Biology subjects

Frank, A. T.

Publications and source records attributed to Frank, A. T..

9 recordsLinked to original sources

bioRxiv2022

N1-Methylpseudouridine and pseudouridine modifications modulate mRNA decoding during translation

The ribosome relies on hydrogen bonding interactions between mRNA codons and incoming aminoacyl-tRNAs to ensure rapid and accurate protein production. The inclusion of chemically modified bases into mRNAs has the potential to alter the strength and pattern of hydrogen bonding between mRNAs and aminoacyl-tRNAs to alter protein synthesis. We investigated how the Nl-methylpseudouridine (m1{Psi}) modification, commonly incorporated into therapeutic and vaccine mRNA sequences, influences the ability of codons to react with cognate and near-cognate tRNAs and release factors. We find that the presence of a single m1{Psi} does not substantially change the rate constants for amino acid addition by cognate tRNAs or termination by release factors. However, insertion of m1{Psi} can affect the selection of near-cognate tRNAs both in vitro and in human cells. Our observations demonstrate that m1{Psi}, and the related naturally occurring pseudouridine ({Psi}) modification, exhibit the ability to both increase and decrease the extent of amino acid misincorporation in a codon-position and tRNA dependent manner. To ascertain the chemical logic for our biochemical and cellular observations, we computationally modeled tRNAIle(GAU) bound to unmodified and m1{Psi}- or {Psi}-modified phenylalanine codons (UUU). Our modeling suggests that changes in the energetics of mRNA:tRNA interactions largely correlate with the context specificity of Ile-miscoding events we observe on {Psi} and m1{Psi} containing Phe codons. These studies reveal that the sequence context of a given modification within an mRNA plays a large role in determining how (and if) the modification impacts the number and distribution of proteoforms synthesized by the ribosome.

biochemistry↗

bioRxiv2022

AI-Generated Virtual Libraries Could Help Uncover RNA-Specific Regions of Chemical Space

RNAs can recognize small-molecule ligands. However, the extent to which the molecules that they recognize differ from those recognized by proteins remains an open question. Cheminformatics analysis of experimentally validated RNA binders strongly suggests that RNA binders occupy a specific region of chemical space. However, less than 100 validated small molecule ligands are currently known. Here, we demonstrate how structure-based approaches could be used to navigate vast regions of the chemical space specific to ligand binding sites in five highly-structured RNAs. Our method involves using generative-AI to design target- and site-specific virtual libraries and then analyzing them using similar cheminformatics approaches as those used to assess experimentally validated RNA binders. Despite employing a completely orthogonal strategy, our results essentially reproduce the trends observed by analyzing the experimentally validated RNA binders. Large-scale generation of target and site-specific libraries may therefore prove to be helpful in simultaneously mapping the regions of chemical space unique to RNA and generating libraries that could be mined to identify novel RNA binders. TOC IMAGE O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biophysics↗

bioRxiv2022

Profiling Unbinding Events to Identify Near-Native RNA-Ligand Poses

Predicting the structure (or pose) of RNA-ligand complexes is an important problem in RNA structural biology and drug discovery. Although one could use molecular docking procedures to rapidly sample putative poses of RNA-ligand complexes, accurately dis-criminating the native-like poses from non-native, decoy structures remains a formidable challenge. Here, we started from the assumption that native-like RNA-ligand poses are less likely to dissociate during molecular dynamics simulations, and then we used enhanced simulations to promote ligand for diverse poses of a handful of RNA-ligand complexes. By fitting unbinding profiles obtained from the simulations to a single-exponential, we identified the characteristic decay time ({tau}) as particularly effective at resolving native poses from decoys. Remarkably, a simple regression model trained to predict the simulation-derived parameters directly from structure could also discriminate poses. As such, when molecular dynamics simulations are feasible, one could use them to simulate ligand unbinding to aid in the identification of near-native poses. On the other hand, when speed is more important, one could use a simple structure-based regression model, like the ones described in this study, to analyze and filter RNA-ligand poses.

biophysics↗

bioRxiv2022

Using Unassigned NMR Chemical Shifts to Model RNA Secondary Structure

NMR-derived chemical shifts are sensitive probes of RNA structure. However, the need to assign NMR spectra hampers their utility as a direct source of structural information. In this report, we describe a simple method that uses unassigned 2D NMR spectra to model the secondary structure of RNAs. Similar to assigned chemical shifts, we could use unassigned chemical shift data to reweight conformational libraries such that the highest weighted structure closely resembles their reference NMR structure. Furthermore, the application of our approach to the 3- and 5-UTR of the SARS-CoV-2 genome yields structures that are, for the most part, consistent with the secondary structure models derived from chemical probing data. Therefore, we expect the framework we describe here will be useful as a general strategy for rapidly generating preliminary structural RNA models directly from unassigned 2D NMR spectra. As we demonstrated for the 337-nt and 472-nt UTRs of SARS-CoV-2, our approach could be especially valuable for modeling the secondary structures of large RNA.

biophysics↗

bioRxiv2020

Navigating Chemical Space By Interfacing Generative Artificial Intelligence and Molecular Docking

Here we report the testing and application of a simple, structure-aware framework to design target-specific screening libraries for drug development. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to rapidly explore chemical space conditioned on the unique physiochemical properties of the active site of a biomolecular target. As a proof-of-concept, we used our framework to construct a focused library for cyclin-dependent kinase type-2 (CDK2). We then used it to rapidly generate a library specific to the active site of the main protease (Mpro) of the SARS-CoV-2 virus, which causes COVID-19. By comparing approved and experimental drugs to compounds in our library, we also identified six drugs, namely, Naratriptan, Etryptamine, Panobinostat, Procainamide, Sertraline, and Lidamidine, as possible SARS-CoV-2 Mpro targeting compounds and, as such, potential drug repurposing candidates. To complement the open-science COVID-19 drug discovery initiatives, we make our SARS-CoV-2 Mpro library fully accessible to the research community (https://github.com/atfrank/SARS-CoV-2).

biophysics↗

bioRxiv2020

Structural Ensembles of Ribonucleic Acids From Solvent Accessibility Data: Application to the S-Adenosylmethionine (SAM)-Responsive Riboswitch

Riboswitches are regulatory ribonucleic acid (RNA) elements that act as ligand-dependent conformational switches. In the apo form, the aptamer domain, the region of a riboswitch that binds to its cognate ligand, is dynamic, thus requiring an ensemble-representation of its structure. Analysis of such ensembles can provide molecular insights into the sensing mechanism and capabilities of riboswitches. Here, as a proof-of-concept, we constructed a pair of atomistic ensembles of the well-studied S-adenosylmethionine (SAM)-responsive riboswitch in the absence (-SAM) and presence (+SAM) of SAM. To achieve this, we first generated a large conformational pool and then reweighted conformers in the pool using solvent accessible surface area (SASA) data derived from recently reported light-activated structural examination of RNA (LASER) reactivities, measured in the -SAM and +SAM states of the riboswitch. The differences in the resulting -SAM and +SAM ensembles are consistent with a SAM-dependent reshaping of the free landscape of the riboswitch. Interestingly, within the -SAM ensemble, we identified a conformer that harbors a hidden binding pocket, which was discovered using ensemble docking. The method we have applied to the SAM riboswitch is general, and could, therefore, be used to construct atomistic ensembles for other riboswitches, and more broadly, other classes of structured RNAs.

biophysics↗

bioRxiv2019

RNAPosers: Machine Learning Classifiers For RNA-Ligand Poses

Determining the 3-dimensional (3D) structures of ribonucleic acid (RNA)-small molecule complexes is critical to understanding molecular recognition in RNA. Computer docking can, in principle, be used to predict the 3D structure of RNA-small molecule complexes. Unfortunately, retrospective analysis has shown that the scoring functions that are typically used to rank poses tend to misclassify non-native poses as native, and vice versa. This misclassification of non-native poses severely limits the utility of computer docking in the context pose prediction, as well as in virtual screening. Here, we use machine learning to train a set of pose classifiers that estimate the relative \"nativeness\" of a set of RNA-ligand poses. At the heart of our approach is the use of a pose \"fingerprint\" that is a composite of a set of atomic fingerprints, which individually encode the local \"RNA environment\" around ligand atoms. We found that by ranking poses based on the classification scores from our machine learning classifiers, we were able to recover native-like poses better than when we ranked poses based on their docking scores. With a leave-one-out training and testing approach, we found that one of our classifiers could recover poses that were within 2.5 [A] of the native poses in [~]80% of the 88 cases we examined, and similarly, on a separate validation set, we could recover such poses in [~]70% of the cases. Our set of classifiers, which we refer to as RNAPosers, should find utility as a tool to aid in RNA-ligand pose prediction and so we make RNAPosers open to the academic community via https://github.com/atfrank/RNAPosers.

biophysics↗

bioRxiv2019

Conditional Prediction of RNA Secondary Structure Using NMR Chemical Shifts

Inspired by methods that utilize chemical-mapping data to guide secondary structure prediction, we sought to develop a framework for using assigned chemical shift data to guide RNA secondary structure prediction. We first used machine learning to develop classifiers which predict the base-pairing status of individual residues in an RNA based on their assigned chemical shifts. Then, we used these base-pairing status predictions as restraints to guide RNA folding algorithms. Our results showed that we could recover the correct secondary folds for nearly all of the 108 RNAs in our dataset with remarkable accuracy. Finally, we assessed whether we could conditionally predict the structure of the model RNA, microRNA-20b (miR-20b), by folding it using folding restraints derived from chemical shifts associated with two distinct conformational states, one a free (apo) state and the other a protein-bound (holo) state. For this test, we found that by using folding restraints derived from chemical shifts, we could recover the two distinct structures of the miR-20b, confirming our ability to conditionally predict its secondary structure. A command-line tool for Chemical Shifts to Base-Pairing Status (CS2BPS) predictions in RNA has been incorporated into our CS2Structure Git repository and can be accessed via: https://github.com/atfrank/CS2Structure.

biophysics↗

bioRxiv2019

Accelerating Dissociative Events in Molecular Dynamics Simulations by Selective Potential Scaling

Molecular dynamics (or MD) simulations can be a powerful tool for modeling complex dissociative processes such as ligand unbinding. However, many biologically relevant dissociative processes occur on timescales that far exceed the timescales of typical MD simulations. Here, we implement and apply an enhanced sampling method in which specific energy terms in the potential energy function are selectively "scaled" to accelerate dissociative events during simulations. Using ligand unbinding as an example of a complex dissociative process, we selectively scaled-up ligand-water interactions in an attempt to increase the rate of ligand unbinding. By applying our selectively scaled MD (or ssMD) approach to three cyclin-dependent kinase 2 (CDK2)-inhibitor complexes, we were able to significantly accelerate ligand unbinding thereby allowing, in some cases, unbinding events to occur within as little as 2 ns. Moreover, we found that we could make realistic estimates of the unbinding [Formula] as well as the binding free energies ({triangleup}Gsim) of the three inhibitors from our ssMD simulation data. To accomplish this, we employed a previously described Kramers-based rate extrapolation (KRE) method and a newly described free energy extrapolation (FEE) method. Because our ssMD approach is general, it should find utility as an easy-to-deploy, enhanced sampling method for modeling other dissociative processes.

biophysics↗