Biology subjects

Magalhaes, C.

Publications and source records attributed to Magalhaes, C..

4 recordsLinked to original sources

bioRxiv2024

Accelerated enzyme engineering by machine-learning guided cell-free expression

Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we developed a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions. We applied this platform to engineer amide synthetases by evaluating substrate preference for 1,217 enzyme variants in 10,953 unique reactions. We used these data to build augmented ridge regression ML models for predicting amide synthetase variants capable of making 9 small molecule pharmaceuticals. Our ML-guided, cell-free framework promises to accelerate enzyme engineering by enabling iterative exploration of protein sequence space to build specialized biocatalysts in parallel.

synthetic biology↗

bioRxiv2024

Environmental DNA as a complementary tool for biodiversity monitoring: A multi-technique and multi-trophic approach to investigate cetacean distribution and feeding ecology

The use of environmental DNA (eDNA) to assess the presence of biological communities has emerged as a promising monitoring tool in the marine conservation landscape. Moreover, advances in Next-Generation Sequencing techniques, such as DNA metabarcoding, enable multi-species detection in mixed samples, allowing the study of complex ecosystems such as oceanic ones. We aimed at using these molecular-based techniques to characterise cetacean communities, as well as potential prey in the northern coast of Mainland Portugal. During seasonal campaigns, we collected seawater samples, along with visual records of cetacean occurrence. The eDNA extracted from 64 environmental samples was sequenced in an Illumina platform, with universal primers targeting marine vertebrates. Five cetacean species were identified by molecular detection: common dolphin (Delphinus delphis), bottlenose dolphin (Tursiops truncatus), Rissos dolphin (Grampus griseus), harbour porpoise (Phocoena phocoena) and fin whale (Balaenoptera physalus). Overall, except for the fin whale (not sighted during the campaigns), this cetacean community composition was similar to that obtained through visual monitoring, and the complementary results suggest their presence in the region all year round. In addition, the positive molecular detections of B. physalus are of special relevance since there are no visual records reported in the area. The detection of multiple known preys of the identified dolphins indicates they use these coastal areas for feeding purposes. While this methodological approach remains in a development stage, the present work highlights the benefits of using eDNA to study marine communities, with specific applications for research on cetacean distribution and feeding ecology, ultimately serving as the baseline of a methodological approach for biodiversity monitoring and marine conservation.

ecology↗

bioRxiv2020

Copper nanoparticles prompt the activity of estuarine denitrifying bacterial communities at relevant environmental concentrations

Effects of metallic nanoparticles (NPs) to the estuarine biota have mostly been shown for concentrations higher than those actually measured or predicted in these environments. To address this gap, a range of concentrations expected to occur in estuarine environments (from 0.01 to 1 μg g-1) was employed in microcosms studies to assess the impact of Cu NPs in the denitrification pathway. That was achieved by quantifying gene expression and the potential denitrification rate in estuarine sediments exposed to Cu NPs for up to six days. Expression of nitrite (nirS) and nitrous oxide (nosZ) reductase genes was enhanced in a timewise manner. For the highest Cu NPs (1 μg g-1) an increase in gene expression could be seen immediately after 1 h of exposure, and continuing to be enhanced up until 7 h of exposure. For the lowest Cu NPs (0.01 μg g-1) an increase in gene expression could only be seen after 4 h or 7 h of exposure; however it continued to rise up until 24 h of exposure. In any case, after 48 h the expression levels were no longer different from the non-exposed control. Concomitantly to increased gene expression the potential denitrification rate was increased by 30 %. Our results suggest that deposition and adsorption of Cu NPs to estuarine sediments promotes the immediate and transient expression of key genes of the denitrification pathway. The long term impact of continuous inputs of Cu NPs into estuaries deserves renewed analysis to account for their effects, not just on the biota, but especially on ecosystems services.Environmental significance Interactions of metallic nanoparticles with microbial communities of estuarine sediments are poorly characterized and its impact towards ecosystem services even less. By assessing the effect of copper nanoparticles on the expression of key genes of the denitrification pathway, an essential step for nitrogen (N) removal, we were able to show that denitrifying communities are immediately activated after exposure, increasing the denitrification rates in estuaries. The importance of denitrification lies in its release of dinitrogen (N2) to the atmosphere but also in the emissions of N2O (a potent greenhouse gas). The results obtained in this study gather data that contribute information on the denitrification dynamics in estuaries, invaluable for a timely response to the expected upcoming changes in coastal areas.Table of contentsIn estuaries the deposition upon the sediments of copper nanoparticles can contribute to change metal availability and promote the activity of denitrifying bacteriaCompeting Interest StatementThe authors have declared no competing interest.View Full Text

microbiology↗

bioRxiv2019

HABIT - a webserver for interactive T cell neoepitope discovery

Neoepitopes generated by amino acid variants specifically found in pathogens or cancer cells are gaining momentum in immunotherapy development. HABIT (HLA Binding InTelligence) is a web platform designed to generate and analyse machine learning-based T cell epitope predictions for improved neoepitope discovery. Availability and ImplementationHABIT is available at http://habit.evobiomed.com. Peptide-HLA binding predictions by NetMHCpan 4.0 and NetMHCIIpan 3.1 were implemented in a web application for interactive data exploration using shiny package powered by RStudio. Contactnosorio@med.uminho.pt Supplementary informationSupplementary data and video tutorials are available online.

bioinformatics↗