Biology subjects

Portela, F.

Publications and source records attributed to Portela, F..

4 recordsLinked to original sources

bioRxiv2024

Compact RNA sensors for increasingly complex functions of multiple inputs

Designing single molecules that compute general functions of input molecular partners represents a major unsolved challenge in molecular design. Here, we demonstrate that high-throughput, iterative experimental testing of diverse RNA designs crowdsourced from Eterna yields sensors of increasingly complex functions of input oligonucleotide concentrations. After designing single-input RNA sensors with activation ratios beyond our detection limits, we created logic gates, including challenging XOR and XNOR gates, and sensors that respond to the ratio of two inputs. Finally, we describe the OpenTB challenge, which elicited 85-nucleotide sensors that compute a score for diagnosing active tuberculosis, based on the ratio of products of three gene segments. Building on OpenTB design strategies, we created an algorithm Nucleologic that produces similarly compact sensors for the three-gene score based on RNA and DNA. These results open new avenues for diverse applications of compact, single molecule sensors previously limited by design complexity.

synthetic biology↗

bioRxiv2019

Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular sensors

Internet-based scientific communities promise a means to apply distributed, diverse human intelligence towards previously intractable scientific problems. However, current implementations have not allowed communities to propose experiments to test all emerging hypotheses at scale or to modify hypotheses in response to experiments. We report high-throughput methods for molecular characterization of nucleic acids that enable the large-scale videogame-based crowdsourcing of functional RNA sensor design, followed by high-throughput functional characterization. Iterative design testing of thousands of crowdsourced RNA sensor designs produced near-thermodynamically optimal and reversible RNA switches that act as self-contained molecular sensors and couple five distinct small molecule inputs to three distinct protein binding and fluorogenic outputs--results that surpass computational and expert-based design. This work represents a new paradigm for widely distributed experimental bioscience. One Sentence SummaryOnline community discovers standalone RNA sensors.

synthetic biology↗

bioRxiv2018

An unexpectedly effective Monte Carlo technique for the RNA inverse folding problem

Solving the RNA inverse folding problem, also known as the RNA design problem, is critical to advance several scientific fields like bioengineering, yet existing approaches have had limited success. The problem has several features that resist traditional computational techniques, such as its exponential complexity and the chaotic behavior of its cost function. Although some state-of-the-art AI approaches have reported promising results, all existing computational methods substantially underperform expert human designers. I combine a different technique, Nested Monte Carlo Search (NMCS), with domain-specific knowledge to create an algorithm that outperforms all prior published methods by wide margins and solves 95 of the 100 puzzles listed in a recently proposed RNA solving difficulty benchmark.

bioinformatics↗

bioRxiv2018

EternaBrain: Automated RNA design through move sets from an Internet-scale RNA videogame

Emerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these secondary structure design tasks. The Eterna project has crowdsourced RNA design to human video game players in the form of puzzles that reach extraordinary difficulty. Here, we present an eternamoves-large repository consisting of 1.8 million of player moves on 12 of the most-played Eterna puzzles as well as an eternamoves-select repository of 30,477 moves from the top 72 players on a select set of more advanced puzzles. On eternamoves-select, a multilayer convolutional neural network (CNN) EternaBrain achieves test accuracies of 51% and 34% in base prediction and location prediction, respectively, suggesting that top players moves are partially stereotyped. We then show that while this CNNs move predictions are not enough to solve numerous new puzzles, inclusion of six additional strategies compiled by human players solves 61 out of 100 independent puzzles in the Eterna100 benchmark. This EternaBrain-SAP performance is better than previously published methods and in the middle of the performance range of newer algorithms developed by Eterna participants and other groups. Our study provides useful lessons for efforts to achieve human-competitive performance with automated RNA design algorithms.

bioinformatics↗