Bound water at protein-protein interfaces: partners, roles and hydrophobic bubbles as a conserved motif

Three-Dimensional Interaction Homology: Deconstructing Residue-Residue and Residue-Lipid Interactions in Membrane Proteins

A method is described to deconstruct the network of hydropathic interactions within and between a protein's sidechain and its environment into residue-based three-dimensional maps. These maps encode favorable and unfavorable hydrophobic and polar interactions, in terms of spatial positions for optimal interactions, relative interaction strength, as well as character. In addition, these maps are backbone angle-dependent. After map calculation and clustering, a finite number of unique residue sidechain interaction maps exist for each backbone conformation, with the number related to the residue's size and interaction complexity. Structures for soluble proteins (~749,000 residues) and membrane proteins (~387,000 residues) were analyzed, with the latter group being subdivided into three subsets related to the residue's position in the membrane protein: soluble domain, core-facing transmembrane domain, and lipid-facing transmembrane domain. This work suggests that maps representing residue types and their backbone conformation can be reassembled to optimize the medium-to-high resolution details of a protein structure. In particular, the information encoded in maps constructed from the lipid-facing transmembrane residues appears to paint a clear picture of the protein-lipid interactions that are difficult to obtain experimentally.

Merging cultures and disciplines to create a drug discovery ecosystem at Virginia commonwealth university: Medicinal chemistry, structural biology, molecular and behavioral pharmacology and computational chemistry

The Department of Medicinal Chemistry, together with the Institute for Structural Biology, Drug Discovery and Development, at Virginia Commonwealth University (VCU) has evolved, organically with quite a bit of bootstrapping, into a unique drug discovery ecosystem in response to the environment and culture of the university and the wider research enterprise. Each faculty member that joined the department and/or institute added a layer of expertise, technology and most importantly, innovation, that fertilized numerous collaborations within the University and with outside partners. Despite moderate institutional support with respect to a typical drug discovery enterprise, the VCU drug discovery ecosystem has built and maintained an impressive array of facilities and instrumentation for drug synthesis, drug characterization, biomolecular structural analysis and biophysical analysis, and pharmacological studies. Altogether, this ecosystem has had major impacts on numerous therapeutic areas, such as neurology, psychiatry, drugs of abuse, cancer, sickle cell disease, coagulopathy, inflammation, aging disorders and others. Novel tools and strategies for drug discovery, design and development have been developed at VCU in the last five decades; e.g., fundamental rational structure-activity relationship (SAR)-based drug design, structure-based drug design, orthosteric and allosteric drug design, design of multi-functional agents towards polypharmacy outcomes, principles on designing glycosaminoglycans as drugs, and computational tools and algorithms for quantitative SAR (QSAR) and understanding the roles of water and the hydrophobic effect.

HINT, a code for understanding the interaction between biomolecules: a tribute to Donald J. Abraham

A long-lasting goal of computational biochemists, medicinal chemists, and structural biologists has been the development of tools capable of deciphering the molecule-molecule interaction code that produces a rich variety of complex biomolecular assemblies comprised of the many different simple and biological molecules of life: water, small metabolites, cofactors, substrates, proteins, DNAs, and RNAs. Software applications that can mimic the interactions amongst all of these species, taking account of the laws of thermodynamics, would help gain information for understanding qualitatively and quantitatively key determinants contributing to the energetics of the bimolecular recognition process. This, in turn, would allow the design of novel compounds that might bind at the intermolecular interface by either preventing or reinforcing the recognition. HINT, hydropathic interaction, was a model and software code developed from a deceptively simple idea of Donald Abraham with the close collaboration with Glen Kellogg at Virginia Commonwealth University. HINT is based on a function that scores atom-atom interaction using LogP, the partition coefficient of any molecule between two phases; here, the solvents are water that mimics the cytoplasm milieu and octanol that mimics the protein internal hydropathic environment. This review summarizes the results of the extensive and successful collaboration between Abraham and Kellogg at VCU and the group at the University of Parma for testing HINT in a variety of different biomolecular interactions, from proteins with ligands to proteins with DNA.

Antibody CDR amino acids underlying the functionality of antibody repertoires in recognizing diverse protein antigens

Antibodies recognize protein antigens with exquisite specificity in a complex aqueous environment, where interfacial waters are an integral part of the antibody–protein complex interfaces. In this work, we elucidate, with computational analyses, the principles governing the antibodies’ specificity and affinity towards their cognate protein antigens in the presence of explicit interfacial waters. Experimentally, in four model antibody–protein complexes, we compared the contributions of the interaction types in antibody–protein antigen complex interfaces with the antibody variants selected from phage-displayed synthetic antibody libraries. Evidently, the specific interactions involving a subset of aromatic CDR (complementarity determining region) residues largely form the predominant determinant underlying the specificity of the antibody–protein complexes in nature. The interfacial direct/water-mediated hydrogen bonds accompanying the CDR aromatic interactions are optimized locally but contribute little in determining the epitope location. The results provide insights into the phenomenon that natural antibodies with limited sequence and structural variations in an antibody repertoire can recognize seemingly unlimited protein antigens. Our work suggests guidelines in designing functional artificial antibody repertoires with practical applications in developing novel antibody-based therapeutics and diagnostics for treating and preventing human diseases.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC₅₀ values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

Rational Design of a User-Friendly Aptamer/Peptide-Based Device for the Detection of Staphylococcus aureus

The urgent need to develop a detection system for Staphylococcus aureus, one of the most common causes of infection, is prompting research towards novel approaches and devices, with a particular focus on point-of-care analysis. Biosensors are promising systems to achieve this aim. We coupled the selectivity and affinity of aptamers, short nucleic acids sequences able to recognize specific epitopes on bacterial surface, immobilized at high density on a nanostructured zirconium dioxide surface, with the rational design of specifically interacting fluorescent peptides to assemble an easy-to-use detection device. We show that the displacement of fluorescent peptides upon the competitive binding of S. aureus to immobilized aptamers can be detected and quantified through fluorescence loss. This approach could be also applied to the detection of other bacterial species once aptamers interacting with specific antigens will be identified, allowing the development of a platform for easy detection of a pathogen without requiring access to a healthcare environment.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Water envelope has a critical impact on the design of protein–protein interaction inhibitors

We show that a water envelope network plays a critical role in protein–protein interactions (PPI).

The urokinase plasminogen activator binding to its receptor: a quantum biochemistry description within an in/homogeneous dielectric function framework with application to uPA–uPAR peptide inhibitors

DFT calculations using the MFCC fragment-based model considering a spatial-dependent dielectric function based on the Poisson–Boltzmann approximation were performed to describe the uPA–uPAR interactions.

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies.

The ammonium transporter AmtB and the PII signal transduction protein GlnZ are required to inhibit DraG in Azospirillum brasilense

The ammonium-dependent posttranslational regulation of nitrogenase activity in Azospirillum brasilense requires dinitrogenase reductase ADP-ribosyl transferase (DraT) and dinitrogenase reductase ADP-glycohydrolase (DraG). These enzymes are reciprocally regulated by interaction with the PII proteins, GlnB and GlnZ. In this study, purified ADP-ribosylated Fe-protein was used as substrate to study the mechanism involved in the regulation of A. brasilense DraG in vitro. The data show that DraG is partially inhibited by GlnZ and that DraG inhibition is further enhanced by the simultaneous presence of GlnZ and AmtB. These results are the first to demonstrate experimentally that DraG inactivation requires the formation of a ternary DraG-GlnZ-AmtB complex in vitro. Previous structural data have revealed that when the DraG-GlnZ complex associates with AmtB, the flexible T-loops of the trimeric GlnZ bind to AmtB and become rigid; these molecular events stabilize the DraG-GlnZ complex, resulting in DraG inactivation. To determine whether restraining the flexibility of the GlnZ T-loops is a limiting factor in DraG inhibition, we used a GlnZ variant that carries a partial deletion of the T-loop (GlnZΔ42-54). However, although the GlnZΔ42-54 variant was more effective in inhibiting DraG in vitro, it bound to DraG with a slightly lower affinity than does wild-type GlnZ and was not competent to completely inhibit DraG activity either in vitro or in vivo. We, therefore, conclude that the formation of a ternary complex between DraG-GlnZ-AmtB is necessary for the inactivation of DraG.

Development and Validation of Molecular Overlays Derived from Three-Dimensional Hydrophobic Similarity with PharmScreen

Molecular alignment is a standard procedure for three-dimensional (3D) similarity measurements and pharmacophore elucidation. This process is influenced by several factors, such as the physicochemical descriptors utilized to account for the molecular determinants of biological activity and the reference templates. Relying on the hypothesis that the maximal achievable binding affinity for a drug-like molecule is largely due to desolvation, we explore a novel strategy for 3D molecular overlays that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond (HB) donor/acceptor groups. A brief description of the method, as implemented in the software package PharmScreen, including the derivation of the fractional hydrophobic contributions within the quantum mechanical version of the Miertus-Scrocco-Tomasi (MST) continuum model, and the procedure utilized for the optimal superposition between molecules, is presented. The computational procedure is calibrated by using a data set of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the AstraZeneca test, which comprises 121 experimentally derived sets of molecular overlays. The results point out the suitability of the MST-based hydrophobic parameters for generating molecular overlays, as correct predictions were obtained for 94%, 79%, and 54% of the molecules classified into easy, moderate, and hard sets, respectively. Moreover, the results point out that this accuracy is attained at a much lower degree of identity between the templates used by hydrophobic/HB fields and electrostatic/steric ones. These findings support the usefulness of the hydrophobic/HB descriptors to generate complementary overlays that may be valuable to rationalize structure-activity relationships and for virtual screening campaigns.

Local and global anatomy of antibody-protein antigen recognition

Deciphering antibody-protein antigen recognition is of fundamental and practical significance. We constructed an antibody structural dataset, partitioned it into human and murine subgroups, and compared it with nonantibody protein-protein complexes. We investigated the physicochemical properties of regions on and away from the antibody-antigen interfaces, including net charge, overall antibody charge distributions, and their potential role in antigen interaction. We observed that amino acid preference in antibody-protein antigen recognition is entropy driven, with residues having low side-chain entropy appearing to compensate for the high backbone entropy in interaction with protein antigens. Antibodies prefer charged and polar antigen residues and bridging water molecules. They also prefer positive net charge, presumably to promote interaction with negatively charged protein antigens, which are common in proteomes. Antibody-antigen interfaces have large percentages of Tyr, Ser, and Asp, but little Lys. Electrostatic and hydrophobic interactions in the Ag binding sites might be coupled with Fab domains through organized charge and residue distributions away from the binding interfaces. Here we describe some features of antibody-antigen interfaces and of Fab domains as compared with nonantibody protein-protein interactions. The distributions of interface residues in human and murine antibodies do not differ significantly. Overall, our results provide not only a local but also a global anatomy of antibody structures.

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

Complex structure of cytochrome c-cytochrome c oxidase reveals a novel protein-protein interaction mode

Mitochondrial cytochrome c oxidase (CcO) transfers electrons from cytochrome c (Cyt.c) to O₂ to generate H₂O, a process coupled to proton pumping. To elucidate the mechanism of electron transfer, we determined the structure of the mammalian Cyt.c–CcO complex at 2.0‐Å resolution and identified an electron transfer pathway from Cyt.c to CcO. The specific interaction between Cyt.c and CcO is stabilized by a few electrostatic interactions between side chains within a small contact surface area. Between the two proteins are three water layers with a long inter‐molecular span, one of which lies between the other two layers without significant direct interaction with either protein. Cyt.c undergoes large structural fluctuations, using the interacting regions with CcO as a fulcrum. These features of the protein–protein interaction at the docking interface represent the first known example of a new class of protein–protein interaction, which we term “soft and specific”. This interaction is likely to contribute to the rapid association/dissociation of the Cyt.c–CcO complex, which facilitates the sequential supply of four electrons for the O₂ reduction reaction.

Conformational selection in amyloid-based immunotherapy: Survey of crystal structures of antibody-amyloid complexes

BACKGROUND

The dominant feature in neurodegenerative diseases is protein aggregations that lead to neuronal loss. Immunotherapies using antibodies or antibody fragments to target the aggregations are a highly perused approach. The molecular mechanisms underlying the amyloid-based immunotherapy are complex. Deciphering the properties of amyloidogenic proteins responsible for these diseases is essential to obtain insights into antibody recognition of the amyloid antigens.

SCOPE OF REVIEW

We systematically explore all available crystal structures of antibody-amyloid complexes related to neurodegenerative diseases, including antibodies that recognize the Aβ peptide, tau protein, prion protein, alpha-synuclein, huntingtin protein (mHTT), and polyglutamine.

MAJOR CONCLUSIONS

We found that antibodies mostly use the conformational selection mechanism to recognize the highly flexible amyloid antigens. In particular, solanezumab bound to Aβ12-28 tripeptide motif conformation (F19F20A21), which is shared with the Aβ42 fibril. This motif, which is trapped by the antibody, may provide the missing link in amyloid formation. Water molecules often bridge between the antibody and amyloid, contributing to the recognition.

GENERAL SIGNIFICANCE

This paper provides the structural basis for antibody recognition of amyloidogenic proteins. The analysis and discussion of known structures are expected to help in the design and optimization of antibodies in neurodegenerative diseases. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Molecular docking as a popular tool in drug design, an in silico travel

New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism-or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein-protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.

Molecular dynamics of zinc-finger ubiquitin binding domains: a comparative study of histone deacetylase 6 and ubiquitin-specific protease 5

HDAC6 is a unique cytoplasmic histone deacetylase characterized by two deacetylase domains, and by a zinc-finger ubiquitin binding domain (ZnF-UBP) able to recognize ubiquitin (Ub). The latter has recently been demonstrated to be involved in the progression of neurodegenerative diseases and in mediating infection by the influenza A virus. Nowadays, understanding the dynamic and energetic features of HDAC6 ZnF-UBP-Ub recognition is considered as a crucial step for the conception of HDAC6 potential modulators. In this study, the atomic, solvent-related, and thermodynamic features behind HDAC6 ZnF-UBP-Ub recognition have been analyzed through molecular dynamics simulations. The behavior was then compared to the prototypical ZnF-UBP from ubiquitin-specific protease 5 (USP5) in order to spot relevant differences useful for selective drug design. Principal component analysis highlighted flapping motions of the L2A loop which were lowered down upon Ub binding in both systems. While polar and nonpolar interactions involving Ub G75 and G76 residues were also common features stabilizing both complexes, salt bridges showed a different pattern, more significant in HDAC6 ZnF-UBP-Ub, whose energetic contribution in USP5 ZnF-UBP-Ub was compensated by the presence of a more stable bridging water molecule. Whereas molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) free energies of binding were comparable for both systems, in agreement with experiments, computational alanine scanning and free energy decomposition data revealed that HDAC6 E1141 and D1178 are potential hotspots for the design of selective HDAC6 modulators.

Interaction between bound water molecules and local protein structures: A statistical analysis of the hydrogen bond structures around bound water molecules

Water molecules play an important role in protein folding and protein interactions through their structural association with proteins. Examples of such structural association can be found in protein crystal structures, and can often explain protein functionality in the context of structure. We herein report the systematic analysis of the local structures of proteins interacting with water molecules, and the characterization of their geometric features. We first examined the interaction of water molecules with a large local interaction environment by comparing the preference of water molecules in three regions, namely, the protein-protein interaction (PPI) interfaces, the crystal contact (CC) interfaces, and the non-interfacial regions. High preference of water molecules to the PPI and CC interfaces was found. In addition, the bound water on the PPI interface was more favorably associated with the complex interaction structure, implying that such water-mediated structures may participate in the shaping of the PPI interface. The pairwise water-mediated interaction was then investigated, and the water-mediated residue-residue interaction potential was derived. Subsequently, the types of polar atoms surrounding the water molecules were analyzed, and the preference of the hydrogen bond acceptor was observed. Furthermore, the geometries of the structures interacting with water were analyzed, and it was found that the major structure on the protein surface exhibited planar geometry rather than tetrahedral geometry. Several previously undiscovered characteristics of water-protein interactions were unfolded in this study, and are expected to lead to a better understanding of protein structure and function.

Structure of the ordered hydration of amino acids in proteins: analysis of crystal structures

Crystallography provides unique information about the arrangement of water molecules near protein surfaces. Using a nonredundant set of 2818 protein crystal structures with a resolution of better than 1.8 Å, the extent and structure of the hydration shell of all 20 standard amino-acid residues were analyzed as function of the residue conformation, secondary structure and solvent accessibility. The results show how hydration depends on the amino-acid conformation and the environment in which it occurs. After conformational clustering of individual residues, the density distribution of water molecules was compiled and the preferred hydration sites were determined as maxima in the pseudo-electron-density representation of water distributions. Many hydration sites interact with both main-chain and side-chain amino-acid atoms, and several occurrences of hydration sites with less canonical contacts, such as carbon-donor hydrogen bonds, OH-π interactions and off-plane interactions with aromatic heteroatoms, are also reported. Information about the location and relative importance of the empirically determined preferred hydration sites in proteins has applications in improving the current methods of hydration-site prediction in molecular replacement, ab initio protein structure prediction and the set-up of molecular-dynamics simulations.

High-resolution protein-protein docking by global optimization: recent advances and future challenges

A computational protein-protein docking method that predicts atomic details of protein-protein interactions from protein monomer structures is an invaluable tool for understanding the molecular mechanisms of protein interactions and for designing molecules that control such interactions. Compared to low-resolution docking, high-resolution docking explores the conformational space in atomic resolution to provide predictions with atomic details. This allows for applications to more challenging docking problems that involve conformational changes induced by binding. Recently, high-resolution methods have become more promising as additional information such as global shapes or residue contacts are now available from experiments or sequence/structure data. In this review article, we highlight developments in high-resolution docking made during the last decade, specifically regarding global optimization methods employed by the docking methods. We also discuss two major challenges in high-resolution docking: prediction of backbone flexibility and water-mediated interactions.

Inferring the microscopic surface energy of protein-protein interfaces from mutation data

Mutations at protein-protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical ΔΔG values, yielding mean energies of -48 cal mol(-1) Å(-2) for interactions between hydrophobic surfaces, -51 to -80 cal mol(-1) Å(-2) for surfaces of complementary charge, and 66-83 cal mol(-1) Å(-2) for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at -24 cal mol(-1) Å(-2) . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid-body interactions (r = 0.66). When used to rerank docking poses, it can place near-native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50-95% of the cohesive energy. The model is available open-source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/.

IFACEwat: the interfacial water-implemented re-ranking algorithm to improve the discrimination of near native structures for protein rigid docking

Background

Protein-protein docking is an in silico method to predict the formation of protein complexes. Due to limited computational resources, the protein-protein docking approach has been developed under the assumption of rigid docking, in which one of the two protein partners remains rigid during the protein associations and water contribution is ignored or implicitly presented. Despite obtaining a number of acceptable complex predictions, it seems to-date that most initial rigid docking algorithms still find it difficult or even fail to discriminate successfully the correct predictions from the other incorrect or false positive ones. To improve the rigid docking results, re-ranking is one of the effective methods that help re-locate the correct predictions in top high ranks, discriminating them from the other incorrect ones.

In this paper, we propose a new re-ranking technique using a new energy-based scoring function, namely IFACEwat - a combined Interface Atomic Contact Energy (IFACE) and water effect. The IFACEwat aims to further improve the discrimination of the near-native structures of the initial rigid docking algorithm ZDOCK3.0.2. Unlike other re-ranking techniques, the IFACEwat explicitly implements interfacial water into the protein interfaces to account for the water-mediated contacts during the protein interactions.

Results

Our results showed that the IFACEwat increased both the numbers of the near-native structures and improved their ranks as compared to the initial rigid docking ZDOCK3.0.2. In fact, the IFACEwat achieved a success rate of 83.8% for Antigen/Antibody complexes, which is 10% better than ZDOCK3.0.2. As compared to another re-ranking technique ZRANK, the IFACEwat obtains success rates of 92.3% (8% better) and 90% (5% better) respectively for medium and difficult cases. When comparing with the latest published re-ranking method F²Dock, the IFACEwat performed equivalently well or even better for several Antigen/Antibody complexes.

Conclusions

With the inclusion of interfacial water, the IFACEwat improves mostly results of the initial rigid docking, especially for Antigen/Antibody complexes. The improvement is achieved by explicitly taking into account the contribution of water during the protein interactions, which was ignored or not fully presented by the initial rigid docking and other re-ranking techniques. In addition, the IFACEwat maintains sufficient computational efficiency of the initial docking algorithm, yet improves the ranks as well as the number of the near native structures found. As our implementation so far targeted to improve the results of ZDOCK3.0.2, and particularly for the Antigen/Antibody complexes, it is expected in the near future that more implementations will be conducted to be applicable for other initial rigid docking algorithms.

Direct and indirect interactions in the recognition between a cross-neutralizing antibody and the four serotypes of dengue virus

Dengue fever is the most important vector-borne viral disease. Four serotypes of dengue virus, DENV1 to DENV4, coexist. Secondary infection by a different serotype is a risk factor for severe dengue. Monoclonal antibody mAb4E11 neutralizes the four serotypes of DENV with varying efficacies by recognizing an epitope located within domain-III (ED3) of the viral envelope (E) protein. To better understand the cross-reactivities between mAb4E11 and the four serotypes of DENV, we constructed mutations in both Fab4E11 fragment and ED3, and we searched for indirect interactions in the crystal structures of the four complexes. According to the serotype, 7 to 12 interactions are mediated by one water molecule, 1 to 10 by two water molecules, and several of these interactions are conserved between serotypes. Most interfacial water molecules make hydrogen bonds with both antibody and antigen. Some residues or atomic groups are engaged in both direct and water-mediated interactions. The doubly-indirect interactions are more numerous in the complex of lowest affinity. The third complementarity determining region of the light chain (L-CDR3) of mAb4E11 does not contact ED3. The structures and double-mutant thermodynamic cycles showed that the effects of (hyper)-mutations in L-CDR3 on affinity were caused by conformational changes and indirect interactions with ED3 through other CDRs. Exchanges of residues between ED3 serotypes showed that their effects on affinity were context dependent. Thus, conformational changes, structural context, and indirect interactions should be included when studying cross-reactivity between antibodies and different serotypes of viral antigens for a better design of diagnostics, vaccine, and therapeutic tools against DENV and other Flaviviruses.

Promiscuous and specific recognition among ephrins and Eph receptors

Eph-ephrin interactions control the signal transduction between cells and play an important role in carcinogenesis and other diseases. The interactions between Eph receptors and ephrins of the same subclass are promiscuous; there are cross-interactions between some subclasses, but not all. To understand how Eph-ephrin interactions can be both promiscuous and specific, we investigated sixteen energy landscapes of four Eph receptors (A2, A4, B2, and B4) interacting with four ephrin ligands (A1, A2, A5, and B2). We generated conformational ensembles and recognition energy landscapes starting from separated Eph and ephrin molecules and proceeding up to the formation of Eph-ephrin complexes. Analysis of the Eph-ephrin recognition trajectories and the co-evolution entropy of 400 ligand binding domains of Eph receptor and 241 ephrin ligands identified conserved residues during the recognition process. Our study correctly predicted the promiscuity and specificity of the interactions and provided insights into their recognition. The dynamic conformational changes during Eph-ephrin recognition can be described by progressive conformational selection and population shift events, with two dynamic salt bridges between EphB4 and ephrin-B2 contributing to the specific recognition. EphA3 cancer-related mutations lowered the binding energies. The specificity is not only controlled by the final stage of the interaction across the protein-protein interface, but also has large contributions from binding kinetics with the help of dynamic intermediates along the pathway from the separated Eph and ephrin to the Eph-ephrin complex.

Protein-ligand docking using hamiltonian replica exchange simulations with soft core potentials

Molecular dynamics (MD) simulations in explicit solvent allow studying receptor-ligand binding processes including full flexibility of the binding partners and an explicit inclusion of solvation effects. However, in MD simulations, the search for an optimal ligand-receptor complex geometry is frequently trapped in locally stable non-native binding geometries. A Hamiltonian replica-exchange (H-REMD)-based protocol has been designed to enhance the sampling of putative ligand-receptor complexes. It is based on softening nonbonded ligand-receptor interactions along the replicas and one reference replica under the control of the original force field. The efficiency of the method has been evaluated on two receptor-ligand systems and one protein-peptide complex. Starting from misplaced initial docking geometries, the H-REMD method reached in each case the known binding geometry significantly faster than a standard MD simulation. The approach could also be useful to identify and evaluate alternative binding geometries in a given binding region with small relative differences in binding free energy.

Targeting cystalysin, a virulence factor of treponema denticola-supported periodontitis

Cystalysin from Treponema denticola is a pyridoxal 5'-phosphate dependent lyase that catalyzes the formation of pyruvate, ammonia, and sulfide from cysteine. It is a virulence factor in adult periodontitis because its reaction contributes to hemolysis, which sustains the pathogen. Therefore, it was proposed as a potential antimicrobial target. To identify specific inhibitors by structure-based in silico methods, we first validated the crystal structure of cystalysin as a reliable starting point for the design of ligands. By using single-crystal absorption microspectrophotometry, we found that the enzyme in the crystalline state, with respect to that in solution, exhibits: 1) the same absorption spectra for the catalytic intermediates, 2) a close pKa value for the residue controlling the keto enamine ionization, and 3) similar reactivity with glycine, L-serine, L-methionine, and the nonspecific irreversible inhibitor aminoethoxyvinylglycine. Next, we screened in silico a library of 9357 compounds with the Fingerprints for Ligands and Proteins (FLAP) software, by using the three-dimensional structure of cystalysin as a template. From the library, 17 compounds were selected and experimentally evaluated by enzyme assays and spectroscopic methods. Two compounds were found to competitively inhibit recombinant T. denticola cystalysin, with inhibition constant (Ki ) values of 25 and 37 μM. One of them exhibited a minimum inhibitory concentration (MIC) value of 64 μg mL(-1) on Moraxella catarrhalis ATCC 23246, which proves its ability to cross bacterial membranes.

Intuitive, but not simple: including explicit water molecules in protein-protein docking simulations improves model quality

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high-resolution crystallographically characterized "dry" protein-protein complexes and was shown to reliably identify native-like models. However, most current protein-protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the "truly" bridging waters at the 30 protein-protein interfaces and we utilized them in "solvated" docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ∼24% in the average hit-count within the top-10 predictions the protein-protein dataset was seen, compared to standard "dry" docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native-like structure predictions.

De-peptidising protein-protein interactions - big jobs for small molecules

Virtually all biological processes rely on protein-protein interactions (PPIs) for signal propagation, therefore representing a vast array of potentially viable therapeutic intervention points. Targeting PPIs is a relatively novel drug development strategy so computational approaches towards analysing the interface between protein partners and predicting the likelihood of developing a small molecule inhibitor are still progressing. This review provides an overview of recent successful examples of computational methodologies used to predict druggable PPIs and small molecules designed to inhibit them.

Quantifying the molecular origins of opposite solvent effects on protein-protein interactions

Although the nature of solvent-protein interactions is generally weak and non-specific, addition of cosolvents such as denaturants and osmolytes strengthens protein-protein interactions for some proteins, whereas it weakens protein-protein interactions for others. This is exemplified by the puzzling observation that addition of glycerol oppositely affects the association constants of two antibodies, D1.3 and D44.1, with lysozyme. To resolve this conundrum, we develop a methodology based on the thermodynamic principles of preferential interaction theory and the quantitative characterization of local protein solvation from molecular dynamics simulations. We find that changes of preferential solvent interactions at the protein-protein interface quantitatively account for the opposite effects of glycerol on the antibody-antigen association constants. Detailed characterization of local protein solvation in the free and associated protein states reveals how opposite solvent effects on protein-protein interactions depend on the extent of dewetting of the protein-protein contact region and on structural changes that alter cooperative solvent-protein interactions at the periphery of the protein-protein interface. These results demonstrate the direct relationship between macroscopic solvent effects on protein-protein interactions and atom-scale solvent-protein interactions, and establish a general methodology for predicting and understanding solvent effects on protein-protein interactions in diverse biological environments.

Large Scale Chemical Cross-linking Mass Spectrometry Perspectives

The spectacular heterogeneity of a complex protein mixture from biological samples becomes even more difficult to tackle when one’s attention is shifted towards different protein complex topologies, transient interactions, or localization of PPIs. Meticulous protein-by-protein affinity pull-downs and yeast-two-hybrid screens are the two approaches currently used to decipher proteome-wide interaction networks. Another method is to employ chemical cross-linking, which gives not only identities of interactors, but could also provide information on the sites of interactions and interaction interfaces. Despite significant advances in mass spectrometry instrumentation over the last decade, mapping Protein-Protein Interactions (PPIs) using chemical cross-linking remains time consuming and requires substantial expertise, even in the simplest of systems. While robust methodologies and software exist for the analysis of binary PPIs and also for the single protein structure refinement using cross-linking-derived constraints, undertaking a proteome-wide cross-linking study is highly complex. Difficulties include i) identifying cross-linkers of the right length and selectivity that could capture interactions of interest; ii) enrichment of the cross-linked species; iii) identification and validation of the cross-linked peptides and cross-linked sites.

In this review we examine existing literature aimed at the large-scale protein cross-linking and discuss possible paths for improvement. We also discuss short-length cross-linkers of broad specificity such as formaldehyde and diazirine-based photo-cross-linkers. These cross-linkers could potentially capture many types of interactions, without strict requirement for a particular amino-acid to be present at a given protein-protein interface. How these shortlength, broad specificity cross-linkers be applied to proteome-wide studies? We will suggest specific advances in methodology, instrumentation and software that are needed to make such a leap.

On the binding affinity of macromolecular interactions: daring to ask why proteins interact

Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (K_d), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition.