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

Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.

Different combinations of atomic interactions predict protein-small molecule and protein-DNA/RNA affinities with similar accuracy

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein−ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein−protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

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.

AlphaSpace: Fragment-Centric Topographical Mapping To Target Protein-Protein Interaction Interfaces

Inhibition of protein–protein interactions (PPIs) is emerging as a promising therapeutic strategy despite the difficulty in targeting such interfaces with drug-like small molecules. PPIs generally feature large and flat binding surfaces as compared to typical drug targets. These features pose a challenge for structural characterization of the surface using geometry-based pocket-detection methods. An attractive mapping strategy—that builds on the principles of fragment-based drug discovery (FBDD)—is to detect the fragment-centric modularity at the protein surface and then characterize the large PPI interface as a set of localized, fragment-targetable interaction regions. Here, we introduce AlphaSpace, a computational analysis tool designed for fragment-centric topographical mapping (FCTM) of PPI interfaces. Our approach uses the alpha sphere construct, a geometric feature of a protein’s Voronoi diagram, to map out concave interaction space at the protein surface. We introduce two new features—alpha-atom and alpha-space—and the concept of the alpha-atom/alpha-space pair to rank pockets for fragment-targetability and to facilitate the evaluation of pocket/fragment complementarity. The resulting high-resolution interfacial map of targetable pocket space can be used to guide the rational design and optimization of small molecule or biomimetic PPI inhibitors.

Modeling for (physical) biologists: an introduction to the rule-based approach

Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.

Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method

A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).

Mechanotransduction Mechanisms for Intraventricular Diastolic Vortex Forces and Myocardial Deformations: Part 2

Epigenetic mechanisms are fundamental in cardiac adaptations, remodeling, reverse remodeling, and disease. A primary goal of translational cardiovascular research is recognizing whether disease-related changes in phenotype can be averted by eliminating or reducing the effects of environmental epigenetic risks. There may be significant medical benefits in using gene-by-environment interaction knowledge to prevent or reverse organ abnormalities and disease. This survey proposes that "environmental" forces associated with diastolic RV/LV rotatory flows exert important, albeit still unappreciated, epigenetic actions influencing functional and morphological cardiac adaptations. Mechanisms analogous to Murray's law of hydrodynamic shear-induced endothelial cell modulation of vascular geometry are likely to link diastolic vortex-associated shear, torque and "squeeze" forces to RV/LV adaptations. The time has come to explore a new paradigm in which such forces play a fundamental epigenetic role, and to work out how heart cells react to them. Findings from various imaging modalities, computational fluid dynamics, molecular cell biology and cytomechanics are considered. The following are examined, among others: structural dynamics of myocardial cells (endocardium, cardiomyocytes, and fibroblasts), cytoskeleton, nucleoskeleton, and extracellular matrix; mechanotransduction and signaling; and mechanical epigenetic influences on genetic expression. To help integrate and focus relevant pluridisciplinary research, rotatory RV/LV filling flow is placed within a working context that has a cytomechanics perspective. This new frontier in cardiac research should uncover versatile mechanistic insights linking filling vortex patterns and attendant forces to variable expressions of gene regulation in RV/LV myocardium. In due course, it should reveal intrinsic homeostatic arrangements that support ventricular myocardial function and adaptability.

Detection and characterization of nonspecific, sparsely populated binding modes in the early stages of complexation

A method is proposed to study protein-ligand binding in a system governed by specific and nonspecific interactions. Strong associations lead to narrow distributions in the proteins configuration space; weak and ultraweak associations lead instead to broader distributions, a manifestation of nonspecific, sparsely populated binding modes with multiple interfaces. The method is based on the notion that a discrete set of preferential first-encounter modes are metastable states from which stable (prerelaxation) complexes at equilibrium evolve. The method can be used to explore alternative pathways of complexation with statistical significance and can be integrated into a general algorithm to study protein interaction networks. The method is applied to a peptide-protein complex. The peptide adopts several low-population conformers and binds in a variety of modes with a broad range of affinities. The system is thus well suited to analyze general features of binding, including conformational selection, multiplicity of binding modes, and nonspecific interactions, and to illustrate how the method can be applied to study these problems systematically. The equilibrium distributions can be used to generate biasing functions for simulations of multiprotein systems from which bulk thermodynamic quantities can be calculated.

Alternative modulation of protein-protein interactions by small molecules

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Protein–protein interactions can be modulated by more than orthosteric disruption.

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Modulator categories: ‘orthosteric versus allosteric’ and ‘disrupting versus stabilising’.

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Interfacial binders exert secondary effects.

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Non-competitive modulation is a way around low affinity molecules.

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Non-competitive modulators require tailored screening strategies.

Protein–protein interactions (PPI) have become increasingly popular drug targets, with a number of promising compounds currently in clinical trials. Recent research shows, that PPIs can be modulated in more ways than direct inhibition, where novel non-competitive modes of action promise a solution for the difficult nature of PPI drug discovery.

Here, we review recently discovered PPI modulators in light of their mode of action and categorise them as disrupting versus stabilising, orthosteric versus allosteric and by their ability to affect the proteins’ dynamics. We also give recent examples of compounds successful in the clinic, analyse their physicochemical properties and discuss how to overcome the hurdles in discovering alternative modes of modulation.

Structural basis for assembly and function of the Nup82 complex in the nuclear pore scaffold

The yeast Nup82 complex forms an unusual asymmetric structure with a dimeric array of subunits that mediate its anchorage to the NPC scaffold and its concomitant interaction with the soluble nucleocytoplasmic transport machinery.

Nuclear pore complexes (NPCs) are huge assemblies formed from ∼30 different nucleoporins, typically organized in subcomplexes. One module, the conserved Nup82 complex at the cytoplasmic face of NPCs, is crucial to terminate mRNA export. To gain insight into the structure, assembly, and function of the cytoplasmic pore filaments, we reconstituted in yeast the Nup82–Nup159–Nsp1–Dyn2 complex, which was suitable for biochemical, biophysical, and electron microscopy analyses. Our integrative approach revealed that the yeast Nup82 complex forms an unusual asymmetric structure with a dimeric array of subunits. Based on all these data, we developed a three-dimensional structural model of the Nup82 complex that depicts how this module might be anchored to the NPC scaffold and concomitantly can interact with the soluble nucleocytoplasmic transport machinery.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Use of fluorescence spectroscopy for quantitative investigations of ubiquitin interactions with the ubiquitin-binding domains of NEMO

Ubiquitin serves as a signal for a variety of cellular processes and its specific interaction with ubiquitin-binding domain (UBD) regulates key cellular events including protein degradation, cell-cycle control, DNA repair, and kinase activation. Several binding mechanisms for isolated UBDs have been reported in recent years. However, little is known about the mechanism through which proteins containing multiple-UBDs achieve specificity for a particular oligomer of polyUb. The NF-κB essential modulator (NEMO, also known IKKγ), which plays a key role in the NF-κB signaling pathway, belongs to the latter family of proteins since it contains two distal NOA (also known UBAN/CC2-LZ/NUB) and ZF UBDs, separated by an unstructured proline-rich linker of about 40 residues in length. Here, we show a new procedure for fast purification of this bipartite domain. We also describe the use of intrinsic fluorescence spectroscopy for quantitative investigations of ubiquitin interactions between two distal ubiquitin-binding domains of NEMO (NOA and ZF). This spectroscopic method has many advantages over other techniques like GST pulldown and Biacore's SPR for monitoring avid interactions between two UBDs, especially when UBDs are located at significant distance from each other within the protein.

Affinity proteomics to study endogenous protein complexes: pointers, pitfalls, preferences and perspectives

Dissecting and studying cellular systems requires the ability to specifically isolate distinct proteins along with the co-assembled constituents of their associated complexes. Affinity capture techniques leverage high affinity, high specificity reagents to target and capture proteins of interest along with specifically associated proteins from cell extracts. Affinity capture coupled to mass spectrometry (MS)-based proteomic analyses has enabled the isolation and characterization of a wide range of endogenous protein complexes. Here, we outline effective procedures for the affinity capture of protein complexes, highlighting best practices and common pitfalls.

Using nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM) for simultaneous determination of concentration and equilibrium constant

Nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM) is a versatile tool for studying affinity binding. Here we describe a NECEEM-based approach for simultaneous determination of both the equilibrium constant, K(d), and the unknown concentration of a binder that we call a target, T. In essence, NECEEM is used to measure the unbound equilibrium fraction, R, for the binder with a known concentration that we call a ligand, L. The first set of experiments is performed at varying concentrations of T, prepared by serial dilution of the stock solution, but at a constant concentration of L, which is as low as its reliable quantitation allows. The value of R is plotted as a function of the dilution coefficient, and dilution corresponding to R = 0.5 is determined. This dilution of T is used in the second set of experiments in which the concentration of T is fixed but the concentration of L is varied. The experimental dependence of R on the concentration of L is fitted with a function describing their theoretical dependence. Both K(d) and the concentration of T are used as fitting parameters, and their sought values are determined as the ones that generate the best fit. We have fully validated this approach in silico by using computer-simulated NECEEM electropherograms and then applied it to experimental determination of the unknown concentration of MutS protein and K(d) of its interactions with a DNA aptamer. The general approach described here is applicable not only to NECEEM but also to any other method that can determine a fraction of unbound molecules at equilibrium.

Flexibility and small pockets at protein-protein interfaces: New insights into druggability

The transient assembly of multiprotein complexes mediates many aspects of cell regulation and signalling in living organisms. Modulation of the formation of these complexes through targeting protein-protein interfaces can offer greater selectivity than the inhibition of protein kinases, proteases or other post-translational regulatory enzymes using substrate, co-factor or transition state mimetics. However, capitalising on protein-protein interaction interfaces as drug targets has been hindered by the nature of interfaces that tend to offer binding sites lacking the well-defined large cavities of classical drug targets. In this review we posit that interfaces formed by concerted folding and binding (disorder-to-order transitions on binding) of one partner and other examples of interfaces where a protein partner is bound through a continuous epitope from a surface-exposed helix, flexible loop or chain extension may be more tractable for the development of "orthosteric", competitive chemical modulators; these interfaces tend to offer small-volume but deep pockets and/or larger grooves that may be bound tightly by small chemical entities. We discuss examples of such protein-protein interaction interfaces for which successful chemical modulators are being developed.

Serum albumin binding analysis and toxicological screening of novel chroman-2,4-diones as oral anticoagulants

Two chroman-2,4-dione derivatives, namely 2a and 2f, were tested as in vivo anticoagulants by seven days of continuous per os application to adult male Wistar rats in a concentration of 20 mg/kg of body weight. Derivatives were selected from a group of six previously intraperitoneally applied compounds on the basis of presenting remarkable activity in a concentration of 2 mg/kg of body weight. The derivatives 2a and 2f are VKORC1 inhibitors, and comparison of the absorption spectra, association, and dissociation constants suggested that the compounds will be bound to serum albumin in the same manner as warfarin is, leading to transfer towards the molecular target VKORC1. After oral administration, the compounds proved to be anticoagulants comparable with warfarin, inasmuch as the measured prothrombin times for 2a and 2f were 56.63 and 60.08 s, respectively. The INR values of 2a and 2f ranged from 2.6 to 2.8, recommending them as useful therapeutics in the treatment of patients suffering from thromboembolic events and atrial fibrillation. The high percentage of binding and high binding affinity of 2a and 2f towards serum albumin reduced the risk of induced internal bleeding. Several kinds of toxicity studies were performed to investigate whether or not 2a and 2f can cause pathological changes in the liver, kidneys, and DNA. The catalytic activity of serum enzymes, concentration and catalytic activity of liver and kidney oxidative stress markers and enzymes, respectively, as well as the observed hepatic and renal morphological changes indicated that the compounds in relation to warfarin induced irrelevant hepatic toxicity, no increment of necrosis, and inconsiderable oxidative damage in the liver and kidneys. Estimation of DNA damage using the comet assay confirmed that 2a and 2f caused no clinically significant genotoxicity. The higher activity and lower toxicity of 2f recommended this compound as a better drug candidate than 2a.

Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins

This review emphasizes the effects of naturally occurring mutations on structural features and physico-chemical properties of proteins. The basic protein characteristics considered are stability, dynamics, and the binding of proteins and methods for assessing effects of mutations on these macromolecular characteristics are briefly outlined. It is emphasized that the above entities mostly reflect global characteristics of considered macromolecules, while given mutations may alter the local structural features such as salt bridges and hydrogen bonds without affecting the global ones. Furthermore, it is pointed out that disease-causing mutations frequently involve a drastic change of amino acid physico-chemical properties such as charge, hydrophobicity, and geometry, and are less surface exposed than polymorphic mutations.

Small molecules, peptides and natural products: getting a grip on 14-3-3 protein-protein modulation

One of the proteins that is found in a diverse range of eukaryotic protein-protein interactions is the adaptor protein 14-3-3. As 14-3-3 is a hub protein with very diverse interactions, it is a good model to study various protein-protein interactions. A wide range of classes of molecules, peptides, small molecules or natural products, has been used to modify the protein interactions, providing both stabilization or inhibition of the interactions of 14-3-3 with its binding partners. The first protein crystal structures were solved in 1995 and gave molecular insights for further research. The plant analog of 14-3-3 binds to a plant plasma membrane H(+)-ATPase and this protein complex is stabilized by the fungal phytotoxin fusicoccin A. The knowledge gained from the process in plants was transferred to and applied in human models to find stabilizers or inhibitors of 14-3-3 interaction in human cellular pathways.

Structural and electrostatic analysis of HLA B-cell epitopes: inference on immunogenicity and prediction of humoral alloresponses

PURPOSE OF REVIEW

The immunogenic capacity of donor human leukocyte antigen (HLA) to induce humoral immune responses is not an intrinsic property of the mismatched alloantigen but depends on the HLA phenotype of the recipient. In recent years, advances in molecular sequence technology and information from X-ray crystallography have enabled structural comparison of donor and recipient HLA type providing an opportunity for a more rational approach for determining HLA compatibility. In this article, we review studies investigating the molecular basis of antibody-antigen interactions and present computational approaches to determine the complex physiochemical and structural properties of B-cell epitopes.

RECENT FINDINGS

The relative immunogenicity of individual HLA mismatches may be predicted from analysis of polymorphic amino acids at continuous and discontinuous HLA sequence positions. The use of alloantigen sequence information alone, however, provides limited insight into key determinants of B-cell epitope immunogenicity, such as the orientation, accessibility and physiochemical properties of amino acid side chains. Advances in computational molecular modelling techniques now enable assessment of HLA-alloantibody interactions at the atomic level. Recent evidence supports a strong link between HLA B-cell epitope surface electrostatic potential and their immunogenicity.

SUMMARY

Assessment of the surface electrostatic properties of HLA alloantigens and computational analyses of HLA-alloantibody interactions represent a promising area for future research into the molecular basis of HLA immunogenicity and antigenicity.

In silico affinity profiling of neuroactive polyphenols for post-traumatic calpain inactivation: a molecular docking and atomistic simulation sensitivity analysis

Calcium-activated nonlysosomal neutral proteases, calpains, are believed to be early mediators of neuronal damage associated with neuron death and axonal degeneration after traumatic neural injuries. In this study, a library of biologically active small molecular weight calpain inhibitors was used for model validation and inhibition site recognition. Subsequently, two natural neuroactive polyphenols, curcumin and quercetin, were tested for their sensitivity and activity towards calpain's proteolytic sequence and compared with the known calpain inhibitors via detailed molecular mechanics (MM), molecular dynamics (MD), and docking simulations. The MM and MD energy profiles (SJA6017 < AK275 < AK295 < PD151746 < quercetin < leupeptin < PD150606 < curcumin < ALLN < ALLM < MDL-28170 < calpeptin) and the docking analysis (AK275 < AK295 < PD151746 < ALLN < PD150606 < curcumin < leupeptin < quercetin < calpeptin < SJA6017 < MDL-28170 < ALLM) demonstrated that polyphenols conferred comparable calpain inhibition profiling. The modeling paradigm used in this study provides the first detailed account of corroboration of enzyme inhibition efficacy of calpain inhibitors and the respective calpain-calpain inhibitor molecular complexes' energetic landscape and in addition stimulates the polyphenol bioactive paradigm for post-SCI intervention with implications reaching to experimental in vitro, in cyto, and in vivo studies.

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.

Flexible docking-based molecular dynamics/steered molecular dynamics calculations of protein-protein contacts in a complex of cytochrome P450 1A2 with cytochrome b5

Formation of transient complexes of cytochrome P450 (P450) with another protein of the endoplasmic reticulum membrane, cytochrome b5 (cyt b5), dictates the catalytic activities of several P450s. Therefore, we examined formation and binding modes of the complex of human P450 1A2 with cyt b5. Docking of soluble domains of these proteins was performed using an information-driven flexible docking approach implemented in HADDOCK. Stabilities of the five unique binding modes of the P450 1A2-cyt b5 complex yielded by HADDOCK were evaluated using explicit 10 ns molecular dynamics (MD) simulations in aqueous solution. Further, steered MD was used to compare the stability of the individual P450 1A2-cyt b5 binding modes. The best binding mode was characterized by a T-shaped mutual orientation of the porphyrin rings and a 10.7 Å distance between the two redox centers, thus satisfying the condition for a fast electron transfer. Mutagenesis studies and chemical cross-linking, which, in the absence of crystal structures, were previously used to deduce specific P450-cyt b5 interactions, indicated that the negatively charged convex surface of cyt b5 binds to the positively charged concave surface of P450. Our simulations further elaborate structural details of this interface, including nine ion pairs between R95, R100, R138, R362, K442, K455, and K465 side chains of P450 1A2 and E42, E43, E49, D65, D71, and heme propionates of cyt b5. The universal heme-centric system of internal coordinates was proposed to facilitate consistent classification of the orientation of the two porphyrins in any protein complex.

Constructing arrays of proteins

The construction of crystalline arrays allows proteins to be presented in a dense, oriented and functional way that also facilitates determination of their structure. Rational design of these supramolecular structures is becoming increasingly tractable with recent successes exploiting both innate protein symmetry and advances in protein–protein interface design. Pre-existing symmetry minimizes the number of non-native interfaces that must be produced, and the use of symmetric interfaces facilitates protein alignment. Arrays in which metal coordination or peptide binding are responsible for the inter-particle associations show particular promise due to the malleable and reversible nature of these interactions. Cross-pollination of the principles that underlie successful strategies is likely to produce rapid advances in this field and consequent benefits to both nanotechnology and structural biology.

Protein-protein interaction networks studies and importance of 3D structure knowledge

Protein-protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes' stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype-phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Interaction studies of the human and Arabidopsis thaliana Med25-ACID proteins with the herpes simplex virus VP16- and plant-specific Dreb2a transcription factors

Mediator is an evolutionary conserved multi-protein complex present in all eukaryotes. It functions as a transcriptional co-regulator by conveying signals from activators and repressors to the RNA polymerase II transcription machinery. The Arabidopsis thaliana Med25 (aMed25) ACtivation Interaction Domain (ACID) interacts with the Dreb2a activator which is involved in plant stress response pathways, while Human Med25-ACID (hMed25) interacts with the herpes simplex virus VP16 activator. Despite low sequence similarity, hMed25-ACID also interacts with the plant-specific Dreb2a transcriptional activator protein. We have used GST pull-down-, surface plasmon resonance-, isothermal titration calorimetry and NMR chemical shift experiments to characterize interactions between Dreb2a and VP16, with the hMed25 and aMed25-ACIDs. We found that VP16 interacts with aMed25-ACID with similar affinity as with hMed25-ACID and that the binding surface on aMed25-ACID overlaps with the binding site for Dreb2a. We also show that the Dreb2a interaction region in hMed25-ACID overlaps with the earlier reported VP16 binding site. In addition, we show that hMed25-ACID/Dreb2a and aMed25-ACID/Dreb2a display similar binding affinities but different binding energetics. Our results therefore indicate that interaction between transcriptional regulators and their target proteins in Mediator are less dependent on the primary sequences in the interaction domains but that these domains fold into similar structures upon interaction.

Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches

Protein-protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein-protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein-protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein-protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein-protein complexes into high and low affinity groups based on their Kd values. Our results showed a 10-fold cross-validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein-protein interaction networks and human-pathogen interactions based on the strength of interactions.

Impact of subdomain D1 of the short form S1b of the human prolactin receptor on its inhibitory action on the function of the long form of the receptor induced by prolactin

BACKGROUND

Long-form (LF) homodimers of the human prolactin receptor (PRLR) mediate prolactin's diverse actions. Short form S1b inhibits the LF function through heterodimerization. Reduced S1b/LF-ratio in breast cancer could contribute to tumor development/progression. Current work defines the structural and functional relevance of the D1 domain of S1b on its inhibitory function on prolactin-induced LF function.

METHODS

Studies were conducted using mutagenesis, promoter/signaling analyses, bioluminescence resonance energy transfer (BRET) and molecular modeling approaches.

RESULTS

Mutation of E69 in D1 S1b or adjacent residues at the receptor surface near to the binding pocket (S) causes loss of its inhibitory effect while mutations away from this region (A) or in the D2 domain display inhibitory action as the wild-type. All S1b mutants preserved prolactin-induced Jak2 activation. BRET reveals an increased affinity in D1 mutated S1b (S) homodimers in transfected cells stably expressing LF. In contrast, affinity in S1b homodimers with either D1 (A) or D2 mutations remained unchanged. This favors LF mediated signaling induced by prolactin. Molecular dynamics simulations show that mutations (S) elicit major conformational changes that propagate downward to the D1/D2 interface and change their relative orientation in the dimers.

CONCLUSIONS

These findings demonstrate the essential role of D1 on the S1b structure and its inhibitory action on prolactin-induced LF-mediated function.

GENERAL SIGNIFICANCE

Major changes in receptor conformation and dimerization affinity are triggered by single mutations in critical regions of D1. Our structure-function/simulation studies provide a basis for modeling and design of small molecules to enhance inhibition of LF activation for potential use in breast cancer treatment.

Protein modeling: what happened to the "protein structure gap"?

Computational modeling of three-dimensional macromolecular structures and complexes from their sequence has been a long-standing vision in structural biology. Over the last 2 decades, a paradigm shift has occurred: starting from a large "structure knowledge gap" between the huge number of protein sequences and small number of known structures, today, some form of structural information, either experimental or template-based models, is available for the majority of amino acids encoded by common model organism genomes. With the scientific focus of interest moving toward larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows the study of large and complex molecular machines. One of the open challenges for computational modeling and prediction techniques is to convey the underlying assumptions, as well as the expected accuracy and structural variability of a specific model, which is crucial to understanding its limitations.

HADDOCK(2P2I): a biophysical model for predicting the binding affinity of protein-protein interaction inhibitors

The HADDOCK score, a scoring function for both protein-protein and protein-nucleic acid modeling, has been successful in selecting near-native docking poses in a variety of cases, including those of the CAPRI blind prediction experiment. However, it has yet to be optimized for small molecules, and in particular inhibitors of protein-protein interactions, that constitute an "unmined gold reserve" for drug design ventures. We describe here HADDOCK(2P2I), a biophysical model capable of predicting the binding affinity of protein-protein complex inhibitors close to experimental error (~2-fold larger). The algorithm was trained and 4-fold cross-validated against experimental data for 27 inhibitors targeting 7 protein-protein complexes of various functions and tested on an independent set of 24 different inhibitors for which K(d)/IC50 data are available. In addition, two popular ligand topology generation and parametrization methods (ACPYPE and PRODRG) were assessed. The resulting HADDOCK(2P2I) model, derived from the original HADDOCK score, provides insights into inhibition determinants: while the role of electrostatics and desolvation energies is case-dependent, the interface area plays a more critical role compared to protein-protein interactions.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Coarse-grain modelling of protein-protein interactions

Here, we review recent advances towards the modelling of protein-protein interactions (PPI) at the coarse-grained (CG) level, a technique that is now widely used to understand protein affinity, aggregation and self-assembly behaviour. PPI models of soluble proteins and membrane proteins are separately described, but we note the parallel development that is present in both research fields with three important themes: firstly, combining CG modelling with knowledge-based approaches to predict and refine protein-protein complexes; secondly, using physics-based CG models for de novo prediction of protein-protein complexes; and thirdly modelling of large scale protein aggregates.

Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization

Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.

Molecular origins of binding affinity: seeking the Archimedean point

Connecting three dimensional structure and affinity is analogous to seeking the 'Archimedean point', a vantage point from where any observer can quantitatively perceive the subject of inquiry. Here we review current knowledge and challenges that lie ahead of us in the quest for this Archimedean point. We argue that current models are limited in reproducing measured data because molecular description of binding affinity must expand beyond the interfacial contribution and also incorporate effects stemming from conformational changes/dynamics and long-range interactions. Fortunately, explicit modeling of various kinetic schemes underlying biomolecular recognition and confined systems that reflect in vivo interactions are coming within reach. This quest will hopefully lead to an accurate biophysical interpretation of binding affinity that would allow unprecedented understanding of the molecular basis of life through unraveling the why's of interaction networks.