The structure of protein-protein recognition sites

Encoding the space of protein-protein binding interfaces by artificial intelligence

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.

Crystal structure of human peptidylarginine deiminase type VI (PAD6) provides insights into its inactivity

Human peptidylarginine deiminase isoform VI (PAD6), which is predominantly limited to cytoplasmic lattices in the mammalian oocytes in ovarian tissue, is essential for female fertility. It belongs to the peptidylarginine deiminase (PAD) enzyme family that catalyzes the conversion of arginine residues to citrulline in proteins. In contrast to other members of the family, recombinant PAD6 was previously found to be catalytically inactive. We sought to provide structural insight into the human homologue to shed light on this observation. We report here the first crystal structure of PAD6, determined at 1.7 Å resolution. PAD6 follows the same domain organization as other structurally known PAD isoenzymes. Further structural analysis and size-exclusion chromatography show that PAD6 behaves as a homodimer similar to PAD4. Differential scanning fluorimetry suggests that PAD6 does not coordinate Ca2+ which agrees with acidic residues found to coordinate Ca2+ in other PAD homologs not being conserved in PAD6. The crystal structure of PAD6 shows similarities with the inactive state of apo PAD2, in which the active site conformation is unsuitable for catalytic citrullination. The putative active site of PAD6 adopts a non-productive conformation that would not allow protein-substrate binding due to steric hindrance with rigid secondary structure elements. This observation is further supported by the lack of activity on the histone H3 and cytokeratin 5 substrates. These findings suggest a different mechanism for enzymatic activation compared with other PADs; alternatively, PAD6 may exert a non-enzymatic function in the cytoplasmic lattice of oocytes and early embryos.

Characterization of epitopes of human monoclonal antibodies against cytomegalovirus glycoprotein B for neutralization and antibody-dependent phagocytosis

As congenital cytomegalovirus (CMV) infections are the leading non-genetic cause of sensorineural hearing loss and significant neurological disabilities in children, the development of CMV vaccines should be given the highest public health priority. Although MF59-adjuvanted glycoprotein B (gB) vaccine (gB/MF59) is safe and immunogenic, its efficacy in terms of protection from natural infection was around 50 % in clinical trials. Although gB/MF59 induced high antibody titers, anti-gB antibodies contributed little to the neutralization of infection. Recent studies have found that non-neutralizing functions, including antibody-dependent phagocytosis of virions and virus-infected cells, are likely to play important roles in pathogenesis and vaccine design. Previously, we isolated human monoclonal antibodies (MAbs) that reacted with the trimeric form of gB ectodomain and found that preferential epitopes for neutralization were present on Domains (Doms) I and II of gB, while there were abundant non-neutralizing antibodies targeting Dom IV. In this study, we analyzed the phagocytosis activities of these MAbs and found the following: 1) MAbs effective for phagocytosis of the virions targeted Doms I and II, 2) the MAbs effective for phagocytosis of the virions and those of virus-infected cells were generally distinct, and 3) the antibody-dependent phagocytosis showed little correlation with neutralizing activities. Taking account of the frequency and levels of neutralization and phagocytosis, incorporation of the epitopes on Doms I and II into developing vaccines is considered desirable for the prevention of viremia.

Intracellular Antibodies for Drug Discovery and as Drugs of the Future

The application of antibodies in cells was first shown in the early 1990s, and subsequently, the field of intracellular antibodies has expanded to encompass antibody fragments and their use in target validation and as engineered molecules that can be fused to moieties (referred to as warheads) to replace the Fc effector region of a whole immunoglobulin to elicit intracellular responses, such as cell death pathways or protein degradation. These various forms of intracellular antibodies have largely been used as research tools to investigate function within cells by perturbing protein activity. New applications of such molecules are on the horizon, namely their use as drugs per se and as templates for small-molecule drug discovery. The former is a potential new pharmacology that could harness the power and flexibility of molecular biology to generate new classes of drugs (herein referred to as macrodrugs when used in the context of disease control). Delivery of engineered intracellular antibodies, and other antigen-binding macromolecules formats, into cells to produce a therapeutic effect could be applied to any therapeutic area where regulation, degradation or other kinds of manipulation of target proteins can produce a therapeutic effect. Further, employing single-domain antibody fragments as competitors in small-molecule screening has been shown to enable identification of drug hits from diverse chemical libraries. Compounds selected in this way can mimic the effects of the intracellular antibodies that have been used for target validation. The capability of intracellular antibodies to discriminate between closely related proteins lends a new dimension to drug screening and drug development.

Analytical Method for Experimental Validation of Computer-Designed Antibody

In the computational design of antibodies, the interaction analysis between target antigen and antibody is an essential process to obtain feedback for validation and optimization of the design. Kinetic and thermodynamic parameters as well as binding affinity (K_D) allow for a more detailed evaluation and understanding of the molecular recognition. In this chapter, we summarize the conventional experimental methods which can calculate K_D value (ELISA, FP), analyze a binding activity to actual cells (FCM), and evaluate the kinetic and thermodynamic parameters (ITC, SPR, BLI), including high-throughput analysis and a recently developed experimental technique.

EXCESP: A Structure-Based Online Database for Extracellular Interactome of Cell Surface Proteins in Humans

The interactions between ectodomains of cell surface proteins are vital players in many important cellular processes, such as regulating immune responses, coordinating cell differentiation, and shaping neural plasticity. However, while the construction of a large-scale protein interactome has been greatly facilitated by the development of high-throughput experimental techniques, little progress has been made to support the discovery of extracellular interactome for cell surface proteins. Harnessed by the recent advances in computational modeling of protein-protein interactions, here we present a structure-based online database for the extracellular interactome of cell surface proteins in humans, called EXCESP. The database contains both experimentally determined and computationally predicted interactions among all type-I transmembrane proteins in humans. All structural models for these interactions and their binding affinities were further computationally modeled. Moreover, information such as expression levels of each protein in different cell types and its relation to various signaling pathways from other online resources has also been integrated into the database. In summary, the database serves as a valuable addition to the existing online resources for the study of cell surface proteins. It can contribute to the understanding of the functions of cell surface proteins in the era of systems biology.

Using Surface Hydrophobicity Together with Empirical Potentials to Identify Protein-Protein Binding Sites: Application to the Interactions of E-cadherins

Studying the interactions within protein structures can inform about the details of how proteins of various types interact and aggregate. Empirical contact potentials have proven to be extremely important in the evaluation of individual modeled protein structures, but have found few applications to protein-protein interactions. In part, this is caused by a lack of properly formulated potentials with a proper reference state. Since the comparisons are made between different bound structures, the proper reference state should take into account other contacts. Therefore, a preferred reference state should be defined with respect to a given residue type interacting with an average residue instead of interacting with solvent as typically is used in derivation of statistical contact potentials. Here, a two-stage procedure for generating and evaluating interacting protein pairs is described, and an example of E-cadherin interactions is shown.

Classification of protein-protein association rates based on biophysical informatics

BACKGROUND

Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles.

RESULTS

To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model.

CONCLUSIONS

In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein-protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.

Fragment-Based Ligand Discovery Using Protein-Observed 19F NMR: A Second Semester Organic Chemistry CURE Project

Curriculum-based undergraduate research experiences (CUREs) have been shown to increase student retention in STEM fields and are starting to become more widely adopted in chemistry curricula. Here we describe a 10-week CURE that is suitable for a second-semester organic chemistry laboratory course. Students synthesize small molecules and use protein-observed ¹⁹F (PrOF) NMR to assess the small molecule's binding affinity to a target protein. The research project introduced students to multistep organic synthesis, structure-activity relationship studies, quantitative biophysical measurements (measuring K_d from PrOF NMR experiments), and scientific literacy. Docking experiments could be added to help students understand how changes in a ligand structure may affect binding to a protein. Assessment using the CURE survey indicates self-perceived skill gains from the course that exceed gains measured in a traditional and an inquiry-based laboratory experience. Given the speed of the binding experiment and the alignment of the synthetic methods with a second-semester organic chemistry laboratory course, a PrOF NMR fragment-based ligand discovery lab can be readily implemented in the undergraduate chemistry curriculum.

Plausible blockers of Spike RBD in SARS-CoV2-molecular design and underlying interaction dynamics from high-level structural descriptors

Abstract

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently “reaction-prone.” The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic “hydrophobic ↔ polar” mutations serve as a potential breakthrough.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00894-021-04779-0.

Computational Assessment of Protein-Protein Binding Affinity by Reverse Engineering the Energetics in Protein Complexes

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.

Robust principal component analysis-based prediction of protein-protein interaction hot spots

Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help design protein-protein interaction inhibitors for therapy. Unfortunately, current machine learning methods to predict hot spots, suffer from limitations caused by gross errors in the data matrices. Here, we present a novel data pre-processing pipeline that overcomes this problem by recovering a low rank matrix with reduced noise using Robust Principal Component Analysis. Application to existing databases shows the predictive power of the method.

Design of Functional Nanoparticles for Intractable Disease Therapy

Protein affinity reagents are widely used for basic research, diagnostics, and disease therapy. Antibodies and their fragments are known as the most common protein affinity reagents. They specifically and strongly bind to target molecules and inhibit their functions. Thus, antibody drugs have increased in the recent two decades for disease therapy, such as cancer. These strong protein-protein interactions are composed of a nexus of multiple weak interactions. Synthetic polymers that bind to target molecules have been developed by the imitation of protein-protein interactions. These polymers show nanomolar affinity for the target and neutralize their functions; thus, they are of significant interest as a cost-effective protein affinity reagent. We have been developing synthetic polymer nanoparticles (NPs) that bind to target peptides and proteins by the inclusion of several functional monomers, such as charged and hydrophobic monomers. In this review, the focus is on the design of synthetic polymer NPs that bind to target molecules for disease therapy. We succeeded in neutralization of toxic peptides and signaling proteins both in vitro and in vivo. Additionally, linear polymers were modified on a lipid nanoparticle surface to improve polymer biodistribution. Our recent findings should provide useful information for the development of abiotic protein affinity reagents.

Anomalously Slow Conformational Change Dynamics of Polar Groups Anchored to Hydrophobic Surfaces in Aqueous Media

Water molecules within a thin hydration layer, spontaneously generated on hydrophobic protein surfaces, are reported to form a poorly dynamic network structure. However, how such a water network affects the conformational change dynamics of polar groups has never been explored, although such polar groups play a critical role in protein-protein and protein-ligand interactions. In the present work, we utilized as model protein surfaces a series of self-assembled monolayers (SAMs) appended with polar (Fmoc) or ionic (FITC) fluorescent head groups that were tethered via a 1.5-nm-long flexible oligoether chain to a hydrophobic silicon wafer surface, which was densely covered with paraffinic chains. We found that, not only in deionized water but also in aqueous buffer, these oligoether-appended head groups at ambient temperatures both displayed an anomalously slow conformational change, which required ∼10 h to reach a thermodynamically equilibrated state. We suppose that these behaviors reflect the poorly dynamic and low-permittivity natures of the thin hydration layer.

In Silico Drug Repurposing for SARS-CoV-2 Main Proteinase and Spike Proteins

The pandemic caused by SARS-CoV-2 is currently representing a major health and economic threat to humanity. So far, no specific treatment to this viral infection has been developed and the emergency still requires an efficient intervention. In this work, we used virtual screening to facilitate drug repurposing against SARS-CoV-2, targeting viral main proteinase and spike protein with 3000 existing drugs. We used a protocol based on a docking step followed by a short molecular dynamic simulation and rescoring by the Nwat-MMGBSA approach. Our results provide suggestions for prioritizing in vitro and/or in vivo tests of already available compounds.

Understanding the Impacts of Conformational Dynamics on the Regulation of Protein-Protein Association by a Multiscale Simulation Method

Complexes formed among diverse proteins carry out versatile functions in nearly all physiological processes. Association rates which measure how fast proteins form various complexes are of fundamental importance to characterize their functions. The association rates are not only determined by the energetic features at binding interfaces of a protein complex but also influenced by the intrinsic conformational dynamics of each protein in the complex. Unfortunately, how this conformational effect regulates protein association has never been calibrated on a systematic level. To tackle this problem, we developed a multiscale strategy to incorporate the information on protein conformational variations from Langevin dynamic simulations into a kinetic Monte Carlo algorithm of protein-protein association. By systematically testing this approach against a large-scale benchmark set, we found the association of a protein complex with a relatively rigid structure tends to be reduced by its conformational fluctuations. With specific examples, we further show that higher degrees of structural flexibility in various protein complexes can facilitate the searching and formation of intermolecular interactions and thereby accelerate their associations. In general, the integration of conformational dynamics can improve the correlation between experimentally measured association rates and computationally derived association probabilities. Finally, we analyzed the statistical distributions of different secondary structural types on protein-protein binding interfaces and their preference to the change of association rates. Our study, to the best of our knowledge, is the first computational method that systematically estimates the impacts of protein conformational dynamics on protein-protein association. It throws lights on the molecular mechanisms of how protein-protein recognition is kinetically modulated.

Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

Protein-protein interactions: a structural view of inhibition strategies and the IL-23/IL-17 axis

Protein-protein interactions are central to biology and provide opportunities to modulate disease with small-molecule or protein therapeutics. Recent developments in the understanding of the tractability of protein-protein interactions are discussed with a focus on the ligandable nature of protein-protein interaction surfaces. General principles of inhibiting protein-protein interactions are illustrated with structural biology examples from six members of the IL-23/IL-17 signaling family (IL-1, IL-6, IL-17, IL-23 RORγT and TNFα). These examples illustrate the different approaches to discover protein-protein interaction inhibitors on a target-specific basis that has proven fruitful in terms of discovering both small molecule and biologic based protein-protein interaction inhibitors.

Small protein-protein interfaces rich in electrostatic are often linked to regulatory function

Protein-protein interaction (PPI) is critical for several biological functions in living cells through the formation of an interface. Therefore, it is of interest to characterize protein-protein interfaces using an updated non-redundant structural dataset of 2557 homo (identical subunits) and 393 hetero (different subunits) dimer protein complexes determined by X-ray crystallography. We analyzed the interfaces using van der Waals (vdW), hydrogen bonding and electrostatic energies. Results show that on average homo and hetero interfaces are similar. Hence, we further grouped the 2950 interfaces based on percentage vdW to total energies into dominant (≥60%) and sub-dominant (<60%) vdW interfaces. Majority (92%) of interfaces have dominant vdW energy with large interface size (146 ± 87 (homo) and 137 ± 76 (hetero) residues) and interface area (1622 ± 1135 Ų (homo) and 1579 ± 1060 Ų (hetero)). However, a proportion (8%) of interfaces have sub-dominant vdW energy with small interface size (85 ± 46 (homo) and 88 ± 36 (hetero) residues) and interface area (823 ± 538 Ų (homo) and 881 ± 377 Ų (hetero)). It is found that large interfaces have two-fold more interface area and interface size than small interfaces with increasing hydrogen bonding energy to interface size. However, small interfaces have three-fold more electrostatics energy than large interfaces with increasing electrostatics to interface size. Thus, 8% of complexes having small interfaces with limited interface area and sub-dominant vdW energy are rich in electrostatics. It is interesting to observe that complexes having small interfaces are often associated with regulatory function. Hence, the observed structural features with known molecular function provide insights for the better understanding of PPI.Communicated by Ramaswamy H. Sarma.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

Translational Entropy and DNA Duplex Stability

Investigation of folding/unfolding DNA duplexes of various size and composition by superprecise calorimetry has revised several long-held beliefs concerning the forces responsible for the formation of the double helix. It was established that: 1) the enthalpy and the entropy of duplex unfolding are temperature dependent, increasing with temperature rise and having the same heat capacity increment for CG and AT pairs; 2) the enthalpy of AT melting is greater than that of the CG pair, so the stabilizing effect of the CG pair in comparison with AT results not from its larger enthalpic contribution (as expected from its extra hydrogen bond), but from the larger entropic contribution of the AT pair that results from its ability to fix ordered water in the minor groove and release it upon duplex unfolding; 3) the translation entropy, resulting from the appearance of a new kinetic unit on duplex dissociation, determines the dependence of duplex stability on its length and its concentration (it is an order-of-magnitude smaller than predicted from the statistical mechanics of gases and is fully expressed by the stoichiometric correction term); 4) changes in duplex stability on reshuffling the sequence (the "nearest-neighbor effect") result from the immobilized water molecules fixed by AT pairs in the minor groove; and 5) the evaluated thermodynamic components permit a quantitative expression of DNA duplex stability.

In silico identification of epitopes present in human heat shock proteins (HSPs) overexpressed by tumour cells

Although many of heat shock proteins (HSPs) are crucial in homeostasis due to their role in maintaining cellular proteostasis by the integration of two pivotal processes-folding and degradation, several decades of cancer proteomics suggest that HSPs may improve cancer establishment and progression. Therefore, it is imperative to explore how these molecules impact patient outcomes and whether their interaction with the immune systems improves the protumour or antitumour environment. Here, using an immunoinformatics approach were investigated the best probable epitopes from ten HSPs (HSP90α, HSP90β, HSPA1A, HSPA1L, HSPA2, HSPA5, HSPA6, HSPB1, HSPB5 and HSP60/HSP10). To achieve this aim, antigenicity, immunogenicity (prediction of continuous and discontinuous B cell epitopes, binding peptides to HLA class I and HLA class II, and overlapping epitopes), analysis of conservancy and population coverage, and prediction of IgE epitopes were evaluated. According to the physicochemical properties used for their prediction (hydrophilicity, flexibility, accessibility and antigenicity propensity), ten continuous epitopes (one per HSPs) were considered as the best and also several regions of each molecule were identified as B discontinuous epitopes. Interestingly, peptides of HSP90β, HSPA2, HSPB1, and HSPB5 were predicted as both continuous and discontinuous B cell epitopes. For all the HSPs evaluated were identified potential overlapping epitopes ("NTFYSNKEI", "TTYSCVGVF", "TADRWRVSL", "VKHFSPEEL" and "CEFQDAYVL"). Moreover, these peptides were negative for IgE epitopes and showed a large coverage in the human population (HLA-A*02, HLA-B*15, HLA-C*03, and HLA-C*12). Taken together, these data indicate that such epitopes may activate both the humoral and cell-mediated response, and thus serve as therapeutic targets for cancer. However, it must be assessed their efficacy and safety in vitro and in vivo before their translation in clinical trials.

Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery

Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.

Computational studies of protein-protein dissociation by statistical potential and coarse-grained simulations: a case study on interactions between colicin E9 endonuclease and immunity proteins

Proteins carry out their diverse functions in cells by forming interactions with each other. The dynamics of these interactions are quantified by the measurement of association and dissociation rate constants. Relative to the efforts made to model the association of biomolecules, little has been studied to understand the principles of protein complex dissociation. Using the interaction between colicin E9 endonucleases and immunity proteins as a test system, here we develop a coarse-grained simulation method to explore the dissociation mechanisms of protein complexes. The interactions between proteins in the complex are described by the knowledge-based potential that was constructed by the statistics from available protein complexes in the structural database. Our study provides the supportive evidences to the dual recognition mechanism for the specificity of binding between E9 DNase and immunity proteins, in which the conserved residues of helix III of Im2 and Im9 proteins act as the anchor for binding, while the sequence variations in helix II make positive or negative contributions to specificity. Beyond that, we further suggest that this binding specificity is rooted in the process of complex dissociation instead of association. While we increased the flexibility of protein complexes, we further found that they are less prone to dissociation, suggesting that conformational fluctuations of protein complexes play important functional roles in regulating their binding and dissociation. Our studies therefore bring new insights to the molecule mechanisms of protein-protein interactions, while the method can serve as a new addition to a suite of existing computational tools for the simulations of protein complexes.

Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells

The information of how two proteins interact is embedded in the atomic details of their binding interfaces. These interactions, spatial-temporally coordinating each other as a network in a variable cytoplasmic environment, dominate almost all biological functions. A feasible and reliable computational model is highly demanded to realistically simulate these cellular processes and unravel the complexities beneath them. We therefore present a multiscale framework that integrates simulations on two different scales. The higher-resolution model incorporates structural information of proteins and energetics of their binding, while the lower-resolution model uses a highly simplified representation of proteins to capture the long-time-scale dynamics of a system with multiple proteins. Through a systematic benchmark test and two practical applications of biomolecular systems with specific cellular functions, we demonstrated that this method could be a powerful approach to understand molecular mechanisms of dynamic interactions between biomolecules and their functional impacts with high computational efficiency.

An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.

Crystal structures of eukaryote glycosyltransferases reveal biologically relevant enzyme homooligomers

Glycosyltransferases (GTases) transfer sugar moieties to proteins, lipids or existing glycan or polysaccharide molecules. GTases form an important group of enzymes in the Golgi, where the synthesis and modification of glycoproteins and glycolipids take place. Golgi GTases are almost invariably type II integral membrane proteins, with the C-terminal globular catalytic domain residing in the Golgi lumen. The enzymes themselves are divided into 103 families based on their sequence homology. There is an abundance of published crystal structures of GTase catalytic domains deposited in the Protein Data Bank (PDB). All of these represent either of the two main characteristic structural folds, GT-A or GT-B, or present a variation thereof. Since GTases can function as homomeric or heteromeric complexes in vivo, we have summarized the structural features of the dimerization interfaces in crystal structures of GTases, as well as considered the biochemical data available for these enzymes. For this review, we have considered all 898 GTase crystal structures in the Protein Data Bank and highlight the dimer formation characteristics of various GTases based on 24 selected structures.

Protein-protein interface hot spots prediction based on a hybrid feature selection strategy

Background

Hot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model.

Results

In this study, three different feature selection methods were compared to propose a new hybrid feature selection strategy. This new strategy was proved to effectively reduce the feature space when we were building the prediction models for identifying hotspot residues. It was tested on eighty-two features, both conventional and newly proposed. According to the strategy, combining the feature subsets selected by decision tree and mRMR (maximum Relevance Minimum Redundancy) individually, we were able to build a model with 6 features by using a PSFS (Pseudo Sequential Forward Selection) process. Compared with other state-of-art methods for the independent test set, our model had shown better or comparable predictive performances (with F-measure 0.622 and recall 0.821). Analysis of the 6 features confirmed that our newly proposed feature CNSV_REL1 was important for our model. The analysis also showed that the complementarity between features should be considered as an important aspect when conducting the feature selection.

Conclusion

In this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2009-5) contains supplementary material, which is available to authorized users.

Docking, thermodynamics and molecular dynamics (MD) studies of a non-canonical protease inhibitor, MP-4, from Mucuna pruriens

Sequence and structural homology suggests that MP-4 protein from Mucuna pruriens belongs to Kunitz-type protease inhibitor family. However, biochemical assays showed that this protein is a poor inhibitor of trypsin. To understand the basis of observed poor inhibition, thermodynamics and molecular dynamics (MD) simulation studies on binding of MP-4 to trypsin were carried out. Molecular dynamics simulations revealed that temperature influences the spectrum of conformations adopted by the loop regions in the MP-4 structure. At an optimal temperature, MP-4 achieves maximal binding while above and below the optimum temperature, its functional activity is hampered due to unfavourable flexibility and relative rigidity, respectively. The low activity at normal temperature is due to the widening of the conformational spectrum of the Reactive Site Loop (RSL) that reduces the probability of formation of stabilizing contacts with trypsin. The unique sequence of the RSL enhances flexibility at ambient temperature and thus reduces its ability to inhibit trypsin. This study shows that temperature influences the function of a protein through modulation in the structure of functional domain of the protein. Modulation of function through appearance of new sequences that are more sensitive to temperature may be a general strategy for evolution of new proteins.

Are there physicochemical differences between allosteric and competitive ligands?

Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes.

Selectivity determinants of GPCR-G-protein binding

The selective coupling of G-protein-coupled receptors (GPCRs) to specific G proteins is critical to trigger the appropriate physiological response. However, the determinants of selective binding have remained elusive. Here we reveal the existence of a selectivity barcode (that is, patterns of amino acids) on each of the 16 human G proteins that is recognized by distinct regions on the approximately 800 human receptors. Although universally conserved positions in the barcode allow the receptors to bind and activate G proteins in a similar manner, different receptors recognize the unique positions of the G-protein barcode through distinct residues, like multiple keys (receptors) opening the same lock (G protein) using non-identical cuts. Considering the evolutionary history of GPCRs allows the identification of these selectivity-determining residues. These findings lay the foundation for understanding the molecular basis of coupling selectivity within individual receptors and G proteins.

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning

Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Specificity and stability of transient protein-protein interactions

Remarkable features that are achieved in a protein-protein complex to precise levels are stability and specificity. Deviation from the normal levels of specificity and stability, which is often caused by mutations, could result in disease conditions. Chemical nature, 3-D arrangement and dynamics of interface residues code for both specificity and stability. This article reviews roles of interfacial residues in transient protein-protein complexes. It is proposed that aside from hotspot residues conferring stability to the complex, a small set of 'rigid' residues at the interface that maintain conformation between complexed and uncomplexed forms, play a major role in conferring specificity. Exceptionally, 'super hotspot' residues, which confer both stability and specificity, are attractive sites for interaction with small molecule inhibitors.

Dimeric and Trimeric Fusion Proteins Generated with Fimbrial Adhesins of Uropathogenic Escherichia coli

Urinary tract infections (UTIs) are associated with high rates of morbidity and mortality worldwide, and uropathogenic Escherichia coli (UPEC) is the main etiologic agent. Fimbriae assembled on the bacterial surface are essential for adhesion to the urinary tract epithelium. In this study, the FimH, CsgA, and PapG adhesins were fused to generate biomolecules for use as potential target vaccines against UTIs. The fusion protein design was generated using bioinformatics tools, and template fusion gene sequences were synthesized by GenScript in the following order fimH-csgA-papG-fimH-csgA (fcpfc) linked to the nucleotide sequence encoding the [EAAAK]₅ peptide. Monomeric (fimH, csgA, and papG), dimeric (fimH-csgA), and trimeric (fimH-csgA-papG) genes were cloned into the pLATE31 expression vector and generated products of 1040, 539, 1139, 1442, and 2444 bp, respectively. Fusion protein expression in BL21 E. coli was induced with 1 mM IPTG, and His-tagged proteins were purified under denaturing conditions and refolded by dialysis using C-buffer. Coomassie blue-stained SDS-PAGE gels and Western blot analysis revealed bands of 29.5, 11.9, 33.9, 44.9, and 82.1 kDa, corresponding to FimH, CsgA, PapG, FC, and FCP proteins, respectively. Mass spectrometry analysis by MALDI-TOF/TOF revealed specific peptides that confirmed the fusion protein structures. Dynamic light scattering analysis revealed the polydispersed state of the fusion proteins. FimH, CsgA, and PapG stimulated the release of 372–398 pg/mL IL-6; interestingly, FC and FCP stimulated the release of 464.79 pg/mL (p ≤ 0.018) and 521.24 pg/mL (p ≤ 0.002) IL-6, respectively. In addition, FC and FCP stimulated the release of 398.52 pg/mL (p ≤ 0.001) and 450.40 pg/mL (p ≤ 0.002) IL-8, respectively. High levels of IgA and IgG antibodies in human sera reacted against the fusion proteins, and under identical conditions, low levels of IgA and IgG antibodies were detected in human urine. Rabbit polyclonal antibodies generated against FimH, CsgA, PapG, FC, and FCP blocked the adhesion of E. coli strain CFT073 to HTB5 bladder cells. In conclusion, the FC and FCP proteins were highly stable, demonstrated antigenic properties, and induced cytokine release (IL-6 and IL-8); furthermore, antibodies generated against these proteins showed protection against bacterial adhesion.

Intracellular targeting with engineered proteins

If the isolation, production, and clinical use of insulin marked the inception of the age of biologics as therapeutics, the convergence of molecular biology and combinatorial engineering techniques marked its coming of age. The first wave of recombinant protein-based drugs in the 1980s demonstrated emphatically that proteins could be engineered, formulated, and employed for clinical advantage. Yet despite the successes of protein-based drugs such as antibodies, enzymes, and cytokines, the druggable target space for biologics is currently restricted to targets outside the cell. Insofar as estimates place the number of proteins either secreted or with extracellular domains in the range of 8000 to 9000, this represents only one-third of the proteome and circumscribes the pathways that can be targeted for therapeutic intervention. Clearly, a major objective for this field to reach maturity is to access, interrogate, and modulate the majority of proteins found inside the cell. However, owing to the large size, complex architecture, and general cellular impermeability of existing protein-based drugs, this poses a daunting challenge. In recent years, though, advances on the two related fronts of protein engineering and drug delivery are beginning to bring this goal within reach. First, prompted by the restrictions that limit the applicability of antibodies, intense efforts have been applied to identifying and engineering smaller alternative protein scaffolds for the modulation of intracellular targets. In parallel, innovative solutions for delivering proteins to the intracellular space while maintaining their stability and functional activity have begun to yield successes. This review provides an overview of bioactive intrabodies and alternative protein scaffolds amenable to engineering for intracellular targeting and also outlines advances in protein engineering and formulation for delivery of functional proteins to the interior of the cell to achieve therapeutic action.

Single Mutations in the Transmembrane Domains of Maize Plasma Membrane Aquaporins Affect the Activity of Monomers within a Heterotetramer

Aquaporins are channels facilitating the diffusion of water and/or small uncharged solutes across biological membranes. They assemble as homotetramers but some of them also form heterotetramers, especially in plants. In Zea mays, aquaporins belonging to the plasma membrane intrinsic protein (PIP) subfamily are clustered into two groups, PIP1 and PIP2, which exhibit different water-channel activities when expressed in Xenopus oocytes. When PIP1 and PIP2 isoforms are co-expressed, they physically interact to modulate their subcellular localization and channel activity. Here, we demonstrated by affinity chromatography purification that, when co-expressed in Xenopus oocytes, the maize PIP1;2 and PIP2;5 isoforms assemble as homo- and heterodimers within heterotetramers. We built the 3D structure of such heterotetramers by comparative modeling on the basis of the spinach SoPIP2;1 X-ray structure and identified amino acid residues in the transmembrane domains which putatively interact at the interfaces between monomers. Their roles in the water-channel activity, subcellular localization, protein abundance, and physical interaction were investigated by mutagenesis. We highlighted single-residue substitutions that either inactivated PIP2;5 or activated PIP1;2 without affecting their interaction. Interestingly, the Phe220Ala mutation in the transmembrane domain 5 of PIP1;2 activated its water-channel activity and, at the same time, inactivated PIP2;5 within a heterotetramer. Altogether, these data contribute to a better understanding of the interaction mechanisms between PIP isoforms and the role of heterotetramerization on their water-channel activity.

Great interactions: How binding incorrect partners can teach us about protein recognition and function

Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Nonspecific native elution of proteins and mumps virus in immunoaffinity chromatography

Immunoaffinity chromatography, based on the antigen-antibody recognition, enables specific purification of any antigen (protein, virus) by its antibody. The problem with immunoaffinity chromatography is the harsh elution conditions required for disrupting strong antigen-antibody interactions, such as low pH buffers, which are often deleterious for the immobilized protein and the protein to be isolated since they can also disrupt the intramolecular forces. Therefore, immunoaffinity chromatography can only be partially used for protein and virus purification. Here we report on a nonspecific elution in immunoaffinity chromatography using native conditions by elution with amino acid solution at physiological pH for which we suppose possible competing mechanism of action. Elution potential of various amino acid solutions was tested using immunoaffinity columns specific for ovalbumin and mumps virus, and protein G affinity column. Results have shown that the most successful elution solutions were those containing imidazole and arginine of high molarity. Imidazole represents aromatic residues readily found at the antigen-antibody interaction surface and arginine is most frequently found on protein surface in general. Therefore, results on their eluting power in immunoaffinity chromatography, which increases with increasing molarity, are in line with the competing mechanism of action. Virus immunoaffinity chromatography resulted in removal on nonviable virus particles, which is important for research and biotechnology purposes. In addition, amino acids are proven stabilizers for proteins and viruses making approach presented in this work a very convenient purification method.

De novo design of protein-protein interactions through modification of inter-molecular helix-helix interface residues

For de novo design of protein-protein interactions (PPIs), information on the shape and chemical complementarity of their interfaces is generally required. Recent advances in computational PPI design have allowed for de novo design of protein complexes, and several successful examples have been reported. In addition, a simple and easy-to-use approach has also been reported that arranges leucines on a solvent-accessible region of an α-helix and places charged residues around the leucine patch to induce interactions between the two helical peptides. For this study, we adopted this approach to de novo design a new PPI between the helical bundle proteins sulerythrin and LARFH. A non-polar patch was created on an α-helix of LARFH around which arginine residues were introduced to retain its solubility. The strongest interaction found was for the LARFH variant cysLARFH-IV-3L3R and the sulerythrin mutant 6L6D (KD=0.16 μM). This artificial protein complex is maintained by hydrophobic and ionic interactions formed by the inter-molecular helical bundle structure. Therefore, by the simple and easy-to-use approach to create de novo interfaces on the α-helices, we successfully generated an artificial PPI. We also created a second LARFH variant with the non-polar patch surrounded by positively charged residues at each end. Upon mixing this LARFH variant with 6L6D, mesh-like fibrous nanostructures were observed by atomic force microscopy. Our method may, therefore, also be applicable to the de novo design of protein nanostructures.

Binding of small molecules at interface of protein-protein complex - A newer approach to rational drug design

Protein–protein interaction is a vital process which drives many important physiological processes in the cell and has also been implicated in several diseases. Though the protein–protein interaction network is quite complex but understanding its interacting partners using both in silico as well as molecular biology techniques can provide better insights for targeting such interactions. Targeting protein–protein interaction with small molecules is a challenging task because of druggability issues. Nevertheless, several studies on the kinetics as well as thermodynamic properties of protein–protein interactions have immensely contributed toward better understanding of the affinity of these complexes. But, more recent studies on hot spots and interface residues have opened up new avenues in the drug discovery process. This approach has been used in the design of hot spot based modulators targeting protein–protein interaction with the objective of normalizing such interactions.

Classification epitopes in groups based on their protein family

Background

The humoral immune system response is based on the interaction between antibodies and antigens for the clearance of pathogens and foreign molecules. The interaction between these proteins occurs at specific positions known as antigenic determinants or B-cell epitopes. The experimental identification of epitopes is costly and time consuming. Therefore the use of in silico methods, to help discover new epitopes, is an appealing alternative due the importance of biomedical applications such as vaccine design, disease diagnostic, anti-venoms and immune-therapeutics. However, the performance of predictions is not optimal been around 70% of accuracy. Further research could increase our understanding of the biochemical and structural properties that characterize a B-cell epitope.

Results

We investigated the possibility of linear epitopes from the same protein family to share common properties. This hypothesis led us to analyze physico-chemical (PCP) and predicted secondary structure (PSS) features of a curated dataset of epitope sequences available in the literature belonging to two different groups of antigens (metalloproteinases and neurotoxins). We discovered statistically significant parameters with data mining techniques which allow us to distinguish neurotoxin from metalloproteinase and these two from random sequences. After a five cross fold validation we found that PCP based models obtained area under the curve values (AUC) and accuracy above 0.9 for regression, decision tree and support vector machine.

Conclusions

We demonstrated that antigen's family can be inferred from properties within a single group of linear epitopes (metalloproteinases or neurotoxins). Also we discovered the characteristics that represent these two epitope groups including their similarities and differences with random peptides and their respective amino acid sequence. These findings open new perspectives to improve epitope prediction by considering the specific antigen's protein family. We expect that these findings will help to improve current computational mapping methods based on physico-chemical due it's potential application during epitope discovery.

The putative role of some conserved water molecules in the structure and function of human transthyretin

Human transthyretin (hTTR) is a multifunctional protein that is involved in several neurodegenerative diseases. Besides the transportation of thyroxin and vitamin A, it is also involved in the proteolysis of apolipoprotein A1 and Aβ peptide. Extensive analyses of 32 high-resolution X-ray and neutron diffraction structures of hTTR followed by molecular-dynamics simulation studies using a set of 15 selected structures affirmed the presence of 44 conserved water molecules in its dimeric structure. They are found to play several important roles in the structure and function of the protein. Eight water molecules stabilize the dimeric structure through an extensive hydrogen-bonding network. The absence of some of these water molecules in highly acidic conditions (pH ≤ 4.0) severely affects the interfacial hydrogen-bond network, which may destabilize the native tetrameric structure, leading to its dissociation. Three pairs of conserved water molecules contribute to maintaining the geometry of the ligand-binding cavities. Some other water molecules control the orientation and dynamics of different structural elements of hTTR. This systematic study of the location, absence, networking and interactions of the conserved water molecules may shed some light on various structural and functional aspects of the protein. The present study may also provide some rational clues about the conserved water-mediated architecture and stability of hTTR.

The energetic basis of the DNA double helix: a combined microcalorimetric approach

Microcalorimetric studies of DNA duplexes and their component single strands showed that association enthalpies of unfolded complementary strands into completely folded duplexes increase linearly with temperature and do not depend on salt concentration, i.e. duplex formation results in a constant heat capacity decrement, identical for CG and AT pairs. Although duplex thermostability increases with CG content, the enthalpic and entropic contributions of an AT pair to duplex formation exceed that of a CG pair when compared at the same temperature. The reduced contribution of AT pairs to duplex stabilization comes not from their lower enthalpy, as previously supposed, but from their larger entropy contribution. This larger enthalpy and particularly the greater entropy results from water fixed by the AT pair in the minor groove. As the increased entropy of an AT pair exceeds that of melting ice, the water molecule fixed by this pair must affect those of its neighbors. Water in the minor groove is, thus, orchestrated by the arrangement of AT groups, i.e. is context dependent. In contrast, water hydrating exposed nonpolar surfaces of bases is responsible for the heat capacity increment on dissociation and, therefore, for the temperature dependence of all thermodynamic characteristics of the double helix.

Structure of HCMV glycoprotein B in the postfusion conformation bound to a neutralizing human antibody

Human cytomegalovirus (HCMV) poses a significant threat to immunocompromised individuals and neonates infected in utero. Glycoprotein B (gB), the herpesvirus fusion protein, is a target for neutralizing antibodies and a vaccine candidate due to its indispensable role in infection. Here we show the crystal structure of the HCMV gB ectodomain bound to the Fab fragment of 1G2, a neutralizing human monoclonal antibody isolated from a seropositive subject. The gB/1G2 interaction is dominated by aromatic residues in the 1G2 heavy chain CDR3 protruding into a hydrophobic cleft in the gB antigenic domain 5 (AD-5). Structural analysis and comparison with HSV gB suggest the location of additional neutralizing antibody binding sites on HCMV gB. Finally, immunoprecipitation experiments reveal that 1G2 can bind to HCMV virion gB suggesting that its epitope is exposed and accessible on the virus surface. Our data will support the development of vaccines and therapeutic antibodies against HCMV infection.

Cytomegalovirus is a danger to individuals with compromised immune systems and neonates infected in utero. Here the authors show the structure of a neutralizing antibody-bound viral fusion protein glycoprotein B, supporting the development of therapeutic antibodies and vaccines.

Epitopes in α8β1 and other RGD-binding integrins delineate classes of integrin-blocking antibodies and major binding loops in α subunits

Identification of epitopes for integrin-blocking monoclonal antibodies (mAbs) has aided our understanding of structure-function relationship of integrins. We mapped epitopes of chicken anti-integrin-α8-subunit-blocking mAbs by mutational analyses, examining regions that harboured all mapped epitopes recognized by mAbs against other α-subunits in the RGD-binding-integrin subfamily. Six mAbs exhibited blocking function, and these mAbs recognized residues on the same W2:41-loop on the top-face of the β-propeller. Loop-tips sufficiently close to W2:41 (<25 Å) contained within a footprint of the mAbs were mutated, and the loop W3:34 on the bottom face was identified as an additional component of the epitope of one antibody, clone YZ5. Binding sequences on the two loops were conserved in virtually all mammals, and that on W3:34 was also conserved in chickens. These indicate 1) YZ5 binds both top and bottom loops, and the binding to W3:34 is by interactions to conserved residues between immunogen and host species, 2) five other blocking mAbs solely bind to W2:41 and 3) the α8 mAbs would cross-react with most mammals. Comparing with the mAbs against the other α-subunits of RGD-integrins, two classes were delineated; those binding to "W3:34 and an top-loop", and "solely W2:41", accounting for 82% of published RGD-integrin-mAbs.

The molecular mode of action and species specificity of canakinumab, a human monoclonal antibody neutralizing IL-1β

Interleukin-1β (IL-1β) plays a key role in autoinflammatory diseases, such as systemic juvenile idiopathic arthritis (sJIA) or cryopyrin-associated periodic syndrome (CAPS). Canakinumab, a human monoclonal anti-IL-1β antibody, was recently approved for human use under the brand name Ilaris®. Canakinumab does not cross-react with IL-1β from mouse, rat, rabbit, or macaques. The crystal structure of the canakinumab Fab bound to human IL-1β was determined in an attempt to rationalize the species specificity. The X-ray analysis reveals a complex surface epitope with an intricate network of well-ordered water molecules at the antibody-antigen interface. The canakinumab paratope is largely pre-organized, as demonstrated by the structure determination of the free Fab. Glu 64 of human IL-1β is a pivotal epitope residue explaining the exquisite species specificity of canakinumab. We identified marmoset as the only non-human primate species that carries Glu 64 in its IL-1β and demonstrates full cross-reactivity of canakinumab, thereby enabling toxicological studies in this species. As demonstrated by the X-ray structure of the complex with IL-1β, canakinumab binds IL-1β on the opposite side with respect to the IL-1RAcP binding site, and in an approximately orthogonal orientation with respect to IL-1RI. However, the antibody and IL-1RI binding sites slightly overlap and the VH region of canakinumab would sterically interfere with the D1 domain of IL-1RI, as shown by a structural overlay with the IL-1β:IL-1RI complex. Therefore, direct competition with IL-1RI for IL-1β binding is the molecular mechanism of neutralization by canakinumab, which is also confirmed by competition assays with recombinant IL-1RI and IL-1RII.