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

Crystal structure of the plant symporter STP10 illuminates sugar uptake mechanism in monosaccharide transporter superfamily

Plants are dependent on controlled sugar uptake for correct organ development and sugar storage, and apoplastic sugar depletion is a defense strategy against microbial infections like rust and mildew. Uptake of glucose and other monosaccharides is mediated by Sugar Transport Proteins, proton-coupled symporters from the Monosaccharide Transporter (MST) superfamily. We present the 2.4 Å structure of Arabidopsis thaliana high affinity sugar transport protein, STP10, with glucose bound. The structure explains high affinity sugar recognition and suggests a proton donor/acceptor pair that links sugar transport to proton translocation. It contains a Lid domain, conserved in all STPs, that locks the mobile transmembrane domains through a disulfide bridge, and creates a protected environment which allows efficient coupling of the proton gradient to drive sugar uptake. The STP10 structure illuminates fundamental principles of sugar transport in the MST superfamily with implications for both plant antimicrobial defense, organ development and sugar storage.

Principles of Inter-Amino-Acid Recognition Revealed by Binding Energies between Homogeneous Oligopeptides

We have determined the interaction strengths of the common naturally occurring amino acids using a complete binding affinity matrix of 20 × 20 pairs of homo-octapeptides consisting of the 20 common amino acids between stationary and mobile states. We used a bead-based fluorescence assay for these measurements. The results provide a basis for analyzing specificity, polymorphisms, and selectivity of inter-amino-acid interactions. Comparative analyses of the binding energies, i.e., the free energies of association (ΔG A), reveal contributions assignable to both main-chain-related and side-chain-related interactions originating from the chemical structures of these 20 common amino acids. Side-chain-side-chain and side-chain-main-chain interactions are found to be pronounced in an identified set of amino acid pairs that determine the basis of inter-amino-acid recognition.

3D Modeling of Tumor Necrosis Factor Receptor and Tumor Necrosis Factor-bound Receptor Systems

The interactions between the tumor necrosis factor (TNF) and its receptor molecule are responsible for various signaling networks that are central to the functioning of human immune homeostasis. The present work is a computational study of certain structural aspects of this cell-signaling protein, specifically focusing on the molecular level analyses of the TNF receptor (TNF-R), guided by its crystallographic structure. We also examine the possible binding sites of the TNF onto TNF-R, and the associated interactions. The structural and conformational variations in the TNF-R and TNF bound TNF-R systems are examined in this context using molecular dynamics (MD) simulations. The time dependent variations of the dimeric TNF-R structures are compared with, and shown to be steadier than their isolated monomers. This dimeric stability is favored under acidic conditions. The results are used to further illustrate how 3D modeling and computer simulations can aid the structure-based approach to probing a ligand-receptor system.

How Can Interleukin-1 Receptor Antagonist Modulate Distinct Cell Death Pathways?

Multiple mechanisms of cell death exist (apoptosis, necroptosis, pyroptosis) and the subtle balance of several distinct proteins and inhibitors tightly regulates the cell fate toward one or the other pathway. Here, by combining coimmunoprecipitation, enzyme assays, and molecular simulations, we ascribe a new role, within this entangled regulatory network, to the interleukin-1 receptor antagonist (IL-1Ra). Our study enlightens that IL-1Ra, which usually inhibits the inflammatory effects of IL-1α/β by binding to IL-1 receptor, under advanced pathological states prevents apoptosis and/or necroptosis by noncompetitively inhibiting the activity of caspase-8 and -9. Consensus docking, followed by cumulative 10 μs of molecular dynamics simulations unprecedentedly reveal that IL-1Ra binds both caspases at their dimeric interface, preventing, in this manner, the formation of their catalytically/signaling active form. The resulting IL-1Ra/caspase-8(9) adducts are stabilized by hydrophobic and by few key hydrogen bonding interactions, formed by residues fully conserved across distinct caspases (-3, -6, -7, -8, and -9), and closely resemble the binding mode of the caspases inhibitors XIAP (X-linked inhibitor of apoptosis) and c-FLIP (cellular FLICE-like inhibitory protein). Tight regulation of the different forms of cell death has a major impact on distinct human illnesses (i.e., cancer, neurodegeneration, ischemic injury, atherosclerosis, viral/bacterial infections, and immune reaction). Hence, our study, pinpointing IL-1Ra as new actor of the intricate cell death regulatory network and gaining an atomic-scale understanding of its mechanism may open new avenues toward innovative therapeutic strategies to tackle major human diseases.

Importance of Fluctuating Amino Acid Residues in Folding and Binding of Proteins

BACKGROUND

Conformational flexibility of proteins remains as one of the major events in protein-protein/DNA/ligand/small molecule binding to achieve its biological function in the cell. The availability of high-resolution structures of protein complexes is a valuable resource for researchers to understand the mechanisms behind such interactions and it is found that the flexibility of amino acid residues at binding sites is crucial for many important functions in the cell.

METHODS

In this article, our statistical method (PreFRP) developed based on fluctuating amino acid residues and various amino acid indices related to flexibility/rigidity were used to study the importance of fluctuating amino acid residues in thermonucleases from pathogenic bacteria, cell penetrating peptides and intrinsically disordered proteins responsible for many neural disorders.

RESULTS

The results from our analysis reveal the importance of fluctuating amino acid residues in folding and binding of proteins. The role of moderate and high fluctuating residues in themonucleases, cell penetrating peptide and disordered regions are discussed in detail.

CONCLUSION

Therefore, our analysis will help in understanding the importance of fluctuating amino acid residues in proteins which undergo a conformation change phenomenon.

Refining evERdock: Improved selection of good protein-protein complex models achieved by MD optimization and use of multiple conformations

A method for evaluating binding free energy differences of protein-protein complex structures generated by protein docking was recently developed by some of us. The method, termed evERdock, combined short (2 ns) molecular dynamics (MD) simulations in explicit water and solution theory in the energy representation (ER) and succeeded in selecting the near-native complex structures from a set of decoys. In the current work, we performed longer (up to 100 ns) MD simulations before employing ER analysis in order to further refine the structures of the decoy set with improved binding free energies. Moreover, we estimated the binding free energies for each complex structure based on an average value from five individual MD snapshots. After MD simulations, all decoys exhibit a decrease in binding free energy, suggesting that proper equilibration in explicit solvent resulted in more favourably bound complexes. During the MD simulations, non-native structures tend to become unstable and in some cases dissociate, while near-native structures maintain a stable interface. The energies after the MD simulations show an improved correlation between similarity criteria (such as interface root-mean-square distance) to the native (crystal) structure and the binding free energy. In addition, calculated binding free energies show sensitivity to the number of contacts, which was demonstrated to reflect the relative stability of structures at earlier stages of the MD simulation. We therefore conclude that the additional equilibration step along with the use of multiple conformations can make the evERdock scheme more versatile under low computational cost.

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning‐based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy‐based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein‐protein complexes. It competes with existing state‐of‐the‐art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2–p53 complex.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes

Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (ε_in). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GB^GBn1 model with ε_in = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GB^GBn1 model distinguished the near-native binding structures within the top 10 decoys for 117 out of the 148 protein-RNA systems (79.1%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems.

From radioactive ligands to biosensors: binding methods with olfactory proteins

In this paper, we critically review the binding protocols currently reported in the literature to measure the affinity of odorants and pheromones to soluble olfactory proteins, such as odorant-binding proteins (OBPs), chemosensory proteins (CSPs) and Niemann-Pick class C2 (NPC2) proteins. The first part contains a brief introduction on the principles of binding and a comparison of the techniques adopted or proposed so far, discussing advantages and problems of each technique, as well as their suitable application to soluble olfactory proteins. In the second part, we focus on the fluorescent binding assay, currently the most widely used approach. We analyse advantages and drawbacks, trying to identify the causes of anomalous behaviours that have been occasionally observed, and suggest how to interpret the experimental data when such events occur. In the last part, we describe the state of the art of biosensors for odorants, using soluble olfactory proteins immobilised on biochips, and discuss the possibility of using such approach as an alternative way to measure binding events and dissociation constants.

Predicting Relative Protein Affinity of Novel Per- and Polyfluoroalkyl Substances (PFASs) by An Efficient Molecular Dynamics Approach

With the phasing out of long-chain per- and polyfluoroalkyl substances (PFASs), production of a wide variety of alternative PFASs has increased to meet market demand. However, little is known about the bioaccumulation potential of these replacement compounds. Here, we developed a modeling workflow that combines molecular docking and molecular dynamics simulation techniques to estimate the relative binding affinity of a total of 15 legacy and replacement PFASs for human and rat liver-type fatty acid binding protein (hLFABP and rLFABP). The predicted results were compared with experimental data extracted from three different studies. There was good correlation between predicted free energies of binding and measured binding affinities, with correlation coefficients of 0.97, 0.79, and 0.96, respectively. With respect to replacement PFASs, our results suggest that EEA and ADONA are at least as strongly bound to rLFABP as perfluoroheptanoic acid (PFHpA), and as strongly bound to hLFABP as perfluorooctanoic acid (PFOA). For F-53 and F-53B, both have similar or stronger binding affinities than perfluorooctanesulfonate (PFOS). Given that interactions of PFASs with proteins (e.g., LFABPs) are important determinants of bioaccumulation potential in organisms, these alternatives could be as bioaccumulative as legacy PFASs, and are therefore not necessarily safer alternatives to long-chain PFASs.

Monitoring characteristics and genotoxic effects of engineered nanoparticle-protein corona

Engineered nanoparticles (ENPs) possess different physical and chemical properties compared to their bulk counterparts. These unique properties have found application in various products in the area of therapeutics, consumer goods, environmental remediation, optical and electronic fields. This has also increased the likelihood of their release into the environment thereby affecting human health and ecosystem. ENPs, when in contact with the biological system have various physical and chemical interactions with cellular macromolecules including proteins. These interactions lead to the formation of protein corona around the ENPs. Consequently, living systems interact with the protein-coated ENP rather than with a bare ENP. This ENP-protein interaction influences uptake, accumulation, distribution and clearance and thereby affecting the cytotoxic and genotoxic responses. Although there are few studies which discussed the fate of ENPs, there is a need for extensive research in the field of ENPs, to understand the interaction of ENPs with biological systems for their safe and productive application.

A Diversified Spectrometric and Molecular Docking Technique to Biophysical Study of Interaction between Bovine Serum Albumin and Sodium Salt of Risedronic Acid, a Bisphosphonate for Skeletal Disorders

The binding interaction between bovine serum albumin (BSA) and sodium salt of risedronic acid (RSN) was studied by using the FT-IR (Fourier transform infrared), UV-Vis (ultraviolet–visible), fluorescence (emission and synchronous), CD (circular dichroism) spectrometric, and computational (molecular docking) techniques at 289, 297, and 305 K temperatures with physiological buffer of pH 7.40. The conformational and secondary structural changes observed for BSA from CD spectra and by curve fitting procedure were applied to Fourier self-deconvolution in FT-IR spectra. The formation of a BSA-RSN complex was confirmed from UV-Vis spectroscopy. The static type of quenching shown for RSN to BSA was verified from Stern–Volmer and modified Stern–Volmer equations. The binding constant of order 10⁵ was obtained to be confirming that there exists a strong binding interaction between BSA and RSN. Synchronous fluorescence shows that the microenvironment of tryptophan was altered, not tyrosine of BSA; in addition to this, the distance between tryptophan of BSA and RSN was found out from Forster's theory of nonradiation energy transfer. The interaction between BSA and RSN mainly occurred as a result of hydrogen bonds and van der Waals forces, the process is exothermic and spontaneous, and it was achieved through van 't Hoff equation. This interaction was affected by the presence of biologically active Fe²⁺, Ni²⁺, Ca²⁺, Mg²⁺, and Cd²⁺ ions and was also studied. The subdomain IIIA of BSA involved with RSN interaction was authenticated from molecular docking analysis.

Correlating interfacial water dynamics with protein-protein interaction in complex of GDF-5 and BMPRI receptors

GDF-5 mediated signal transduction regulating chondrogenesis and skeletogenesis involves three different type-I receptors viz. Act-RI, BMPRIA and BMPRIB. BMPRIA and BMPRIB generally shows temporal and spatial co-expression but some spatially different expression pattern has also been observed. BMPRIA receptor is the key receptor implicated in BMP signalling during osteogenesis and is expressed in osteoblasts during the course of bone formation. However, BMPRIB appears to be primarily expressed in mesenchymal pre-cartilage condensations and also found in differentiated osteoblast and chondrocytes. The extracellular pH affects bone cell function and it is experimentally known that mineralization of bone is affected by shift of pH in cultured osteoblast. Here we report the effect of pH on dynamics of water present at the interface of GDF-5:BMPRIA and GDF-5:BMPRIB and binding interaction energy of these complexes. Water dynamics at different pH was analysed using residence time and hydrogen bond relaxation kinetics. pH influences the interaction energy between GDF-5 and BMPRIA and BMPRIB receptors indicating the electrostatic environment modulating the activity of two receptors. This pH dependence of interaction energy is further supported by similar behaviour of hydrogen bond existence of buried water molecules at the interface. In contrast to this the slow and fast exchanging water molecules do not show similar pH dependence of hydrogen bonding relaxation kinetics. Hence; we conclude that only buried water molecule at the interface influences the protein-protein interaction and the electrostatic environment of the extracellular fluid might decide the specificity of the two receptors.

Inhibition of TRAF6-Ubc13 interaction in NFkB inflammatory pathway by analyzing the hotspot amino acid residues and protein-protein interactions using molecular docking simulations

Protein-protein interactions (PPIs) are important in most of the biochemical processes. Hotspot amino acid residues in proteins are the most important contributors for proper protein-protein interactions. Hotspot amino acid residues have been looked down upon as important therapeutic targets in inhibiting PPIs. Interaction between TRAF6 and Ubc13 is a crucial point in the NFkB inflammatory pathway. Dysfunction of the NFkB pathway is associated with numerous human diseases including cancer and neurodenegeration disorders. Ubc13 also interacts specifically to TRAF6 and not with other proteins of the TRAF family and this makes the TRAF6-Ubc13 complex an important target for specific inhibition. Hence, interfering with the TRAF6-Ubc13 association may prove effective in suppressing the NFkB disease pathway. In the present study, we searched the TRAF6-Ubc13 interaction interface to analyze their binding hotspot amino acid residues using various computational techniques. Heterocyclic compounds are known for their medicinal properties. We screened for heterocyclic analogues to the known TRAF6 inhibitor PDTC, to predict a better inhibitor using in silico protein-ligand and protein-protein interaction studies. Our in silico prediction results suggest that tetrahydro-2-thiophenecarbothioamide (Chemspider ID 36027528) binds one of the major hot-spot residues of TRAF6-Ubc13 interface and can be a better alternative in suppressing TRA6-Ubc13 complex formation in chronic inflammation than PDTC.

Using thermodynamic parameters to calibrate a mechanistic dose-response for infection of a host by a virus

Assessing the risk of infection from emerging viruses or of existing viruses jumping the species barrier into novel hosts is limited by the lack of dose response data. The initial stages of the infection of a host by a virus involve a series of specific contact interactions between molecules in the host and on the virus surface. The strength of the interaction is quantified in the literature by the dissociation constant (K_d) which is determined experimentally and is specific for a given virus molecule/host molecule combination. Here, two stages of the initial infection process of host intestinal cells are modelled, namely escape of the virus in the oral challenge dose from the innate host defenses (e.g. mucin proteins in mucus) and the subsequent binding of any surviving virus to receptor molecules on the surface of the host epithelial cells. The strength of virus binding to host cells and to mucins may be quantified by the association constants, K_a and K_mucin, respectively. Here, a mechanistic dose-response model for the probability of infection of a host by a given virus dose is constructed using K_a and K_mucin which may be derived from published K_d values taking into account the number of specific molecular interactions. It is shown that the effectiveness of the mucus barrier is determined not only by the amount of mucin but also by the magnitude of K_mucin. At very high K_mucin values, slight excesses of mucin over virus are sufficient to remove all the virus according to the model. At lower K_mucin values, high numbers of virus may escape even with large excesses of mucin. The output from the mechanistic model is the probability (p₁) of infection by a single virion which is the parameter used in conventional dose-response models to predict the risk of infection of the host from the ingested dose. It is shown here how differences in K_a (due to molecular differences in an emerging virus strain or new host) affect p₁, and how these differences in K_a may be quantified in terms of two thermodynamic parameters, namely enthalpy and entropy. This provides the theoretical link between sequencing data and risk of infection. Lack of data on entropy is a limitation at present and may also affect our interpretation of K_d in terms of infectivity. It is concluded that thermodynamic approaches have a major contribution to make in developing dose-response models for emerging viruses.

Assessment of a Single Decoupling Alchemical Approach for the Calculation of the Absolute Binding Free Energies of Protein-Peptide Complexes

The computational modeling of peptide inhibitors to target protein-protein binding interfaces is growing in interest as these are often too large, too shallow, and too feature-less for conventional small molecule compounds. Here, we present a rare successful application of an alchemical binding free energy method for the calculation of converged absolute binding free energies of a series of protein-peptide complexes. Specifically, we report the binding free energies of a series of cyclic peptides derived from the LEDGF/p75 protein to the integrase receptor of the HIV1 virus. The simulations recapitulate the effect of mutations relative to the wild-type binding motif of LEDGF/p75, providing structural, energetic and dynamical interpretations of the observed trends. The equilibration and convergence of the calculations are carefully analyzed. Convergence is aided by the adoption of a single-decoupling alchemical approach with implicit solvation, which circumvents the convergence difficulties of conventional double-decoupling protocols. We hereby present the single-decoupling methodology and critically evaluate its advantages and limitations. We also discuss some of the challenges and potential pitfalls of binding free energy calculations for complex molecular systems which have generally limited their applicability to the quantitative study of protein-peptide binding equilibria.

Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies

The need for accurate yet efficient representation of the aqueous environment in biomolecular modeling has led to the development of a variety of generalized Born (GB) implicit solvent models. While many studies have focused on the accuracy of available GB models in predicting solvation free energies, a systematic assessment of the quality of these models in binding free energy calculations, crucial for rational drug design, has not been undertaken. Here, we evaluate the accuracies of eight common GB flavors (GB-HCT, GB-OBC, GB-neck2, GBNSR6, GBSW, GBMV1, GBMV2, and GBMV3), available in major molecular dynamics packages, in predicting the electrostatic binding free energies ( ΔΔ G_el) for a diverse set of 60 biomolecular complexes belonging to four main classes: protein-protein, protein-drug, RNA-peptide, and small complexes. The GB flavors are examined in terms of their ability to reproduce the results from the Poisson-Boltzmann (PB) model, commonly used as accuracy reference in this context. We show that the agreement with the PB of ΔΔ G_el estimates varies widely between different GB models and also across different types of biomolecular complexes, with R² correlations ranging from 0.3772 to 0.9986. A surface-based "R6" GB model recently implemented in AMBER shows the closest overall agreement with reference PB ( R² = 0.9949, RMSD = 8.75 kcal/mol). The RNA-peptide and protein-drug complex sets appear to be most challenging for all but one model, as indicated by the large deviations from the PB in ΔΔ G_el. Small neutral complexes present the least challenge for most of the GB models tested. The quantitative demonstration of the strengths and weaknesses of the GB models across the diverse complex types provided here can be used as a guide for practical computations and future development efforts.

What factors determine the stability of a weak protein-protein interaction in a charged aqueous droplet?

Maintaining the interface of a weak transient protein complex transferred from bulk solution to the gaseous state via evaporating droplets is a critical question in the detection of the complex association (dissociation) constant by using electrospray ionization mass spectrometry (ESI-MS). Here we explore the factors that may affect the stability of a protein-protein interaction (PPI) using atomistic molecular dynamics (MD) modelling of a complex of ubiquitin (Ub) and the ubiquitin-associated domain (UbA) (RCSB PDB code ) and a non-covalent complex of diubiquitin (RCSB PDB code ) in aqueous droplets. A general method is presented to determine the protonation states of the complexes we investigate in particular, and that of a protein in general, under various pH conditions that an evaporating droplet acquires due to its change in size. We find that the combination of high temperature and high charge states of the protein complexes may destabilize the interface by creating new interfaces instead of a direct rupture of the initial stable interface. We provide evidence that highly charged protein complexes are found in droplets that form conical extrusions of the solvent on the surface due to charge-induced instability. This distinct droplet morphology leads to a higher solvent evaporation rate that assists in transferring the complex in the gaseous state without dissociation. The conical solvent protrusions expose on the droplet surface certain amino acids that otherwise would be solvated in a droplet with the protein complex of low charge states. The new vapor-protein interface does not have a direct effect on the stability of the PPI. A common way in experiments to stabilize the protein complexes in droplets is to reduce the protonation state of the proteins. Here we find that weakly bound protein complexes even at high protonation states can be stabilized by the presence of a small number of counterions, without affecting the protonation state of the protein. Our findings may provide guiding principles in ESI-MS experiments to stabilize weak transient PPIs.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking

Understanding protein-protein interactions (PPIs) is quite important to elucidate crucial biological processes and even design compounds that interfere with PPIs with pharmaceutical significance. Protein-protein docking can afford the atomic structural details of protein-protein complexes, but the accurate prediction of the three-dimensional structures for protein-protein systems is still notoriously difficult due in part to the lack of an ideal scoring function for protein-protein docking. Compared with most scoring functions used in protein-protein docking, the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) methodologies are more theoretically rigorous, but their overall performance for the predictions of binding affinities and binding poses for protein-protein systems has not been systematically evaluated. In this study, we first evaluated the performance of MM/PBSA and MM/GBSA to predict the binding affinities for 46 protein-protein complexes. On the whole, different force fields, solvation models, and interior dielectric constants have obvious impacts on the prediction accuracy of MM/GBSA and MM/PBSA. The MM/GBSA calculations based on the ff02 force field, the GB model developed by Onufriev et al. and a low interior dielectric constant (εin = 1) yield the best correlation between the predicted binding affinities and the experimental data (rp = -0.647), which is better than MM/PBSA (rp = -0.523) and a number of empirical scoring functions used in protein-protein docking (rp = -0.141 to -0.529). Then, we examined the capability of MM/GBSA to identify the possible near-native binding structures from the decoys generated by ZDOCK for 43 protein-protein systems. The results illustrate that the MM/GBSA rescoring has better capability to distinguish the correct binding structures from the decoys than the ZDOCK scoring. Besides, the optimal interior dielectric constant of MM/GBSA for re-ranking docking poses may be determined by analyzing the characteristics of protein-protein binding interfaces. Considering the relatively high prediction accuracy and low computational cost, MM/GBSA may be a good choice for predicting the binding affinities and identifying correct binding structures for protein-protein systems.

Leishmania donovani Inhibitor of Serine Peptidases 2 Mediated Inhibition of Lectin Pathway and Upregulation of C5aR Signaling Promote Parasite Survival inside Host

Leishmania donovani, the causative agent of Indian visceral leishmaniasis has to face several barriers of the immune system inside the mammalian host for its survival. The complement system is one of the first barriers and consists of a well-balanced network of proteases including S1A family serine proteases (SPs). Inhibitor of serine peptidases (ISPs) is considered as inhibitor of S1A family serine peptidases and is reported to be present in trypanosomes, including Leishmania. In our previous study, we have deciphered the role of ISPs [LdISP1 and L. donovani inhibitor of serine peptidases 2 (LdISP2)] in the survival of L. donovani inside the sandfly midgut. However, the role of theses ISPs in the survival of L. donovani inside mammalian host still remains elusive. In the present study, we have deciphered the inhibitory effect of LdISPs on the host complement S1A serine peptidases, such as C1r/C1s and MASP1/MASP2. Our study suggested that although both rLdISP1 and rLdISP2 inferred strong interaction with C1complex and MBL-associated serine proteases (MASPs) but rLdISP2 showed the stronger inhibitory effect on MASP2 than rLdISP1. Moreover, we found that rLdISP2 significantly reduces the formation of C3, C5 convertase, and membrane attacking complex (MAC) by lectin pathway (LP) resulting in significant reduction in serum mediated lysis of the parasites. The role of LdISP2 on neutrophil elastase-mediated C5aR signaling was also evaluated. Notably, our results showed that infection of macrophages with ISP2-overexpressed Leishmania parasites significantly induces the expression of C5aR both at the transcript and translational level. Simultaneously, infection with ISP2KD parasites results in downregulation of host PI3K/AKT phosphorylation and increased in IL-12 production. Taken together, our findings clearly suggest that LdISP2 promotes parasite survival inside host by inhibiting MAC formation and complement-mediated lysis via LP and by upregulation of C5aR signaling.

Arsenic exposure during embryonic development alters the expression of the long noncoding RNA growth arrest specific-5 (Gas5) in a sex-dependent manner

Our previous studies suggest that prenatal arsenic exposure (50ppb) modifies epigenetic control of the programming of the glucocorticoid receptor (GR) signaling system in the developing mouse brain. These deficits may lead to long-lasting consequences, including deficits in learning and memory, increased depressive-like behaviors, and an altered set-point of GR feedback throughout life. To understand the arsenic-induced changes within the GR system, we assessed the impact of in utero arsenic exposure on the levels of the GR and growth arrest-specific-5 (Gas5), a noncoding RNA, across a key gestational period for GR programming (gestational days, GD 14-18) in mice. Gas5 contains a glucocorticoid response element (GRE)-like sequence that binds the GR, thereby decreasing GR-GRE-dependent gene transcription and potentially altering GR programming. Prenatal arsenic exposure resulted in sex-dependent and age-dependent shifts in the levels of GR and Gas5 expression in fetal telencephalon. Nuclear GR levels were reduced in males, but unchanged in females, at all gestational time points tested. Total cellular Gas5 levels were lower in arsenic-exposed males with no changes seen in arsenic-exposed females at GD16 and 18. An increase in total cellular Gas-5 along with increased nuclear levels in GD14 arsenic-exposed females, suggests a differential regulation of cellular compartmentalization of Gas5. RIP assays revealed reduced Gas5 associated with the GR on GD14 in the nuclear fraction prepared from arsenic-exposed males and females. This decrease in levels of GR-Gas5 binding continued only in the females at GD18. Thus, nuclear GR signaling potential is decreased in prenatal arsenic-exposed males, while it is increased or maintained at levels approaching normal in prenatal arsenic-exposed females. These findings suggest that females, but not males, exposed to arsenic are able to regulate the levels of nuclear free GR by altering Gas5 levels, thereby keeping GR nuclear signaling closer to control (unexposed) levels.

Interactome evolution: insights from genome-wide analyses of protein-protein interactions

We highlight new evolutionary insights enabled by recent genome-wide studies on protein-protein interaction (PPI) networks ('interactomes'). While most PPIs are mediated by a single sequence region promoting or inhibiting interactions, many PPIs are mediated by multiple sequence regions acting cooperatively. Most PPIs perform important functions maintained by negative selection: we estimate that less than ∼10% of the human interactome is effectively neutral upon perturbation (i.e. 'junk' PPIs), and the rest are deleterious upon perturbation; interfacial sites evolve more slowly than other sites; many conserved PPIs show signatures of co-evolution at the interface; PPIs evolve more slowly than protein sequence. At the same time, many PPIs undergo rewiring during evolution for lineage-specific adaptation. Finally, chaperone-protein and host-pathogen interactomes are governed by distinct evolutionary principles.

Binding Affinity of a Highly Sensitive Au/Ag/Au/Chitosan-Graphene Oxide Sensor Based on Direct Detection of Pb2+ and Hg2+ Ions

The study of binding affinity is essential in surface plasmon resonance (SPR) sensing because it allows researchers to quantify the affinity between the analyte and immobilised ligands of an SPR sensor. In this study, we demonstrate the derivation of the binding affinity constant, K, for Pb²⁺ and Hg²⁺ ions according to their SPR response using a gold/silver/gold/chitosan-graphene oxide (Au/Ag/Au/CS-GO) sensor for the concentration range of 0.1-5 ppm. The higher affinity of Pb²⁺ to binding with the CS-GO sensor explains the outstanding sensitivity of 2.05 °ppm^-1 against 1.66 °ppm^-1 of Hg²⁺. The maximum signal-to-noise ratio (SNR) upon detection of Pb²⁺ is 1.53, and exceeds the suggested logical criterion of an SNR. The Au/Ag/Au/CS-GO SPR sensor also exhibits excellent repeatability in Pb²⁺ due to the strong bond between its functional groups and this cation. The adsorption data of Pb²⁺ and Hg²⁺ on the CS-GO sensor fits well with the Langmuir isotherm model where the affinity constant, K, of Pb²⁺ and Hg²⁺ ions is computed. The affinity of Pb²⁺ ions to the Au/Ag/Au/CS-GO sensor is significantly higher than that of Hg²⁺ based on the value of K, 7 × 10⁵ M^-1 and 4 × 10⁵ M^-1, respectively. The higher shift in SPR angles due to Pb²⁺ and Hg²⁺ compared to Cr³⁺, Cu²⁺ and Zn²⁺ ions also reveals the greater affinity of the CS-GO SPR sensor to them, thus supporting the rationale for obtaining K for these two heavy metals. This study provides a better understanding on the sensing performance of such sensors in detecting heavy metal ions.

Impact of assay temperature on antibody binding characteristics in living cells: A case study

Kinetic and thermodynamic studies of ligand-receptor interactions are essential for increasing the understanding of receptor activation mechanisms and drug behavior. The characterization of molecular interactions on living cells in real-time goes beyond most current binding assays, and provides valuable information about the dynamics and underlying mechanism of the molecules in a living system. The effect of temperature on interactions in cell-based assays is, however, rarely discussed. In the present study, the effect of temperature on binding of monoclonal antibodies, cetuximab and pertuzumab to specific receptors on living cancer cells was evaluated, and the affinity and kinetics of the interactions were estimated at selected key temperatures. Changes in the behavior of the interactions, particularly in the on- and off-rates were observed, leading to greatly extended time to reach the equilibrium at 21°C compared with at 37°C. However, the observed changes in kinetic characteristics were less than a factor of 10. It was concluded that it is possible to conduct real-time measurements with living cells at different temperatures, and demonstrated that influences of the ambient temperature on the interaction behavior are likely to be less than one order of magnitude.

Analysis of Protein Interactions by Surface Plasmon Resonance

Surface plasmon resonance is an optical technique that is utilized for detecting molecular interactions, such as interactions that occur between proteins or other classes of molecules. Binding of a mobile molecule (analyte) to a molecule immobilized on a thin metal film (ligand) changes the refractive index of the film. The angle of extinction of light that is completely reflected after polarized light impinges upon the film, is altered and monitored as a change in detector position for a dip in reflected intensity (the surface plasmon resonance phenomenon). Because the method strictly detects mass, there is no need to label the interacting components, thus eliminating possible changes of their molecular properties. In this chapter, we review essential SPR methodology and present applications to basic science and human disease.

Dynamic Modulation of Binding Affinity as a Mechanism for Regulating Interferon Signaling

How structural dynamics affects cytokine signaling is under debate. Here, we investigated the dynamics of the type I interferon (IFN) receptor, IFNAR1, and its effect on signaling upon binding IFN and IFNAR2 using a combination of structure-based mechanistic studies, in situ binding, and gene induction assays. Our study reveals that IFNAR1 flexibility modulates ligand-binding affinity, which, in turn, regulates biological signaling. We identified the hinge sites and key interactions implicated in IFNAR1 inter-subdomain (SD1-SD4) movements. We showed that the predicted cooperative movements are essential to accommodate intermolecular interactions. Engineered disulfide bridges, computationally predicted to interfere with IFNAR1 dynamics, were experimentally confirmed. Notably, introducing disulfide bonds between subdomains SD2 and SD3 modulated IFN binding and activity in accordance with the relative attenuation of cooperative movements with varying distance from the hinge center, whereas locking the SD3-SD4 interface flexibility in favor of an extended conformer increased activity.

Measurement of In Vivo Protein Binding Affinities in a Signaling Network with Mass Spectrometry

Protein interaction networks play a key role in signal processing. Despite the progress in identifying the interactions, the quantification of their strengths lags behind. Here we present an approach to quantify the in vivo binding of proteins to their binding partners in signaling-transcriptional networks, by the pairwise genetic isolation of each interaction and by varying the concentration of the interacting components over time. The absolute quantification of the protein concentrations was performed with targeted mass spectrometry. The strengths of the interactions, as defined by the apparent dissociation constants, ranged from subnanomolar to micromolar values in the yeast galactose signaling network. The weak homodimerization of the Gal4 activator amplifies the signal elicited by glucose. Furthermore, combining the binding constants in a feedback loop correctly predicted cellular memory, a characteristic network behavior. Thus, this genetic-proteomic binding assay can be used to faithfully quantify how strongly proteins interact with proteins, DNA and metabolites.

Interaction of Aldehyde dehydrogenase with acetaminophen as examined by spectroscopies and molecular docking

The interaction of acetaminophen, a non-substrate anionic ligand, with Aldehyde Dehydrogenase was studied by fluorescence, UV-Vis absorption, and circular dichroism spectroscopies under simulated physiological conditions. The fluorescence spectra and data generated showed that acetaminophen binding to ALDH is purely dynamic quenching mechanism. The acetaminophen-ALDH is kinetically rapid reversible interaction with a binding constant, Ka, of 4.91×103 L mol-1. There was an existence of second binding site of ALDH for acetaminophen at saturating acetaminophen concentration. The binding sites were non-cooperative. The thermodynamic parameters obtained suggest that Van der Waal force and hydrogen bonding played a major role in the binding of acetaminophen to ALDH. The interaction caused perturbation of the ALDH structures with an obvious reduction in the α-helix. The binding distance of 4.43 nm was obtained between Acetaminophen and ALDH. Using Ficoll 400 as macro-viscosogen and glycerol as micro-viscosogen, Stoke-Einstein empirical plot demonstrated that acetaminophen-ALDH binding was diffusion controlled. Molecular docking showed the participation of some amino acids in the complex formation with -5.3 kcal binding energy. With these, ALDH might not an excipient detoxifier of acetaminophen but could be involved in its pegylation/encapsulation.

A structural perspective on the interactions of TRAF6 and Basigin during the onset of melanoma: A molecular dynamics simulation study

Metastatic melanoma is the most fatal type of skin cancer. The roles of matrix metalloproteinases (MMPs) have well been established in the onset of melanoma. Basigin (BSG) belongs to the immunoglobulin superfamily and is critical for induction of extracellular MMPs during the onset of various cancers including melanoma. Tumor necrosis factor receptor-associated factor 6 (TRAF6) is an E3-ligase that interacts with BSG and mediates its membrane localization, which leads to MMP expression in melanoma cells. This makes TRAF6 a potential therapeutic target in melanoma. We here conducted protein-protein interaction studies on TRAF6 and BSG to get molecular level insights of the reactions. The structure of human BSG was constructed by protein threading. Molecular-docking method was applied to develop the TRAF6-BSG complex. The refined docked complex was further optimized by molecular dynamics simulations. Results from binding free energy, surface properties, and electrostatic interaction analysis indicate that Lys340 and Glu417 of TRAF6 play as the anchor residues in the protein interaction interface. The current study will be helpful in designing specific modulators of TRAF6 to control melanoma metastasis.

Bioengineering Microgels and Hydrogel Microparticles for Sensing Biomolecular Targets

Hydrogels, and in particular microgels, are playing an increasingly important role in a diverse range of applications due to their hydrophilic, biocompatible, and highly flexible chemical characteristics. On this basis, solution-like environment, non-fouling nature, easy probe accessibility and target diffusion, effective inclusion of reporting moieties can be achieved, making them ideal substrates for bio-sensing applications. In fact, hydrogels are already successfully used in immunoassays as well as sensitive nucleic acid assays, also enabling hydrogel-based suspension arrays. In this review, we discuss key parameters of hydrogels in the form of micron-sized particles to be used in sensing applications, paying attention to the protein and oligonucleotides (i.e., miRNAs) targets as most representative kind of biomarkers.

Elucidating endotoxin-biomolecule interactions with FRET: extending the frontiers of their supramolecular complexation

Endotoxin has been one of the topical chemical contaminants of major concern to researchers, especially in the field of bioprocessing. This major concern of researchers stems from the fact that the presence of Gram-negative bacterial endotoxin in intracellular products is unavoidable and requires complex downstream purification steps. For instance, endotoxin interacts with recombinant proteins, peptides, antibodies and aptamers and these interactions have formed the foundation for most biosensors for endotoxin detection. It has become imperative for researchers to engineer reliable means/techniques to detect, separate and remove endotoxin, without compromising the quality and quantity of the end-product. However, the underlying mechanism involved during endotoxin-biomolecule interaction is still a gray area. The use of quantitative molecular microscopy that provides high resolution of biomolecules is highly promising, hence, may lead to the development of improved endotoxin detection strategies in biomolecule preparation. Förster resonance energy transfer (FRET) spectroscopy is one of the emerging most powerful tools compatible with most super-resolution techniques for the analysis of molecular interactions. However, the scope of FRET has not been well-exploited in the analysis of endotoxin-biomolecule interaction. This article reviews endotoxin, its pathophysiological consequences and the interaction with biomolecules. Herein, we outline the common potential ways of using FRET to extend the current understanding of endotoxin-biomolecule interaction with the inference that a detailed understanding of the interaction is a prerequisite for the design of strategies for endotoxin identification and removal from protein milieus.

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.

Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data

Background

One goal of structural biology is to understand how a protein’s 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex into its constituent atom-atom interactions is typically sufficient to rapidly predict ligand affinity with high accuracy (>70% correlation between predicted and experimentally-determined affinity), a fact that is exploited to support structure-based drug design. We recently found that protein-DNA/RNA affinity can also be predicted with high accuracy using extensions of existing techniques, but protein-protein affinity could not be predicted with >60% correlation, even when the protein-protein complex was available.

Methods

X-ray and NMR structures of protein-protein complexes, their associated binding affinities and experimental conditions were obtained from different binding affinity and structural databases. Statistical models were implemented using a generalized linear model framework, including the experimental conditions as new model features. We evaluated the potential for new features to improve affinity prediction models by calculating the Pearson correlation between predicted and experimental binding affinities on the training and test data after model fitting and after cross-validation. Differences in accuracy were assessed using two-sample t test and nonparametric Mann–Whitney U test.

Results

Here we evaluate a range of potential factors that may interfere with accurate protein-protein affinity prediction. We find that X-ray crystal resolution has the strongest single effect on protein-protein affinity prediction. Limiting our analyses to only high-resolution complexes (≤2.5 Å) increased the correlation between predicted and experimental affinity from 54 to 68% (p = 4.32x10⁻³). In addition, incorporating information on the experimental conditions under which affinities were measured (pH, temperature and binding assay) had significant effects on prediction accuracy. We also highlight a number of potential errors in large structure-affinity databases, which could affect both model training and accuracy assessment.

Conclusions

The results suggest that the accuracy of statistical models for protein-protein affinity prediction may be limited by the information present in databases used to train new models. Improving our capacity to integrate large-scale structural and functional information may be required to substantively advance our understanding of the general principles by which a protein’s structure determines its function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1533-z) contains supplementary material, which is available to authorized users.

Protein-ligand (un)binding kinetics as a new paradigm for drug discovery at the crossroad between experiments and modelling

In the last three decades, protein and nucleic acid structure determination and comprehension of the mechanisms, leading to their physiological and pathological functions, have become a cornerstone of biomedical sciences. A deep understanding of the principles governing the fates of cells and tissue at the molecular level has been gained over the years, offering a solid basis for the rational design of drugs aimed at the pharmacological treatment of numerous diseases. Historically, affinity indicators (i.e. Kd and IC50/EC50) have been assumed to be valid indicators of the in vivo efficacy of a drug. However, recent studies pointed out that the kinetics of the drug-receptor binding process could be as important or even more important than affinity in determining the drug efficacy. This eventually led to a growing interest in the characterisation and prediction of the rate constants of protein-ligand association and dissociation. For instance, a drug with a longer residence time can kinetically select a given receptor over another, even if the affinity for both receptors is comparable, thus increasing its therapeutic index. Therefore, understanding the molecular features underlying binding and unbinding processes is of central interest towards the rational control of drug binding kinetics. In this review, we report the theoretical framework behind protein-ligand association and highlight the latest advances in the experimental and computational approaches exploited to investigate the binding kinetics.

High-Throughput, Protein-Targeted Biomolecular Detection Using Frequency-Domain Faraday Rotation Spectroscopy

A clinically relevant magneto-optical technique (fd-FRS, frequency-domain Faraday rotation spectroscopy) for characterizing proteins using antibody-functionalized magnetic nanoparticles (MNPs) is demonstrated. This technique distinguishes between the Faraday rotation of the solvent, iron oxide core, and functionalization layers of polyethylene glycol polymers (spacer) and model antibody-antigen complexes (anti-BSA/BSA, bovine serum albumin). A detection sensitivity of ≈10 pg mL^-1 and broad detection range of 10 pg mL^-1 ≲ c_BSA ≲ 100 µg mL^-1 are observed. Combining this technique with predictive analyte binding models quantifies (within an order of magnitude) the number of active binding sites on functionalized MNPs. Comparative enzyme-linked immunosorbent assay (ELISA) studies are conducted, reproducing the manufacturer advertised BSA ELISA detection limits from 1 ng mL^-1 ≲ c_BSA ≲ 500 ng mL^-1 . In addition to the increased sensitivity, broader detection range, and similar specificity, fd-FRS can be conducted in less than ≈30 min, compared to ≈4 h with ELISA. Thus, fd-FRS is shown to be a sensitive optical technique with potential to become an efficient diagnostic in the chemical and biomolecular sciences.

Folate binding protein: therapeutic natural nanotechnology for folic acid, methotrexate, and leucovorin

Blood serum proteins play a critical role in the transport, biodistribution, and efficacy of systemically-delivered therapeutics. Here, we have investigated the concentration- and ligand-dependent aggregation of folate binding protein (FBP), focusing in particular on folic acid, an important vitamin and targeting agent; methotrexate, an antifolate drug used to treat cancer and rheumatoid arthritis; and leucovorin which is used to decrease methotrexate toxicity. We employed atomic force microscopy to characterize, on a particle-by-particle basis, the volumes of the FBP nanoparticles that form upon ligand binding. We measured the distribution of FBP nanoparticle volumes as a function of ligand concentration over physiologically- and therapeutically-relevant ranges. At physiologically-relevant concentrations, significant differences in particle volume distributions exist that we hypothesize are consistent with different trafficking mechanisms for folic acid and methotrexate. In addition, we hypothesize leucovorin is trafficked and delivered like folic acid at therapeutically-relevant concentrations. We propose that changes in dosing procedures could improve the delivery and therapeutic index for methotrexate and other folic acid-targeted drug conjugates and imaging agents. Specifically, we suggest pre-binding the drugs to FBP may provide a better formulation for drug delivery of methotrexate for both cancer and rheumatoid arthritis. This would be analogous to pre-binding paclitaxel to albumin, which is already used in the clinic.

Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes

Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.