A simple physical model for binding energy hot spots in protein-protein complexes

KFC Server: interactive forecasting of protein interaction hot spots

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model-a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein-protein or protein-DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.

Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction

Incorporation of effective backbone sampling into protein simulation and design is an important step in increasing the accuracy of computational protein modeling. Recent analysis of high-resolution crystal structures has suggested a new model, termed backrub, to describe localized, hinge-like alternative backbone and side-chain conformations observed in the crystal lattice. The model involves internal backbone rotations about axes between C-alpha atoms. Based on this observation, we have implemented a backrub-inspired sampling method in the Rosetta structure prediction and design program. We evaluate this model of backbone flexibility using three different tests. First, we show that Rosetta backrub simulations recapitulate the correlation between backbone and side-chain conformations in the high-resolution crystal structures upon which the model was based. As a second test of backrub sampling, we show that backbone flexibility improves the accuracy of predicting point-mutant side-chain conformations over fixed backbone rotameric sampling alone. Finally, we show that backrub sampling of triosephosphate isomerase loop 6 can capture the millisecond/microsecond oscillation between the open and closed states observed in solution. Our results suggest that backrub sampling captures a sizable fraction of localized conformational changes that occur in natural proteins. Application of this simple model of backbone motions may significantly improve both protein design and atomistic simulations of localized protein flexibility.

The crystal structure and dimerization interface of GADD45gamma

Gadd45 proteins are recognized as tumor and autoimmune suppressors whose expression can be induced by genotoxic stresses. These proteins are involved in cell cycle control, growth arrest, and apoptosis through interactions with a wide variety of binding partners. We report here the crystal structure of Gadd45gamma, which reveals a fold comprising an alphabetaalpha sandwich with a central five-stranded mixed beta-sheet with alpha-helices packed on either side. Based on crystallographic symmetry we identified the dimer interface of Gadd45gamma dimers by generating point mutants that compromised dimerization while leaving the tertiary structure of the monomer intact. The dimer interface comprises a four-helix bundle involving residues that are the most highly conserved among Gadd45 isoforms. Cell-based assays using these point mutants demonstrate that dimerization is essential for growth inhibition. This structural information provides a new context for evaluation of the plethora of protein-protein interactions that govern the many functions of the Gadd45 family of proteins.

Molecular dynamics-solvated interaction energy studies of protein-protein interactions: the MP1-p14 scaffolding complex

Using the MP1-p14 scaffolding complex from the mitogen-activated protein kinase signaling pathway as model system, we explored a structure-based computational protocol to probe and characterize binding affinity hot spots at protein-protein interfaces. Hot spots are located by virtual alanine-scanning consensus predictions over three different energy functions and two different single-structure representations of the complex. Refined binding affinity predictions for select hot-spot mutations are carried out by applying first-principle methods such as the molecular mechanics generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) to the molecular dynamics (MD) trajectories for mutated and wild-type complexes. Here, predicted hot-spot residues were actually mutated to alanine, and crystal structures of the mutated complexes were determined. Two mutated MP1-p14 complexes were investigated, the p14(Y56A)-mutated complex and the MP1(L63A,L65A)-mutated complex. Alternative ways to generate MD ensembles for mutant complexes, not relying on crystal structures for mutated complexes, were also investigated. The SIE function, fitted on protein-ligand binding affinities, gave absolute binding affinity predictions in excellent agreement with experiment and outperformed standard MM-GBSA predictions when tested on the MD ensembles of Ras-Raf and Ras-RalGDS protein-protein complexes. For wild-type and mutant MP1-p14 complexes, SIE predictions of relative binding affinities were supported by a yeast two-hybrid assay that provided semiquantitative relative interaction strengths. Results on the MP1-mutated complex suggested that SIE predictions deteriorate if mutant MD ensembles are approximated by just mutating the wild-type MD trajectory. The SIE data on the p14-mutated complex indicated feasibility for generating mutant MD ensembles from mutated wild-type crystal structure, despite local structural differences observed upon mutation. For energetic considerations, this would circumvent costly needs to produce and crystallize mutated complexes. The sensitized protein-protein interface afforded by the p14(Y56A) mutation identified here has practical applications in screening-based discovery of first-generation small-molecule hits for further development into specific modulators of the mitogen-activated protein kinase signaling pathway.

Experimental and computational analyses of the energetic basis for dual recognition of immunity proteins by colicin endonucleases

Colicin endonucleases (DNases) are bound and inactivated by immunity (Im) proteins. Im proteins are broadly cross-reactive yet specific inhibitors binding cognate and non-cognate DNases with K(d) values that vary between 10(-4) and 10(-14) M, characteristics that are explained by a 'dual-recognition' mechanism. In this work, we addressed for the first time the energetics of Im protein recognition by colicin DNases through a combination of E9 DNase alanine scanning and double-mutant cycles (DMCs) coupled with kinetic and calorimetric analyses of cognate Im9 and non-cognate Im2 binding, as well as computational analysis of alanine scanning and DMC data. We show that differential DeltaDeltaGs observed for four E9 DNase residues cumulatively distinguish cognate Im9 association from non-cognate Im2 association. E9 DNase Phe86 is the primary specificity hotspot residue in the centre of the interface, which is coordinated by conserved and variable hotspot residues of the cognate Im protein. Experimental DMC analysis reveals that only modest coupling energies to Im9 residues are observed, in agreement with calculated DMCs using the program ROSETTA and consistent with the largely hydrophobic nature of E9 DNase-Im9 specificity contacts. Computed values for the 12 E9 DNase alanine mutants showed reasonable agreement with experimental DeltaDeltaG data, particularly for interactions not mediated by interfacial water molecules. DeltaDeltaG predictions for residues that contact buried water molecules calculated using solvated rotamer models met with mixed success; however, we were able to predict with a high degree of accuracy the location and energetic contribution of one such contact. Our study highlights how colicin DNases are able to utilise both conserved and variable amino acids to distinguish cognate from non-cognate Im proteins, with the energetic contributions of the conserved residues modulated by neighbouring specificity sites.

A new twist in TCR diversity revealed by a forbidden alphabeta TCR

We report crystal structures of a negatively selected T cell receptor (TCR) that recognizes two I-A(u)-restricted myelin basic protein peptides and one of its peptide/major histocompatibility complex (pMHC) ligands. Unusual complementarity-determining region (CDR) structural features revealed by our analyses identify a previously unrecognized mechanism by which the highly variable CDR3 regions define ligand specificity. In addition to the pMHC contact residues contributed by CDR3, the CDR3 residues buried deep within the V alpha/V beta interface exert indirect effects on recognition by influencing the V alpha/V beta interdomain angle. This phenomenon represents an additional mechanism for increasing the potential diversity of the TCR repertoire. Both the direct and indirect effects exerted by CDR residues can impact global TCR/MHC docking. Analysis of the available TCR structures in light of these results highlights the significance of the V alpha/V beta interdomain angle in determining specificity and indicates that TCR/pMHC interface features do not distinguish autoimmune from non-autoimmune class II-restricted TCRs.

Modeling the structure of mAb 14B7 bound to the anthrax protective antigen

The anthrax protective antigen (PA) is a key component of the tripartite anthrax toxin. Monoclonal antibody (mAb) 14B7 and its engineered, affinity-matured variants have been shown to be effective in blocking PA binding to cellular receptors and mitigating anthrax toxicity. Here, we perform computational structural modeling of the mAb 14B7-PA interaction. Our objectives are to determine the structure of the 14B7-PA complex, to deduce a structural explanation for the affinity maturation from the docking models, and to study the effect of inaccuracies in the antibody homology model on docking. We used the RosettaDock program to dock PA with the mAb 14B7 crystal structure or homology model. Our simulations generate two distinct binding orientations consistent with experimental residue mutations that diminish 14B7-PA binding. Furthermore, the models suggest new site-directed mutations to positively identify one of these two solutions as the correct 14B7-PA docking orientation. The models indicate that PA regions 648-660 and 712-720 may be important for 14B7 binding in addition to the known PA epitope, and the binding interfaces are similar to that seen in the PA complex with cellular receptor CMG2. Antibody residues involved in affinity maturation do not contact the antigen in the docking models, suggesting that affinity maturation in the 14B7 family does not result from direct enhancements of antibody-antigen contacts. Docking the homology model produces low-resolution representations of the crystal structure docking orientations, but homology model docking is frustrated by antibody H3 loop conformation errors. This work demonstrates the usefulness and limitations of computational structure prediction for the development of antibody therapeutics, and reemphasizes the need for flexible backbone docking algorithms to achieve high-resolution docking using homology models.

Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6-12

In CAPRI rounds 6-12, RosettaDock successfully predicted 2 of 5 unbound-unbound targets to medium accuracy. Improvement over the previous method was achieved with computational mutagenesis to select decoys that match the energetics of experimentally determined hot spots. In the case of Target 21, Orc1/Sir1, this resulted in a successful docking prediction where RosettaDock alone or with simple site constraints failed. Experimental information also helped limit the interacting region of TolB/Pal, producing a successful prediction of Target 26. In addition, we docked multiple loop conformations for Target 20, and we developed a novel flexible docking algorithm to simultaneously optimize backbone conformation and rigid-body orientation to generate a wide diversity of conformations for Target 24. Continued challenges included docking of homology targets that differ substantially from their template (sequence identity <50%) and accounting for large conformational changes upon binding. Despite a larger number of unbound-unbound and homology model binding targets, Rounds 6-12 reinforced that RosettaDock is a powerful algorithm for predicting bound complex structures, especially when combined with experimental data.

Inhibition of protein-protein interactions with low molecular weight compounds

An overview of issues associated with the design and development of low molecular weight inhibitors of protein-protein interactions is presented. Areas discussed include information on the nature of protein-protein interfaces, methods to characterize those interfaces and methods by which that information is applied towards ligand identification and design. Specific examples of the strategy for the identification of inhibitors of protein-protein interactions involving the proteins p56lck kinase, ERK2 and the calcium-binding protein S100B are presented. Physical characterization of the inhibitors identified in those studies shows them to have drug-like and lead-like properties, indicating their potential to be developed into therapeutic agents.

An object-oriented library for computational protein design

Recent advances in computational protein design have established it as a viable technique for the rational generation of stable protein sequences, novel protein folds, and even enzymatic activity. We present a new and object-oriented library of code, written specifically for protein design applications in C(++), called EGAD Library. The modular fashion in which this library is written allows developers to tailor various energy functions and minimizers for a specific purpose. It also allows for the generation of novel protein design applications with a minimal amount of code investment. It is our hope that this will permit labs that have not considered protein design to apply it to their own systems, thereby increasing its potential as a tool in biology. We also present various uses of EGAD Library: in the development of Interaction Viewer, a PyMOL plug-in for viewing interactions between protein residues; in the repacking of protein cores; and in the prediction of protein-protein complex stabilities.

Evaluation of the influence of the internal aqueous solvent structure on electrostatic interactions at the protein-solvent interface by nonlocal continuum electrostatic approach

The dielectric properties of the polar solvent on the protein-solvent interface at small intercharge distances are still poorly explored. To deconvolute this problem and to evaluate the pair-wise electrostatic interaction (PEI) energies of the point charges located at the protein-solvent interface we used a nonlocal (NL) electrostatic approach along with a static NL dielectric response function of water. The influence of the aqueous solvent microstructure (determined by a strong nonelectrostatic correlation effect between water dipoles within the orientational Debye polarization mode) on electrostatic interactions at the interface was studied in our work. It was shown that the PEI energies can be significantly higher than the energies evaluated by the classical (local) consideration, treating water molecules as belonging to the bulk solvent with a high dielectric constant. Our analysis points to the existence of a rather extended, effective low-dielectric interfacial water shell on the protein surface. The main dielectric properties of this shell (effective thickness together with distance- and orientation-dependent dielectric permittivity function) were evaluated. The dramatic role of this shell was demonstrated when estimating the protein association rate constants.

Statistical analysis of physical-chemical properties and prediction of protein-protein interfaces

We have developed a fully automated method, InterProSurf, to predict interacting amino acid residues on protein surfaces of monomeric 3D structures. Potential interacting residues are predicted based on solvent accessible surface areas, a new scale for interface propensities, and a cluster algorithm to locate surface exposed areas with high interface propensities. Previous studies have shown the importance of hydrophobic residues and specific charge distribution as characteristics for interfaces. Here we show differences in interface and surface regions of all physical chemical properties of residues as represented by five quantitative descriptors. In the current study a set of 72 protein complexes with known 3D structures were analyzed to obtain interface propensities of residues, and to find differences in the distribution of five quantitative descriptors for amino acid residues. We also investigated spatial pair correlations of solvent accessible residues in interface and surface areas, and compared log-odds ratios for interface and surface areas. A new scoring method to predict potential functional sites on the protein surface was developed and tested for a new dataset of 21 protein complexes, which were not included in the original training dataset. Empirically we found that the algorithm achieves a good balance in the accuracy of precision and sensitivity by selecting the top eight highest scoring clusters as interface regions. The performance of the method is illustrated for a dimeric ATPase of the hyperthermophile, Methanococcus jannaschii, and the capsid protein of Human Hepatitis B virus. An automated version of the method can be accessed from our web server at http://curie.utmb.edu/prosurf.html.

HotSprint: database of computational hot spots in protein interfaces

We present a new database of computational hot spots in protein interfaces: HotSprint. Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. HotSprint contains data for 35 776 protein interfaces among 49 512 protein interfaces extracted from the multi-chain structures in Protein Data Bank (PDB) as of February 2006. The conserved residues in interfaces with certain buried accessible solvent area (ASA) and complex ASA thresholds are flagged as computational hot spots. The predicted hot spots are observed to correlate with the experimental hot spots with an accuracy of 76%. Several machine-learning methods (SVM, Decision Trees and Decision Lists) are also applied to predict hot spots, results reveal that our empirical approach performs better than the others. A web interface for the HotSprint database allows users to browse and query the hot spots in protein interfaces. HotSprint is available at http://prism.ccbb.ku.edu.tr/hotsprint; and it provides information for interface residues that are functionally and structurally important as well as the evolutionary history and solvent accessibility of residues in interfaces.

Spatial chemical conservation of hot spot interactions in protein-protein complexes

Background

Conservation of the spatial binding organizations at the level of physico-chemical interactions is important for the formation and stability of protein-protein complexes as well as protein and drug design. Due to the lack of computational tools for recognition of spatial patterns of interactions shared by a set of protein-protein complexes, the conservation of such interactions has not been addressed previously.

Results

We performed extensive spatial comparisons of physico-chemical interactions common to different types of protein-protein complexes. We observed that 80% of these interactions correspond to known hot spots. Moreover, we show that spatially conserved interactions allow prediction of hot spots with a success rate higher than obtained by methods based on sequence or backbone similarity. Detection of spatially conserved interaction patterns was performed by our novel MAPPIS algorithm. MAPPIS performs multiple alignments of the physico-chemical interactions and the binding properties in three dimensional space. It is independent of the overall similarity in the protein sequences, folds or amino acid identities. We present examples of interactions shared between complexes of colicins with immunity proteins, serine proteases with inhibitors and T-cell receptors with superantigens. We unravel previously overlooked similarities, such as the interactions shared by the structurally different RNase-inhibitor families.

Conclusion

The key contribution of MAPPIS is in discovering the 3D patterns of physico-chemical interactions. The detected patterns describe the conserved binding organizations that involve energetically important hot spot residues and are crucial for the protein-protein associations.

Characterization of protomer interfaces in HslV protease; the bacterial homologue of 20S proteasome

HslVU, a two-component proteasome-related prokaryotic system is composed of HslV protease and HslU ATPase. HslV protomers assemble in a dodecamer of two-stacked hexameric rings that form a complex with HslU hexamers. The intra- and inter-ring protomer interfaces in the HslV dodecamer underpin the integrity and functionality of HslVU. Structural characterization of HslV from different bacteria illustrated considerable differences in interacting residues, accessible surface and gap volumes at the intra-ring interface that is primarily stabilized by polar interactions. Amino acid residues Lys28, Arg83 and Asp111 have envisaged as hot spots at this HslU-interacting interface. The inter-ring interfaces that are made up of side chain packing of hydrophobic residues are structurally conserved. Hyperthermostable bacterium T. maritima HslV has extensively networked polar/nonpolar interactions and highly packed environment at all interfaces. Present data demonstrates that HslV protomer interfaces perform distinct functions; whereas intra-ring interface participates in HslV:HslU interaction resulting in allosteric activation of HslV protease by HslU, the inter-ring interfaces uphold the oligomeric form of HslV.

FireDock: fast interaction refinement in molecular docking

Here, we present FireDock, an efficient method for the refinement and rescoring of rigid-body docking solutions. The refinement process consists of two main steps: (1) rearrangement of the interface side-chains and (2) adjustment of the relative orientation of the molecules. Our method accounts for the observation that most interface residues that are important in recognition and binding do not change their conformation significantly upon complexation. Allowing full side-chain flexibility, a common procedure in refinement methods, often causes excessive conformational changes. These changes may distort preformed structural signatures, which have been shown to be important for binding recognition. Here, we restrict side-chain movements, and thus manage to reduce the false-positive rate noticeably. In the later stages of our procedure (orientation adjustments and scoring), we smooth the atomic radii. This allows for the minor backbone and side-chain movements and increases the sensitivity of our algorithm. FireDock succeeds in ranking a near-native structure within the top 15 predictions for 83% of the 30 enzyme-inhibitor test cases, and for 78% of the 18 semiunbound antibody-antigen complexes. Our refinement procedure significantly improves the ranking of the rigid-body PatchDock algorithm for these cases. The FireDock program is fully automated. In particular, to our knowledge, FireDock's prediction results are comparable to current state-of-the-art refinement methods while its running time is significantly lower. The method is available at http://bioinfo3d.cs.tau.ac.il/FireDock/.

An automated decision-tree approach to predicting protein interaction hot spots

Protein-protein interactions can be altered by mutating one or more "hot spots," the subset of residues that account for most of the interface's binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge-based models that improve the ability to predict hot spots: K-FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K-CON uses biochemical contact features. The combined K-FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta-Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta-Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein-2 (BMP-2)/BMP receptor-type I (BMPR-IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface.

Using the natural evolution of a rotavirus-specific human monoclonal antibody to predict the complex topography of a viral antigenic site

BACKGROUND

Understanding the interaction between viral proteins and neutralizing antibodies at atomic resolution is hindered by a lack of experimentally solved complexes. Progress in computational docking has led to the prediction of increasingly high-quality model antibody-antigen complexes. The accuracy of atomic-level docking predictions is improved when integrated with experimental information and expert knowledge.

METHODS

Binding affinity data associated with somatic mutations of a rotavirus-specific human adult antibody (RV6-26) are used to filter potential docking orientations of an antibody homology model with respect to the rotavirus VP6 crystal structure. The antibody structure is used to probe the VP6 trimer for candidate interface residues.

RESULTS

Three conformational epitopes are proposed. These epitopes are candidate antigenic regions for site-directed mutagenesis of VP6, which will help further elucidate antigenic function. A pseudo-atomic resolution RV6-26 antibody-VP6 complex is proposed consistent with current experimental information.

CONCLUSION

The use of mutagenesis constraints in docking calculations allows for the identification of a small number of alternative arrangements of the antigen-antibody interface. The mutagenesis information from the natural evolution of a neutralizing antibody can be used to discriminate between residue-scale models and create distance constraints for atomic-resolution docking. The integration of binding affinity data or other information with computation may be an advantageous approach to assist peptide engineering or therapeutic antibody design.

Robust protein protein interactions in crowded cellular environments

The capacity of proteins to interact specifically with one another underlies our conceptual understanding of how living systems function. Systems-level study of specificity in protein-protein interactions is complicated by the fact that the cellular environment is crowded and heterogeneous; interaction pairs may exist at low relative concentrations and thus be presented with many more opportunities for promiscuous interactions compared with specific interaction possibilities. Here we address these questions by using a simple computational model that includes specifically designed interacting model proteins immersed in a mixture containing hundreds of different unrelated ones; all of them undergo simulated diffusion and interaction. We find that specific complexes are quite robust to interference from promiscuous interaction partners only in the range of temperatures T(design) > T > T(rand). At T > T(design), specific complexes become unstable, whereas at T < T(rand), formation of specific complexes is suppressed by promiscuous interactions. Specific interactions can form only if T(design) > T(rand). This condition requires an energy gap between binding energy in a specific complex and set of binding energies between randomly associating proteins, providing a general physical constraint on evolutionary selection or design of specific interacting protein interfaces. This work has implications for our understanding of how the protein repertoire functions and evolves within the context of cellular systems.

Design of multi-specificity in protein interfaces

Interactions in protein networks may place constraints on protein interface sequences to maintain correct and avoid unwanted interactions. Here we describe a "multi-constraint" protein design protocol to predict sequences optimized for multiple criteria, such as maintaining sets of interactions, and apply it to characterize the mechanism and extent to which 20 multi-specific proteins are constrained by binding to multiple partners. We find that multi-specific binding is accommodated by at least two distinct patterns. In the simplest case, all partners share key interactions, and sequences optimized for binding to either single or multiple partners recover only a subset of native amino acid residues as optimal. More interestingly, for signaling interfaces functioning as network "hubs," we identify a different, "multi-faceted" mode, where each binding partner prefers its own subset of wild-type residues within the promiscuous binding site. Here, integration of preferences across all partners results in sequences much more "native-like" than seen in optimization for any single binding partner alone, suggesting these interfaces are substantially optimized for multi-specificity. The two strategies make distinct predictions for interface evolution and design. Shared interfaces may be better small molecule targets, whereas multi-faceted interactions may be more "designable" for altered specificity patterns. The computational methodology presented here is generalizable for examining how naturally occurring protein sequences have been selected to satisfy a variety of positive and negative constraints, as well as for rationally designing proteins to have desired patterns of altered specificity.

Protein-protein interaction hotspots carved into sequences

Protein-protein interactions, a key to almost any biological process, are mediated by molecular mechanisms that are not entirely clear. The study of these mechanisms often focuses on all residues at protein-protein interfaces. However, only a small subset of all interface residues is actually essential for recognition or binding. Commonly referred to as "hotspots," these essential residues are defined as residues that impede protein-protein interactions if mutated. While no in silico tool identifies hotspots in unbound chains, numerous prediction methods were designed to identify all the residues in a protein that are likely to be a part of protein-protein interfaces. These methods typically identify successfully only a small fraction of all interface residues. Here, we analyzed the hypothesis that the two subsets correspond (i.e., that in silico methods may predict few residues because they preferentially predict hotspots). We demonstrate that this is indeed the case and that we can therefore predict directly from the sequence of a single protein which residues are interaction hotspots (without knowledge of the interaction partner). Our results suggested that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein-protein interaction hotspots in entire organisms and thus may benefit function prediction and drug development. The server for prediction is available at http://www.rostlab.org/services/isis.

In silico screening of mutational effects on enzyme-proteic inhibitor affinity: a docking-based approach

Background

Molecular recognition between enzymes and proteic inhibitors is crucial for normal functioning of many biological pathways. Mutations in either the enzyme or the inhibitor protein often lead to a modulation of the binding affinity with no major alterations in the 3D structure of the complex.

Results

In this study, a rigid body docking-based approach has been successfully probed in its ability to predict the effects of single and multiple point mutations on the binding energetics in three enzyme-proteic inhibitor systems. The only requirement of the approach is an accurate structural model of the complex between the wild type forms of the interacting proteins, with the assumption that the architecture of the mutated complexes is almost the same as that of the wild type and no major conformational changes occur upon binding. The method was applied to 23 variants of the ribonuclease inhibitor-angiogenin complex, to 15 variants of the barnase-barstar complex, and to 8 variants of the bovine pancreatic trypsin inhibitor-β Trypsin system, leading to thermodynamic and kinetic estimates consistent with in vitro data. Furthermore, simulations with and without explicit water molecules at the protein-protein interface suggested that they should be included in the simulations only when their positions are well defined both in the wild type and in the mutants and they result to be relevant for the modulation of mutational effects on the association process.

Conclusion

The correlative models built in this study allow for predictions of mutational effects on the thermodynamics and kinetics of association of three substantially different systems, and represent important extensions of our computational approach to cases in which it is not possible to estimate the absolute free energies. Moreover, this study is the first example in the literature of an extensive evaluation of the correlative weights of the single components of the ZDOCK score on the thermodynamics and kinetics of binding of protein mutants compared to the native state.

Finally, the results of this study corroborate and extend a previously developed quantitative model for in silico predictions of absolute protein-protein binding affinities spanning a wide range of values, i.e. from -10 up to -21 kcal/mol.

The computational approach is simple and fast and can be used for structure-based design of protein-protein complexes and for in silico screening of mutational effects on protein-protein recognition.

Structure-based protocol for identifying mutations that enhance protein-protein binding affinities

The ability to manipulate protein binding affinities is important for the development of proteins as biosensors, industrial reagents, and therapeutics. We have developed a structure-based method to rationally predict single mutations at protein-protein interfaces that enhance binding affinities. The protocol is based on the premise that increasing buried hydrophobic surface area and/or reducing buried hydrophilic surface area will generally lead to enhanced affinity if large steric clashes are not introduced and buried polar groups are not left without a hydrogen bond partner. The procedure selects affinity enhancing point mutations at the protein-protein interface using three criteria: (1) the mutation must be from a polar amino acid to a non-polar amino acid or from a non-polar amino acid to a larger non-polar amino acid, (2) the free energy of binding as calculated with the Rosetta protein modeling program should be more favorable than the free energy of binding calculated for the wild-type complex and (3) the mutation should not be predicted to significantly destabilize the monomers. The performance of the computational protocol was experimentally tested on two separate protein complexes; Galpha(i1) from the heterotrimeric G-protein system bound to the RGS14 GoLoco motif, and the E2, UbcH7, bound to the E3, E6AP from the ubiquitin pathway. Twelve single-site mutations that were predicted to be stabilizing were synthesized and characterized in the laboratory. Nine of the 12 mutations successfully increased binding affinity with five of these increasing binding by over 1.0 kcal/mol. To further assess our approach we searched the literature for point mutations that pass our criteria and have experimentally determined binding affinities. Of the eight mutations identified, five were accurately predicted to increase binding affinity, further validating the method as a useful tool to increase protein-protein binding affinities.

Computational prediction of binding hotspots

We combine side-chain modeling, energy minimization and binding free energy calculation to predict point mutations with significant impacts on binding affinities (binding hotspots). Our method achieves high accuracy for two datasets (alanine-scanning mutations in ASEdb and 570 mutations on protease-inhibitor complexes). In particular, we can predict mutations that lead to improved binding with success. We discuss various factors that may contribute the prediction accuracy, including the amino acid to mutate to, and the position of the mutation.

Hot spots-A review of the protein-protein interface determinant amino-acid residues

Proteins tendency to bind to one another in a highly specific manner forming stable complexes is fundamental to all biological processes. A better understanding of complex formation has many practical applications, which include the rational design of new therapeutic agents, and the analysis of metabolic and signal transduction networks. Alanine-scanning mutagenesis made possible the detection of the functional epitopes, and demonstrated that most of the protein-protein binding energy is related only to a group of few amino acids at intermolecular protein interfaces: the hot spots. The scope of this review is to summarize all the available information regarding hot spots for a better atomic understanding of their structure and function. The ultimate objective is to improve the rational design of complexes of high affinity and specificity as well as that of small molecules, which can mimic the functional epitopes of the proteic complexes.

On the oligomeric state of DJ-1 protein and its mutants associated with Parkinson Disease. A combined computational and in vitro study

Mutations in the DJ-1 protein are present in patients suffering from familial Parkinson disease. Here we use computational methods and biological assays to investigate the relationship between DJ-1 missense mutations and the protein oligomeric state. Molecular dynamics calculations suggest that: (i) the structure of DJ-1 wild type (WT) in aqueous solution, in both oxidized and reduced forms, is similar to the crystal structure of the reduced form; (ii) the Parkinson disease-causing M26I variant is structurally similar to the WT, consistent with the experimental evidence showing the protein is a dimer as WT; (iii) R98Q is structurally similar to the WT, consistent with the fact that this is a physiological variant; and (iv) the L166P monomer rapidly evolves toward a conformation significantly different from WT, suggesting a change in its ability to oligomerize. Our combined computational and experimental approach is next used to identify a mutant (R28A) that, in contrast to L166P, destabilizes the dimer subunit-subunit interface without significantly changing secondary structure elements.

Protein-protein recognition and interaction hot spots in an antigen-antibody complex: free energy decomposition identifies "efficient amino acids"

The molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method was applied to the study of the protein-protein complex between a camelid single chain variable domain (cAb-Lys3) and hen egg white lysozyme (HEL), and between cAb-Lys3 and turkey egg white lysozyme (TEL). The electrostatic energy was estimated by solving the linear Poisson-Boltzmann equation. A free energy decomposition scheme was developed to determine binding energy hot spots of each complex. The calculations identified amino acids of the antibody that make important contributions to the interaction with lysozyme. They further showed the influence of small structural variations on the energetics of binding and they showed that the antibody amino acids that make up the hot spots are organized in such a way as to mimic the lysozyme substrate. Through further analysis of the results, we define the concept of "efficient amino acids," which can provide an assessment of the binding potential of a particular hot spot interaction. This information, in turn, can be useful in the rational design of small molecules that mimic the antibody. The implications of using free energy decomposition to identify regions of a protein-protein complex that could be targeted by small molecules inhibitors are discussed.

Character and evolution of protein-protein interfaces

Protein-protein interactions create the macromolecular assemblies and sequential signaling pathways essential for cell function. Their number far exceeds the number of proteins themselves and their experimental characterization, while improving, remains relatively slow. For these reasons, novel computational methods have important roles to play in understanding the physical basis of protein interactions, and in constraining the molecular basis of their specificity. This paper discusses methods based on multiple sequence alignments of protein homologues and phylogenetic trees.

Prediction of physical protein-protein interactions

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.

Molecular docking and analysis of interactions between vascular endothelial growth factor (VEGF) and SPARC protein

The extracellular module of SPARC/osteonectin binds to vascular endothelial growth factor (VEGF) and inhibits VEGF-stimulated proliferation of endothelial cells. In an attempt to identify the binding site for SPARC on VEGF, we hypothesized that this binding site could overlap at least partially the binding site of VEGF receptor 1 (VEGFR-1), as SPARC acts by preventing VEGF-induced phosphorylation of VEGFR-1. To this end, a docking simulation was carried out using a predictive docking tool to obtain modeled structures of the VEGF-SPARC complex. The predicted structure of VEGF-SPARC complex indicates that the extracellular domain of SPARC interacts with the VEGFR-1 binding site of VEGF, and is consistent with known biochemical data. Following molecular dynamics refinement, side-chain interactions at the protein interface were identified that were predicted to contribute substantially to the free energy of binding. These provide a detailed prediction of key amino acid side-chain interactions at the protein-protein interface. To validate the model further, the identified interactions will be used for designing mutagenesis studies to investigate their effect on binding activity. This model of the VEGF-SPARC complex should provide a basis for future studies aimed at identifying inhibitors of VEGF-induced angiogenesis.

Flexible relaxation of rigid-body docking solutions

Molecular Dynamics (MD) simulations have been performed on a set of rigid-body docking poses, carried out over 25 protein-protein complexes. The results show that fully flexible relaxation increases the fraction of native contacts (NC) by up to 70% for certain docking poses. The largest increase in the fraction of NC is observed for docking poses where anchor residues are able to sample their bound conformation. For each MD simulation, structural snap-shots were clustered and the centre of each cluster used as the MD-relaxed docking pose. A comparison between two energy-based scoring schemes, the first calculated for the MD-relaxed poses, the second for energy minimized poses, shows that the former are better in ranking complexes with large hydrophobic interfaces. Furthermore, complexes with large interfaces are generally ranked well, regardless of the type of relaxation method chosen, whereas complexes with small hydrophobic interfaces remain difficult to rank. In general, the results indicate that current force-fields are able to correctly describe direct intermolecular interactions between receptor and ligand molecules. However, these force-fields still fail in cases where protein-protein complexes are stabilized by subtle energy contributions.

Potential energy functions for protein design

Different potential energy functions have predominated in protein dynamics simulations, protein design calculations, and protein structure prediction. Clearly, the same physics applies in all three cases. The differences in potential energy functions reflect differences in how the calculations are performed. With improvements in computer power and algorithms, the same potential energy function should be applicable to all three problems. In this review, we examine energy functions currently used for protein design, and look to the molecular mechanics field for advances that could be used in the next generation of design algorithms. In particular, we focus on improved models of the hydrophobic effect, polarization and hydrogen bonding.

New algorithms and an in silico benchmark for computational enzyme design

The creation of novel enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Here we describe two new algorithms for enzyme design that employ hashing techniques to allow searching through large numbers of protein scaffolds for optimal catalytic site placement. We also describe an in silico benchmark, based on the recapitulation of the active sites of native enzymes, that allows rapid evaluation and testing of enzyme design methodologies. In the benchmark test, which consists of designing sites for each of 10 different chemical reactions in backbone scaffolds derived from 10 enzymes catalyzing the reactions, the new methods succeed in identifying the native site in the native scaffold and ranking it within the top five designs for six of the 10 reactions. The new methods can be directly applied to the design of new enzymes, and the benchmark provides a powerful in silico test for guiding improvements in computational enzyme design.

Identification of hot spots within druggable binding regions by computational solvent mapping of proteins

Here we apply the computational solvent mapping (CS-Map) algorithm toward the in silico identification of hot spots, that is, regions of protein binding sites that are major contributors to the binding energy and, hence, are prime targets in drug design. The CS-Map algorithm, developed for binding site characterization, moves small organic functional groups around the protein surface and determines their most energetically favorable binding positions. The utility of CS-Map algorithm toward the prediction of hot spot regions in druggable binding pockets is illustrated by three test systems: (1) renin aspartic protease, (2) a set of previously characterized druggable proteins, and (3) E. coli ketopantoate reductase. In each of the three studies, existing literature was used to verify our results. Based on our analyses, we conclude that the information provided by CS-Map can contribute substantially to the identification of hot spots, a necessary predecessor of fragment-based drug discovery efforts.

Fragment-based drug design: how big is too big?

Much has been discussed about the proper physicochemical properties (e.g., molecular weight, hydrophobicity, etc.) that should be considered when utilizing fragment leads in drug design. However, little has been reported as to what emphasis, if any, should be placed on the potency of the resulting fragment leads. In this report, a retrospective analysis of 18 highly optimized inhibitors is described in which the compounds were systematically deconstructed until the minimal binding elements could be identified. An analysis of the potency changes that were observed as the leads were reduced in size indicate that a nearly linear relationship exists between molecular weight and binding affinity over the entire range of sizes and potencies represented in the dataset. On the basis of these observations, prediction maps can be constructed that enable critical and quantitative assessments of the process of lead identification and optimization. These data place well-defined limits on the ideal size and potency of fragment leads that are being considered for use in fragment-based drug design.

Association of putative concave protein-binding sites with the fluctuation behavior of residues

Here, we propose a binding site prediction method based on the high frequency end of the spectrum in the native state of the protein structural dynamics. The spectrum is obtained using an elastic network model (GNM). High frequency vibrating (HFV) residues are determined from the fastest modes dynamics. HFV residue clusters and the associated surface patch residues are tested for their likelihood to locate at the binding interfaces using two different data sets, the Benchmark Set of mainly enzymes and antigen/antibodies and the Cluster Set of more diverse structures. The binding interface is identified to be within 7.5 A of the HFV residue clusters in the Benchmark Set and Cluster Set, for 77% and 70% of the structures, respectively. The success rate increases to 88% and 84%, respectively, by using the surface patches. The results suggest that concave binding interfaces, typically those of enzyme-binding sites, are enriched by the HFV residues. Thus, we expect that the association of HFV residues with the interfaces is mostly for enzymes. If, however, a binding region has invaginations and cavities, as in some of the antigen/antibodies and in cases in the Cluster data set, we expect it would be detected there too. This implies that binding sites possess several (inter-related) properties such as cavities, high packing density, conservation, and disposition for hotspots at binding surfaces. It further suggests that the high frequency vibrating residue-based approach is a potential tool for identification of regions likely to serve as protein-binding sites. The software is available at http://www.prc.boun.edu.tr/PRC/software.html.

Specificity of molecular interactions in transient protein-protein interaction interfaces

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of antibody-antigen complexes, the sign is somewhat ambiguous. From the evolutionary perspective, while protease-inhibitors and sig-naling proteins have optimized their interfaces to suit their biological functions, antibody-antigen interactions are the happenstance, implying that antibody-antigen complexes do not show distinctive interaction types. Persistent interaction types such as pi...pi, amide-carbonyl, and hydroxyl-carbonyl interaction, are also investigated. Analyzing the structural orientations of the pi...pi stacking interactions, we find that herringbone shape is a major configuration in transient protein-protein interfaces. This result is different from that of protein core, where parallel-displaced configurations are the major configuration. We also analyze overall trend of amide-carbonyl and hydroxyl-carbonyl interactions. It is noticeable that nearly 82% of the interfaces have at least one hydroxyl-carbonyl interactions.

Macromolecular recognition in the Protein Data Bank

Crystal structures deposited in the Protein Data Bank illustrate the diversity of biological macromolecular recognition: transient interactions in protein-protein and protein-DNA complexes and permanent assemblies in homodimeric proteins. The geometric and physical chemical properties of the macromolecular interfaces that may govern the stability and specificity of recognition are explored in complexes and homodimers compared with crystal-packing interactions. It is found that crystal-packing interfaces are usually much smaller; they bury fewer atoms and are less tightly packed than in specific assemblies. Standard-size interfaces burying 1200-2000 A2 of protein surface occur in protease-inhibitor and antigen-antibody complexes that assemble with little or no conformation changes. Short-lived electron-transfer complexes have small interfaces; the larger size of the interfaces observed in complexes involved in signal transduction and homodimers correlates with the presence of conformation changes, often implicated in biological function. Results of the CAPRI (critical assessment of predicted interactions) blind prediction experiment show that docking algorithms efficiently and accurately predict the mode of assembly of proteins that do not change conformation when they associate. They perform less well in the presence of large conformation changes and the experiment stimulates the development of novel procedures that can handle such changes.

Binding Hot Spots in the TEM1–BLIP Interface in Light of its Modular Architecture

Proteins bind one another in aqua's solution to form tight and specific complexes. Previously we have shown that this is achieved through the modular architecture of the interaction network formed by the interface residues, where tight cooperative interactions are found within modules but not between them. Here we extend this study to cover the entire interface of TEM1 beta-lactamase and its protein inhibitor BLIP using an improved method for deriving interaction maps based on REDUCE to add hydrogen atoms and then by evaluating the interactions using modifications of the programs PROBE, NCI and PARE. An extensive mutagenesis study of the interface residues indeed showed that each module is energetically independent on other modules, and that cooperativity is found only within a module. By solving the X-ray structure of two interface mutations affecting two different modules, we demonstrated that protein-protein binding occur via the structural reorganization of the binding modules, either by a "lock and key" or an induced fit mechanism. To explain the cooperativity within a module, we performed multiple-mutant cycle analysis of cluster 2 resulting in a high-resolution energy map of this module. Mutant studies are usually done in reference to alanine, which can be regarded as a deletion of a side-chain. However, from a biological perspective, there is a major interest to understand non-Ala substitutions, as they are most common. Using X-ray crystallography and multiple-mutant cycle analysis we demonstrated the added complexity in understanding non-Ala mutations. Here, a double mutation replacing the wild-type Glu,Tyr to Tyr,Asn on TEM1 (res id 104,105) caused a major backbone structural rearrangement of BLIP, changing the composition of two modules but not of other modules within the interface. This shows the robustness of the modular approach, yet demonstrates the complexity of in silico protein design.

Differential recognition of natural and nonnatural substrate by molecular chaperone α-crystallin—A subunit exchange study

alpha-Crystallin is a molecular chaperone that recognizes proteins substrates in stress. It binds to the unstable conformer of a large variety of related or unrelated substrates and thus prevents them aggregating and holds them in a folding competent state. In this article, we have tried to critically analyze, from experimental point of view, whether alpha-crystallin has any preference for its natural substrates compared to the nonnatural one. Our results clearly show that alpha-crystallin is exceptionally active and sensitive in preventing aggregation of its natural substrates and can fully prevent such an aggregation in a substoichiometric ratio, but nonnatural substrates require a considerably higher amount of alpha-crystallin. Using suitable fluorescent-labeled alpha-crystallins and performing fluorescence resonance energy transfer experiments, we were able to determine the subunit exchange kinetics between the alpha-crystallin oligomers. It was found that while alpha-crystallin was bound to its natural substrate, the rate of subunit exchange was slightly decreased. But, when a nonnatural substrate carbonic anhydrase remained bound to the chaperone, further loss in subunit exchange rate was observed. Nonnatural substrate was found to create higher activation energy barrier for the subunit exchange reaction compared to the native substrates. Similarities in major beta-sheet structure of both alpha-crystallin and its natural substrates may be the reason for the preference in molecular recognition in comparison with the nonnatural substrate.

Molecular Basis of TCR Selectivity, Cross-Reactivity, and Allelic Discrimination by a Bacterial Superantigen: Integrative Functional and Energetic Mapping of the SpeC-Vβ2.1 Molecular Interface

Superantigens activate large fractions of T cells through unconventional interactions with both TCR beta-chain V domains (Vbetas) and MHC class II molecules. The bacterial superantigen streptococcal pyrogenic exotoxin C (SpeC) primarily stimulates human Vbeta2(+) T cells. Herein, we have analyzed the SpeC-Vbeta2.1 interaction by mutating all SpeC residues that make contact with Vbeta2.1 and have determined the energetic and functional consequences of these mutations. Our comprehensive approach, including mutagenesis, functional readouts from both bulk T cell populations, and an engineered Vbeta2.1(+) Jurkat T cell, as well as surface plasmon resonance binding analysis, has defined the SpeC "functional epitope" for TCR engagement. Although only two SpeC residues (Tyr(15) and Arg(181)) are critical for activation of virtually all human CD3(+) T cells, a larger cluster of four hot spot residues are required for interaction with Vbeta2.1. Three of these residues (Tyr(15), Phe(75), and Arg(181)) concentrate their binding energy on the CDR2 loop residue Ser(52a), a noncanonical residue insertion found only in Vbeta2 and Vbeta4 chains. Plasticity of this loop is important for recognition by SpeC. Although SpeC interacts with the Vbeta2.1 hypervariable CDR3 loop, our data indicate these contacts have little to no influence on the functional interaction with Vbeta2.1. These studies also provide a molecular basis for selectivity and cross-reactivity of SpeC-TCR recognition and reveal a degree of fine specificity in these interactions, whereby certain SpeC mutants are capable of distinguishing between different alleles of the same Vbeta domain subfamily.

Conformational changes of the p53-binding cleft of MDM2 revealed by molecular dynamics simulations

Two 35-ns molecular dynamics simulations of both ligated [mouse double minute protein 2 (MDM2(p53))] and unligated (MDM2(apo)) structures of human MDM2 bound to the N-terminal domain of the tumor suppressor p53 have been performed. Analysis of the dynamics revealed that the most flexible region of MDM2 was the p53-binding cleft. When MDM2 was bound to p53, a wider and more stable topology of the cleft was obtained, while unligated MDM2 showed a narrower and highly flexible cleft. It was also found that the dynamics involved in the opening/closing motions were due to the movement of different domains of the protein, which is in agreement with recent experimental data. Considering our results, a mechanism in which p53 might be recognized and attached to MDM2 is proposed, and some implications on future directions for in silico anticancer drug design efforts are discussed. In summary, the observations made here would be very useful not only for better understanding of the biological implications of the MDM2 dynamics, but also for future efforts in anticancer drug design and discovery.

Probing hot spots at protein-ligand binding sites: a fragment-based approach using biophysical methods

Mapping interactions at protein-ligand binding sites is an important aspect of understanding many biological reactions and a key part of drug design. In this paper, we have used a fragment-based approach to probe "hot spots" at the cofactor-binding site of a model dehydrogenase, Escherichia coli ketopantoate reductase. Our strategy involved the breaking down of NADPH (Kd = 300 nM) into smaller fragments and the biophysical characterization of their binding using WaterLOGSY NMR spectroscopy, isothermal titration calorimetry (ITC), and inhibition studies. The weak binding affinities of fragments were measured by direct ITC titrations under low c value conditions. The 2'-phosphate and the reduced nicotinamide groups were found to contribute a large part of the binding energy. A combination of ITC and site-directed mutagenesis enabled us to locate the fragments at separate hot spots on opposite ends of the cofactor-binding site. This study has identified structural determinants for cofactor recognition that represent a blueprint for future inhibitor design.

EhPAK2, a novel p21-activated kinase, is required for collagen invasion and capping in Entamoeba histolytica

p21-activated kinases (PAKs) are a highly conserved family of enzymes that are activated by Rho GTPases. All PAKs contain an N-terminal Cdc42/Rac interacting binding (CRIB) domain, which confers binding to these GTPases, and a C-terminal kinase domain. In addition, some PAKs such as Cla4p, Skm1p and Pak2p contain an N-terminal pleckstrin homology (PH) domain and form a distinct group of PAK proteins involved in cell morphology, cell-cycle and gene transcription. Here, we describe a novel p21-activated kinase, denominated EhPAK2, on the parasitic protozoan Entamoeba histolytica. This is the first reported Entamoeba PAK member that contains a N-terminal PH domain and a highly conserved CRIB domain. EhPAK2 CRIB domain shares 29% of amino acid identity and 53% of amino acid homology with these of DdPAKC from Dictyostelium discoideum and Cla4p from Saccharomyces cerevisiae and binds in vitro and in vivo to EhRacA GTPase. This domain also possesses the conserved residues His123, Phe134 and Trp141, which are important for the interaction with the effector loop and strand beta2 of the GTPase; and the residues Met121 and Phe145, which are specific for the interaction of EhPAK2 with EhRacA. Functional studies of EhPAK2 showed that its C-terminal kinase domain had activity toward myelin basic protein. Cellular studies showed that Entamoeba trophozoites transfected with the vector pExEhNeo/kinase-myc, had a 90% decrease in the ability to invade a collagen matrix as well as severe defects in capping, suggesting the involvement of EhPAK2 in these cellular processes.