Over the past decade, protein-protein interactions have emerged as attractive but challenging targets for therapeutic intervention using small molecules. Due to the relatively flat surfaces that ...typify protein interaction sites, modern virtual screening tools developed for optimal performance against "traditional" protein targets perform less well when applied instead at protein interaction sites. Previously, we described a docking method specifically catered to the shallow binding modes characteristic of small-molecule inhibitors of protein interaction sites. This method, called DARC (Docking Approach using Ray Casting), operates by comparing the topography of the protein surface when "viewed" from a vantage point inside the protein against the topography of a bound ligand when "viewed" from the same vantage point. Here, we present five key enhancements to DARC. First, we use multiple vantage points to more accurately determine protein-ligand surface complementarity. Second, we describe a new scheme for rapidly determining optimal weights in the DARC scoring function. Third, we incorporate sampling of ligand conformers "on-the-fly" during docking. Fourth, we move beyond simple shape complementarity and introduce a term in the scoring function to capture electrostatic complementarity. Finally, we adjust the control flow in our GPU implementation of DARC to achieve greater speedup of these calculations. At each step of this study, we evaluate the performance of DARC in a "pose recapitulation" experiment: predicting the binding mode of 25 inhibitors each solved in complex with its distinct target protein (a protein interaction site). Whereas the previous version of DARC docked only one of these inhibitors to within 2 Å RMSD of its position in the crystal structure, the newer version achieves this level of accuracy for 12 of the 25 complexes, corresponding to a statistically significant performance improvement (p < 0.001). Collectively then, we find that the five enhancements described here--which together make up DARC 2.0--lead to dramatically improved speed and performance relative to the original DARC method.
Protein-protein interactions are among today's most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has ...proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more "traditional" drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of "decoys." Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new "Talaris" update and the "pwSHO" solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta's ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta's ability to identify small-molecule inhibitors of protein-protein interactions.
In the Na
/taurocholate cotransporting polypeptide (NTCP), the clinically relevant S267F polymorphism occurs at a "rheostat position". That is, amino acid substitutions at this position ("S267X") ...lead to a wide range of functional outcomes. This result was particularly striking because molecular models predicted the S267X side chains are buried, and thus, usually expected to be less tolerant of substitutions. To assess whether structural tolerance to buried substitutions is widespread in NTCP, here we used Rosetta to model all 19 potential substitutions at another 13 buried positions. Again, only subtle changes in the calculated stabilities and structures were predicted. Calculations were experimentally validated for 19 variants at codon 271 ("N271X"). Results showed near wildtype expression and rheostatic modulation of substrate transport, implicating N271 as a rheostat position. Notably, each N271X substitution showed a similar effect on the transport of three different substrates and thus did
alter substrate specificity. This differs from S267X, which altered
transport kinetics and specificity. As both transport and specificity may change during protein evolution, the recognition of such rheostat positions may be important for evolutionary studies. We further propose that the presence of rheostat positions is facilitated by local plasticity within the protein structure. Finally, we note that identifying rheostat positions may advance efforts to predict new biomedically relevant missense variants in NTCP and other membrane transport proteins.
We describe the Multiscale Modeling Tools for Structural Biology (MMTSB) Tool Set (
http://mmtsb.scripps.edu/software/mmtsbToolSet.html), which is a novel set of utilities and programming libraries ...that provide new enhanced sampling and multiscale modeling techniques for the simulation of proteins and nucleic acids. The tool set interfaces with the existing molecular modeling packages CHARMM and Amber for classical all-atom simulations, and with MONSSTER for lattice-based low-resolution conformational sampling. In addition, it adds new functionality for the integration and translation between both levels of detail. The replica exchange method is implemented to allow enhanced sampling of both the all-atom and low-resolution models. The tool set aims at applications in structural biology that involve protein or nucleic acid structure prediction, refinement, and/or extended conformational sampling. With structure prediction applications in mind, the tool set also implements a facility that allows the control and application of modeling tasks on a large set of conformations in what we have termed ensemble computing. Ensemble computing encompasses loosely coupled, parallel computation on high-end parallel computers, clustered computational grids and desktop grid environments.
This paper describes the design and implementation of the MMTSB Tool Set and illustrates its utility with three typical examples—scoring of a set of predicted protein conformations in order to identify the most native-like structures, ab initio folding of peptides in implicit solvent with the replica exchange method, and the prediction of a missing fragment in a larger protein structure.
Topology has been shown to be an important determinant of many features of protein folding; however, the delineation of sequence effects on folding remains obscure. Furthermore, differentiation ...between the two influences proves difficult due to their intimate relationship. To investigate the effect of sequence in the absence of significant topological differences, we examined the folding mechanisms of segment B1 peptostreptococcal protein L and segment B1 of streptococcal protein G. These proteins share the same highly symmetrical topology. Despite this symmetry, neither protein folds through a symmetrical transition state. We analyzed the origins of this difference using theoretical models. We found that the strength of the interactions present in the N‐terminal hairpin of protein L causes this hairpin to form ahead of the C‐terminal hairpin. The difference in chain entropy associated with the formation of the hairpins of protein G proves sufficient to beget initiation of folding at the shorter C‐terminal hairpin. Our findings suggest that the mechanism of folding may be understood by examination of the free energy associated with the formation of partially folded microstates.
Protein-protein interactions play key roles in all biological processes, motivating numerous campaigns to seek small-molecule disruptors of therapeutically relevant interactions. Two decades ago, the ...prospect of developing small-molecule inhibitors was thought to be perhaps impossible due to the potentially undruggable nature of the protein surfaces involved; this viewpoint was reinforced by the limited successes provided from traditional high-throughput screens. To date, however, refinement of new experimental approaches has led to a multitude of inhibitors against many different targets. Having thus established the feasibility of attaining success in this valuable and diverse target space, attention now turns to incorporating computational techniques that might assist during various stages of drug design and optimization. Here we review cases in which computational approaches - virtual screening, docking, and ligand optimization - have contributed to discovery of new inhibitors of protein-protein interactions. We conclude by providing an outlook into the upcoming challenges and recent advances likely to shape this field moving forward.
Musashi-1 (MSI1) is an RNA-binding protein that acts as a translation activator or repressor of target mRNAs. The best-characterized MSI1 target is Numb mRNA, whose encoded protein negatively ...regulates Notch signaling. Additional MSI1 targets include the mRNAs for the tumor suppressor protein APC that regulates Wnt signaling and the cyclin-dependent kinase inhibitor P21WAF−1. We hypothesized that increased expression of NUMB, P21 and APC, through inhibition of MSI1 RNA-binding activity might be an effective way to simultaneously downregulate Wnt and Notch signaling, thus blocking the growth of a broad range of cancer cells. We used a fluorescence polarization assay to screen for small molecules that disrupt the binding of MSI1 to its consensus RNA binding site. One of the top hits was (−)-gossypol (Ki = 476 ± 273 nM), a natural product from cottonseed, known to have potent anti-tumor activity and which has recently completed Phase IIb clinical trials for prostate cancer. Surface plasmon resonance and nuclear magnetic resonance studies demonstrate a direct interaction of (−)-gossypol with the RNA binding pocket of MSI1. We further showed that (−)-gossypol reduces Notch/Wnt signaling in several colon cancer cell lines having high levels of MSI1, with reduced SURVIVIN expression and increased apoptosis/autophagy. Finally, we showed that orally administered (−)-gossypol inhibits colon cancer growth in a mouse xenograft model. Our study identifies (−)-gossypol as a potential small molecule inhibitor of MSI1-RNA interaction, and suggests that inhibition of MSI1's RNA binding activity may be an effective anti-cancer strategy.
•(−)-Gossypol as the first identified natural small molecule inhibitor of the RNA-binding protein Musashi-1.•(−)-Gossypol binding to Musashi-1 directly.•(−)-Gossypol inhibits cell proliferation through MSI1 downstream targets.•(−)-Gossypol inhibits HCT-116 xenograft tumor growth.
The RNA-binding protein Hu antigen R (HuR) binds to AU-rich elements (ARE) in the 3'-untranslated region (UTR) of target mRNAs. The HuR-ARE interactions stabilize many oncogenic mRNAs that play ...important roles in tumorigenesis. Thus, small molecules that interfere with the HuR-ARE interaction could potentially inhibit cancer cell growth and progression. Using a fluorescence polarization (FP) competition assay, we identified the compound azaphilone-9 (AZA-9) derived from the fungal natural product asperbenzaldehyde, binds to HuR and inhibits HuR-ARE interaction (IC50 ~1.2 μM). Results from surface plasmon resonance (SPR) verified the direct binding of AZA-9 to HuR. NMR methods mapped the RNA-binding interface of HuR and identified the involvement of critical RNA-binding residues in binding of AZA-9. Computational docking was then used to propose a likely binding site for AZA-9 in the RNA-binding cleft of HuR. Our results show that AZA-9 blocks key RNA-binding residues of HuR and disrupts HuR-RNA interactions in vitro. This knowledge is needed in developing more potent AZA-9 derivatives that could lead to new cancer therapy.
Traditional hit-to-lead optimization assumes that upon elaboration of chemical structure, the ligand retains its binding mode relative to the receptor. Here, we build a large-scale collection of ...related ligand pairs solved in complex with the same protein partner: we find that for 41 of 297 pairs (14%), the binding mode changes upon elaboration of the smaller ligand. While certain ligand physiochemical properties predispose changes in binding mode, particularly those properties that define fragments, simple structure-based modeling proves far more effective for identifying substitutions that alter the binding mode. Some ligand pairs change binding mode because the added substituent would irreconcilably conflict with the receptor in the original pose, whereas others change because the added substituent enables new, stronger interactions that are available only in a different pose. Scaffolds that can engage their target using alternate poses may enable productive structure-based optimization along multiple divergent pathways.
Therapeutic monoclonal antibodies have transformed medicine, especially with regards to treating cancers and disorders of the immune system. More than 50 antibody-derived drugs have already reached ...the clinic, the majority of which target cytokines or cell-surface receptors. Unfortunately, many of these targets have pleiotropic functions: they serve multiple different roles, and often not all of these roles are disease-related. This can be problematic because antibodies act throughout the body, and systemic neutralization of such targets can lead to safety concerns. To address this, we have developed a strategy whereby an antibody’s ability to recognize its antigen is modulated by a second layer of control, relying on addition of an exogenous small molecule. In previous studies, we began to explore this idea by introducing a deactivating tryptophan-to-glycine mutation in the domain–domain interface of a single-chain variable fragment (scFv), and then restoring activity by adding back indole to fit the designed cavity. Here, we now describe a novel computational strategy for enumerating larger cavities that can be formed by simultaneously introducing multiple adjacent large-to-small mutations; we then carry out a complementary virtual screen to identify druglike compounds to match each candidate cavity. We first demonstrate the utility of this strategy in a fluorescein-binding single-chain variable fragment (scFv) and experimentally characterize a triple mutant with reduced antigen-binding (Rip-3) that can be rescued using a complementary ligand (Stitch-3). Because our design is built upon conserved residues in the antibody framework, we then show that the same mutation/ligand pair can also be used to modulate antigen-binding in an scFv build from a completely unrelated framework. This set of residues is present in many therapeutic antibodies as well, suggesting that this mutation/ligand pair may serve as a general starting point for introducing ligand-dependence into many clinically relevant antibodies.
PDF
