Computational analysis of binding of P1 variants to trypsin

The binding of P1 variants of bovine pancreatic trypsin inhibitor (BPTI) to trypsin has been investigated by means of molecular dynamics simulations. The specific interaction formed between the amino acid at the primary binding (P1) position of the binding loop of BPTI and the specificity pocket of trypsin was estimated by use of the linear interaction energy (LIE) method. Calculations for 13 of the naturally occurring amino acids at the P1 position were carried out, and the results obtained were found to correlate well with the experimental binding free energies. The LIE calculations rank the majority of the 13 variants correctly according to the experimental association energies and the mean error between calculated and experimental binding free energies is only 0.38 kcal/mole, excluding the Glu and Asp variants, which are associated with some uncertainties regarding protonation and the possible presence of counter-ions. The three-dimensional structures of the complex with three of the P1 variants (Asn, Tyr, and Ser) included in this study have not at present been solved by any experimental techniques and, therefore, were modeled on the basis of experimental data from P1 variants of similar size. Average structures were calculated from the MD simulations, from which specific interactions explaining the broad variation in association energies were identified. The present study also shows that explicit treatment of the complex water-mediated hydrogen bonding network at the protein-protein interface is of crucial importance for obtaining reliable binding free energies. The successful reproduction of relative binding energies shows that this type of methodology can be very useful as an aid in rational design and redesign of biologically active macromolecules.     

Free energy calculations show that acidic P1 variants undergo large pKa shifts upon binding to trypsin

Serine proteinases and their protein inhibitors belong to one of the most comprehensively studied models of protein-protein interactions. It is well established that the narrow trypsin specificity is caused by the presence of a negatively charged aspartate at the specificity pocket. X-ray crystallography as well as association measurements revealed, surprisingly, that BPTI with glutamatic acid as the primary binding (P1) residue was able to bind to trypsin. Previous free energy calculations showed that there was a substantially unfavorable binding free energy associated with accommodation of ionized P1 Glu at the S1-site of trypsin. In this study, the binding of P1 Glu to trypsin has been systematically investigated in terms of the protonation states of P1 Glu and Asp189, the orientation of Gln192, as well as the possible presence of counterions using the linear interaction energy (LIE) approach and the free energy perturbation (FEP) method. Twenty-four conceivable binding arrangements were evaluated and quantitative agreement with experiments is obtained when the P1 Glu binds in its protonated from. The results suggest that P1 Glu is one of the variants of BPTI that inhibit trypsin strongest at low pH, contrary to the specificity profile of trypsin, suggesting a new regulation mechanism of trypsin-like enzymes.     

Evaluation of protein-protein association energies by free energy perturbation calculations

The association energy upon binding of different amino acids in the specificity pocket of trypsin was evaluated by free energy perturbation calculations on complexes between bovine trypsin (BT) and bovine pancreatic trypsin inhibitor (BPTI). Three simulations of mutations of the primary binding residue (P(1)) were performed (P(1)-Ala to Gly, P(1)-Met to Gly and P(1)-Met to Ala) and the resulting differences in association energy (DeltaDeltaG(a)) are 2. 28, 5.08 and 2.93 kcal/mol for P(1)-Ala to Gly, P(1)-Met to Gly and to Ala with experimental values of 1.71, 4.62 and 2.91 kcal/mol, respectively. The calculated binding free energy differences are hence in excellent agreement with the experimental binding free energies. The binding free energies, however, were shown to be highly dependent on water molecules at the protein-protein interface and could only be quantitatively estimated if the correct number of such water molecules was included. Furthermore, the cavities that were formed when a large amino acid side-chain is perturbed to a smaller one seem to create instabilities in the systems and had to be refilled with water molecules in order to obtain reliable results. In addition, if the protein atoms that were perturbed away were not replaced by water molecules, the simulations dramatically overestimated the initial state of the free energy perturbations.     

Accounting for conformational entropy in predicting binding free energies of protein-protein interactions

Protein-protein interactions are governed by the change in free energy upon binding, ΔG = ΔH - TΔS. These interactions are often marginally stable, so one must examine the balance between the change in enthalpy, ΔH, and the change in entropy, ΔS, when investigating known complexes, characterizing the effects of mutations, or designing optimized variants. To perform a large-scale study into the contribution of conformational entropy to binding free energy, we developed a technique called GOBLIN (Graphical mOdel for BiomoLecular INteractions) that performs physics-based free energy calculations for protein-protein complexes under both side-chain and backbone flexibility. Goblin uses a probabilistic graphical model that exploits conditional independencies in the Boltzmann distribution and employs variational inference techniques that approximate the free energy of binding in only a few minutes. We examined the role of conformational entropy on a benchmark set of more than 700 mutants in eight large, well-studied complexes. Our findings suggest that conformational entropy is important in protein-protein interactions--the root mean square error (RMSE) between calculated and experimentally measured ΔΔGs decreases by 12% when explicit entropic contributions were incorporated. GOBLIN models all atoms of the protein complex and detects changes to the binding entropy along the interface as well as positions distal to the binding interface. Our results also suggest that a variational approach to entropy calculations may be quantitatively more accurate than the knowledge-based approaches used by the well-known programs FOLDX and Rosetta--GOBLIN's RMSEs are 10 and 36% lower than these programs, respectively.     

Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method

A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).     

Modulating protein-protein interactions with small molecules: the importance of binding hotspots

The modulation of protein-protein interactions (PPIs) by small drug-like molecules is a relatively new area of research and has opened up new opportunities in drug discovery. However, the progress made in this area is limited to a handful of known cases of small molecules that target specific diseases. With the increasing availability of protein structure complexes, it is highly important to devise strategies exploiting homologous structure space on a large scale for discovering putative PPIs that could be attractive drug targets. Here, we propose a scheme that allows performing large-scale screening of all protein complexes and finding putative small-molecule and/or peptide binding sites overlapping with protein-protein binding sites (so-called "multibinding sites"). We find more than 600 nonredundant proteins from 60 protein families with multibinding sites. Moreover, we show that the multibinding sites are mostly observed in transient complexes, largely overlap with the binding hotspots and are more evolutionarily conserved than other interface sites. We investigate possible mechanisms of how small molecules may modulate protein-protein binding and discuss examples of new candidates for drug design.     

How optimal are the binding energetics of barnase and barstar?

The extracellular ribonuclease barnase and its intracellular inhibitor barstar bind fast and with high affinity. Although extensive experimental and theoretical studies have been carried out on this system, it is unclear what the relative importance of different contributions to the high affinity is and whether binding can be improved through point mutations. In this work, we first applied Poisson-Boltzmann electrostatic calculations to 65 barnase-barstar complexes with mutations in both barnase and barstar. The continuum electrostatic calculations with a van der Waals surface dielectric boundary definition result in the electrostatic interaction free energy providing the dominant contribution favoring barnase-barstar binding. The results show that the computed electrostatic binding free energy can be improved through mutations at W44/barstar and E73/barnase. Furthermore, the determinants of binding affinity were quantified by applying COMparative BINding Energy (COMBINE) analysis to derive quantitative structure-activity relationships (QSARs) for the 65 complexes. The COMBINE QSAR model highlights approximately 20 interfacial residue pairs as responsible for most of the differences in binding affinity between the mutant complexes, mainly due to electrostatic interactions. Based on the COMBINE model, together with Brownian dynamics simulations to compute diffusional association rate constants, several mutants were designed to have higher binding affinities than the wild-type proteins.     

Molecular interactions of c-ABL mutants in complex with imatinib/nilotinib: a computational study using linear interaction energy (LIE) calculations

In spite of the effectiveness of Imatinib for chronic myeloid leukemia (CML) treatment, resistance has repeatedly been reported and is associated with point mutations in the BCR-ABL chimeric gene. To overcome this resistance, several inhibitors of BCR-ABL tyrosine kinase activity were developed. In this context, computational simulations have become a powerful tool for understanding drug-protein interactions. Herein, we report a comparative molecular dynamics analysis of the interaction between two tyrosine kinase inhibitors (imatinib or nilotinib) against wild type c-ABL protein and 12 mutants, using the semi-empirical linear interaction energy (LIE) method, to assess the feasibility of this approach for studying resistance against the inhibitory activity of these drugs. In addition, to understand the structural changes that are associated with resistance, we describe the behavior of water molecules that interact simultaneously with specific residues (Glu286, Lys271 and Asp381) of c-ABL (wild type or mutant) and their relationship with drug resistance. Experimental IC50 values for the interaction between imatinib, wild type c-ABL, and 12 mutants were used to obtain the proper LIE coefficients (α, β and γ) to estimate the free energy of the binding of imatinib with wild-type and mutant proteins, and values were extrapolated for the analysis of the nilotinib/c-ABL interaction. Our results indicate that LIE was suitable to predict the superior inhibitory activity of nilotinib and the resistance to inhibition that was observed in c-ABL mutants. Additionally, for c-ABL mutants, the observed number of water molecules being turned over while interacting with amino acids Glu286, Lys271 and Asp381 was associated with resistance to imatinib, resulting in a less effective inhibition of the kinase activity.     

High-resolution protein-protein docking

The high-resolution prediction of protein-protein docking can now create structures with atomic-level accuracy. This progress arises from both improvements in the rapid sampling of conformations and increased accuracy of binding free energy calculations. Consequently, the quality of models submitted to the blind prediction challenge CAPRI (Critical Assessment of PRedicted Interactions) has steadily increased, including complexes predicted from homology structures of one binding partner and complexes with atomic accuracy at the interface. By exploiting experimental information, docking has created model structures for real applications, even when confronted with challenges such as moving backbones and uncertain monomer structures. Work remains to be done in docking large or flexible proteins, ranking models consistently, and producing models accurate enough to allow computational design of higher affinities or specificities.     

A detailed thermodynamic analysis of ras/effector complex interfaces

Many cellular functions are based on the interaction and crosstalk of various signaling proteins. Among these, members of the Ras family of small GTP-binding proteins are important for communicating signals into different pathways. In order to answer the question of how binding affinity and specificity is achieved, we analyzed binding energetics on the molecular level, with reference to the available structural data. The interaction of two members of the Ras subfamily with two different effector proteins, namely Raf and RalGDS, were investigated using isothermal titration calorimetry and a fluorescence-based method. Experiments with alanine mutants, located in the complex interfaces, yielded an energy map for the contact areas of the Ras/effector complexes, which could be differentiated into enthalpy and entropy contributions. In addition, by using double mutant cycle analysis, we probed the energetic contribution of selected pairs of amino acid residues. The resulting energy landscapes of the Ras/effector interface areas show a highly different topology when comparing the two effectors, Raf and RalGDS, demonstrating the specificity of the respective interactions. Particularly, we observe a high degree of compensating effects between enthalpy and entropy; differences between these components are much greater than the overall free energy differences. This is observed also when using the software FOLD-X to predict the effect of point mutations on the crystal structures of the different complexes. Prediction of the free energy changes shows a very good correlation with the experimentally observed energies. Furthermore, in line with experimental data, energy decomposition indicates that many different components of large magnitude counteract each other to produce a smaller change in overall free energy, illustrating the importance of long-range electrostatic forces in complex formation.     

Wrapping mimicking in drug-like small molecules disruptive of protein-protein interfaces

The discovery of small-molecule drugs aimed at disrupting protein-protein associations is expected to lead to promising therapeutic strategies. The small molecule binds to the target protein thus replacing its natural protein partner. Noteworthy, structural analysis of complexes between successful disruptive small molecules and their target proteins has suggested the possibility that such ligands might somehow mimic the binding behavior of the protein they replace. In these cases, the molecules show a spatial and "chemical" (i.e., hydrophobicity) similarity with the residues of the partner protein involved in the protein-protein complex interface. However, other disruptive small molecules do not seem to show such spatial and chemical correspondence with the replaced protein. In turn, recent progress in the understanding of protein-protein interactions and binding hot spots has revealed the main role of intermolecular wrapping interactions: three-body cooperative correlations in which nonpolar groups in the partner protein promote dehydration of a two-body electrostatic interaction of the other protein. Hence, in the present work, we study some successful complexes between already discovered small disruptive drug-like molecules and their target proteins already reported in the literature and we compare them with the complexes between such proteins and their natural protein partners. Our results show that the small molecules do in fact mimic to a great extent the wrapping behavior of the protein they replace. Thus, by revealing the replacement the small molecule performs of relevant wrapping interactions, we convey precise physical meaning to the mimicking concept, a knowledge that might be exploited in future drug-design endeavors.     

Enzymatic properties of alpha 2-macroglobulin-proteinase complexes. Apparent discrimination between covalently and noncovalently bound trypsin by reaction with soybean trypsin inhibitor

The binding of trypsin to alpha 2-macroglobulin, the appearance of free beta-cysteinyl thiol groups of the formed complexes, the steady-state kinetics of their enzymic hydrolysis of carbobenzoxy-L-valyl-glycyl-L-arginyl-4-nitroanilide and finally their reactions with soybean trypsin inhibitor leading to the formation of ternary alpha 2-macroglobulin-trypsin-soybean trypsin inhibitor complexes were investigated. Each alpha 2-macroglobulin molecule binds two trypsin tightly; the dissociation constants were found to be unmeasureably small, but the extent of formation of 1:1 and 1:2 complexes at different molar ratios of alpha 2-macroglobulin to trypsin as determined from the appearance of thiol groups clearly indicated that binding of trypsin to alpha 2-macroglobulin shows negative cooperativity. Binding of the first trypsin makes the access of the second less easy. The kinetic results showed a decrease of the kc/Km value of hydrolysis of the tripeptide substrate by approx. 4-fold compared to that of free trypsin for each alpha 2-macroglobulin-bound trypsin. Here no differences were seen between the bound trypsins. The analysis of the reactions between the alpha 2-macroglobulin-trypsin complexes and soybean trypsin inhibitor shows that ternary complexes do form, although slowly, and that two processes occur, not only when 1:2 complexes but also when 1:1 complexes react with soybean trypsin inhibitor. Soybean trypsin inhibitor apparently discriminates between two distinct binding modes of trypsin to alpha 2-macroglobulin, the covalently and the noncovalently alpha 2-macroglobulin-bound trypsins.     

Predicting where Small Molecules Bind at Protein-Protein Interfaces

Small molecules that bind at protein-protein interfaces may either block or stabilize protein-protein interactions in cells. Thus, some of these binding interfaces may turn into prospective targets for drug design. Here, we collected 175 pairs of protein-protein (PP) complexes and protein-ligand (PL) complexes with known three-dimensional structures for which (1) one protein from the PP complex shares at least 40% sequence identity with the protein from the PL complex, and (2) the interface regions of these proteins overlap at least partially with each other. We found that those residues of the interfaces that may bind the other protein as well as the small molecule are evolutionary more conserved on average, have a higher tendency of being located in pockets and expose a smaller fraction of their surface area to the solvent than the remaining protein-protein interface region. Based on these findings we derived a statistical classifier that predicts patches at binding interfaces that have a higher tendency to bind small molecules. We applied this new prediction method to more than 10 000 interfaces from the protein data bank. For several complexes related to apoptosis the predicted binding patches were in direct contact to co-crystallized small molecules.     

Grafting of protein-protein interaction epitope

Transferring the biological function of one protein to another is a key issue in understanding the structure and function relationship of proteins. We have developed a strategy for grafting protein-protein interaction epitopes. As a first step, residues at the interface of the ligand protein which strongly interact with the receptor protein were identified. Then protein scaffolds were docked onto receptor protein based on geometric complementarity. Only high docking score matches were saved. For each saved match, the scaffold protein was accepted if it had suitable positions for grafting key interaction residues of the ligand protein. These candidate residues were mutated to corresponding residues in the ligand protein at each relevant position and the mutated scaffold protein was co-minimized with receptor protein. Finally, the minimized complexes were evaluated by a scoring function deduced from statistical analysis of rigid binding data sets. As a test case, the binding epitope of barstar, the inhibitor of barnase, was grafted onto smaller proteins. Pheromone Er-1 (PDB entry 1erc) has been found to be a good scaffold. The calculated binding free energy for mutated Pheromone Er-1 is equivalent to that of barstar.     

Structural Model for the Interaction of a Designed Ankyrin Repeat Protein with the Human Epidermal Growth Factor Receptor 2

Designed Ankyrin Repeat Proteins are a class of novel binding proteins that can be selected and evolved to bind to targets with high affinity and specificity. We are interested in the DARPin H10-2-G3, which has been evolved to bind with very high affinity to the human epidermal growth factor receptor 2 (HER2). HER2 is found to be over-expressed in 30% of breast cancers, and is the target for the FDA-approved therapeutic monoclonal antibodies trastuzumab and pertuzumab and small molecule tyrosine kinase inhibitors. Here, we use computational macromolecular docking, coupled with several interface metrics such as shape complementarity, interaction energy, and electrostatic complementarity, to model the structure of the complex between the DARPin H10-2-G3 and HER2. We analyzed the interface between the two proteins and then validated the structural model by showing that selected HER2 point mutations at the putative interface with H10-2-G3 reduce the affinity of binding up to 100-fold without affecting the binding of trastuzumab. Comparisons made with a subsequently solved X-ray crystal structure of the complex yielded a backbone atom root mean square deviation of 0.84–1.14 Ångstroms. The study presented here demonstrates the capability of the computational techniques of structural bioinformatics in generating useful structural models of protein-protein interactions.     

Compromise and accommodation in ecotin, a dimeric macromolecular inhibitor of serine proteases

Ecotin is a dimeric serine protease inhibitor from Escherichia coli which binds proteases to form a hetero-tetramer with three distinct interfaces: an ecotin-ecotin dimer interface, a larger primary ecotin-protease interface, and a smaller secondary ecotin-protease interface. The contributions of these interfaces to binding and inhibition are unequal. To investigate the contribution and adaptability of each interface, we have solved the structure of two mutant ecotin-trypsin complexes and compared them to the structure of the previously determined wild-type ecotin-trypsin complex. Wild-type ecotin has an affinity of 1 nM for trypsin, while the optimized mutant, ecotin Y69F, D70P, which was found using phage display technologies, inhibits rat trypsin with a K(i) value of 0.08 nM. Ecotin 67-70A, M84R which has four alanine substitutions in the ecotin-trypsin secondary binding site, along with the M84R mutation at the primary site, has a K(i) value against rat trypsin of 0.2 nM. The structure of the ecotin Y69F, D70P-trypsin complex shows minor structural changes in the ecotin-trypsin tetramer. The structure of the ecotin 67-70A, M84R mutant bound to trypsin shows large deviations in the tertiary and quaternary structure of the complex. The trypsin structure shows no significant changes, but the conformation of several loop regions of ecotin are altered, resulting in the secondary site releasing its hold on trypsin. The structure of several regions previously considered to be rigid is also significantly modified. The inherent flexibility of ecotin allows it to accommodate these mutations and still maintain tight binding through the compromises of the protein-protein interfaces in the ecotin-trypsin tetramer. A comparison with two recently described ecotin-like genes from other bacteria suggests that these structural and functional features are conserved in otherwise distant bacterial lineages.     

Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes

Absolute binding free energy calculations and free energy decompositions are presented for the protein-protein complexes H-Ras/C-Raf1 and H-Ras/RalGDS. Ras is a central switch in the regulation of cell proliferation and differentiation. In our study, we investigate the capability of the molecular mechanics (MM)-generalized Born surface area (GBSA) approach to estimate absolute binding free energies for the protein-protein complexes. Averaging gas-phase energies, solvation free energies, and entropic contributions over snapshots extracted from trajectories of the unbound proteins and the complexes, calculated binding free energies (Ras-Raf: -15.0(+/-6.3)kcal mol(-1); Ras-RalGDS: -19.5(+/-5.9)kcal mol(-1)) are in fair agreement with experimentally determined values (-9.6 kcal mol(-1); -8.4 kcal mol(-1)), if appropriate ionic strength is taken into account. Structural determinants of the binding affinity of Ras-Raf and Ras-RalGDS are identified by means of free energy decomposition. For the first time, computationally inexpensive generalized Born (GB) calculations are applied in this context to partition solvation free energies along with gas-phase energies between residues of both binding partners. For selected residues, in addition, entropic contributions are estimated by classical statistical mechanics. Comparison of the decomposition results with experimentally determined binding free energy differences for alanine mutants of interface residues yielded correlations with r(2)=0.55 and 0.46 for Ras-Raf and Ras-RalGDS, respectively. Extension of the decomposition reveals residues as far apart as 25A from the binding epitope that can contribute significantly to binding free energy. These "hotspots" are found to show large atomic fluctuations in the unbound proteins, indicating that they reside in structurally less stable regions. Furthermore, hotspot residues experience a significantly larger-than-average decrease in local fluctuations upon complex formation. Finally, by calculating a pair-wise decomposition of interactions, interaction pathways originating in the binding epitope of Raf are found that protrude through the protein structure towards the loop L1. This explains the finding of a conformational change in this region upon complex formation with Ras, and it may trigger a larger structural change in Raf, which is considered to be necessary for activation of the effector by Ras.     

Superimposing the 27 crystal protein/inhibitor complexes of β-secretase to calculate the binding affinities by the linear interaction energy method

BACE-1 is an important target for designing therapeutic agents for the treatment of Alzheimer's disease. An improved linear interaction energy (LIE) model has been developed to calculate the binding free energies of β-secretase (BACE-1) by superimposing the 27 crystal BACE-1/inhibitor complexes to put a diverse set of 27 co-crystallized ligands into the binding pocket. These co-crystallized conformations of ligands were set as the initial binding conformations for LIE simulation. The effects of two protein conformations (i.e., 1W51 and 1FKN), two sampling methods (i.e., energy minimization and hybrid Monte Carlo [HMC]), and energy terms were studied. Using 1W51 crystal structure and HMC sampling technique, the best binding affinity model for the full set of ligands was found to have a root-mean-square error of 0.996 kcal/mol.     

Molecular dynamic simulation of wild type and mutants of the polymorphic amyloid NNQNTF segments of elk prion: structural stability and thermodynamic of association

A hexapeptide with amino acid sequence NNQNTF from the elk prion protein forms amyloid fibrils. Here we use molecular dynamic simulations of the oligomers and their single point glycine mutants to study their stability. In an effort to probe the structural stability and association thermodynamic in a realistic environment, all wildtype of NNQNTF polymorphic forms with different size and their corresponding double layer 5 strands single point glycine mutants were subjected to a total of 500 ns of explicit-solvent molecular dynamics (MD) simulation. Our results show that the structural stability of the NNQNTF oligomers increases with increasing the number of β-strands for double layers. Our results also demonstrated that hydrophobic interaction is the principle driving force to stabilize the adjacent β-strands while the steric zipper is responsible for holding the neighboring β-sheet layers together. We used MM-PBSA approach free energy calculations to determine the role of nonpolar effects, electrostatics and entropy in binding. Nonpolar effects remained consistently more favorable in wild type and mutants reinforcing the importance of hydrophobic effects in protein-protein binding. While entropy systematically opposed binding in all cases, there was no observed trend in the entropy difference between wildtype and glycine mutant. Free energy decomposition shows residues situated at the interface were found to make favorable contributions to the peptide-peptide association. The study of the wild type and mutants in an explicit solvent may provide valuable insight for amyloid aggregation inhibitor design efforts.