generated after independent sorting of the pHDock and RosettaDock models based on (A, B) increasing Irmsd values and (C) decreasing fnat.
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pcbi-1004018-g004: Distribution curves of interface RMSDs (Irmsd) and fraction of recovered native contacts (fnat) for the docking models.(A) Irmsd distribution curve of the lowest-Irmsd models generated using pHDock (orange) and RosettaDock (grey). (B, C) Irmsd and fnat distribution curve for the top-ranked models according to interface scores (Isc) for each protein complex. The distribution curves are generated after independent sorting of the pHDock and RosettaDock models based on (A, B) increasing Irmsd values and (C) decreasing fnat.

Mentions: Since pHDock is a stochastic docking algorithm that generates several candidate models, the performance of the algorithm broadly depends on (i) the quality and diversity of the generated ensemble of models, or ‘sampling’, and (ii) the ability of the final score function to discriminate native-like models from non-native-like models, or ‘scoring’. To test the sampling performance of pHDock, we examined the lowest-Irmsd models for all the complexes in the dataset. The Irmsd distribution for pHDock is similar to RosettaDock (Fig. 4A), and in 92% of the docking targets, it generates at least one model within 4 Å from the native interface. Out of 1000 models generated for each target, pHDock creates on average 1.9, 18.5, and 90.8 high-, medium-, and acceptable-quality models, respectively. In comparison, RosettaDock samples 7–12% fewer medium- and high-quality models (S6 Figure). To test the scoring performance of pHDock, we calculated the Irmsd and fnat distributions of the top-scoring models for each target (Figs. 4B–C). pHDock generates top-ranked models within 4 Å in 57% of the targets (RosettaDock 51%), and 52% of the time these models recover more than 30% of the native residue-residue contacts (RosettaDock 46%).


Protein-protein docking with dynamic residue protonation states.

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

Distribution curves of interface RMSDs (Irmsd) and fraction of recovered native contacts (fnat) for the docking models.(A) Irmsd distribution curve of the lowest-Irmsd models generated using pHDock (orange) and RosettaDock (grey). (B, C) Irmsd and fnat distribution curve for the top-ranked models according to interface scores (Isc) for each protein complex. The distribution curves are generated after independent sorting of the pHDock and RosettaDock models based on (A, B) increasing Irmsd values and (C) decreasing fnat.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4263365&req=5

pcbi-1004018-g004: Distribution curves of interface RMSDs (Irmsd) and fraction of recovered native contacts (fnat) for the docking models.(A) Irmsd distribution curve of the lowest-Irmsd models generated using pHDock (orange) and RosettaDock (grey). (B, C) Irmsd and fnat distribution curve for the top-ranked models according to interface scores (Isc) for each protein complex. The distribution curves are generated after independent sorting of the pHDock and RosettaDock models based on (A, B) increasing Irmsd values and (C) decreasing fnat.
Mentions: Since pHDock is a stochastic docking algorithm that generates several candidate models, the performance of the algorithm broadly depends on (i) the quality and diversity of the generated ensemble of models, or ‘sampling’, and (ii) the ability of the final score function to discriminate native-like models from non-native-like models, or ‘scoring’. To test the sampling performance of pHDock, we examined the lowest-Irmsd models for all the complexes in the dataset. The Irmsd distribution for pHDock is similar to RosettaDock (Fig. 4A), and in 92% of the docking targets, it generates at least one model within 4 Å from the native interface. Out of 1000 models generated for each target, pHDock creates on average 1.9, 18.5, and 90.8 high-, medium-, and acceptable-quality models, respectively. In comparison, RosettaDock samples 7–12% fewer medium- and high-quality models (S6 Figure). To test the scoring performance of pHDock, we calculated the Irmsd and fnat distributions of the top-scoring models for each target (Figs. 4B–C). pHDock generates top-ranked models within 4 Å in 57% of the targets (RosettaDock 51%), and 52% of the time these models recover more than 30% of the native residue-residue contacts (RosettaDock 46%).

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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