Aaron T. Frank

Discovery of selective bioactive small molecules by targeting an RNA dynamic ensemble

Current approaches used to identify protein-binding small molecules are not suited for identifying small molecules that can bind emerging RNA drug targets. By docking small molecules onto an RNA dynamic ensemble constructed by combining NMR spectroscopy and computational molecular dynamics, we virtually screened small molecules that target the entire structure landscape of the transactivation response element (TAR) from HIV type 1 (HIV-1). We quantitatively predict binding energies for small molecules that bind different RNA conformations and report the de novo discovery of six compounds that bind TAR with high affinity and inhibit its interaction with a Tat peptide in vitro (K(i) values of 710 nM-169 μM). One compound binds HIV-1 TAR with marked selectivity and inhibits Tat-mediated activation of the HIV-1 long terminal repeat by 81% in T-cell lines and HIV replication in an HIV-1 indicator cell line (IC(50) ∼23.1 μM).

Constructing RNA dynamical ensembles by combining MD and motionally decoupled NMR RDCs: new insights into RNA dynamics and adaptive ligand recognition

We describe a strategy for constructing atomic resolution dynamical ensembles of RNA molecules, spanning up to millisecond timescales, that combines molecular dynamics (MD) simulations with NMR residual dipolar couplings (RDC) measured in elongated RNA. The ensembles are generated via a Monte Carlo procedure by selecting snap-shot from an MD trajectory that reproduce experimentally measured RDCs. Using this approach, we construct ensembles for two variants of the transactivation response element (TAR) containing three (HIV-1) and two (HIV-2) nucleotide bulges. The HIV-1 TAR ensemble reveals significant mobility in bulge residues C24 and U25 and to a lesser extent U23 and neighboring helical residue A22 that give rise to large amplitude spatially correlated twisting and bending helical motions. Omission of bulge residue C24 in HIV-2 TAR leads to a significant reduction in both the local mobility in and around the bulge and amplitude of inter-helical bending motions. In contrast, twisting motions of the helices remain comparable in amplitude to HIV-1 TAR and spatial correlations between them increase significantly. Comparison of the HIV-1 TAR dynamical ensemble and ligand bound TAR conformations reveals that several features of the binding pocket and global conformation are dynamically preformed, providing support for adaptive recognition via a ‘conformational selection’ type mechanism.

Structure and Function of Glycosylated Tandem Repeats from Candida albicans Als Adhesins

ABSTRACT Tandem repeat (TR) regions are common in yeast adhesins, but their structures are unknown, and their activities are poorly understood. TR regions in Candida albicans Als proteins are conserved glycosylated 36-residue sequences with cell-cell aggregation activity (J. M. Rauceo, R. De Armond, H. Otoo, P. C. Kahn, S. A. Klotz, N. K. Gaur, and P. N. Lipke, Eukaryot. Cell 5:1664–1673, 2006). Ab initio modeling with either Rosetta or LINUS generated consistent structures of three-stranded antiparallel β-sheet domains, whereas randomly shuffled sequences with the same composition generated various structures with consistently higher energies. O- and N-glycosylation patterns showed that each TR domain had exposed hydrophobic surfaces surrounded by glycosylation sites. These structures are consistent with domain dimensions and stability measurements by atomic force microscopy (D. Alsteen, V. Dupres, S. A. Klotz, N. K. Gaur, P. N. Lipke, and Y. F. Dufrene, ACS Nano 3:1677–1682, 2009) and with circular dichroism determination of secondary structure and thermal stability. Functional assays showed that the hydrophobic surfaces of TR domains supported binding to polystyrene surfaces and other TR domains, leading to nonsaturable homophilic binding. The domain structures are like “classic” subunit interaction surfaces and can explain previously observed patterns of promiscuous interactions between TR domains in any Als proteins or between TR domains and surfaces of other proteins. Together, the modeling techniques and the supporting data lead to an approach that relates structure and function in many kinds of repeat domains in fungal adhesins.

Utility of 1H NMR Chemical Shifts in Determining RNA Structure and Dynamics

The development of methods for predicting NMR chemical shifts with high accuracy and speed is increasingly allowing use of these abundant, readily accessible measurements in determining the structure and dynamics of proteins. For nucleic acids, however, despite the availability of semiempirical methods for predicting (1)H chemical shifts, their use in determining the structure and dynamics has not yet been examined. Here, we show that (1)H chemical shifts offer powerful restraints for RNA structure determination, allowing discrimination of native structure from non-native states to within 2-4 Å, and <3 Å when highly flexible residues are ignored. Theoretical simulations shows that although (1)H chemical shifts can provide valuable information for constructing RNA dynamic ensembles, large uncertainties in the chemical shift predictions and inherent degeneracies lead to higher uncertainties as compared to residual dipolar couplings.

Constructing Atomic-Resolution RNA Structural Ensembles Using MD and Motionally Decoupled NMR RDCs

A broad structural landscape often needs to be characterized in order to fully understand how regulatory RNAs perform their biological functions at the atomic level. We present a protocol for visualizing thermally accessible RNA conformations at atomic-resolution and with timescales extending up to milliseconds. The protocol combines molecular dynamics (MD) simulations with experimental residual dipolar couplings (RDCs) measured in partially aligned (13)C/(15)N isotopically enriched elongated RNA samples. The structural ensembles generated in this manner provide insights into RNA dynamics and its role in functionally important transitions.

Prediction of RNA 1H and 13C Chemical Shifts: A Structure Based Approach

The use of NMR-derived chemical shifts in protein structure determination and prediction has received much attention, and, as such, many methods have been developed to predict protein chemical shifts from three-dimensional (3D) coordinates. In contrast, little attention has been paid to predicting chemical shifts from RNA coordinates. Using the random forest machine learning approach, we developed RAMSEY, which is capable of predicting both (1)H and protonated (13)C chemical shifts from RNA coordinates. In this report, we introduce RAMSEY, assess its accuracy, and demonstrate the sensitivity of RAMSEY-predicted chemical shifts to RNA 3D structure.

PCASSO: A fast and efficient Cα‐based method for accurately assigning protein secondary structure elements

Proteins are often characterized in terms of their primary, secondary, tertiary, and quaternary structure. Algorithms such as define secondary structure of proteins (DSSP) can automatically assign protein secondary structure based on the backbone hydrogen‐bonding pattern. However, the assignment of secondary structure elements (SSEs) becomes a challenge when only the Cα coordinates are available. In this work, we present protein C‐alpha secondary structure output (PCASSO), a fast and accurate program for assigning protein SSEs using only the Cα positions. PCASSO achieves ∼95% accuracy with respect to DSSP and takes ∼0.1 s using a single processor to analyze a 1000 residue system with multiple chains. Our approach was compared with current state‐of‐the‐art Cα‐based methods and was found to outperform all of them in both speed and accuracy. A practical application is also presented and discussed.

A Simple and Fast Approach for Predicting 1H and 13C Chemical Shifts: Toward Chemical Shift-Guided Simulations of RNA

We introduce a simple and fast approach for predicting RNA chemical shifts from interatomic distances that performs with an accuracy similar to existing predictors and enables the first chemical shift-restrained simulations of RNA to be carried out. Our analysis demonstrates that the applied restraints can effectively guide conformational sampling toward regions of space that are more consistent with chemical shifts than the initial coordinates used for the simulations. As such, our approach should be widely applicable in mapping the conformational landscape of RNAs via chemical shift-guided molecular dynamics simulations. The simplicity and demonstrated sensitivity to three-dimensional structure should also allow our method to be used in chemical shift-based RNA structure prediction, validation, and refinement.

Coming to Grips with Ambiguity: Ion Mobility-Mass Spectrometry for Protein Quaternary Structure Assignment

AbstractMultiprotein complexes are central to our understanding of cellular biology, as they play critical roles in nearly every biological process. Despite many impressive advances associated with structural characterization techniques, large and highly-dynamic protein complexes are too often refractory to analysis by conventional, high-resolution approaches. To fill this gap, ion mobility-mass spectrometry (IM-MS) methods have emerged as a promising approach for characterizing the structures of challenging assemblies due in large part to the ability of these methods to characterize the composition, connectivity, and topology of large, labile complexes. In this Critical Insight, we present a series of bioinformatics studies aimed at assessing the information content of IM-MS datasets for building models of multiprotein structure. Our computational data highlights the limits of current coarse-graining approaches, and compelled us to develop an improved workflow for multiprotein topology modeling, which we benchmark against a subset of the multiprotein complexes within the PDB. This improved workflow has allowed us to ascertain both the minimal experimental restraint sets required for generation of high-confidence multiprotein topologies, and quantify the ambiguity in models where insufficient IM-MS information is available. We conclude by projecting the future of IM-MS in the context of protein quaternary structure assignment, where we predict that a more complete knowledge of the ultimate information content and ambiguity within such models will undoubtedly lead to applications for a broader array of challenging biomolecular assemblies. Graphical Abstractᅟ

Predicting protein backbone chemical shifts from Cα coordinates: extracting high resolution experimental observables from low resolution models.

Given the demonstrated utility of coarse-grained modeling and simulations approaches in studying protein structure and dynamics, developing methods that allow experimental observables to be directly recovered from coarse-grained models is of great importance. In this work, we develop one such method that enables protein backbone chemical shifts (1HN, 1Hα, 13Cα, 13C, 13Cβ, and 15N) to be predicted from Cα coordinates. We show that our Cα-based method, LARMORCα, predicts backbone chemical shifts with comparable accuracy to some all-atom approaches. More importantly, we demonstrate that LARMORCα predicted chemical shifts are able to resolve native structure from decoy pools that contain both native and non-native models, and so it is sensitive to protein structure. As an application, we use LARMORCα to characterize the transient state of the fast-folding protein gpW using recently published NMR relaxation dispersion derived backbone chemical shifts. The model we obtain is consistent with the previously proposed model based on independent analysis of the chemical shift dispersion pattern of the transient state. We anticipate that LARMORCα will find utility as a tool that enables important protein conformational substates to be identified by “parsing” trajectories and ensembles generated using coarse-grained modeling and simulations.