Biology subjects

Barazandeh, S.

Publications and source records attributed to Barazandeh, S..

3 recordsLinked to original sources

bioRxiv2025

RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation

Protein-RNA interactions are essential in gene regulation, splicing, RNA stability, and translation, making RNA a promising therapeutic agent for targeting proteins, including those considered undruggable. However, designing RNA sequences that selectively bind to proteins remains a significant challenge due to the vast sequence space and limitations of current experimental and computational methods. Traditional approaches rely on in vitro selection techniques or computational models that require post-generation optimization, restricting their applicability to well-characterized proteins. We introduce RNAtranslator, a generative language model that formulates protein-conditional RNA design as a sequence-to-sequence natural language translation problem for the first time. By learning a joint representation of RNA and protein interactions from large-scale datasets, RNAtranslator directly generates binding RNA sequences for any given protein target without the need for additional optimization. Our results demonstrate that RNAtranslator produces RNA sequences with natural-like properties, high novelty, and enhanced binding affinity compared to existing methods. This approach enables efficient RNA design for a wide range of proteins, paving the way for new RNA-based therapeutics and synthetic biology applications. The model and the code is released at github.com/ciceklab/RNAtranslator.

bioinformatics↗

bioRxiv2023

RNAGEN: A generative adversarial network-based model to generate synthetic RNA sequences to target proteins

RNA - protein binding plays an important role in regulating protein activity by affecting localization and stability. While proteins are usually targeted via small molecules or other proteins, easy-to-design and synthesize small RNAs are a rather unexplored and promising venue. The problem is the lack of methods to generate RNA molecules that have the potential to bind to certain proteins. Here, we propose a method based on generative adversarial networks (GAN) that learn to generate short RNA sequences with natural RNA-like properties such as secondary structure and free energy. Using an optimization technique, we fine-tune these sequences to have them bind to a target protein. We use RNA-protein binding prediction models from the literature to guide the model. We show that even if there is no available guide model trained specifically for the target protein, we can use models trained for similar proteins, such as proteins from the same family, to successfully generate a binding RNA molecule to the target protein. Using this approach, we generated piRNAs that are tailored to bind to SOX2 protein using models trained for its relative (SOX10, SOX14, and SOX8) and experimentally validated in vitro that the top-2 molecules we generated specifically bind to SOX2.

bioinformatics↗

bioRxiv2023

Learning to Generate 5' UTR Sequences for Optimized Ribosome Load and Gene Expression

The 5 untranslated region (5 UTR) of mRNA is crucial for the molecules translatability and stability, making it essential for designing synthetic biological circuits for high and stable protein expression. Several UTR sequences are patented and widely used in laboratories. This paper presents UTRGAN, a Generative Adversarial Network (GAN)-based model for generating 5 UTR sequences, coupled with an optimization procedure to ensure high expression for target gene sequences or high ribosome load and translation efficiency. The model generates sequences mimicking various properties of natural UTR sequences and optimizes them to achieve (i) up to 5-fold higher average expression on target genes, (ii) up to 2-fold higher mean ribosome load, and (iii) a 34-fold higher average translation efficiency compared to initial UTR sequences. UTRGAN-generated sequences also exhibit higher similarity to known regulatory motifs in regions such as internal ribosome entry sites, upstream open reading frames, G-quadruplexes, and Kozak and initiation start codon regions. In-vitro experiments show that the UTR sequences designed by UTRGAN result in a higher translation rate for the human TNF- protein compared to the human Beta Globin 5 UTR, a UTR with high production capacity.

bioinformatics↗