Molecular interaction among proteins drives the cellular processes through the formation of complexes that perform the requisite biochemical function. While some of the complexes are obligate (i.e., they fold together while complexation) others are non-obligate, and are formed through macromolecular recognition. Macromolecular recognition in proteins is highly specific, yet it can be both permanent and non permanent in nature. Hallmarks of permanent recognition complexes include large surface of interaction, stabilization by hydrophobic interaction and other noncovalent forces. Several amino acids which contribute critically to the free energy of binding at these interfaces are called as “hot spot” residues. The non permanent recognition complexes, on the other hand, usually show small interface of interaction, with limited stabilization from non covalent forces. For both the permanent and non permanent complexes, the specificity of molecular interaction is governed by the geometric compatibility of the interaction surface, and the noncovalent forces that anchor them. A great deal of studies has already been performed in understanding the basis of protein macromolecular recognition.1; 2 Based on these studies efforts have been made to develop protein-protein docking algorithms that can predict the geometric orientation of the interacting molecules from their individual unbound states. Despite advances in docking methodologies, several significant difficulties remain.1 Therefore, in this thesis, we start with literature review to understand the individual merits and demerits of the existing approaches (Chapter 1),3 and then, we attempt to address some of the problems by developing methods to infer protein quaternary structure from the crystalline state, and improve structural and chemical understanding of protein-protein interactions through biological complex prediction. The understanding of the interaction geometry is the first step in a protein-protein interaction study. Yet, no consistent method exists to assess the geometric compatibility of the interacting interface because of its highly rugged nature. This suggested that new sensitive measures and methods are needed to tackle the problem. We, therefore, developed two new and conceptually different measures using the Delaunay tessellation and interface slice selection to compute the surface complementarity and atom packing at the protein-protein interface (Chapter 2).4 We called these Normalized Surface Complementarity (NSc) and Normalized Interface Packing (NIP). We rigorously benchmarked the measures on the non redundant protein complexes available in the Protein Data Bank (PDB) and found that they efficiently segregate the biological protein-protein contacts from the non biological ones, especially those derived from X-ray crystallography. Sensitive surface packing/complementarity recognition algorithms are usually computationally expensive and thus limited in application to high-throughput screening. Therefore, special emphasis was given to make our measure compute-efficient as well. Our final evaluation showed that NSc, and NIP have very strong correlation among themselves, and with the interface area normalized values available from the Surface Complementarity program (CCP4 Suite: <http://smb.slac.stanford.edu/facilities/software/ccp4/html/sc.html>); but at a fraction of the computing cost. After building the geometry based surface complementarity and packing assessment methods to assess the rugged protein surface, we advanced our goal to determine the stabilities of the geometrically compatible interfaces formed. For doing so, we needed to survey the quaternary structure of proteins with various affinities. The emphasis on affinity arose due to its strong relationship with the permanent and non permanent life-time of the complex. We, therefore, set up data mining studies on two databases named PQS (Protein Quaternary structure database: http://pqs.ebi.ac.uk) and PISA (Protein Interfaces, Surfaces and Assemblies: www.ebi.ac.uk/pdbe/prot_int/pistart.html) that offered downloads on quaternary structure data on protein complexes derived from X-ray crystallographic methods. To our surprise, we found that above mentioned databases provided the valid quaternary structure mostly for moderate to strong affinity complexes. The limitation could be ascertained by browsing annotations from another curated database of protein quaternary structure (PiQSi:5 supfam.mrc-lmb.cam.ac.uk/elevy/piqsi/piqsi_home.cgi) and literature surveys. This necessitated that we at first develop a more robust method to infer quaternary structures of all affinity available from the PDB. We, therefore, developed a new scheme focused on covering all affinity category complexes, especially the weak/very weak ones, and heteromeric quaternary structures (Chapter 3).6 Our scheme combined the naïve Bayes classifier and point-group symmetry under a Boolean framework to detect all categories of protein quaternary structures in crystal lattice. We tested it on a standard benchmark consisting of 112 recognition heteromeric complexes, and obtained a correct recall in 95% cases, which are significantly better than 53% achieved by the PISA,7 a state-of-art quaternary structure detection method hosted at the European Bioinformatics Institute, Hinxton, UK. A few cases that failed correct detection through our scheme, offered interesting insights into the intriguing nature of protein contacts in the lattice. The findings have implications for accurate inference of quaternary states of proteins, especially weak affinity complexes, where biological protein contacts tend to be sacrificed for the energetically optimal ones that favor the formation/stabilization of the crystal lattice. We expect our method to be used widely by all researchers interested in protein quaternary structure and interaction. Having developed a method that allows us to sample all categories of quaternary structures in PDB, we set our goal in addressing the next problem that of accurately determining stabilities of the geometrically compatible protein surfaces involved in interaction. Reformulating the question in terms of protein-protein docking, we sought to ask how we could reliably infer the stabilities of any arbitrary interface that is formed when two protein molecules are brought sterically closer. In a real protein docking exercise this question is asked innumerable times during energy-based screening of thousands of decoys geometrically sampled (through rotation+translation) from the unbound subunits. The current docking methods face problems in two counts: (i), the number of interfaces from decoys to evaluate energies is rather large (64320 for a 9º rotation and translation for a dimeric complex), and (ii) the energy based screening is not quite efficient such that the decoys with native-like quaternary structure are rarely selected at high ranks. We addressed both the problems with interesting results. Intricate decoy filtering approaches have been developed, which are either applied during the search stage or the sampling stage, or both. For filtering, usually statistical information, such as 3D conservation information of the interfacial residues, or similar facts is used; more expensive approaches screen for orientation, shape complementarity and electrostatics. We developed an interface area based decoy filter for the sampling stage, exploiting an assumption that native-like decoys must have the largest, or close to the largest, interface (Chapter 4).8 Implementation of this assumption and standard benchmarking showed that in 91% of the cases, we could recover native-like decoys of bound and unbound binary docking-targets of both strong and weak affinity. This allowed us to propose that “native-like decoys must have the largest, or close to the largest, interface” can be used as a rule to exclude non native decoys efficiently during docking sampling. This rule can dramatically clip the needle-in-a-haystack problem faced in a docking study by reducing >95% of the decoy set available from sampling search. We incorporated the rule as a central part of our protein docking strategy. While addressing the question of energy based screening to rank the native-like decoys at high rank during docking, we came across a large volume of work already published. The mainstay of most of the energy based screenings that avoid statistical potential, involve some form of the Coulomb’s potential, Lennard Jones potential and solvation energy. Different flavors of the energy functions are used with diverse preferences and weights for individual terms. Interestingly, in all cases the energy functions were of the unnormalized form. Individual energy terms were simply added to arrive at a final score that was to be used for ranking. Proteins being large molecules, offer limited scope of applying semi-empirical or quantum mechanical methods for large scale evaluation of energy. We, therefore, developed a de novo empirical scoring function in the normalized form. As already stated, we found NSc and NIP to be highly discriminatory for segregating biological and non biological interface. We, therefore, incorporated them as parameters for our scoring function. Our data mining study revealed that there is a reasonable correlation of -0.73 between normalized solvation energy and normalized nonbonding energy (Coulombs + van der Waals) at the interface. Using the information, we extended our scoring function by combining the geometric measures and the normalized interaction energies. Tests on 30 unbound binary protein-protein complexes showed that in 16 cases we could identify at least one decoy in top three ranks with ≤10 Å backbone root-mean-square-deviation (RMSD) from true binding geometry. The scoring results were compared with other state-of-art methods, which returned inferior results. The salient feature of our scoring function was exclusion of any experiment guided restraints, evolutionary information, statistical propensities or modified interaction energy equations, commonly used by others. Tests on 118 less difficult bound binary protein-protein complexes with ≤35% sequence redundancy at the interface gave first rank in 77% cases, where the native like decoy was chosen among 1 in 10,000 and had ≤5 Å backbone RMSD from true geometry. The details about the scoring function, results and comparison with the other methods are extensively discussed in Chapter 5.9 The method has been implemented and made available for public use as a web server - PROBE (http://pallab.serc.iisc.ernet.in/probe). The development and use of PROBE has been elaborated in Chapter 7.10 On course of this work, we generated huge amounts of data, which is useful information that could be used by others, especially “protein dockers”. We, therefore, developed dockYard (http://pallab.serc.iisc.ernet.in/dockYard) - a repository for protein-protein docking decoys (Chapter 6).11 dockYard offers four categories of docking decoys derived from: Bound (native dimer co-crystallized), Unbound (individual subunits as well as the target are crystallized), Variants (match the previous two categories in at least one subunit with 100% sequence identity), and Interlogs (match the previous categories in at least one subunit with ≥90% or ≥50% sequence identity). There is facility for full or selective download based on search parameters. The portal also serves as a repository to modelers who may want to share their decoy sets with the community. In conclusion, although we made several contributions in development of algorithms for improved protein-protein docking and quaternary structure inference, a lot of challenges remain (Chapter 8). The principal challenge arises by considering proteins as flexible bodies, whose conformational states may change on quaternary structure formation. In addition, solvent plays a major role in the free energy of binding, but its exact contribution is not straightforward to estimate. Undoubtedly, the cost of computation is one of the limiting factors apart from good energy functions to evaluate the docking decoys. Therefore, the next generation of algorithms must focus on improved docking studies that realistically incorporate flexibility and solvent environment in all their evaluations.

Le développement des anticorps thérapeutiques s'est rapidement accéléré dans les 10 dernières années et concerne un nombre croissant de pathologies. La connaissance de l'épitope, à savoir la région de la cible à laquelle l'anticorps se fixe, est essentielle pour la compréhension des effets fonctionnels de ce dernier. Nous avons développé une méthode in silico, MAbTope, qui permet une prédiction précise de cet épitope, quand bien même aucune structure 3D de l'anticorps d’intérêt n'est résolue. Cette méthode se base sur une méthode d'amarrage protéine-protéine développée auparavant dans l’équipe BIOS. Le jeu d'apprentissage a été fortement enrichi en complexes anticorps-cibles, de nouvelles fonctions de score spécifiques ont été mises au point, et le plus important, l'objectif de l'apprentissage-machine a été modifié pour optimiser non plus la conformation de !'assemblage, mais la prédiction de l'épitope. Nous montrons que la méthode qui en résulte permet une prédiction précise et robuste de l'épitope, que la structure 3D de l'anticorps soit connue ou non. Nous montrons également comment les prédictions peuvent être facilement exploitées pour la validation expérimentale. Enfin, nous montrons comment la méthode peut être utilisée pour étudier à haut-débit le recouvrement d'épitopes par des anticorps ayant la même cible. / The development of therapeutic antibodies has been rapidly increasing in the last 10 years, with application to an increasing number of pathologies. The knowledge of the epitope, the region of the antigen to which the antibody binds, is crucial for understanding its functional effects. We have developed an in silico method, MAbTope, which allows the accurate prediction of the epitope, regardless of the availability of the 3D structure of the antibody of interest. This method is based on a protein-protein docking method previously developed in the BIOS group. The learning dataset was enlarged in antibody-antigen complexes, new specific scoring functions have been designed, and very importantly, the objective of machine-learning was switched from the conformational perspective towards the epitope determination perspective. We show that the resulting method allows robust and accurate prediction, whether or not the 3D structure of the antibody is available. We also show how the predictions can be easily exploited for experimental validation. Finally, we show how this method can be used for high-throughput epitope binning.

<p dir="ltr">Proteins and their interactions with each other, with nucleic acids, and with other molecules are foundational to all known forms of life. The three-dimensional structures of these interactions are an essential component of a comprehensive understanding of how they function. Molecular-biological hypothesis formulation and rational drug design are both often predicated on a particular structure model of the molecule or complex of interest. While experimental methods capable of determining atomic-detail structures of molecules and complexes exist, such as the popular X-ray crystallography and cryo-electron microscopy, these methods require both laborious sample preparation and expensive instruments with limited throughput. Computational methods of predicting complex structures are therefore desirable if they can enable cheap, high-throughput virtual screening of the space of biological hypotheses. Many common biomolecular contexts have largely been blind spots for predictive modeling of complex structures. In this direction, docking methods are proposed to address extreme conformational change, nonuniform environments, and distance-geometric priors. Flex-LZerD deforms a flexible protein using a novel fitting procedure based on iterated normal mode decomposition and was shown to construct accurate complex models even when an initial input subunit structure exhibits extreme conformational differences from its bound state. Mem-LZerD efficiently constrains the docking search space by augmenting the geometric hashing data structure at the core of the LZerD algorithm and enabled membrane protein complexes to be efficiently and accurately modeled. Finally, atomic distance-based approaches developed during modeling competitions and collaborations with wet lab biologists were shown to effectively integrate domain knowledge into complex modeling pipelines.</p>

Analysis of genome sequences is enabling identification of numerous novel characteristics that provide valuable means for genetic and biochemical studies. Of these characteristics, Conserved Signature Indels (CSIs) in proteins which are specific for a given group of organisms have proven particularly useful for evolutionary and biochemical studies. My research work focused on using comparative genomics techniques to identify a large number of CSIs which are distinctive characteristics of fungi and other important groups of organisms. These CSIs were utilized to understand the evolutionary relationships among different proteins (species), and also regarding their structural features and functional significance. Based on multiple CSIs that I have identified for the PIP4K/PIP5K family of proteins, different isozymes of these proteins and also their subfamilies can now be reliably distinguished in molecular terms. Further, the species distribution of CSIs in the PIP4K/PIP5K proteins and phylogenetic analyses of these protein sequences, my work provides important insights into the evolutionary history of this protein family. The functional significance of one of the CSI in the PIP5K proteins, specific for the Saccharomycetaceae family of fungi, was also investigated. The results from structural analysis and molecular dynamics (MD) simulation studies show that this 8 aa CSI plays an important role in facilitating the binding of fungal PIP5K protein to the membrane surface. In other work, we identified multiple highly-specific CSIs in the phosphoketolase (PK) proteins, which clearly distinguish the bifunctional form of PK found in bifidobacteria from its homologs (monofunctional) found in other organisms. Structural analyses and docking studies with these proteins indicate that the CSIs in bifidobacterial PK, which are located on the subunit interface, play a role in the formation/stabilization of the protein dimer. We have also identified 2 large CSIs in SecA proteins that are uniquely found in thermophilic species from two different phyla of bacteria. Detailed bioinformatics analyses on one of these CSIs show that a number of residues from this CSI, through their interaction with a conserved network of water molecules, play a role in stabilizing the binding of ADP/ATP to the SecA protein at high temperature. My work also involved developing an integrated software pipeline for homology modeling of proteins and analyzing the location of CSIs in protein structures. Overall, my thesis work establishes the usefulness of CSIs in protein sequences as valuable means for genetic, biochemical, structural and evolutionary studies. / Dissertation / Doctor of Philosophy (PhD)

Protein structure predication is a field of computational molecular modeling with an enormous potential for improvement. Side-chain geometry prediction is a critical component of this process that is crucial for computational protein structure predication as well as crystallographers in refining experimentally determined protein crystal structures. The cornerstone of side-chain geometry prediction are side-chain rotamer libraries, usually obtained through exhaustive statistical analysis of existing protein structures. Little is known, however, about the driving forces leading to the preference or suitability of one rotamer over another. Construction of 3D hydropathic interaction maps for nearly 30,000 tyrosines extracted from the PDB reveals their environments, in terms of hydrophobic and polar (collectively “hydropathic”) interactions. Using a unique 3D similarity metric, these environments were clustered with k-means. In the ϕ, ψ region (–200° < ϕ < –155°; –205° < ψ < –160°) representing 631 tyrosines, clustering reduced the set to 14 unique hydropathic environments, with most diversity arising from favorable hydrophobic interactions. Polar interactions for tyrosine include ubiquitous hydrogen bonding with the phenolic OH and a handful of unique environments surrounding the backbone. The memberships of all but one of the 14 environments are dominated by a single χ1/χ2 rotamer. Each tyrosine residue attempts to fulfill its hydropathic valence. Structural water molecules are thus used in a variety of roles throughout protein structure. A second project involves elucidating the 3D structure of CRIP1a, a cannabinoid 1 receptor (CB1R) binding protein that could provide information for designing small molecules targeting the CRIP1a-CB1R interaction. The CRIP1a protein was produced in high purity. Crystallization experiments failed, both with and without the last 9 or 12 amino acid peptide of the CB1R C-terminus. Attempts were made to use NMR for structure determination; however, the protein precipitated out during data acquisition. A model was thus built computationally to which the CB1R C-terminus peptide was docked. HINT was used in selecting optimum models and analyzing interactions involved in the CRIP1a-CB1R complex. The final model demonstrated key putative interactions between CRIP1a and CB1R while also predicting highly flexible areas of the CRIP1a possibly contributing to the difficulties faced during crystallization.

Proteins and the formation of large protein complexes are essential parts of living organisms. Proteins are present in all aspects of life processes, performing a multitude of various functions ranging from being structural components of cells, to facilitating the passage of certain molecules between various regions of cells. The 'protein docking problem' refers to the computational method of predicting the appropriate matching pair of a protein (receptor) with respect to another protein (ligand), when attempting to bind to one another to form a stable complex. Research shows that matching the three-dimensional (3D) geometric structures of candidate proteins plays a key role in determining a so-called docking pair, which is one of the key aspects of the Computer Aided Drug Design process. However, the active sites which are responsible for binding do not always present a rigid-body shape matching problem. Rather, they may undergo sufficient deformation when docking occurs, which complicates the problem of finding a match. To address this issue, we present an isometry-invariant and topologically robust partial shape matching method for finding complementary protein binding sites, which we call the ProtoDock algorithm. The ProtoDock algorithm comes in two variations. The first version performs a partial shape complementarity matching by initially segmenting the underlying protein object mesh into smaller portions using a spectral mesh segmentation approach. The Heat Kernel Signature (HKS), the underlying basis of our shape descriptor, is subsequently computed for the obtained segments. A final descriptor vector is constructed from the Heat Kernel Signatures and used as the basis for the segment matching. The three different descriptor methods employed are, the accepted Bag of Features (BoF) technique, and our two novel approaches, Closest Medoid Set (CMS) and Medoid Set Average (MSA). The second variation of our ProtoDock algorithm aims to perform the partial matching by utilizing the pointwise HKS descriptors. The use of the pointwise HKS is mainly motivated by the suggestion that, at adequate times, the Heat Kernel Signature of a point on a surface sufficiently describes its neighbourhood. Hence, the HKS of a point may serve as the representative descriptor of its given region of which it forms a part. We propose three (3) sampling methods---Uniform, Random, and Segment-based Random sampling---for selecting these points for the partial matching. Random and Segment-based Random sampling both prove superior to the Uniform sampling method. Our experimental results, run against the Protein-Protein Benchmark 4.0, demonstrate the viability of our approach, in that, it successfully returns known binding segments for known pairing proteins. Furthermore, our ProtoDock-1 algorithm still still yields good results for low resolution protein meshes. This results in even faster processing and matching times with sufficiently reduced computational requirements when obtaining the HKS.

Proteins and the formation of large protein complexes are essential parts of living organisms. Proteins are present in all aspects of life processes, performing a multitude of various functions ranging from being structural components of cells, to facilitating the passage of certain molecules between various regions of cells. The 'protein docking problem' refers to the computational method of predicting the appropriate matching pair of a protein (receptor) with respect to another protein (ligand), when attempting to bind to one another to form a stable complex. Research shows that matching the three-dimensional (3D) geometric structures of candidate proteins plays a key role in determining a so-called docking pair, which is one of the key aspects of the Computer Aided Drug Design process. However, the active sites which are responsible for binding do not always present a rigid-body shape matching problem. Rather, they may undergo sufficient deformation when docking occurs, which complicates the problem of finding a match. To address this issue, we present an isometry-invariant and topologically robust partial shape matching method for finding complementary protein binding sites, which we call the ProtoDock algorithm. The ProtoDock algorithm comes in two variations. The first version performs a partial shape complementarity matching by initially segmenting the underlying protein object mesh into smaller portions using a spectral mesh segmentation approach. The Heat Kernel Signature (HKS), the underlying basis of our shape descriptor, is subsequently computed for the obtained segments. A final descriptor vector is constructed from the Heat Kernel Signatures and used as the basis for the segment matching. The three different descriptor methods employed are, the accepted Bag of Features (BoF) technique, and our two novel approaches, Closest Medoid Set (CMS) and Medoid Set Average (MSA). The second variation of our ProtoDock algorithm aims to perform the partial matching by utilizing the pointwise HKS descriptors. The use of the pointwise HKS is mainly motivated by the suggestion that, at adequate times, the Heat Kernel Signature of a point on a surface sufficiently describes its neighbourhood. Hence, the HKS of a point may serve as the representative descriptor of its given region of which it forms a part. We propose three (3) sampling methods---Uniform, Random, and Segment-based Random sampling---for selecting these points for the partial matching. Random and Segment-based Random sampling both prove superior to the Uniform sampling method. Our experimental results, run against the Protein-Protein Benchmark 4.0, demonstrate the viability of our approach, in that, it successfully returns known binding segments for known pairing proteins. Furthermore, our ProtoDock-1 algorithm still still yields good results for low resolution protein meshes. This results in even faster processing and matching times with sufficiently reduced computational requirements when obtaining the HKS.

Three-finger toxins (3FTX) are a group of peptides that affect multiple receptor types. One group of proteins affected by 3FTX are nicotinic acetylcholine receptors (nAChR). Structural information on how neurotoxins interact with nAChR is limited and is confined to a small group of neurotoxins. Therefore, in silico methods are valuable in understanding the interactions between 3FTX and different nAChR subtypes, but there are no established protocols to model 3FTX–nAChR interactions. We followed a homology modeling and protein docking protocol to address this issue and tested its success on three different systems. First, neurotoxin peptides co-crystallized with acetylcholine binding protein (AChBP) were re-docked to assess whether Rosetta protein–protein docking can reproduce the native poses. Second, experimental data on peptide binding to AChBP was used to test whether the docking protocol can qualitatively distinguish AChBP-binders from non-binders. Finally, we docked eight peptides with known α7 and muscle-type nAChR binding properties to test whether the protocol can explain the differential activities of the peptides at the two receptor subtypes. Overall, the docking protocol predicted the qualitative and some specific aspects of 3FTX binding to nAChR with reasonable success and shed light on unknown aspects of 3FTX binding to different receptor subtypes.

Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.

Computationally driven drug design/discovery efforts generally rely on accurate assessment of the forces that guide the molecular recognition process. HINT (Hydropathic INTeraction) is a natural force field, derived from experimentally determined partition coefficients that quantifies all non-bonded interactions in the biological environment, including hydrogen bonding, electrostatic and hydrophobic interactions, and the energy of desolvation. The overall goal of this work is to apply the HINT-based atomic level description of molecular systems to biologically important proteins, to better understand their biochemistry – a key step in exploiting them for therapeutic purposes. This dissertation discusses the results of three diverse projects: i) structural modeling of human sphingosine kinase 2 (SphK2, a novel anticancer target) and binding mode determination of an isoform selective thiazolidine-2,4-dione (TZD) analog; ii) structural modeling of human cytomegalorvirus (HCMV) alkaline nuclease (AN) UL98 (a novel antiviral target) and subsequent virtual screening of its active site; and iii) explicit treatment of interfacial waters during protein-protein docking process using HINT-based computational tools. SphK2 is a key regulator of the sphingosine-rheostat, and its upregulation /overexpression has been associated with cancer development. We report structural modeling studies of a novel TZD-analog that selectively inhibits SphK2, in a HINT analysis that identifies the key structural features of ligand and protein binding site responsible for isoform selectivity. The second aim was to build a three-dimensional structure of a novel HCMV target – AN UL98, to identify its catalytically important residues. HINT analysis of the interaction of 5’ DNA end at its active site is reported. A parallel aim to perform in silico screening with a site-based pharmacophore model, identified several novel hits with potentially desirable chemical features for interaction with UL98 AN. The majority of current protein-protein docking algorithms fail to account for water molecules involved in bridging interactions between partners, mediating and stabilizing their association. HINT is capable of reproducing the physical and chemical properties of such waters, while accounting for their energetic stabilizing contributions. We have designed a solvated protein-protein docking protocol that explicitly models the Relevant bridging waters, and demonstrate that more accurate results are obtained when water is not ignored.