Protein–protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area

Protein–protein interactions play key roles in many cellular processes and their affinities and specificities are finely tuned to the functions they perform. Here, we present a study on the relationship between binding affinity and the size and chemical nature of protein–protein interfaces. Our analysis focuses on heterodimers and includes curated structural and thermodynamic data for 113 complexes. We observe a direct correlation between binding affinity and the amount of surface area buried at the interface. For a given amount of surface area buried, the binding affinity spans four orders of magnitude in terms of the dissociation constant (K_d). Across the entire dataset, we observe no obvious relationship between binding affinity and the chemical composition of the interface. We also calculate the free energy per unit surface area buried, or “surface energy density,” of each heterodimer. For interfacial surface areas between 500 and 2000 Ų, the surface energy density decreases as the buried surface area increases. As the buried surface area increases beyond about 2000 Ų, the surface energy density levels off to a constant value. We believe that these analyses and data will be useful for researchers with an interest in understanding, designing or inhibiting protein–protein interfaces.     

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

Protein–protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein–protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein–protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein–protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein‐protein complexes into high and low affinity groups based on their K_d values. Our results showed a 10‐fold cross‐validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein–protein interaction networks and human–pathogen interactions based on the strength of interactions. Proteins 2014; 82:2088–2096. © 2014 Wiley Periodicals, Inc.     

Locating the binding sites of retinol and retinoic acid with milk β-lactoglobulin

β-lactoglobulin (β-LG) is a member of lipocalin superfamily of transporters for small hydrophobic molecules such as retinoids. We located the binding sites of retinol and retinoic acid on β-LG in aqueous solution at physiological conditions, using FTIR, CD, fluorescence spectroscopic methods, and molecular modeling. The retinoid-binding sites and the binding constants as well as the effect of retinol and retinoic acid complexation on protein stability and secondary structure were determined. Structural analysis showed that retinoids bind strongly to β-LG via both hydrophilic and hydrophobic contacts with overall binding constants of K _{retinol- β -LG?}=?6.4 (±?.6)?×?10⁶?M^?1 and K _{retinoic acid- β -LG?}=?3.3 (±?.5)?×?10⁶?M^?1. The number of retinoid molecules bound per protein (n) is 1.1 (±?.2) for retinol and 1.5 (±?.3) for retinoic acid. Molecular modeling showed the participation of several amino acids in the retinoid–protein complexes with the free binding energy of ?8.11?kcal/mol for retinol and ?7.62?kcal/mol for retinoic acid. Protein conformation was altered with reduction of β-sheet from 59 (free protein) to 52–51% and a major increase in turn structure from 13 (free protein) to 24–22%, in the retinoid–β-LG complexes, indicating a partial protein destabilization.     

Exploring additivity effects of double mutations on the binding affinity of protein‐protein complexes

Additivity in binding affinity of protein‐protein complexes refers to the change in free energy of binding (ΔΔG_bind) for double (or multiple) mutations which is approximately equal to the sum of their corresponding single mutation ΔΔG_bind values. In this study, we have explored the additivity effect of double mutants, which shows a linear relationship between the binding affinity of double and sum of single mutants with a correlation of 0.90. However, the comparison of ΔΔG_bind values showed a mean absolute deviation of 0.86 kcal/mol, and 25.6% of the double mutants show a deviation of more than 1 kcal/mol, which are identified as non‐additive. The additivity effects have been analyzed based on the influence of structural features such as accessible surface area, long range order, binding propensity change, surrounding hydrophobicity, flexibility, atomic contacts between the mutations and distance between the 2 mutations. We found that non‐additive mutations tend to be closer to each other and have more contacts. We have also used machine learning methods to discriminate additive and non‐additive mutations using structure‐based features, which showed the accuracies in the range of 0.77–0.92 for protein‐protein complexes belonging to different functions. Further, we have compared the additivity effects of protein stability along with binding affinity and explored the similarities and differences between them. The results obtained in this study provide insights into the effects of various structural features on binding affinity of double mutants, and will aid the development of accurate methods to predict the binding affinity of double mutants.     

Discovery of binding proteins for a protein target using protein–protein docking‐based virtual screening

Target structure‐based virtual screening, which employs protein‐small molecule docking to identify potential ligands, has been widely used in small‐molecule drug discovery. In the present study, we used a protein–protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all‐to‐all protein–protein docking run on a large dataset was performed. The three‐dimensional rigid docking program SDOCK was used to examine protein–protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z‐score, and convergency of the low‐score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all‐to‐all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor‐α (TNFα), which is a well‐known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top‐ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein–protein docking for the discovery of novel binding proteins for specific protein targets. Proteins 2014; 82:2472–2482. © 2014 Wiley Periodicals, Inc.     

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the E nergetic CONT ributions of A mino acid residues and its possible C ross-T alk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact /.     

A minimal model of protein–protein binding affinities

A minimal model of protein–protein binding affinity that takes into account only two structural features of the complex, the size of its interface, and the amplitude of the conformation change between the free and bound subunits, is tested on the 144 complexes of a structure‐affinity benchmark. It yields K_d values that are within two orders of magnitude of the experiment for 67% of the complexes, within three orders for 88%, and fails on 12%, which display either large conformation changes, or a very high or a low affinity. The minimal model lacks the specificity and accuracy needed to make useful affinity predictions, but it should help in assessing the added value of parameters used by more elaborate models, and set a baseline for evaluating their performances.     

Protein�Cprotein binding affinities by pulse proteolysis: Application to TEM-1/BLIP protein complexes

Efficient methods for quantifying dissociation constants have become increasingly important for high‐throughput mutagenesis studies in the postgenomic era. However, experimentally determining binding affinity is often laborious, requires large amounts of purified protein, and utilizes specialized equipment. Recently, pulse proteolysis has been shown to be a robust and simple method to determine the dissociation constants for a protein–ligand pair based on the increase in thermodynamic stability upon ligand binding. Here, we extend this technique to determine binding affinities for a protein–protein complex involving the β‐lactamase TEM‐1 and various β‐lactamase inhibitor protein (BLIP) mutants. Interaction with BLIP results in an increase in the denaturation curve midpoint, C_m, of TEM‐1, which correlates with the rank order of binding affinities for several BLIP mutants. Hence, pulse proteolysis is a simple, effective method to assay for mutations that modulate binding affinity in protein–protein complexes. From a small set (n = 4) of TEM‐1/BLIP mutant complexes, a linear relationship between energy of stabilization (dissociation constant) and ΔC_m was observed. From this “calibration curve,” accurate dissociation constants for two additional BLIP mutants were calculated directly from proteolysis‐derived ΔC_m values. Therefore, in addition to qualitative information, armed with knowledge of the dissociation constants from the WT protein and a limited number of mutants, accurate quantitation of binding affinities can be determined for additional mutants from pulse proteolysis. Minimal sample requirements and the suitability of impure protein preparations are important advantages that make pulse proteolysis a powerful tool for high‐throughput mutagenesis binding studies.     

Interplay between binding affinity and kinetics in protein–protein interactions

To clarify the interplay between the binding affinity and kinetics of protein–protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound‐state valley is deep with a barrier height of 12 ? 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920–933. © 2016 Wiley Periodicals, Inc.     

An explorative study on Staphylococcus aureus MurE inhibitor: induced fit docking,binding free energy calculation,and molecular dynamics

Staphylococcus aureus MurE enzyme catalyzes the addition of l-lysine as third residue of the peptidoglycan peptide moiety. Due to the high substrate specificity and its ubiquitous nature among bacteria, MurE enzyme is considered as one of the potential target for the development of new therapeutic agents. In the present work, induced fit docking (IFD), binding free energy calculation, and molecular dynamics (MD) simulation were carried out to elucidate the inhibition potential of 2-thioxothiazolidin-4-one based inhibitor 1 against S. aureus MurE enzyme. The inhibitor 1 formed majority of hydrogen bonds with the central domain residues Asn151, Thr152, Ser180, Arg187, and Lys219. Binding free-energy calculation by MM-GBSA approach showed that van der Waals (ΔG_vdW, ?57.30?kcal/mol) and electrostatic solvation (ΔG_solv, ?36.86?kcal/mol) energy terms are major contributors for the inhibitor binding. Further, 30-ns MD simulation was performed to validate the stability of ligand–protein complex and also to get structural insight into mode of binding. Based on the IFD and MD simulation results, we designed four new compounds D1–D4 with promising binding affinity for the S. aureus MurE enzyme. The designed compounds were subjected to the extra-precision docking and binding free energy was calculated for complexes. Further, a 30-ns MD simulation was performed for D1/4C13 complex.     

An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes

We report here a computationally fast protocol for predicting binding affinities of non-metallo protein-ligand complexes. The protocol builds in an all atom energy based empirical scoring function comprising electrostatics, van der Waals, hydrophobicity and loss of conformational entropy of protein side chains upon ligand binding. The method is designed to ensure transferability across diverse systems and has been validated on a heterogenous dataset of 161 complexes consisting of 55 unique protein targets. The scoring function trained on a dataset of 61 complexes yielded a correlation of r=0.92 for the predicted binding free energies against the experimental binding affinities. Model validation and parameter analysis studies ensure the predictive ability of the scoring function. When tested on the remaining 100 protein-ligand complexes a correlation of r=0.92 was recovered. The high correlation obtained underscores the potential applicability of the methodology in drug design endeavors. The scoring function has been web enabled at as binding affinity prediction of protein-ligand (BAPPL) server.     

Reassessing buried surface areas in protein–protein complexes

The buried surface area (BSA), which measures the size of the interface in a protein–protein complex may differ from the accessible surface area (ASA) lost upon association (which we call DSA), if conformation changes take place. To evaluate the DSA, we measure the ASA of the interface atoms in the bound and unbound states of the components of 144 protein–protein complexes taken from the Protein–Protein Interaction Affinity Database of Kastritis et al. (2011). We observe differences exceeding 20%, and a systematic bias in the distribution. On average, the ASA calculated in the bound state of the components is 3.3% greater than in their unbound state, and the BSA, 7% greater than the DSA. The bias is observed even in complexes where the conformation changes are small. An examination of the bound and unbound structures points to a possible origin: local movements optimize contacts with the other component at the cost of internal contacts, and presumably also the binding free energy.     

Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods.     

Stability of Ligand-induced Protein Conformation Influences Affinity in Maltose-binding Protein

Our understanding of what determines ligand affinity of proteins is poor, even with high-resolution structures available. Both the non-covalent ligand–protein interactions and the relative free energies of available conformations contribute to the affinity of a protein for a ligand. Distant, non-binding site residues can influence the ligand affinity by altering the free energy difference between a ligand-free and ligand-bound conformation. Our hypothesis is that when different ligands induce distinct ligand-bound conformations, it should be possible to tweak their affinities by changing the free energies of the available conformations. We tested this idea for the maltose-binding protein (MBP) from Escherichia coli. We used single-molecule Förster resonance energy transfer (smFRET) to distinguish several unique ligand-bound conformations of MBP. We engineered mutations, distant from the binding site, to affect the stabilities of different ligand-bound conformations. We show that ligand affinity can indeed be altered in a conformation-dependent manner. Our studies provide a framework for the tuning of ligand affinity, apart from modifying binding site residues.     

Insight into the binding modes and inhibition mechanisms of adamantyl-based 1,3-disubstituted urea inhibitors in the active site of the human soluble epoxide hydrolase

Soluble epoxide hydrolase (sEH) is a promising new target for treating hypertension and inflammation. Considerable efforts have been devoted to develop novel inhibitors. In this study, the binding modes and interaction mechanisms of a series of adamantyl-based 1,3-disubstituted urea inhibitors were investigated by molecular docking, molecular dynamics simulations, binding free energy calculations, and binding energy decomposition analysis. Based on binding affinity, the most favorable binding mode was determined for each inhibitor. The calculation results indicate that the total binding free energy (ΔG_TOT, the sum of enthalpy ΔG_MM-GB/SA, and entropy ?TΔS) presents a good correlation with the experimental inhibitory activity (IC₅₀, r²?=?.99). The van der Waals energy contributes most to the total binding free energy (ΔG_TOT). A detailed discussion on the interactions between inhibitors and those residues located in the active pocket is made based on hydrogen bond and binding modes analysis. According to binding energy decomposition, the residues Asp333 and Trp334 contribute the most to binding free energy in all systems. Furthermore, Hip523 plays a major role in determining this class of inhibitor-binding orientations. Combined with the results of hydrogen bond analysis and binding free energy, we believe that the conserved hydrogen bonds play a role only in anchoring the inhibitors to the exact site for binding and the number of hydrogen bonds may not directly relate to the binding free energy. The results we obtained will provide valuable information for the design of high potency sEH inhibitors.     

Prediction of estrogen receptor β ligands potency and selectivity by docking and MM-GBSA scoring methods using three different scaffolds

This study aimed to identify the docking and molecular mechanics-generalized born surface area (MM-GBSA) re-scoring parameters which can correlate the binding affinity and selectivity of the ligands towards oestrogen receptor β (ERβ). Three different series of ERβ ligands were used as dataset and the compounds were docked against ERβ (protein data bank (PDB) ID: 1QKM) using Glide and ArgusLab. Glide docking showed superior results when compared with ArgusLab. Docked poses were then rescored using Prime-MM-GBSA to calculate free energy binding. Correlations were made between observed activities of ERβ ligands with computationally predicted values from docking, binding energy parameters. ERβ ligands experimental binding affinity/selectivity did not correlate well with Glide and ArgusLab score. Whereas calculated Glide energy (coulomb-van der Waal interaction energy) correlated significantly with binding affinity of ERβ ligands (r²?=?0.66). MM-GBSA re-scoring showed correlation of r²?=?0.74 with selectivity of ERβ ligands. These results will aid the discovery of novel ERβ ligands with isoform selectivity.     

Quantitative prediction of protein–protein binding affinity with a potential of mean force considering volume correction

Quantitative prediction of protein–protein binding affinity is essential for understanding protein–protein interactions. In this article, an atomic level potential of mean force (PMF) considering volume correction is presented for the prediction of protein–protein binding affinity. The potential is obtained by statistically analyzing X‐ray structures of protein–protein complexes in the Protein Data Bank. This approach circumvents the complicated steps of the volume correction process and is very easy to implement in practice. It can obtain more reasonable pair potential compared with traditional PMF and shows a classic picture of nonbonded atom pair interaction as Lennard‐Jones potential. To evaluate the prediction ability for protein–protein binding affinity, six test sets are examined. Sets 1–5 were used as test set in five published studies, respectively, and set 6 was the union set of sets 1–5, with a total of 86 protein–protein complexes. The correlation coefficient (R) and standard deviation (SD) of fitting predicted affinity to experimental data were calculated to compare the performance of ours with that in literature. Our predictions on sets 1–5 were as good as the best prediction reported in the published studies, and for union set 6, R = 0.76, SD = 2.24 kcal/mol. Furthermore, we found that the volume correction can significantly improve the prediction ability. This approach can also promote the research on docking and protein structure prediction.     

Virtual screening to identify novel potential inhibitors for Glutamine synthetase of Mycobacterium tuberculosis

Abstract

Glutamine synthetase (GS) of Mycobacterium tuberculosis (Mtb) is an essential enzyme which is involved in nitrogen metabolism and cell wall synthesis. It is involved in the inhibition of phagosome-lysosome fusion by preventing acidification. Targeting GS can be helpful to control the infection of Mtb. In order to identify potential inhibitors, we screened chemical libraries (56,400 compounds of ChEMBL anti-mycobacterial, 1596 FDA approved drugs, 419 Natural product and 916 phytochemical) against this target using the virtual screening approach. Screening by molecular docking identified ten top-ranked compounds as GSMtb inhibitors and they were compared with known inhibitors (as control). Since GS enzyme (GSHs) is also present in human. We have compared the protein sequence of GS from Mtb and human using the P-BLAST in NCBI. We found ～27% identity in between these two sequences, so we also compared the binding affinity of inhibitor between Mtb and human. Finally, we identified top two compounds namely CHEMBL387509, CHEMBL226198 from ChEMBL anti-mycobacterial dataset, and Eriocitrin and Malvidin from phytochemical dataset which showed lees binding affinity towards GSHs whereas Pamidronate, and Phentermine from FDA approved drugs and (-)-Quinic Acid, Hexopyranuronic acid, Quebrachit, and Castanospermine from natural product showed protein-ligand interaction with Mtb protein while no interaction with GSHs. The top two docked complexes were subjected to molecular dynamic simulation to understand the stability of the molecule. Further, we calculated the binding free energy of the docked complex and analyzed hydrogen bond, salt bridge, pie stacking, and hydrophobic interaction in the docking region. These ligands exhibited very good binding affinity GSMtb enzymes. Therefore, these ligands are novel and drug-likeness compounds, and they may be potential inhibitors of M tuberculosis.

Communicated by Ramaswamy H. Sarma     

Alu-family repeat binding protein from HeLa cells which interacts with regulatory region of SV40 virus genome

Using a gel retardation assay the protein which binds selectively to the Alu-family repeat (AFR) has been identified and partially purified from HeLa cell nuclear extract. The protein (AFR-binding protein, ABP) forms multiple discrete complexes with AFR even in the presence of 200 to 2000-fold excess of non-specific (E. coli) DNA. The most stable complex has a relative mobility in 4% polyacrylamide gel (as compared to the free Alu-fragment) of 0.54. Heterogeneity of protein-DNA bands seen in the polyacrylamide gel suggests that ABP is able to form multimeric complexes with AFR. Competition experiments show that ABP do not interact with the RNA polymerase III promoter and with the TGGCA-sequence, but a high affinity binding site for ABP was found within a 660 bp restriction fragment containing the SV40 virus promoter and replication origin.