A simple reference state makes a significant improvement in near-native selections from structurally refined docking decoys

Near-native selections from docking decoys have proved challenging especially when unbound proteins are used in the molecular docking. One reason is that significant atomic clashes in docking decoys lead to poor predictions of binding affinities of near native decoys. Atomic clashes can be removed by structural refinement through energy minimization. Such an energy minimization, however, will lead to an unrealistic bias toward docked structures with large interfaces. Here, we extend an empirical energy function developed for protein design to protein-protein docking selection by introducing a simple reference state that removes the unrealistic dependence of binding affinity of docking decoys on the buried solvent accessible surface area of interface. The energy function called EMPIRE (EMpirical Protein-InteRaction Energy), when coupled with a refinement strategy, is found to provide a significantly improved success rate in near native selections when applied to RosettaDock and refined ZDOCK docking decoys. Our work underlines the importance of removing nonspecific interactions from specific ones in near native selections from docking decoys.     

Protonation state of Asp30 exerts crucial influence over surface loop rearrangements responsible for NO release in nitrophorin 4

pK(a) values of ionizable residues were calculated for the crystal structures describing the pH and NO binding dependant conformations of nitrophorin 4, a pH sensitive NO carrier heme protein. Comparison of resultant H-bonding patterns allowed the identification of the amino acids that take part in signaling pH change. We carried out MD simulations to show that the protonation state of Asp30, buried in the closed conformation, is crucial for maintaining the tight packed conformation of the closed form of the complex - presenting a model for the functional decrease of NO binding affinity of nitrophorins at physiological pH.     

Protein-protein binding is often associated with changes in protonation state

pK(a) values of ionizable residues have been calculated using the PROPKA method and structures of 75 protein-protein complexes and their corresponding free forms. These pK(a) values were used to compute changes in protonation state of individual residues, net changes in protonation state of the complex relative to the uncomplexed proteins, and the correction to a binding energy calculated assuming standard protonation states at pH 7. For each complex, two different structures for the uncomplexed form of the proteins were used: the X-ray structures determined for the proteins in the absence of the other protein and the individual protein structures taken from the structure of the complex (referred to as unbound and bound structures, respectively). In 28 and 77% of the cases considered here, protein-protein binding is accompanied by a complete (>95%) or significant (>50%) change in protonation state of at least one residue using unbound structures. Furthermore, in 36 and 61% of the cases, protein-protein binding is accompanied by a complete or significant net change in protonation state of the complex relative to the separated monomers. Using bound structures, the corresponding values are 12, 51, 20, and 48%. Comparison to experimental data suggest that using unbound and bound structures lead to over- and underestimation of binding-induced protonation state changes, respectively. Thus, we conclude that protein-protein binding is often associated with changes in protonation state of amino acid residues and with changes in the net protonation state of the proteins. The pH-dependent correction to the binding energy contributes at least one order of magnitude to the binding constant in 45 and 23%, using unbound and bound structures, respectively.     

A Combinatorial Histidine Scanning Library Approach to Engineer Highly pH-Dependent Protein Switches

There is growing interest in the development of protein switches, which are proteins whose function, such as binding a target molecule, can be modulated through environmental triggers. Efforts to engineer highly pH sensitive protein–protein interactions typically rely on the rational introduction of ionizable groups in the protein interface. Such experiments are typically time intensive and often sacrifice the protein's affinity at the permissive pH. The underlying thermodynamics of proton‐linkage dictate that the presence of multiple ionizable groups, which undergo a pK_a change on protein binding, are necessary to result in highly pH‐dependent binding. To test this hypothesis, a novel combinatorial histidine library was developed where every possible combination of histidine and wild‐type residue is sampled throughout the interface of a model anti‐RNase A single domain VHH antibody. Antibodies were coselected for high‐affinity binding and pH‐sensitivity using an in vitro, dual‐function selection strategy. The resulting antibodies retained near wild‐type affinity yet became highly sensitive to small decreases in pH, drastically decreasing their binding affinity, due to the incorporation of multiple histidine groups. Several trends were observed, such as histidine “hot‐spots,” which will help enhance the development of pH switch proteins as well as increase our understanding of the role of ionizable residues in protein interfaces. Overall, the combinatorial approach is rapid, general, and robust and should be capable of producing highly pH‐sensitive protein affinity reagents for a number of different applications.     

A combination of rescoring and refinement significantly improves protein docking performance

To determine the structures of protein-protein interactions, protein docking is a valuable tool that complements experimental methods to characterize protein complexes. Although protein docking can often produce a near-native solution within a set of global docking predictions, there are sometimes predictions that require refinement to elucidate correct contacts and conformation. Previously, we developed the ZRANK algorithm to rerank initial docking predictions from ZDOCK, a docking program developed by our lab. In this study, we have applied the ZRANK algorithm toward refinement of protein docking models in conjunction with the protein docking program RosettaDock. This was performed by reranking global docking predictions from ZDOCK, performing local side chain and rigid-body refinement using RosettaDock, and selecting the refined model based on ZRANK score. For comparison, we examined using RosettaDock score instead of ZRANK score, and a larger perturbation size for the RosettaDock search, and determined that the larger RosettaDock perturbation size with ZRANK scoring was optimal. This method was validated on a protein-protein docking benchmark. For refining docking benchmark predictions from the newest ZDOCK version, this led to improved structures of top-ranked hits in 20 of 27 cases, and an increase from 23 to 27 cases with hits in the top 20 predictions. Finally, we optimized the ZRANK energy function using refined models, which provides a significant improvement over the original ZRANK energy function. Using this optimized function and the refinement protocol, the numbers of cases with hits ranked at number one increased from 12 to 19 and from 7 to 15 for two different ZDOCK versions. This shows the effective combination of independently developed docking protocols (ZDOCK/ZRANK, and RosettaDock), indicating that using diverse search and scoring functions can improve protein docking results.     

Immobilization of arginase and its application in an enzymatic chromatographic column: thermodynamic studies of nor-NOHA/arginase binding and role of the reactive histidine residue

A biochromatographic approach is developed to measure for the first time changes in enthalpy, heat capacity change and protonation for the binding of nor-NOHA to arginase in a wide temperature range. For this, the arginase enzyme was immobilized on a chromatographic support. It was established that this novel arginase column was stable during an extended period of time. The affinity of nor-NOHA to arginase is high and changes slightly with the pH, because the number of protons linked to binding is low. The determination of the enthalpy change at different pH values suggested that the protonated group in the nor-NOHA-arginase complex exhibits a heat protonation of approximately -33 kJ/mol. This value agrees with the protonation of an imidazole group. Our result confirmed that active-site residue Hist 141 is protonated as imidazolium cation. Hist 141 can function as a general acid to protonate the leaving amino group of L-ornithine during catalysis. The thermodynamic data showed that nor-NOHA-arginase binding, for low temperature (<15 degrees C), is enthalpically unfavourable and being dominated by a positive entropy change. This result suggests that dehydration at the binding interface and charge-charge interactions contribute to the nor-NOHA-arginase complex formation. The temperature dependence of the free energy of binding is weak because of the enthalpy-entropy compensation caused by a large heat capacity change, DeltaC(p)=-2.43 kJ/mol/K, of arginase. Above 15 degrees C, the thermodynamic data DeltaH and DeltaS became negative due to van der Waals interactions and hydrogen bonding which are engaged at the complex interface confirming strong enzyme-inhibitor hydrogen bond networks. As well, by the use of these thermodynamic data and known correlations it was clearly demonstrated that the binding of nor-NOHA to arginase produces slight conformational changes in the vicinity of the active site. Our work indicated that our biochromatographic approach could soon become very attractive for studying other enzyme-ligand binding.     

Electrostatic interactions in the acid denaturation of alpha-lactalbumin determined by NMR.

alpha-Lactalbumin (alpha-LA) undergoes a pH-dependent unfolding from the native state to a partially unfolded state (the molten globule state). To understand the role of electrostatic interactions in protein denaturation, NMR and CD pH titration experiments are performed on guinea pig alpha-LA. Variation of pH over the range of 7.0 to 2.0 simultaneously leads to the acid denaturation of the protein and the titration of individual ionizable groups. The pH titrations are interpreted in the context of these coupled events, and indicate that acid denaturation in alpha-LA is a cooperative event that is triggered by the protonation of two ionizable residues. Our NMR results suggest that the critical electrostatic interactions that contribute to the denaturation of alpha-LA are concentrated in the calcium binding region of the protein.     

Cross Talk between KGF and KITLG Proteins Implicated with Ovarian Folliculogenesis in Buffalo Bubalus bubalis

Molecular interactions between mesenchymal-derived Keratinocyte growth factor (KGF) and Kit ligand (KITLG) are essential for follicular development. These factors are expressed by theca and granulosa cells. We determined full length coding sequence of buffalo KGF and KITLG proteins having 194 and 274 amino acids, respectively. The recombinant KGF and KITLG proteins were solubilized in 10 mM Tris, pH 7.5 and 50 mM Tris, pH 7.4 and purified using Ni-NTA column and GST affinity chromatography, respectively. The purity and molecular weight of His-KGF (~23 kDa) and GST-KITLG (~57 kDa) proteins were confirmed by SDS-PAGE and western blotting. The co-immunoprecipitation assay accompanied with computational analysis demonstrated the interaction between KGF and KITLG proteins. We deduced 3D structures of the candidate proteins and assessed their binding based on protein docking. In the process, KGF specific residues, Lys123, Glu135, Lys140, Lys155 and Trp156 and KITLG specific ones, Ser226, Phe233, Gly234, Ala235, Phe236, Trp238 and Lys239 involved in the formation of KGF-KITLG complex were detected. The hydrophobic interactions surrounding KGF-KITLG complex affirmed their binding affinity and stability to the interacting interface. Additionally, in-silico site directed mutagenesis enabled the assessment of changes that occurred in the binding energies of mutated KGF-KITLG protein complex. Our results demonstrate that in the presence of KITLG, KGF mimics its native binding mode suggesting all the KGF residues are specific to their binding complex. This study provides an insight on the critical amino acid residues participating in buffalo ovarian folliculogenesis.     

Benchmarking and analysis of protein docking performance in Rosetta v3.2

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 'other' complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of 'other' targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction.     

Calbindin D(9k): a protein optimized for calcium binding at neutral pH.

The binding of calcium ions by EF-hand proteins depends strongly on the electrostatic interactions between Ca(2+) ions and negatively charged residues of these proteins. We have investigated the pH dependence of the binding of Ca(2+) ions by calbindin D(9k). This protein offers a unique possibility for interpretation of such data since the pK(a) values of all ionizable groups are known. The binding is independent of pH between 7 and 9, where maximum calcium affinity is observed. An abrupt decrease in the binding affinity is observed at pH values below 7. This decrease is due to protonation of acidic groups, leading to modification of protein charges. The pH dependence of the product of the two macroscopic Ca(2+)-binding constants can be formally described by the involvement of two acidic groups with pK(a) = 6.6. Monte Carlo calculations show that the reduction of Ca(2+) binding is strictly determined by variable electrostatic interactions due to pH-dependent changes not only in the binding sites, but also of the overall charge of the protein.     

Environmental effects on the protonation states of active site residues in bacteriorhodopsin.

Finite difference solutions of the Poisson-Boltzmann equation are used to calculate the pKa values of the functionally important ionizable groups in bacteriorhodopsin. There are strong charge-charge interactions between the residues in the binding site leading to the possibility of complex titration behavior. Structured water molecules, if they exist in the binding site, can have significant effects on the calculated pKa by strongly stabilizing ionized species. The ionization states of the Schiff base and Asp-85 are found to be strongly coupled. Small environmental changes, which might occur as a consequence of trans-cis isomerization, are capable of causing large shifts in the relative pKa values of these two groups. This provides an explanation for the protonation of Asp-85 and the deprotonation of the Schiff base in the M state of bacteriorhodopsin. The different behavior of Asp-85 and Asp-212 is discussed in this regard.     

RosettaDock in CAPRI rounds 6-12

A challenge in protein-protein docking is to account for the conformational changes in the monomers that occur upon binding. The RosettaDock method, which incorporates sidechain flexibility but keeps the backbone fixed, was found in previous CAPRI rounds (4 and 5) to generate docking models with atomic accuracy, provided that conformational changes were mainly restricted to protein sidechains. In the recent rounds of CAPRI (6-12), large backbone conformational changes occur upon binding for several target complexes. To address these challenges, we explicitly introduced backbone flexibility in our modeling procedures by combining rigid-body docking with protein structure prediction techniques such as modeling variable loops and building homology models. Encouragingly, using this approach we were able to correctly predict a significant backbone conformational change of an interface loop for Target 20 (12 A rmsd between those in the unbound monomer and complex structures), but accounting for backbone flexibility in protein-protein docking is still very challenging because of the significantly larger conformational space, which must be surveyed. Motivated by these CAPRI challenges, we have made progress in reformulating RosettaDock using a "fold-tree" representation, which provides a general framework for treating a wide variety of flexible-backbone docking problems.     

Computational investigation of proton transfer,pKa shifts and pH‐optimum of protein–DNA and protein–RNA complexes

Protein–nucleic acid interactions play a crucial role in many biological processes. This work investigates the changes of pKa values and protonation states of ionizable groups (including nucleic acid bases) that may occur at protein–nucleic acid binding. Taking advantage of the recently developed pKa calculation tool DelphiPka, we utilize the large protein–nucleic acid interaction database (NPIDB database) to model pKa shifts caused by binding. It has been found that the protein's interfacial basic residues experience favorable electrostatic interactions while the protein acidic residues undergo proton uptake to reduce the energy cost upon the binding. This is in contrast with observations made for protein–protein complexes. In terms of DNA/RNA, both base groups and phosphate groups of nucleotides are found to participate in binding. Some DNA/RNA bases undergo pKa shifts at complex formation, with the binding process tending to suppress charged states of nucleic acid bases. In addition, a weak correlation is found between the pH‐optimum of protein–DNA/RNA binding free energy and the pH‐optimum of protein folding free energy. Overall, the pH‐dependence of protein–nucleic acid binding is not predicted to be as significant as that of protein–protein association. Proteins 2017; 85:282–295. © 2016 Wiley Periodicals, Inc.     

Energetics of hydrogen bond switch, residue burial and cavity analysis reveals molecular basis of improved heparin binding to antithrombin

Antithrombin III (ATIII) is the main inhibitor of the coagulation proteases like factor Xa and thrombin. Anticoagulant activity of ATIII is increased by several thousand folds when activated by vascular wall heparan sulfate proteoglycans (HSPGs) and pharmaceutical heparins. ATIII isoforms in human plasma, alpha-ATIII and beta-ATIII differ in the amount of glycosylation which is the basis of differences in their heparin binding affinity and function. Crystal structures and site directed mutagenesis studies have mapped the heparin binding site in ATIII, however the hydrogen bond switch and energetics of interaction during the course of heparin dependent conformational change remains largely unclear. An analysis of heparin bound conformational states of ATIII using PEARLS software showed that in heparin bound intermediate state, Arg 47 and Arg 13 residues make hydrogen bonds with heparin but in the activated conformation Lys 11 and Lys 114 have more hydrogen bond interactions. In the protease bound-antithrombin-pentasaccharide complex Lys 114, Pro 12 and Lys 125 form important hydrogen bonding interactions. The results showed that A-helix and N-terminal end residues are more important in the initial interactions but D-helix is more important during the latter stage of conformational activation and during the process of protease inhibition. We carried out the residue wise Accessible Surface Area (ASA) analysis of alpha and beta ATIII native states and the results indicated major differences in burial of residues from Ser 112 to Ser 116 towards the N-terminal end. This region is involved in the P-helix formation on account of heparin binding. A cavity analysis showed a progressively larger cavity formation during activation in the region just adjacent to the heparin binding site towards the C-terminal end. We hypothesize that during the process of conformational change after heparin binding beta form of antithrombin has low energy barrier to form D-helix extension toward N and C-terminal end as compared to alpha isoform.     

Protein-protein interactions: hot spots and structurally conserved residues often locate in complemented pockets that pre-organized in the unbound states: implications for docking

Energetic hot spots account for a significant portion of the total binding free energy and correlate with structurally conserved interface residues. Here, we map experimentally determined hot spots and structurally conserved residues to investigate their geometrical organization. Unfilled pockets are pockets that remain unfilled after protein-protein complexation, while complemented pockets are pockets that disappear upon binding, representing tightly fit regions. We find that structurally conserved residues and energetic hot spots are strongly favored to be located in complemented pockets, and are disfavored in unfilled pockets. For the three available protein-protein complexes with complemented pockets where both members of the complex were alanine-scanned, 62% of all hot spots (DeltaDeltaG>2kcal/mol) are within these pockets, and 60% of the residues in the complemented pockets are hot spots. 93% of all red-hot residues (DeltaDeltaG>/=4kcal/mol) either protrude into or are located in complemented pockets. The occurrence of hot spots and conserved residues in complemented pockets highlights the role of local tight packing in protein associations, and rationalizes their energetic contribution and conservation. Complemented pockets and their corresponding protruding residues emerge among the most important geometric features in protein-protein interactions. By screening the solvent, this organization shields backbone hydrogen bonds and charge-charge interactions. Complemented pockets often pre-exist binding. For 18 protein-protein complexes with complemented pockets whose unbound structures are available, in 16 the pockets are identified to pre-exist in the unbound structures. The root-mean-squared deviations of the atoms lining the pockets between the bound and unbound states is as small as 0.9A, suggesting that such pockets constitute features of the populated native state that may be used in docking.     

iATTRACT: Simultaneous global and local interface optimization for protein–protein docking refinement

Protein‐protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure‐based force field for intramolecular contributions. The approach was systematically evaluated on a large protein‐protein docking benchmark, starting from an enriched decoy set of rigidly docked protein–protein complexes deviating by up to 15 Å from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 Å were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%. Proteins 2015; 83:248–258. © 2014 Wiley Periodicals, Inc.     

CAPRI rounds 3-5 reveal promising successes and future challenges for RosettaDock

CAPRI Rounds 3, 4, and 5 are the first public test of the published RosettaDock algorithm. The targets cover a wide range of sizes and shapes. For most targets, published biological information indicated the region of the binding site on at least one docking partner. The RosettaDock algorithm produced high accuracy predictions for three targets, medium-accuracy predictions for two targets, and an acceptable prediction for one target. RosettaDock predicted all five targets with less than 450 residues to high or medium accuracy, but it predicted only one of seven targets with above 450 residues to acceptable accuracy. RosettaDock's high-accuracy predictions for small to moderately large targets reveal the predictive power and fidelity of the algorithm, especially the high-resolution refinement and scoring protocol. In addition, RosettaDock can predict complexes from at least one homology-modeled docking partner with comparable accuracy to unbound cases of similar size. Larger targets present a more intensive sampling problem, and some large targets present repulsive barriers to entering the binding site. Ongoing improvements to RosettaDock's low-resolution search may alleviate this problem. This first public test suggests that RosettaDock can be useful in a significant range of applications in biochemistry and cell biology.     

The pH-induced release of iron from transferrin investigated with a continuum electrostatic model.

A reduction in pH induces the release of iron from transferrin in a process that involves a conformational change in the protein from a closed to an open form. Experimental evidence suggests that there must be changes in the protonation states of certain, as yet not clearly identified, residues in the protein accompanying this conformational change. Such changes in protonation states of residues and the consequent changes in electrostatic interactions are assumed to play a large part in the mechanism of release of iron from transferrin. Using the x-ray crystal structures of human ferri- and apo-lactoferrin, we calculated the pKa values of the titratable residues in both the closed (iron-loaded) and open (iron-free) conformations with a continuum electrostatic model. With the knowledge of a residue's pKa value, its most probable protonation state at any specified pH may be determined. The preliminary results presented here are in good agreement with the experimental observation that the binding of ferric iron and the synergistic anion bicarbonate/carbonate results in the release of approximately three H+ ions. It is suggested that the release of these three H+ ions may be accounted for, in most part, by the deprotonation of the bicarbonate and residues Tyr-92, Lys-243, Lys-282, and Lys-285 together with the protonation of residues Asp-217 and Lys-277.     

Binding interface prediction by combining protein–protein docking results

We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein–protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision‐recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta‐PPISP (AUC = 0.43), which is one of the best monomer‐based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta‐PPISP and another monomer‐based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta‐PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues. Proteins 2014; 82:57–66. © 2013 Wiley Periodicals, Inc.