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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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pHDock flowchart.Each step in the pHDock workflow is colored based on the differences compared to RosettaDock: unmodified steps are colored in grey, and steps with minor (light orange) and major (dark orange) modifications are colored in shades of orange.
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pcbi-1004018-g001: pHDock flowchart.Each step in the pHDock workflow is colored based on the differences compared to RosettaDock: unmodified steps are colored in grey, and steps with minor (light orange) and major (dark orange) modifications are colored in shades of orange.

Mentions: The pHDock algorithm is illustrated in Fig. 1. In the first pre-packing step, the protein complex side chains are idealized, and the residue ionization states are equilibrated with the solution pH using Rosetta-pH [25]. Then, following the standard RosettaDock low-resolution stage, the residue side chains are represented by coarse-grained centroid atoms. This stage comprises i) a random initial perturbation of the partners, and ii) rigid-body ligand moves relative to the receptor which are accepted/rejected based on the Metropolis criteria. In the high-resolution stage, the side-chain centroid pseudo-atoms are replaced by the side-chain atoms from the initial unbound conformation. The high-resolution stage involves i) repacking the residue side chains with simultaneous evaluation of the most favorable residue protonation states at the environmental pH, and ii) minimization of the side-chain torsion angles and rigid-body orientation of the ligand relative to the receptor with an accompanying Metropolis criteria check. One thousand candidate structures, or models, are generated for each target and then ranked according to their interface scores, and the top-ranked model is picked as the final prediction.


Protein-protein docking with dynamic residue protonation states.

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

pHDock flowchart.Each step in the pHDock workflow is colored based on the differences compared to RosettaDock: unmodified steps are colored in grey, and steps with minor (light orange) and major (dark orange) modifications are colored in shades of orange.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1004018-g001: pHDock flowchart.Each step in the pHDock workflow is colored based on the differences compared to RosettaDock: unmodified steps are colored in grey, and steps with minor (light orange) and major (dark orange) modifications are colored in shades of orange.
Mentions: The pHDock algorithm is illustrated in Fig. 1. In the first pre-packing step, the protein complex side chains are idealized, and the residue ionization states are equilibrated with the solution pH using Rosetta-pH [25]. Then, following the standard RosettaDock low-resolution stage, the residue side chains are represented by coarse-grained centroid atoms. This stage comprises i) a random initial perturbation of the partners, and ii) rigid-body ligand moves relative to the receptor which are accepted/rejected based on the Metropolis criteria. In the high-resolution stage, the side-chain centroid pseudo-atoms are replaced by the side-chain atoms from the initial unbound conformation. The high-resolution stage involves i) repacking the residue side chains with simultaneous evaluation of the most favorable residue protonation states at the environmental pH, and ii) minimization of the side-chain torsion angles and rigid-body orientation of the ligand relative to the receptor with an accompanying Metropolis criteria check. One thousand candidate structures, or models, are generated for each target and then ranked according to their interface scores, and the top-ranked model is picked as the final prediction.

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
Related in: MedlinePlus