Wipapat Kladwang

FAST-MaP provides a protocol for per-nucleotide RNA structure characterization using chemical modification and primer-less sequencing without requiring sequencing infrastructure or bioinformatics expertise.

This paper demonstrates that deep learning can accurately design RNA pseudoknots, achieving results comparable to human experts in a competition.

The paper designs compact RNA sensors for complex functions of multiple inputs using crowdsourced designs and an algorithm, achieving applications like tuberculosis diagnosis.

This study develops PERSIST-seq and In-line-seq to optimize mRNA stability and translation, finding that in-cell stability is more critical than ribosome load for protein output.

A crowdsourced RNA design approach discovers efficient, reversible molecular sensors that couple small molecule inputs to protein binding and fluorogenic outputs.

This paper evaluates a recurrent neural network model, Ribonet, for learning RNA biophysics from high-throughput data, showing it can predict RNA interactions and design functional RNAs but requires more diverse datasets for accurate biophysical modeling.

The paper introduces RNAMake, a tool for designing asymmetric RNA structures, validated through experimental methods and demonstrating successful creation of complex 3D RNA folds and synthetic ribosomes.

This paper presents RECCES-Rosetta, a computational framework that accurately predicts RNA nearest neighbor energy parameters, including non-standard nucleotides, with RMSD below 1 kcal/mol in blind tests.

MOHCA-seq provides nucleotide-resolution RNA 3D structure models from single experiments, revealing new insights into ligand-induced conformational changes in riboswitches.

The paper corrects an erroneous SHAPE-directed RNA structure inference using a mutate-map-rescue approach and identifies a functional conformational state in 16S rRNA.