Unraveling the importance of protein-protein interaction: application of a computational alanine-scanning mutagenesis to the study of the IgG1 streptococcal protein G (C2 fragment) complex

Alanine-scanning mutagenesis of protein-protein interfacial residues is a very important process for rational drug design. In this study, we have used the improved MM-PBSA approach that combining molecular mechanics and continuum solvent permits one to calculate the free energy differences through alanine mutation. To identify the binding determinants of the complex formed between the IgG1 (immunoglobulin-binding protein G) and protein G, we have extended the experimental alanine scanning mutagenesis study to both proteins of this complex and, therefore, to all interfacial residues of this binding complex. As a result, we present new residues that can be characterized as warm spots and, therefore, are important for complex formation. We have further increased the understanding of the functionality of this improved computational alanine-scanning mutagenesis approach testing its sensitivity to a protein-protein complex with an interface made up of residues mainly polar. In this study, we also have improved the method for the detection of an important amino acid residue that frequently constitutes a hot spot--tryptophan.     

Relationship between Hot Spot Residues and Ligand Binding Hot Spots in Protein-Protein Interfaces

In the context of protein-protein interactions, the term "hot spot" refers to a residue or cluster of residues that makes a major contribution to the binding free energy, as determined by alanine scanning mutagenesis. In contrast, in pharmaceutical research, a hot spot is a site on a target protein that has high propensity for ligand binding and hence is potentially important for drug discovery. Here we examine the relationship between these two hot spot concepts by comparing alanine scanning data for a set of 15 proteins with results from mapping the protein surfaces for sites that can bind fragment-sized small molecules. We find the two types of hot spots are largely complementary; the residues protruding into hot spot regions identified by computational mapping or experimental fragment screening are almost always themselves hot spot residues as defined by alanine scanning experiments. Conversely, a residue that is found by alanine scanning to contribute little to binding rarely interacts with hot spot regions on the partner protein identified by fragment mapping. In spite of the strong correlation between the two hot spot concepts, they fundamentally differ, however. In particular, while identification of a hot spot by alanine scanning establishes the potential to generate substantial interaction energy with a binding partner, there are additional topological requirements to be a hot spot for small molecule binding. Hence, only a minority of hot spots identified by alanine scanning represent sites that are potentially useful for small inhibitor binding, and it is this subset that is identified by experimental or computational fragment screening.     

Extending the applicability of the O-ring theory to protein–DNA complexes

Many biological processes depend on protein-based interactions, which are governed by central regions with higher binding affinities, the hot-spots. The O-ring theory or the “Water Exclusion” hypothesis states that the more deeply buried central regions are surrounded by areas, the null-spots, whose role would be to shelter the hot-spots from the bulk solvent. Although this theory is well-established for protein–protein interfaces, its applicability to other protein interfaces remains unclear. Our goal was to verify its applicability to protein–DNA interfaces. We performed Molecular Dynamics simulations in explicit solvent of several protein–DNA complexes and measured a variety of solvent accessible surface area (SASA) features, as well as, radial distribution functions of hot-spots and null-spots. Our aim was to test the influence of water in their coordination sphere. Our results show that hot-spots tend to have fewer water molecules in their neighborhood when compared to null-spots, and higher values of ΔSASA, which confirms their occlusion from solvent. This study provides evidence in support of the O-ring theory with its applicability to a new type of protein-based interface: protein–DNA.     

Crucial importance of the water-entropy effect in predicting hot spots in protein-protein complexes

"Hot spots" are residues accounting for the majority of the protein-protein binding free energy (BFE) despite that they comprise only a small fraction of the protein-protein interface. A hot spot can be found experimentally by measuring the BFE change upon mutating it to alanine: the mutation gives rise to a significantly large increase in the BFE. Theoretical prediction of hot spots is an enthusiastic subject in biophysics, biochemistry, and bioinformatics. For the development of a reliable prediction method, it is essential to understand the physical origin of hot spots. To this end, we calculate the water-entropy gains upon binding both for a wild-type complex and for its mutant complex using a hybrid method of the angle-dependent integral equation theory applied to a molecular model for water and the morphometric approach. We note that this type of calculation has never been employed in the previously reported methods. The BFE change due to alanine mutation is evaluated only from the change in the water-entropy gain with no parameters fitted to the experimental data. It is shown that the overall performance of predicting hot spots in our method is higher than that in Robetta, a standard free-energy-based method using fitting parameters, when the most widely used criterion for defining an actual hot spot is adopted. This result strongly suggests that the water-entropy effect we calculate is the key factor governing basic physics of hot spots.     

Use of selective Trp side chain labeling to characterize protein-protein and protein-ligand interactions by NMR spectroscopy

Recent studies on amino acid occurrence in protein binding sites suggest that only a reduced number of residues are responsible for most interaction energy in protein-protein and protein-ligand interactions. Above all, tryptophan (Trp) seems to be the most frequent residue in protein's hot spots. Here we report a novel, efficient, and cost-effective method to selectively incorporate specific isotope labels into the side chains of Trp residues in recombinant proteins. We show that the method proposed allows selective NMR observation of Trp side chains that enables studies of ligand binding, protein-protein interactions, hydrogen binding, protein folding, and side chain dynamics. Examples with the protein BIR3 will be given.     

Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface

Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.     

Protein–protein recognition: a computational mutagenesis study of the MDM2–P53 complex

Protein P53 is involved in more than 50% of the human cancers and the P53–MDM2 complex is a target for anticancer drug design. It is possible to engineer small P53 mimics that would be expected to disrupt the P53–MDM2 complex, and release P53 to initiate cell-cycle arrest or apoptosis. These small peptides should bind to the functional epitopes of the protein–protein interface, and prevent the interaction between P53 and MDM2. Here, we apply an improved computational alanine scanning mutagenesis method, which allows the determination of the hot spots present in both monomers, P53 and MDM2, of three protein complexes (the P53-binding domain of human MDM2, its analogue from Xenopus laevis, and the structure of human MDM2 in complex with an optimized P53 peptide). The importance of the hydrogen bonds formed by the protein backbone has been neglected due to the difficulty of measuring experimentally their contribution to the binding free energy. In this study we present a computational approach that allows the estimation of the contribution to the binding free energy of the C=O and N–H groups in the backbone of the P53 and MDM2 proteins. We have noticed that the hydrogen bond between the HE1 atom of the hot spot Trp23 and the O atom of the residue Leu54, as well as the NH-pi hydrogen bond between the Ile57 and Met58 were observed in the Molecular dynamics simulation, and their contribution to the binding free energy measured. This study not only shows the reliability of the computational mutagenesis method to detect hot spots but also demonstrates an excellent correlation between the quantitative calculated binding free energy contribution of the C=O and N–H backbone groups of the interfacial residues and the qualitative values expected for this kind of interaction. The study also increases our understanding of the P53–MDM2 interaction.     

Empirical estimation of the energetic contribution of individual interface residues in structures of protein–protein complexes

We report a simple algorithm to scan interfaces in protein–protein complexes for identifying binding ‘hot spots’. The change in side-chain solvent accessible area (ΔASA) of interface residues has been related to change in binding energy due to mutating interface residues to Ala (ΔΔG _X → ALA) based on two criteria—hydrogen bonding across the interface and location in the interface core—both of which are major determinants in specific, high-affinity binding. These relationships are used to predict the energetic contribution of individual interface residues. The predictions are tested against 462 experimental X → ALA mutations from 28 interfaces with an average unsigned error of 1.04 kcal/mol. More than 80% of interface hot spots (with experimental ΔΔG ≥ 2 kcal/mol) could be identified as being energetically important. From the experimental values, Asp, Lys, Tyr and Trp are found to contribute most of the binding energy, burying >45 Å² on average. The method described here would be useful to understand and interfere with protein interactions by assessing the energetic importance of individual interface residues. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Calculation of hot spots for protein–protein interaction in p53/PMI-MDM2/MDMX complexes

The recently developed MM/GBSA_IE method is applied to computing hot and warm spots in p53/PMI-MDM2/MDMX protein–protein interaction systems. Comparison of the calculated hot (>2 kcal/mol) and warm spots (>1 kcal/mol) in P53 and PMI proteins interacting with MDM2 and MDMX shows a good quantitative agreement with the available experimental data. Further, our calculation predicted hot spots in MDM2 and MDMX proteins in their interactions with P53 and PMI and they help elucidate the interaction mechanism underlying this important PPI system. In agreement with the experimental result, the present calculation shows that PMI has more hot and warm spots and binds stronger to MDM2/MDMX. The analysis of these hot and warm spots helps elucidate the fundamental difference in binding between P53 and PMI to the MDM2/MDMX systems. Specifically, for p53/PMI-MDM2 systems, p53 and PMI use essentially the same residues (L54, I61, Y67, Q72, V93, H96, and I99) of MDM2 for binding. However, PMI enhanced interactions with residues L54, Y67, and Q72 of MDM2. For the p53/PMI-MDMX system, p53 and PMI use similar residues (M53, I60, Y66, Q71, V92, and Y99) of MDMX for binding. However, PMI exploited three extra residues (M61, K93, and L98) of MDMX for enhanced binding. In addition, PMI enhanced interaction with four residues (M53, Y66, Q71, and Y99) of MDMX. These results gave quantitative explanation on why the binding affinities of PMI-MDM2/MDMX interactions are stronger than that of p53-MDM2/MDMX although their binding modes are similar. © 2018 Wiley Periodicals, Inc.     

Robust identification of binding hot spots using continuum electrostatics: application to hen egg-white lysozyme

Binding hot spots, protein regions with high binding affinity, can be identified by using X-ray crystallography or NMR spectroscopy to screen libraries of small organic molecules that tend to cluster at such hot spots. FTMap, a direct computational analogue of the experimental screening approaches, uses 16 different probe molecules for global sampling of the surface of a target protein on a dense grid and evaluates the energy of interaction using an empirical energy function that includes a continuum electrostatic term. Energy evaluation is based on the fast Fourier transform correlation approach, which allows for the sampling of billions of probe positions. The grid sampling is followed by off-grid minimization that uses a more detailed energy expression with a continuum electrostatics term. FTMap identifies the hot spots as consensus clusters formed by overlapping clusters of several probes. The hot spots are ranked on the basis of the number of probe clusters, which predicts their binding propensity. We applied FTMap to nine structures of hen egg-white lysozyme (HEWL), whose hot spots have been extensively studied by both experimental and computational methods. FTMap found the primary hot spot in site C of all nine structures, in spite of conformational differences. In addition, secondary hot spots in sites B and D that are known to be important for the binding of polysaccharide substrates were found. The predicted probe-protein interactions agree well with those seen in the complexes of HEWL with various ligands and also agree with an NMR-based study of HEWL in aqueous solutions of eight organic solvents. We argue that FTMap provides more complete information on the HEWL binding site than previous computational methods and yields fewer false-positive binding locations than the X-ray structures of HEWL from crystals soaked in organic solvents.     

Peptide inhibitors of viral assembly: a novel route to broad-spectrum antivirals

We investigated the potential of small peptide segments to function as broad-spectrum antiviral drug leads. We extracted the α-helical peptide segments that share common secondary-structure environments in the capsid protein-protein interfaces of three unrelated virus classes (PRD1-like, HK97-like, and BTV-like) that encompass different levels of pathogenicity to humans, animals, and plants. The potential for the binding of these peptides to the individual capsid proteins was then investigated using blind docking simulations. Most of the extracted α-helical peptides were found to interact favorably with one or more of the protein-protein interfaces within the capsid in all three classes of virus. Moreover, binding of these peptides to the interface regions was found to block one or more of the putative "hot spot" regions on the protein interface, thereby providing the potential to disrupt virus capsid assembly via competitive interaction with other capsid proteins. In particular, binding of the GDFNALSN peptide was found to block interface "hot spot" regions in most of the viruses, providing a potential lead for broad-spectrum antiviral drug therapy.     

Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design

Use of solvent mapping, based on multiple-copy minimization (MCM) techniques, is common in structure-based drug discovery. The minima of small-molecule probes define locations for complementary interactions within a binding pocket. Here, we present improved methods for MCM. In particular, a Jarvis-Patrick (JP) method is outlined for grouping the final locations of minimized probes into physical clusters. This algorithm has been tested through a study of protein-protein interfaces, showing the process to be robust, deterministic, and fast in the mapping of protein "hot spots." Improvements in the initial placement of probe molecules are also described. A final application to HIV-1 protease shows how our automated technique can be used to partition data too complicated to analyze by hand. These new automated methods may be easily and quickly extended to other protein systems, and our clustering methodology may be readily incorporated into other clustering packages.     

Unravelling Hot Spots: a comprehensive computational mutagenesis study

As protein–protein interactions are critical for all biological functions, representing a large and important class of targets for human therapeutics, identification of protein–protein interaction sites and detection of specific amino acid residues that contribute to the specificity and strength of protein interactions is very important in the biochemistry field. Alanine scanning mutagenesis has allowed the discovery of energetically crucial determinants for protein association that have been defined as hot spots. Systematic experimental mutagenesis is very laborious and time-consuming to perform, and thus it is important to achieve an accurate, predictive computational methodology for alanine scanning mutagenesis, capable of reproducing the experimental mutagenesis values. Having as a basis the MM–PBSA approach first developed by Massova et al., we performed a complete study of the influence of the variation of different parameters, such as the internal dielectric constant, the solvent representation, and the number of trajectories, in the accuracy of the free energy binding differences. As a result, we present here a very simple and fast methodological approach that achieved an overall success rate of 82% in reproducing the experimental mutagenesis data.Electronic Supplementary Material  Supplementary material is available for this article at and is accessible for authorized users.     

Computational alanine scanning mutagenesis--an improved methodological approach

Alanine scanning mutagenesis of protein-protein interfacial residues can be applied to a wide variety of protein complexes to understand the structural and energetic characteristics of the hot-spots. Binding free energies have been estimated with reasonable accuracy with empirical methods, such as Molecular Mechanics/Poisson-Boltzmann surface area (MM-PBSA), and with more rigorous computational approaches like Free Energy Perturbation (FEP) and Thermodynamic Integration (TI). The main objective of this work is the development of an improved methodological approach, with less computational cost, that predicts accurately differences in binding free energies between the wild-type and alanine mutated complexes (DeltaDeltaG(binding)). The method was applied to three complexes, and a mean unsigned error of 0.80 kcal/mol was obtained in a set of 46 mutations. The computational method presented here achieved an overall success rate of 80% and an 82% success rate in residues for which alanine mutation causes an increase in the binding free energy > 2.0 kcal/mol (warm- and hot-spots). This fully atomistic computational methodological approach consists in a computational Molecular Dynamics simulation protocol performed in a continuum medium using the Generalized Born model. A set of three different internal dielectric constants, to mimic the different degree of relaxation of the interface when different types of amino acids are mutated for alanine, have to be used for the proteins, depending on the type of amino acid that is mutated. This method permits a systematic scanning mutagenesis of protein-protein interfaces and it is capable of anticipating the experimental results of mutagenesis, thus guiding new experimental investigations.     

On the nonpolar hydration free energy of proteins: surface area and continuum solvent models for the solute-solvent interaction energy

Implicit solvent hydration free energy models are an important component of most modern computational methods aimed at protein structure prediction, binding affinity prediction, and modeling of conformational equilibria. The nonpolar component of the hydration free energy, consisting of a repulsive cavity term and an attractive van der Waals solute-solvent interaction term, is often modeled using estimators based on the solvent exposed solute surface area. In this paper, we analyze the accuracy of linear surface area models for predicting the van der Waals solute-solvent interaction energies of native and non-native protein conformations, peptides and small molecules, and the desolvation penalty of protein-protein and protein-ligand binding complexes. The target values are obtained from explicit solvent simulations and from a continuum solvent van der Waals interaction energy model. The results indicate that the standard surface area model, while useful on a coarse-grained scale, may not be accurate or transferable enough for high resolution modeling studies of protein folding and binding. The continuum model constructed in the course of this study provides one path for the development of a computationally efficient implicit solvent nonpolar hydration free energy estimator suitable for high-resolution structural and thermodynamic modeling of biological macromolecules.     

Detection of ligand binding hot spots on protein surfaces via fragment-based methods: application to DJ-1 and glucocerebrosidase

The identification of hot spots, i.e., binding regions that contribute substantially to the free energy of ligand binding, is a critical step for structure-based drug design. Here we present the application of two fragment-based methods to the detection of hot spots for DJ-1 and glucocerebrosidase (GCase), targets for the development of therapeutics for Parkinson’s and Gaucher’s diseases, respectively. While the structures of these two proteins are known, binding information is lacking. In this study we employ the experimental multiple solvent crystal structures (MSCS) method and computational fragment mapping (FTMap) to identify regions suitable for the development of pharmacological chaperones for DJ-1 and GCase. Comparison of data derived via MSCS and FTMap also shows that FTMap, a computational method for the identification of fragment binding hot spots, is an accurate and robust alternative to the performance of expensive and difficult crystallographic experiments.     

Computational Analysis for Residue-Speci c CDK2-Inhibitor Bindings

Cyclin-dependent kinase 2 (CDK2) is a key macromolecule in cell cycle regulation. In cancer cells, CDK2 is often overexpressed and its inhibition is an effective therapy of many cancers including breast carcinomas, leukemia, and lymphomas. Quantitative characterization of the interactions between CDK2 and its inhibitors at atomic level may provide a deep understanding of protein-inhibitor interactions and clues for more effective drug discovery. In this study, we have used the computational alanine scanning approach in combination with an efficient interaction entropy method to study the microscopic mechanism of binding between CDK2 and its 13 inhibitors. The total binding free energy from the method shows a correlation of 0.76?0.83 with the experimental values. The free energy component reveals two binding mode in the 13 complexes, namely van der Waals dominant, and electrostatic dominant. Decomposition of the total energy to per-residue contribution allows us to identify five hydrophobic residues as hot spots during the binding. Residues that are responsible for determining the strength of the binding were also analyzed.     

Focused grid‐based resampling for protein docking and mapping

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein‐protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near‐native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands. © 2016 Wiley Periodicals, Inc.     

Diverse fragment clustering and water exclusion identify protein hot spots

Simulated annealing of chemical potential located the highest affinity positions of eight organic probes and water on eight static structures of hen egg white lysozyme (HEWL) in various conformational states. In all HELW conformations, a diverse set of organic probes clustered in the known binding site (hot spot). Fragment clusters at other locations were excluded by tightly-bound waters so that only the hot-spot cluster remained in each case. The location of the hot spot was correctly predicted irrespective of the protein conformation and without accounting for protein flexibility during the simulations. Any one of the static structures could have been used to locate the hot spot. A site on a protein where a diversity of organic probes is calculated to cluster, but where water specifically does not bind, identifies a potential small-molecule binding site or protein-protein interaction hot spot.     

Calculations of pH-dependent binding of proteins to biological membranes

Binding of proteins to membranes is often accompanied by titration of ionizable residues and is, therefore, dependent on pH. We present a theoretical treatment and computational approach for predicting absolute, pH-dependent membrane binding free energies. The standard free energy of binding, DeltaG, is defined as -RTln(P(b)/P(f)), where P(b) and P(f) are the amounts of bound and free protein. The apparent pK(a) of binding is the pH value at which P(b) and P(f) are equal. Proteins bind to the membrane in the pH range where DeltaG is negative. The components of the binding free energy are (a) the free energy cost of ionization state changes (DeltaG(ion)), (b) the effective energy of transfer from solvent to the membrane surface, (c) the translational/rotational entropy cost of binding, and (d) an ideal entropy term that depends on the relative volume of the bound and free state and therefore depends on lipid concentration. Calculation of the first term requires determination of pK(a) values in solvent and on the membrane surface. All energies required by the method are obtained from molecular dynamics trajectories on an implicit membrane (IMM1-GC). The method is tested on pentalysine and the helical peptide VEEKS, derived from the membrane-binding domain of phosphocholine cytidylyltransferase. The agreement between the measured and the calculated free energies of binding of pentalysine is good. The extent of membrane binding of VEEKS is, however, underestimated compared to experiment. Calculations of the interaction energy between two VEEKS helices on the membrane suggest that the discrepancy is mainly due to the neglect of protein-protein interactions on the membrane surface.