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.     

Determination of binding affinity upon mutation for type I dockerin–cohesin complexes from Clostridium thermocellum and Clostridium cellulolyticum using deep sequencing

The comprehensive sequence determinants of binding affinity for type I cohesin toward dockerin from Clostridium thermocellum and Clostridium cellulolyticum was evaluated using deep mutational scanning coupled to yeast surface display. We measured the relative binding affinity to dockerin for 2970 and 2778 single point mutants of C. thermocellum and C. cellulolyticum, respectively, representing over 96% of all possible single point mutants. The interface ΔΔG for each variant was reconstructed from sequencing counts and compared with the three independent experimental methods. This reconstruction results in a narrow dynamic range of ?0.8–0.5 kcal/mol. The computational software packages FoldX and Rosetta were used to predict mutations that disrupt binding by more than 0.4 kcal/mol. The area under the curve of receiver operator curves was 0.82 for FoldX and 0.77 for Rosetta, showing reasonable agreements between predictions and experimental results. Destabilizing mutations to core and rim positions were predicted with higher accuracy than support positions. This benchmark dataset may be useful for developing new computational prediction tools for the prediction of the mutational effect on binding affinities for protein–protein interactions. Experimental considerations to improve precision and range of the reconstruction method are discussed. Proteins 2016; 84:1914–1928. © 2016 Wiley Periodicals, Inc.     

The dataset for protein–RNA binding affinity

We have developed a non‐redundant protein–RNA binding benchmark dataset derived from the available protein–RNA structures in the Protein Database Bank. It consists of 73 complexes with measured binding affinity. The experimental conditions (pH and temperature) for binding affinity measurements are also listed in our dataset. This binding affinity dataset can be used to compare and develop protein–RNA scoring functions. The predicted binding free energy of the 73 complexes from three available scoring functions for protein–RNA docking has a low correlation with the binding Gibbs free energy calculated from K_d. © 2013 The Protein Society     

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.     

PCA-MutPred: Prediction of Binding Free Energy Change Upon Missense Mutation in Protein-carbohydrate Complexes

Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter the binding affinity, affect the functions and lead to diseases. Elucidating the factors influencing the binding affinity change (ΔΔG) of protein-carbohydrate complexes upon mutation is a challenging task. In this work, we have collected the experimental data for the binding affinity change of 318 unique mutants and related with sequence and structural features of amino acid residues at the mutant sites. We found that accessible surface area, secondary structure, mutation preference, conservation score, hydrophobicity and contact energies are important to understand the binding affinity change upon mutation. We have developed multiple regression equations for predicting the binding affinity change upon mutation and our method showed an average correlation of 0.74 and a mean absolute error of 0.70 kcal/mol between experimental and predicted ΔΔG on a 10-fold cross-validation. Further, we have validated our method using an independent test data set of 124 (62 unique) mutations, which showed a correlation and MAE of 0.79 and 0.56 kcal/mol, respectively. We have developed a web server PCA-MutPred, Protein-CArbohydrate complex Mutation affinity Predictor, for predicting the change in binding affinity of protein–carbohydrate complexes and it is freely accessible at https://web.iitm.ac.in/bioinfo2/pcamutpred. We suggest that the method could be a useful resource for designing protein-carbohydrate complexes with desired affinities.     

Computational Assessment of Protein–protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes

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

Crucial Roles of Single Residues in Binding Affinity,Specificity, and Promiscuity in the Cellulosomal Cohesin-Dockerin Interface

Interactions between cohesin and dockerin modules play a crucial role in the assembly of multienzyme cellulosome complexes. Although intraspecies cohesin and dockerin modules bind in general with high affinity but indiscriminately, cross-species binding is rare. Here, we combined ELISA-based experiments with Rosetta-based computational design to evaluate the contribution of distinct residues at the Clostridium thermocellum cohesin-dockerin interface to binding affinity, specificity, and promiscuity. We found that single mutations can show distinct and significant effects on binding affinity and specificity. In particular, mutations at cohesin position Asn³⁷ show dramatic variability in their effect on dockerin binding affinity and specificity: the N37A mutant binds promiscuously both to cognate (C. thermocellum) as well as to non-cognate Clostridium cellulolyticum dockerin. N37L in turn switches binding specificity: compared with the wild-type C. thermocellum cohesin, this mutant shows significantly increased preference for C. cellulolyticum dockerin combined with strongly reduced binding to its cognate C. thermocellum dockerin. The observation that a single mutation can overcome the naturally observed specificity barrier provides insights into the evolutionary dynamics of this system that allows rapid modulation of binding specificity within a high affinity background.     

Diabetes mellitus caused by mutations in human insulin: analysis of impaired receptor binding of insulins Wakayama,Los Angeles and Chicago using pharmacoinformatics

Several naturally occuring mutations in the human insulin gene are associated with diabetes mellitus. The three known mutant molecules, Wakayama, Los Angeles and Chicago were evaluated using molecular docking and molecular dynamics (MD) to analyse mechanisms of deprived binding affinity for insulin receptor (IR). Insulin Wakayama, is a variant in which valine at position A3 is substituted by leucine, while in insulin Los Angeles and Chicago, phenylalanine at positions B24 and B25 is replaced by serine and leucine, respectively. These mutations show radical changes in binding affinity for IR. The ZDOCK server was used for molecular docking, while AMBER 14 was used for the MD study. The published crystal structure of IR bound to natural insulin was also used for MD. The binding interactions and MD trajectories clearly explained the critical factors for deprived binding to the IR. The surface area around position A3 was increased when valine was substituted by leucine, while at positions B24 and B25 aromatic amino acid phenylalanine replaced by non-aromatic serine and leucine might be responsible for fewer binding interactions at the binding site of IR that leads to instability of the complex. In the MD simulation, the normal mode analysis, rmsd trajectories and prediction of fluctuation indicated instability of complexes with mutant insulin in order of insulin native insulin < insulin Chicago < insulin Los Angeles < insulin Wakayama molecules which corresponds to the biological evidence of the differing affinities of the mutant insulins for the IR.     

POLYGENIC MUTATION IN DROSOPHILA MELANOGASTER: ESTIMATES FROM DIVERGENCE AMONG INBRED STRAINS

A highly inbred line of Drosophila melanogaster was subdivided into 25 replicate sublines, which were independently maintained for 100 generations with 10 pairs of unselected flies per generation. The polygenic mutation rate (V_M) for two quantitative traits, abdominal and sternopleural bristle number, was estimated from divergence among sublines at 10 generation intervals from generations 30-100, and from response of each line to divergent selection after more than 65 generations of mutation accumulation. Estimates of V_M averaged over males and females both from divergence among lines and from response to selection within lines were 3.3 × 10^-3 V_E for abdominal bristles and 1.5 × 10^-3 V_E for sternopleural bristles, where V_E is the environmental variance. The actual rate of production of mutations affecting these traits may be considerably higher if the traits are under stabilizing selection, and if mutations affecting bristle number have deleterious effects on fitness. There was a substantial component of variance for sex × mutant effect interaction and the sublines evolved highly significant mutational variation in sex dimorphism of abdominal bristle number. Pleiotropic effects on sex dimorphism may be a general property of mutations at loci determining bristle number.     

High‐resolution crystal structures leverage protein binding affinity predictions

Predicting protein binding affinities from structural data has remained elusive, a difficulty owing to the variety of protein binding modes. Using the structure‐affinity‐benchmark (SAB, 144 cases with bound/unbound crystal structures and experimental affinity measurements), prediction has been undertaken either by fitting a model using a handfull of predefined variables, or by training a complex model from a large pool of parameters (typically hundreds). The former route unnecessarily restricts the model space, while the latter is prone to overfitting. We design models in a third tier, using 12 variables describing enthalpic and entropic variations upon binding, and a model selection procedure identifying the best sparse model built from a subset of these variables. Using these models, we report three main results. First, we present models yielding a marked improvement of affinity predictions. For the whole dataset, we present a model predicting K_d within 1 and 2 orders of magnitude for 48% and 79% of cases, respectively. These statistics jump to 62% and 89% respectively, for the subset of the SAB consisting of high resolution structures. Second, we show that these performances owe to a new parameter encoding interface morphology and packing properties of interface atoms. Third, we argue that interface flexibility and prediction hardness do not correlate, and that for flexible cases, a performance matching that of the whole SAB can be achieved. Overall, our work suggests that the affinity prediction problem could be partly solved using databases of high resolution complexes whose affinity is known. Proteins 2016; 84:9–20. © 2015 Wiley Periodicals, Inc.     

pH‐selective mutagenesis of protein–protein interfaces: In silico design of therapeutic antibodies with prolonged half‐life

Understanding the effects of mutation on pH‐dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein–protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH‐dependent energy profiles demonstrate excellent agreement with experimentally measured pH‐dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH‐dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH‐selective binding. The method can be used to select mutations that change the pH‐dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH‐dependent binding behavior of IgG to FcRn with the aim of improving the half‐life of therapeutic antibodies in the target organism. © Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Structure-based method for analyzing protein–protein interfaces

Hydrogen bond, hydrophobic and vdW interactions are the three major non-covalent interactions at protein–protein interfaces. We have developed a method that uses only these properties to describe interactions between proteins, which can qualitatively estimate the individual contribution of each interfacial residue to the binding and gives the results in a graphic display way. This method has been applied to analyze alanine mutation data at protein–protein interfaces. A dataset containing 13 protein–protein complexes with 250 alanine mutations of interfacial residues has been tested. For the 75 hot-spot residues (G1.5 kcal mol^-1), 66 can be predicted correctly with a success rate of 88%. In order to test the tolerance of this method to conformational changes upon binding, we utilize a set of 26 complexes with one or both of their components available in the unbound form. The difference of key residues exported by the program is 11% between the results using complexed proteins and those from unbound ones. As this method gives the characteristics of the binding partner for a particular protein, in-depth studies on protein–protein recognition can be carried out. Furthermore, this method can be used to compare the difference between protein–protein interactions and look for correlated mutation. Figure Key interaction grids at the interface between barnase and barstar. Key interaction grid for barnase and barstar are presented in one figure according to their coordinates. In order to distinguish the two proteins, different icons were assigned. Crosses represent key grids for barstar and dots represent key grids for barnase. The four residues in ball and stick are Asp40 in barstar and Arg83, Arg87, His102 in barnase.     

PremPS: Predicting the impact of missense mutations on protein stability

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.     

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.     

Mutation in a flexible linker modulates binding affinity for modular complexes

Tandem beta zippers are modular complexes formed between repeated linear motifs and tandemly arrayed domains of partner proteins in which β-strands form upon binding. Studies of such complexes, formed by LIM domain proteins and linear motifs in their intrinsically disordered partners, revealed spacer regions between the linear motifs that are relatively flexible but may affect the overall orientation of the binding modules. We demonstrate that mutation of a solvent exposed side chain in the spacer region of an LHX4–ISL2 complex has no significant effect on the structure of the complex, but decreases binding affinity, apparently by increasing flexibility of the linker.     

Computational Assessment of Protein–protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes

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

Understanding the resistance mechanism of penicillin binding protein 1a mutant against cefotaxime using molecular dynamic simulation

Antibiotic resistance is a threatening challenge for global health, as the expansion of resistance to current antibiotics has made serious therapeutic problems. Genome mutations are key evolutionary mechanisms conferring antibiotic resistance in bacterial pathogens. For example, penicillin and cephalosporins resistance is mostly mediated by mutations in penicillin binding proteins to change the affinity of the drug. Accordingly, threonine point mutations were reported to develop antibiotic resistance in various bacterial infections including pneumococcal infections. In this study, conventional molecular dynamics simulations, umbrella sampling simulations and MM/GBSA free energy calculations were applied to figure out how the Threonine to Alanine mutation (T to A) at STMK motif affects the binding of cefotaxime to Penicillin Binding Protein 1a and to reveal the resistance mechanism induced by the T to A mutation. The results obtained from the computational methods demonstrate that the T to A mutation increases the flexibility of the binding pocket and changes its conformation, which leads to increased conformational entropy change (?TΔS) and attenuates the bonds between the ligand and the receptor. In brief, our findings indicate that both of the alterations of the conformational enthalpy and entropy contribute to the T to A-induced resistance in the binding of cefotaxime into penicillin binding protein 1a.     

PERSPECTIVE: SPONTANEOUS DELETERIOUS MUTATION

Mildly deleterious mutation has been invoked as a leading explanation for a diverse array of observations in evolutionary genetics and molecular evolution and is thought to be a significant risk of extinction for small populations. However, much of the empirical evidence for the deleterious-mutation process derives from studies of Drosophila melanogaster, some of which have been called into question. We review a broad array of data that collectively support the hypothesis that deleterious mutations arise in flies at rate of about one per individual per generation, with the average mutation decreasing fitness by about only 2% in the heterozygous state. Empirical evidence from microbes, plants, and several other animal species provide further support for the idea that most mutations have only mildly deleterious effects on fitness, and several other species appear to have genomic mutation rates that are of the order of magnitude observed in Drosophila. However, there is mounting evidence that some organisms have genomic deleterious mutation rates that are substantially lower than one per individual per generation. These lower rates may be at least partially reconciled with the Drosophila data by taking into consideration the number of germline cell divisions per generation. To fully resolve the existing controversy over the properties of spontaneous mutations, a number of issues need to be clarified. These include the form of the distribution of mutational effects and the extent to which this is modified by the environmental and genetic background and the contribution of basic biological features such as generation length and genome size to interspecific differences in the genomic mutation rate. Once such information is available, it should be possible to make a refined statement about the long-term impact of mutation on the genetic integrity of human populations subject to relaxed selection resulting from modern medical procedures.     

CScore: a simple yet effective scoring function for protein-ligand binding affinity prediction using modified CMAC learning architecture

Protein-ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein-ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.