The Dipole Potential Modifies the Clustering and Ligand Binding Affinity of ErbB Proteins and Their Signaling Efficiency.
Ontology highlight
ABSTRACT:
SUBMITTER: Kovacs T
PROVIDER: S-EPMC5075772 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The relationship between the structures of protein-ligand complexes existing in the crystal and in solution, essential in the case of fragment-based screening by X-ray crystallography (FBS-X), has been often an object of controversy. To address this question, simultaneous co-crystallization and soaking of two inhibitors with different ratios, Fidarestat (FID; K(d) = 6.5 nM) and IDD594 (594; K(d) = 61 nM), which bind to h-aldose reductase (AR), have been performed. The subatomic resolution of the crystal structures allows the differentiation of both inhibitors, even when the structures are almost superposed. We have determined the occupation ratio in solution by mass spectrometry (MS) Occ(FID)/Occ(594) = 2.7 and by X-ray crystallography Occ(FID)/Occ(594) = 0.6. The occupancies in the crystal and in solution differ 4.6 times, implying that ligand binding potency is influenced by crystal contacts. A structural analysis shows that the Loop A (residues 122-130), which is exposed to the solvent, is flexible in solution, and is involved in packing contacts within the crystal. Furthermore, inhibitor 594 contacts the base of Loop A, stabilizing it, while inhibitor FID does not. This is shown by the difference in B-factors of the Loop A between the AR-594 and AR-FID complexes. A stable loop diminishes the entropic energy barrier to binding, favoring 594 versus FID. Therefore, the effect of the crystal environment should be taken into consideration in the X-ray diffraction analysis of ligand binding to proteins. This conclusion highlights the need for additional methodologies in the case of FBS-X to validate this powerful screening technique, which is widely used.
| S-EPMC4671318 | biostudies-literature
Project description:High-affinity binders are desirable tools to probe the function that specific protein-protein interactions play in cell. In the process of seeking a general strategy to design high-affinity binders, we found a clue from the ?PIX (p21-activated kinase interacting exchange factor)-Shank PDZ interaction in synaptic assembly: three PDZ-binding sites are clustered by a parallel coiled-coil trimer but bind to Shank PDZ protein with 1:1 stoichiometry (1 trimer/1 PDZ). Inspired by this architecture, we proposed that peptide dendrimer, mimicking the ligand clustering in ?PIX, will also show enhanced binding affinity, yet with 1:1 stoichiometry. This postulation has been proven here, as we synthesized a set of monomeric, dimeric and trimeric peptides and measured their binding affinity and stoichiometry with Shank PDZ domains by isothermal titration calorimetry, native mass spectrometry and surface plasmon resonance. This affinity enhancement, best explained by proximity effect, will be useful to guide the design of high-affinity blockers for protein-protein interactions.
| S-EPMC4769301 | biostudies-literature
Project description:The ability to design proteins with high affinity and selectivity for any given small molecule is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition. Attempts to rationally design ligand-binding proteins have met with little success, however, and the computational design of protein-small-molecule interfaces remains an unsolved problem. Current approaches for designing ligand-binding proteins for medical and biotechnological uses rely on raising antibodies against a target antigen in immunized animals and/or performing laboratory-directed evolution of proteins with an existing low affinity for the desired ligand, neither of which allows complete control over the interactions involved in binding. Here we describe a general computational method for designing pre-organized and shape complementary small-molecule-binding sites, and use it to generate protein binders to the steroid digoxigenin (DIG). Of seventeen experimentally characterized designs, two bind DIG; the model of the higher affinity binder has the most energetically favourable and pre-organized interface in the design set. A comprehensive binding-fitness landscape of this design, generated by library selections and deep sequencing, was used to optimize its binding affinity to a picomolar level, and X-ray co-crystal structures of two variants show atomic-level agreement with the corresponding computational models. The optimized binder is selective for DIG over the related steroids digitoxigenin, progesterone and β-oestradiol, and this steroid binding preference can be reprogrammed by manipulation of explicitly designed hydrogen-bonding interactions. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics and diagnostics.
| S-EPMC3898436 | biostudies-literature
Project description:We describe a phage display methodology for engineering synthetic antigen binders (sABs) that recognize either the apo or the ligand-bound conformation of maltose-binding protein (MBP). sABs that preferentially recognize the maltose-bound form of MBP act as positive allosteric effectors by substantially increasing the affinity for maltose. A crystal structure of a sAB bound to the closed form of MBP reveals the basis for this allosteric effect. We show that sABs that recognize the bound form of MBP can rescue the function of a binding-deficient mutant by restoring its natural affinity for maltose. Furthermore, the sABs can enhance maltose binding in vivo, as they provide a growth advantage to bacteria under low-maltose conditions. The results demonstrate that structure-specific sABs can be engineered to dynamically control ligand-binding affinities by modulating the transition between different conformations.
| S-EPMC3077571 | biostudies-literature
Project description:Analysis of the experimental binding thermodynamics for approximately 100 protein-ligand complexes provides important insights into the factors governing ligand affinity and efficiency. The commonly accepted correlation between enthalpy and -T?S is clearly observed for this relatively diverse data set. It is also clear that affinity (i.e., ?G) is not generally correlated to either enthalpy or -T?S. This is a worrisome trend since the vast majority of computational structure-based design is carried out using interaction energies for one, or at most a few, ligand poses. As such, these energies are most closely comparable to enthalpies not free energies. Closer inspection of the data shows that in a few cases the enthalpy (or -T?S) is correlated with free energy. It is tempting to speculate that this could be an important consideration as to why some targets are readily amenable to modeling and others are not. Additionally, analysis of the enthalpy and -T?S efficiencies shows that the trends observed for ligand efficiencies with respect to molecular size are primarily a consequence of enthalpic, not entropic, effects.
| S-EPMC4018058 | biostudies-literature
Project description:Force field accuracy is still one of the "stalemates" in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein-ligand binding, organic host-guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host-guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein-ligand binding in two drug targets against the HIV-1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics-based binding free energy models can be used to evaluate and optimize force fields for protein-ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.
| S-EPMC4715590 | biostudies-literature
Project description:BACKGROUND: Models that are capable of reliably predicting binding affinities for protein-ligand complexes play an important role the field of structure-guided drug design. METHODS: Here, we begin by applying the computational geometry technique of Delaunay tessellation to each set of atomic coordinates for over 1400 diverse macromolecular structures, for the purpose of deriving a four-body statistical potential that serves as a topological scoring function. Next, we identify a second, independent set of three hundred protein-ligand complexes, having both high-resolution structures and known dissociation constants. Two-thirds of these complexes are randomly selected to train a predictive model of binding affinity as follows: two tessellations are generated in each case, one for the entire complex and another strictly for the isolated protein without its bound ligand, and a topological score is computed for each tessellation with the four-body potential. Predicted protein-ligand binding affinity is then based on an empirically derived linear function of the difference between both topological scores, one that appropriately scales the value of this difference. RESULTS: A comparison between experimental and calculated binding affinity values over the two hundred complexes reveals a Pearson's correlation coefficient of r = 0.79 with a standard error of SE = 1.98 kcal/mol. To validate the method, we similarly generated two tessellations for each of the remaining protein-ligand complexes, computed their topological scores and the difference between the two scores for each complex, and applied the previously derived linear transformation of this topological score difference to predict binding affinities. For these one hundred complexes, we again observe a correlation of r = 0.79 (SE = 1.93 kcal/mol) between known and calculated binding affinities. Applying our model to an independent test set of high-resolution structures for three hundred diverse enzyme-inhibitor complexes, each with an experimentally known inhibition constant, also yields a correlation of r = 0.79 (SE = 2.39 kcal/mol) between experimental and calculated binding energies. CONCLUSIONS: Lastly, we generate predictions with our model on a diverse test set of one hundred protein-ligand complexes previously used to benchmark 15 related methods, and our correlation of r = 0.66 between the calculated and experimental binding energies for this dataset exceeds those of the other approaches. Compared with these related prediction methods, our approach stands out based on salient features that include the reliability of our model, combined with the rapidity of the generated predictions, which are less than one second for an average sized complex.
| S-EPMC3952120 | biostudies-literature
Project description:We previously found that EGF (epidermal growth factor) increases the EGFR (EGF receptor) kinase-binding affinity towards the major tyrosine phosphorylation sites in downstream adaptor proteins such as Gab1 (Grb2-associated binding protein 1) and Shc [Src homology 2 (SH2) domain and collagen containing protein], but not that towards EGFR autophosphorylation sites [Fan, Wong, Deb and Johnson (2004) J. Biol. Chem. 279 , 38143-38150]. EGFR activation can also result in transphosphorylation of tyrosine resides in the C-terminal region of the related receptors ErbB2, ErbB3 and ErbB4 in heterodimers which are formed upon ligand stimulation. In the present study, we investigated the specificity of EGFR kinase by comparing the steady state kinetic parameters for peptides derived from all four ErbBs in the absence or presence of EGF. Our results demonstrated that (i) EGFR kinase can efficiently phosphorylate a broad range of diverse peptide sequences representing ErbB sites; (ii) certain ErbB2, ErbB3 and ErbB4 sites had higher specificity constants than any EGFR sequence and (iii) EGF stimulation consistently increases the k(cat) approx. 5-fold, but does not significantly alter the K(m) for any ErbB peptides. Furthermore, peptides containing lysine at position -2 or -3 N-terminal to the target tyrosine were found to be poor EGFR kinase substrates, and substitution of these lysines with glutamine decreased the K(m) and increased the k(cat) for these substrates. We conclude that EGFR kinase-mediated ErbB transphosphorylations are mostly controlled at the level of oligomerization, and not by a preference of the EGFR kinase for phosphorylation sites in any particular ErbB. The results also demonstrated that, unlike phosphorylation sites in select downstream targets, EGF does not regulate the recognition of phosphorylation sites in the C-terminal region of any of the ErbBs.
| S-EPMC1316279 | biostudies-other
Project description:Reconstruction of ancestral protein sequences using phylogenetic methods is a powerful technique for directly examining the evolution of molecular function. Although ancestral sequence reconstruction (ASR) is itself very efficient, downstream functional, and structural studies necessary to characterize when and how changes in molecular function occurred are often costly and time-consuming, currently limiting ASR studies to examining a relatively small number of discrete functional shifts. As a result, we have very little direct information about how molecular function evolves across large protein families. Here we develop an approach combining ASR with structure and function prediction to efficiently examine the evolution of ligand affinity across a large family of double-stranded RNA binding proteins (DRBs) spanning animals and plants. We find that the characteristic domain architecture of DRBs-consisting of 2-3 tandem double-stranded RNA binding motifs (dsrms)-arose independently in early animal and plant lineages. The affinity with which individual dsrms bind double-stranded RNA appears to have increased and decreased often across both animal and plant phylogenies, primarily through convergent structural mechanisms involving RNA-contact residues within the ?1-?2 loop and a small region of ?2. These studies provide some of the first direct information about how protein function evolves across large gene families and suggest that changes in molecular function may occur often and unassociated with major phylogenetic events, such as gene or domain duplications.
| S-EPMC5435087 | biostudies-literature
Project description:Accurate ligand-protein binding affinity prediction, for a set of similar binders, is a major challenge in the lead optimization stage in drug development. In general, docking and scoring functions perform unsatisfactorily in this application. Docking calculations, followed by molecular dynamics simulations and free energy calculations can be applied to improve the predictions. However, for targets with large, flexible binding sites, with no experimentally determined binding modes for a set of ligands, insufficient sampling can decrease the accuracy of the free energy calculations. Cytochrome P450s, a protein family of major importance for drug metabolism, is an example of a challenging target for binding affinity predictions. As a result, the choice of starting structure from the docking solutions becomes crucial. In this study, an iterative scheme is introduced that includes multiple independent molecular dynamics simulations to obtain weighted ensemble averages to be used in the linear interaction energy method. The proposed scheme makes the initial pose selection less crucial for further simulation, as it automatically calculates the relative weights of the various poses. It also properly takes into account the possibility that multiple binding modes contribute similarly to the overall affinity, or of similar compounds occupying very different poses. The method was applied to a set of 12 compounds binding to cytochrome P450 2C9 and it displayed a root mean-square error of 2.9 kJ/mol.
| S-EPMC2877349 | biostudies-literature