Biology subjects

Kappel, K.

Publications and source records attributed to Kappel, K..

8 recordsLinked to original sources

bioRxiv2022

DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design

Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities. Author summaryCompared with small-molecule drugs, aptamer drugs are short RNAs/DNAs that can specifically bind to targets with high strength. With the interest of discovering novel aptamer drugs as an alternative to address the long-lasting COVID-19 pandemic, in this research, we developed an artificial intelligence (AI) framework for the in silico design of novel aptamer drugs that can prevent the SARS-CoV-2 virus from entering human cells. Our research is valuable as we explore a novel approach for the treatment of SARS-CoV-2 infection and the AI framework could be applied to address future health crises.

bioinformatics↗

bioRxiv2019

Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis

Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs (dsRNAs). How the RNA sequence and structure (i.e., the cis-regulation) determine the editing efficiency and specificity is poorly understood, despite a compelling need towards functional understanding of known editing events and transcriptome engineering of desired adenosines. We developed a CRISPR/Cas9-mediated saturation mutagenesis approach to generate comprehensive libraries of point mutations near an editing site and its editing complementary sequence (ECS) at the endogenous genomic locus. We used machine learning to integrate diverse RNA sequence features and computationally predicted structures to model editing levels measured by deep sequencing and identified cis-regulatory features of RNA editing. As proof-of-concept, we applied this integrative approach to three editing substrates. Our models explained over 70% of variation in editing levels. The models indicate that RNA sequence and structure features synergistically determine the editing levels. Our integrative approach can be broadly applied to any editing site towards the goal of deciphering the RNA editing code. It also provides guidance for designing and screening of antisense RNA sequences that form dsRNA duplex with the target transcript for ADAR-mediated transcriptome engineering.

molecular biology↗

bioRxiv2019

Ribosolve: Rapid determination of three-dimensional RNA-only structures

The discovery and design of biologically important RNA molecules is dramatically outpacing three-dimensional structural characterization. To address this challenge, we present Ribosolve, a hybrid method integrating moderate-resolution cryo-EM maps, chemical mapping, and Rosetta computational modeling, and demonstrate its application to thirteen previously unknown 119-to 338-nucleotide protein-free RNA-only structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length V. cholerae and F. nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and computer-designed spinach-TTR-3, eterna3D-JR_1, and ATP-TTR-3 with and without AMP. Blind challenges, prospective compensatory mutagenesis, internal controls, and simulation benchmarks validate the Ribosolve models and establish that modeling convergence is quantitatively predictive of model accuracy. These results demonstrate that RNA-only 3D structure determination can be rapid and routine.

biophysics↗

bioRxiv2019

Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

Nearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features. To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms.\n\nSignificance statementRNA secondary structure prediction algorithms are routinely used to understand, predict and design functional RNA structures in biology and biotechnology. Given the vast number of RNA sequence and structural features, these predictions rely on a series of approximations, and independent tests are needed to quantitatively evaluate the accuracy of predicted RNA structural stabilities. Here we measure the stabilities of over 1000 RNA constructs by using a coupled protein binding assay. Our results reveal substantial deviations from the RNA stabilities predicted by popular algorithms, and identify factors contributing to the observed deviations. We demonstrate the importance of quantitative, experimental tests of computational RNA structure predictions and present an approach that can be used to routinely test and improve the prediction accuracy.

biophysics↗

bioRxiv2018

A quantitative and predictive model for RNA binding by human Pumilio proteins

High-throughput methodologies have enabled routine generation of RNA target sets and sequence motifs for RNA-binding proteins (RBPs). Nevertheless, quantitative approaches are needed to capture the landscape of RNA/RBP interactions responsible for cellular regulation. We have used the RNA-MaP platform to directly measure equilibrium binding for thousands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumilio proteins PUM1 and PUM2. Despite prior findings of linear sequence motifs, our measurements revealed widespread residue flipping and instances of positional coupling. Application of our thermodynamic model to published in vivo crosslinking data reveals quantitative agreement between predicted affinities and in vivo occupancies. Our analyses suggest a thermodynamically driven, continuous Pumilio binding landscape that is negligibly affected by RNA structure or kinetic factors, such as displacement by ribosomes. This work provides a quantitative foundation for dissecting the cellular behavior of RBPs and cellular features that impact their occupancies.

biochemistry↗

bioRxiv2018

Sampling native-like structures of RNA-protein complexes through Rosetta folding and docking

RNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe a Rosetta method called RNP-denovo to simultaneously fold and dock RNA to a protein surface. On a benchmark set of structurally diverse RNA-protein complexes that are not solvable with prior strategies, this fold-and-dock method consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges in which previous methods gave poor results: human telomerase, an RNA methyltransferase with a ribosomal RNA domain, and the spliceosome. When coupled with the same sparse FRET, cross-linking, and functional data used in previous work, RNP-denovo gave models with significantly improved accuracy. These results open a route to computationally modeling global folds of RNA-protein complexes from low-resolution data.

biophysics↗

bioRxiv2018

De novo computational RNA modeling into cryoEM maps of large ribonucleoprotein complexes

RNA-protein assemblies carry out many critical biological functions including translation, RNA splicing, and telomere extension. Increasingly, cryo-electron microscopy (cryoEM) is used to determine the structures of these complexes, but nearly all maps determined with this method have regions in which the local resolution does not permit manual coordinate tracing. Because RNA coordinates typically cannot be determined by docking crystal structures of separate components and existing structure prediction algorithms cannot yet model RNA-protein complexes, RNA coordinates are frequently omitted from final models despite their biological importance. To address these omissions, we have developed a new framework for De novo Ribonucleoprotein modeling in Real-space through Assembly of Fragments Together with Electron density in Rosetta (DRRAFTER). We show that DRRAFTER recovers near-native models for a diverse benchmark set of small RNA-protein complexes, as well as for large RNA-protein machines, including the spliceosome, mitochondrial ribosome, and CRISPR-Cas9-sgRNA complexes where the availability of both high and low resolution maps enable rigorous tests. Blind tests on yeast U1 snRNP and spliceosomal P complex maps demonstrate that the method can successfully build RNA coordinates in real-world modeling scenarios. Additionally, to aid in final model interpretation, we present a method for reliable in situ estimation of DRRAFTER model accuracy. Finally, we apply this method to recently determined maps of telomerase, the HIV-1 reverse transcriptase initiation complex, and the packaged MS2 genome, demonstrating that DRRAFTER can be used to accelerate accurate model building in challenging cases.

biophysics↗

bioRxiv2017

The Rosetta all-atom energy function for macromolecular modeling and design

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosettas success is the energy function: amodel parameterized from small molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, beta_nov15. Applying these concepts,we explain how to use Rosetta energies to identify and analyze the features of biomolecular models.Finally, we discuss the latest advances in the energy function that extend capabilities from soluble proteins to also include membrane proteins, peptides containing non-canonical amino acids, carbohydrates, nucleic acids, and other macromolecules.

biophysics↗