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.     

Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery

Fragment-based drug design (FBDD) starts with finding fragment-sized compounds that are highly ligand efficient and can serve as a core moiety for developing high-affinity leads. Although the core-bound structure of a protein facilitates the construction of leads, effective design is far from straightforward. We show that protein mapping, a computational method developed to find binding hot spots and implemented as the FTMap server, provides information that complements the fragment screening results and can drive the evolution of core fragments into larger leads with a minimal loss or, in some cases, even a gain in ligand efficiency. The method places small molecular probes, the size of organic solvents, on a dense grid around the protein and identifies the hot spots as consensus clusters formed by clusters of several probes. The hot spots are ranked based on the number of probe clusters, which predicts the binding propensity of the subsites and hence their importance for drug design. Accordingly, with a single exception the main hot spot identified by FTMap binds the core compound found by fragment screening. The most useful information is provided by the neighboring secondary hot spots, indicating the regions where the core can be extended to increase its affinity. To quantify this information, we calculate the density of probes from mapping, which describes the binding propensity at each point, and show that the change in the correlation between a ligand position and the probe density upon extending or repositioning the core moiety predicts the expected change in ligand efficiency.     

Hot spot occlusion from bulk water: a comprehensive study of the complex between the lysozyme HEL and the antibody FVD1.3

Alanine scanning of protein-protein interfaces has shown that there are some residues in the protein-protein interfaces, responsible for most of the binding free energy, which are called hot spots. Hot spots tend to exist in densely packed central clusters, and a hypothesis has been proposed that considers that inaccessibility to the solvent must be a necessary condition to define a residue as a binding hot spot. This O-ring hypothesis is mainly based on the analysis of the accessible surface area (ASA) of 23 static, crystallographic structures of protein complexes. It is known, however, that protein flexibility allows for temporary exposures of buried interfacial groups, and even though the ASA provides a general trend of the propensity for hydration, protein/solvent-specific interactions or hydrogen bonding cannot be considered here. Therefore, a microscopic level, atomistic picture of hot spot solvation is needed to support the O-ring hypothesis. In this study, we began by applying a computational alanine-scanning mutagenesis technique, which reproduces the experimental results and allows for decomposing the binding free energy difference in its different energetic factors. Subsequently, we calculated the radial distribution function and residence times of the water molecules near the hot/warm spots to study the importance of the water environment around those energetically important amino acid residues. This study shows that within a flexible, dynamic protein framework, the warm/hot spot residues are, indeed, kept sheltered from the bulk solvent during the whole simulation, which allows a better interacting microenvironment.     

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.     

Evaluation of protein-ligand binding free energy focused on its entropic components

The binding free energy for FK506-binding protein-ligand systems is evaluated as a sum of two entropic components, the water-entropy gain, and the configurational-entropy loss for the protein and ligand molecules upon the binding. The two entropic components are calculated using morphometric thermodynamics combined with a statistical-mechanical theory for molecular liquids and the normal mode analysis, respectively. We find that there is an excellent correlation between the calculated and experimental values of the binding free energy. This result is compared with those of several other binding-free energy calculation methods, including MM-PB/SA. The binding can well be elucidated by competition of the two entropic components. Upon the protein-ligand binding, the total volume available to the translational displacement of the coexisting water molecules increases, leading to an increase in the number of accessible configurations of the water. The water-entropy gain, by which the binding is driven, originates primarily from this effect. This study sheds new light on the theoretical prediction of the protein-ligand binding free energy.     

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.     

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.     

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.     

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.     

Detection of Plasmon Coupling between Silver Nanowires Based on Hyperspectral Darkfield and SERS Imaging and Supported by DDA Theoretical Calculations

We report the unprecedented observation of plasmon coupling between silver nanowires, showing how the surface‐enhanced Raman scattering depends upon this interaction and how the spectrum can be shaped by the hot spot. Such observations were accomplished by Raman spectroscopy mapping of silver nanowires modified with rhodamine. The local spectra on the hot spots were measured by darkfield hyperspectral microscopy, a powerful but uncommonly used technique that is capable of determining the location, structure, and spectra of the hot spots. The result obtained by the simulation of two parallel nanowires based on the discrete dipole approximation (DDA) method was in excellent agreement with the results obtained experimentally.     

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.     

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.     

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.     

Efficient calculation of relative binding free energies by umbrella sampling perturbation

An important task of biomolecular simulation is the calculation of relative binding free energies upon chemical modification of partner molecules in a biomolecular complex. The potential of mean force (PMF) along a reaction coordinate for association or dissociation of the complex can be used to estimate binding affinities. A free energy perturbation approach, termed umbrella sampling (US) perturbation, has been designed that allows an efficient calculation of the change of the PMF upon modification of a binding partner based on the trajectories obtained for the wild type reference complex. The approach was tested on the interaction of modified water molecules in aqueous solution and applied to in silico alanine scanning of a peptide‐protein complex. For the water interaction test case, excellent agreement with an explicit PMF calculation for each modification was obtained as long as no long range electrostatic perturbations were considered. For the alanine scanning, the experimentally determined ranking and binding affinity changes upon alanine substitutions could be reproduced within 0.1–2.0 kcal/mol. In addition, good agreement with explicitly calculated PMFs was obtained mostly within the sampling uncertainty. The combined US and perturbation approach yields, under the condition of sufficiently small system modifications, rigorously derived changes in free energy and is applicable to any PMF calculation. © 2014 Wiley Periodicals, Inc.     

Hot spot computational identification: Application to the complex formed between the hen egg white lysozyme (HEL) and the antibody HyHEL‐10

The definition and comprehension of the hot spots in an interface is a subject of primary interest for a variety of fields, including structure‐based drug design. Therefore, to achieve an alanine mutagenesis computational approach that is at the same time accurate and predictive, capable of reproducing the experimental mutagenesis values is a major challenge in the computational biochemistry field. Antibody/protein antigen complexes provide one of the greatest models to study protein–protein recognition process because they have three fundamentally features: specificity, high complementary association and a small epitope restricted to the diminutive complementary determining regions (CDR) region, while the remainder of the antibody is largely invariant. Thus, we apply a computational mutational methodological approach to the study of the antigen–antibody complex formed between the hen egg white lysozyme (HEL) and the antibody HyHEL‐10. A critical evaluation that focuses essentially on the limitations and advantages between different computational methods for hot spot determination, as well as between experimental and computational methodological approaches, is presented. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2007     

Molecular insight into the interaction between IFABP and PA by using MM-PBSA and alanine scanning methods

The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method combined with alanine-scanning mutagenesis is a very important tool for rational drug design. In this study, molecular dynamics (MD) and MM-PBSA were applied to calculate the binding free energy between the rat intestinal fatty acid binding protein (IFABP) and palmitic acid (PA) to gain insight to the interaction details. Equally spaced snapshots along the trajectory were chosen to perform the binding free energy calculation, which yields a result highly consistent with experimental value with a deviation of 0.4 kcal/mol. Computational alanine scanning was performed on the same set of snapshots by mutating the residues in IFABP to alanine and recomputing the DeltaDeltaG(binding). By postprocessing a single trajectory of the wild-type complex, the average unsigned error of our calculated DeltaDeltaG(binding) is below 1.5 kcal/mol for most of the alanine mutations of the noncharged residues (67% in total). To further investigate some particular mutants, three additional dynamical simulations of IFABP Arg126Ala, Arg106Ala, and Arg106Gln mutants were conducted. Recalculated binding free energies are well consistent with the experimental data. Moreover, the ambiguous role of Arg106 caused by the free energy change of the opposite sign when it is mutated to alanine and glutamine respectively is clarified both structurally and energetically. Typically, this can be attributed to the partial electrostatic compensation mainly from Arg56 and the obvious entropy gain in Arg106Ala mutant while not in Arg106Gln mutant. The presented structural model of IFABP-PA complex could be used to guide future studies.     

UVB-induced Cyclobutane Pyrimidine Dimer Frequency Correlates with Skin Cancer Mutational Hotspots in p53

Abstract— Ultraviolet light has been identified as the major carcinogen in skin cancer and the p53 tumor suppressor gene is a major target for UV-induced mutations. The mutations are probably caused by unrepaired UV-induced cyclobutane pyrimidine dimers (CPD) and possibly by the less frequent pyrimidine (6-4) pyrimidone photoproducts. While hot spots for p53 mutations in human nonmela-noma skin tumors correspond quite well to slow spots for CPD repair in cultured cells irradiated with the model mutagen 254 nm UVC (which is not present in terrestrial sunlight), they do not all coincide with sequences that are initially frequently damaged by 254 nm UVC. Using LMPCR (ligation-mediated polymerase chain reaction), we show that environmentally relevant UVB light induces CPD at CC and PyrmC positions much more frequently than does UVC light, and that all eight skin cancer hot spots in p53 are also hot spots for UVB-induced CPD. Our results show that methylation of dipyrimidine sites (PyrmCpG) is associated with an increase rate of CPD formation upon UVB irradiation. Consequently, DNA methylation may increase the mutagenic potential of UVB and explains that several p53 mutation hot spots are found at PyrmCpG. The distribution patterns of CPD formation and the photofootprint patterns found along exons 5 and 6 of p53 gene are suggestive of DNA folding into nucleosomes.     

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.     

蛋白质表面模块划分及其在结合位点预测中的应用

蛋白质-蛋白质复合物的结合位点预测是计算分子生物学的一个难题. 本文对蛋白质-蛋白质复合物数据集Benchmark 3.0 中的双链蛋白质复合物进行了研究, 计算了单体的残基溶剂可接近表面积和残基间的接触面积, 并据此提出了蛋白质表面模块划分方法. 发现模块的溶剂可接近表面积与其内部接触面积的乘积(PSAIA)值能够提供结合位点的信息. 在78 个双链蛋白质复合物中, 有74 个体系其受体或配体上具有最大或次大PSAIA值的模块是界面模块. 将该方法获得的结合位点信息应用在CAPRI竞赛Target 39 的复合物结构预测中取得了较好的结果. 本文提出的基于模块的蛋白质结合位点预测方法不同于以残基为基础且仅考虑表面残基的传统预测方法, 为蛋白质-蛋白质复合物结合位点预测提供了新思路.