/tools

antaRNA is a tool designed for RNA sequence design using ant-colony optimization techniques. It allows for the modeling of bistable RNA molecules and simulates ligand interactions by applying structural constraints.

This repository contains data and methods for predicting the binding of small molecule ligands to RNA using structural interaction fingerprints and machine learning techniques. It includes datasets for active and inactive ligands, docking poses, and results from various machine learning models, making it a valuable resource for virtual screening in drug discovery.

FRETlabel is a PyMOL plugin designed for labeling nucleic acids with explicit fluorescent dyes, enhancing the setup and evaluation of molecular dynamics simulations. It includes features for building new fragments and calculating FRET distributions from simulations, making it a valuable tool for researchers in molecular biology and computational chemistry.

The proto-Nucleic Acid Builder (pNAB) is a software tool designed for modeling the 3D structures of nucleic acids, including DNA and RNA. It allows for the construction of nucleic acids with alternative backbones or nucleobases and evaluates candidate structures based on geometric and energetic criteria.

RNA-Benchmark is a repository that contains a dataset of RNA and RNA/Protein sequences and structures used for benchmarking various computational models in predicting RNA structures. It includes detailed filtering criteria and multiple sequence alignments to ensure the quality of the dataset for model evaluation.

UNISIS is a software tool designed for coarse-grained molecular dynamics simulations using the Single-Interaction-Site RNA model. It allows researchers to simulate RNA folding and dynamics, contributing to the understanding of molecular behavior in biological systems.

PRA-Pred is a tool designed to predict the binding affinity of protein-RNA complexes. Users can input either a PDB ID or a PDB file to obtain predictions related to the binding affinity and dissociation constant.

OpenKnotAIDesignData provides chemical mapping data for RNA sequences generated from the OpenKnot challenge. This dataset includes various metrics related to RNA design, such as reactivity profiles and structural predictions, making it useful for researchers in molecular biology and RNA design.

This tool implements a self-play reinforcement learning approach to design RNA sequences that match a given target structure. It utilizes a deep value network for state-value evaluation and has shown competitive results on RNA design benchmarks.

The rstudiopymolpysnips library contains Python code snippets designed to enhance productivity when using PyMOL in RStudio. It facilitates the visualization and manipulation of molecular structures, particularly proteins and nucleic acids, by allowing users to run PyMOL commands directly from RStudio.

The espfit repository provides infrastructure to train the espaloma model with quantum chemical data and condensed phase properties specifically for RNA systems. It includes functionalities for molecular dynamics simulations and the computation of RNA J-couplings from trajectories.

OpenKnotScorePipeline is a Python tool designed to generate OpenKnot Scores for Eterna sequence libraries, facilitating the prediction of molecular properties related to RNA sequences. It provides a structured pipeline for running predictions and scoring, making it useful for researchers in molecular biology.

aRNAque is an evolutionary algorithm designed for the inverse folding of RNA secondary structures. It generates RNA sequences that correspond to a specified target structure, utilizing local mutations and evolutionary strategies.

The Gillespie mRNA Simulator is a tool that models mRNA transcription and translation processes using the Doob-Gillespie algorithm. It allows users to simulate the dynamics of dual reporter systems, providing insights into molecular behavior in biological systems.

RNAInverse is a tool aimed at the inverse folding of RNA secondary structures, which involves predicting the sequence of RNA that will fold into a given structure. This process is crucial for understanding RNA function and designing RNA molecules for various applications.

This repository serves as a workspace for testing and experimenting with the espfit tool, which refits the espaloma model using quantum chemistry data and NMR observables to simulate RNA systems. It includes various experiments and scripts for benchmarking protein-ligand binding free energy calculations and RNA nucleoside simulations.

BMF (Bipartite Motif Finder) is an open-source tool designed for de novo discovery of bipartite RNA motifs in genomic sequences. It allows users to predict binding scores for new sequences based on trained models, making it useful for molecular biology applications.

Dual-RAG-IF is a tool that finds RNA sequences with minimal mutations to achieve a target 2D fold using dual graph representations. It employs a genetic algorithm to generate candidate sequences and optimize them based on their folding properties.

Biopython is a collection of Python tools for computational molecular biology, enabling users to work with biological data, including sequences and structures of proteins, DNA, and RNA. It supports various functionalities that are essential for bioinformatics applications.

The rna-3d-folding repository aims to predict the 3D structures of RNA molecules from their sequences using machine learning techniques. It is part of a Kaggle competition and utilizes a custom metric for evaluation, contributing to advancements in understanding RNA's biological roles.

This repository provides tools and datasets for evaluating ligand-based similarity metrics and molecular fingerprints specifically for RNA-targeted small molecule discovery. It includes scripts for molecular docking and statistical analyses of various methods used in the context of nucleic acid binders.

GALS-Fold is a deep learning framework designed for RNA inverse folding, enabling the generation of RNA sequences that fold into specified 3D structures. It utilizes a novel architecture to efficiently capture both local and long-range interactions, making it suitable for designing RNA molecules.

RelRNA is a framework designed to solve the RNA inverse folding problem by generating RNA sequences that correspond to desired secondary structures. It utilizes reinforcement learning to model and produce RNA structures based on a corpus of sample structures.

The rna-evolution repository involves a population of RNA sequences to efficiently solve the problem of inverse RNA folding. This tool is aimed at understanding and designing RNA structures, making it relevant to molecular biology and computational chemistry.