Computational prediction of protein--protein interactions

Protein–protein interactions are vital for cellular function. Computational methods that predict the high-resolution structures of protein–protein complexes offer functional insights and guide rational engineering efforts to identify potential therapeutic targets, or modify protein binding affinities and specificities. With the scope of structural biological simulations expanding rapidly, there is a need for the protein complex prediction methods to adapt to the increasing complexity. So I have developed tools to expand their capacity beyond idealized protein–protein docking. In this thesis, I detail my work focusing on developing new methods to account for the effects of a critical environmental factor, pH, on protein complexes, and further advancements to address other protein interaction challenges. First, I developed Rosetta-pH, a fast and efficient method to calculate the pKa values of protein residues that commonly exhibit variable protonation states. I studied the effects of incorporating increasing levels of protein conformational flexibility on the pKa calculations, and tested the method’s efficacy in capturing large pKa shifts in Staphylococcal nuclease point mutants. Second, I utilized the knowledge of residue pKa values to develop pHDock, the first protein–protein docking method that can sample side-chain protonation states on-the-fly during the docking simulation. pHDock generates more accurate models for the protein complexes compared to the conventional docking method (RosettaDock) with fixed protonation states. I also demonstrated that pHDock can be further expanded to include binding affinity calculations by using it to predict a large pH-dependent binding affinity change in the Fc–FcRn complex. Third, I expanded RosettaDock to address diverse challenges in CAPRI (Critical Assessment of PRediction of Interactions), a world-wide blind protein interaction prediction challenge. Specifically, I developed methods to predict structures of water molecules at protein–protein interfaces, dock flexible sugar–protein complexes, discriminate natural and designed protein interfaces, and predict effects of mutations on binding affinities of protein complexes. Fourth, I used a cross-docking study to develop a structural basis to discriminate protein binders from non-binders by identifying native antibody–antigen interaction pairs among cognate and non-cognate complexes. I demonstrated a decrease in the prediction accuracy when using bound and unbound coordinates, and homology models, respectively, for the antibodies, potentially related to a drop in the interface hydrogen bond formation due to backbone inaccuracies. In summary, I have developed methods to incorporate pH effects on protein–protein complexes and expanded existing docking methods to target binding predictions and interactions involving non-protein biomolecules. The diversity of biomolecular problems requires computational methods to be versatile; my contributions expand their capabilities to encompass more biologically realistic docking problems

Computational prediction of protein--protein interactions

Protein–protein interactions are vital for cellular function. Computational methods that predict the high-resolution structures of protein–protein complexes offer functional insights and guide rational engineering efforts to identify potential therapeutic targets, or modify protein binding affinities and specificities. With the scope of structural biological simulations expanding rapidly, there is a need for the protein complex prediction methods to adapt to the increasing complexity. So I have developed tools to expand their capacity beyond idealized protein–protein docking. In this thesis, I detail my work focusing on developing new methods to account for the effects of a critical environmental factor, pH, on protein complexes, and further advancements to address other protein interaction challenges. First, I developed Rosetta-pH, a fast and efficient method to calculate the pKa values of protein residues that commonly exhibit variable protonation states. I studied the effects of incorporating increasing levels of protein conformational flexibility on the pKa calculations, and tested the method’s efficacy in capturing large pKa shifts in Staphylococcal nuclease point mutants. Second, I utilized the knowledge of residue pKa values to develop pHDock, the first protein–protein docking method that can sample side-chain protonation states on-the-fly during the docking simulation. pHDock generates more accurate models for the protein complexes compared to the conventional docking method (RosettaDock) with fixed protonation states. I also demonstrated that pHDock can be further expanded to include binding affinity calculations by using it to predict a large pH-dependent binding affinity change in the Fc–FcRn complex. Third, I expanded RosettaDock to address diverse challenges in CAPRI (Critical Assessment of PRediction of Interactions), a world-wide blind protein interaction prediction challenge. Specifically, I developed methods to predict structures of water molecules at protein–protein interfaces, dock flexible sugar–protein complexes, discriminate natural and designed protein interfaces, and predict effects of mutations on binding affinities of protein complexes. Fourth, I used a cross-docking study to develop a structural basis to discriminate protein binders from non-binders by identifying native antibody–antigen interaction pairs among cognate and non-cognate complexes. I demonstrated a decrease in the prediction accuracy when using bound and unbound coordinates, and homology models, respectively, for the antibodies, potentially related to a drop in the interface hydrogen bond formation due to backbone inaccuracies. In summary, I have developed methods to incorporate pH effects on protein–protein complexes and expanded existing docking methods to target binding predictions and interactions involving non-protein biomolecules. The diversity of biomolecular problems requires computational methods to be versatile; my contributions expand their capabilities to encompass more biologically realistic docking problems

Protein-protein docking with dynamic residue protonation states.

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

pHDock flowchart.

<p>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.</p

Docking predictions for xylanase – TAXI-IA complex.

<p>Docking plots generated by (A) RosettaDock, and (B) pHDock at pH 4.6. Grey, orange, and red points represent incorrect, acceptable-, and medium- quality predictions, respectively. Discrimination scores are shown in the bottom right corner of the plots. (C) Interface of the top-scoring pHDock prediction (medium accuracy) superimposed on the crystal complex (grey) (2B42 <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004018#pcbi.1004018-Pollet1" target="_blank">[32]</a>). Predicted orientation of the TAXI-IA inhibitor and xylanase, cyan and green, respectively; critical His-374 residue from TAXI-IA, spheres; xylanase active site and other critical binding site residues, sticks.</p

Distribution curves of interface RMSDs (Irmsd) and fraction of recovered native contacts (<i>f</i>_nat) for the docking models.

<p>(A) Irmsd distribution curve of the lowest-Irmsd models generated using pHDock (orange) and RosettaDock (grey). (B, C) Irmsd and <i>f</i>_nat 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 <i>f</i>_nat.</p