Biology subjects

Vieira, L. C.

Publications and source records attributed to Vieira, L. C..

7 recordsLinked to original sources

bioRxiv2026

Intrinsic dataset features drive mutational effect prediction by protein language models

Protein language models (pLMs) are commonly used for predicting protein fitness landscapes, but their wide range of performance across datasets remains poorly understood. We evaluated supervised transfer learning on 41 viral and 33 cellular deep-mutational-scanning (DMS) datasets using embeddings from multiple pLMs. We observed consistently lower predictive performance on viral datasets compared to cellular datasets, independent of model architecture or transfer learning strategy. Surprisingly, a simple baseline model that predicted site mean fitness matched or outperformed supervised models on many datasets, highlighting the dominant role of site effects. Analysis of site variability using two metrics, relative variability of site means (RVSM) and fraction of highly variable sites (FHVS), revealed that patterns of fitness variation within and among sites constrain model performance and largely explain the observed differences between viral and cellular datasets. Moreover, splitting training and test data by site, rather than pooling, revealed that supervised models often rely on site effects rather than capturing broader mutational patterns. These findings highlight limitations of current pLMs for mutational effect prediction and suggest that dataset composition, rather than model architecture or training, is the primary driver of predictive success. Significance StatementMutational effects prediction with protein language models tends to vary widely in prediction accuracy, depending on the dataset considered. While poor performance is commonly equated with poor model quality, we show here that intrinsic dataset features, such as the variability of fitness values within and among sites, are critical predictors of model performance. Moreover, we show that many existing benchmarks overestimate model performance, by allowing training data to leak into the test set. In fact, in many cases, protein language models barely outperform a naive predictor relying entirely on mean fitness values at individual sites. In aggregate, our study reveals that protein language models are not as reliable for predicting mutational effects as is commonly thought.

bioinformatics↗

bioRxiv2025

A fold switch regulates conformation of an alphavirus virus RNA-dependent RNA polymerase

Alphaviruses are mosquito-vectored, positive-strand RNA viruses causing rheumatic and neurological diseases. Like all RNA viruses, they encode an RNA-dependent RNA polymerase (RdRp, nsP4). Purification of an nsP4 derivative capable of processive RNA synthesis from a heteropolymeric template has been unsuccessful. Prior studies indicated Onyong-nyong virus (ONNV) nsP4 is soluble and requires additional non-structural proteins for activity. We performed biochemical and biophysical characterization of ONNV nsP4, including analytical ultracentrifugation and small-angle X-ray scattering (SAXS), revealing an extended conformation inconsistent with AlphaFold predictions of a compact structure. Fold switching was required for the extended conformation. Hydrogen-deuterium exchange mass spectrometry confirmed the fold-switched, extended state. Phylogenetic analysis showed conservation of residues contributing to both extended and compact states, implying functional roles for each. The extended form exhibited weak RNA binding and no polymerase activity on primed templates. The SAXS envelope of a precursor containing 50 amino acids from the nsP3 C-terminus (CT50-P34) matched the compact state. We propose precursor forms adopt the compact conformation. At the replication site, proteolytic cleavage would convert the precursor to an active polymerase. Polymerase dissociation upon completion of synthesis would induce fold switching to the inactive, extended state, precluding cytoplasmic activity that would activate intracellular immune responses.

biophysics↗

bioRxiv2024

Scaling Down for Efficiency: Medium-Sized Transformer Models for Protein Sequence Transfer Learning

Protein language models (pLMs) can offer deep insights into evolutionary and structural properties of proteins. While larger models, such as the 15 billion parameter model ESM-2, promise to capture more complex patterns in sequence space, they also present practical challenges due to their high dimensionality and high computational cost. We systematically evaluated the performance of various ESM-style models across multiple biological datasets to assess the impact of model size on transfer learning via feature extraction. Surprisingly, we found that larger models do not necessarily outperform smaller ones, in particular when data is limited. Medium-sized models, such as ESM-2 650M and ESM C 600M, demonstrated consistently good performance, falling only slightly behind their larger counterparts--ESM-2 15B and ESM C 6B--despite being many times smaller. Additionally, we compared various methods of compressing embeddings prior to transfer learning, and we found that mean embeddings consistently outperformed other compression methods. In summary, ESM C 600M with mean embeddings offers an optimal balance between performance and efficiency, making it a practical and scalable choice for transfer learning in realistic biological applications.

biochemistry↗

bioRxiv2023

Growth-dependent gene expression variation influences the strength of codon usage biases

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

evolutionary biology↗

bioRxiv2022

A long non-coding RNA is a key factor in the evolution of insect eusociality

Insect sociality is a major evolutionary transition based on the suppression of worker reproduction in favor of the reproductive monopoly of the queen. In the honey bee (Apis mellifera) model organism, the development of the two female caste phenotypes, queen and worker, is triggered by differences in their larval diets. However, the mechanistic details underlying their respective developmental trajectories, as well as the maintenance of sterility in the adult workers, are still not fully understood. Here we show that the long non-coding RNA lncov1 interacts with the Tudor staphylococcus nuclease (Tudor-SN) protein to form a regulatory module that promotes apoptosis in the ovaries of worker larvae. In adult workers, the lncov1/Tudor-SN module responds positively to environmental cues that suppress reproductive capacity. As lncov1 is considerably conserved in the Apidae, we propose that, by promoting worker sterility, the lncov1/Tudor-SN module has likely played critical roles in the social evolution of bees.

evolutionary biology↗

bioRxiv2021

EMC is required for biogenesis and membrane insertion of Xport-A, an essential chaperone of rhodopsin-1 and the TRP channel

Insertion of hydrophobic transmembrane domains (TMDs) into the endoplasmic reticulum (ER) lipid bilayer is an essential step during eukaryotic membrane protein biogenesis. The ER membrane complex (EMC) functions as an insertase for TMDs of low hydrophobicity and is required for the biogenesis of a subset of tail-anchored (TA) and polytopic membrane proteins, including rhodopsin-1 (Rh1) and the TRP channel. To better understand the physiological implications of membrane protein biogenesis dependent on the EMC, we performed a bioinformatic analysis to predict TA proteins present in the Drosophila proteome. From 254 predicted TA proteins, subsequent genetic screening in Drosophila larval eye discs led to the identification of 2 proteins that require EMC for their biogenesis: farinelli (fan) and Xport-A. Fan is required for sperm individualization and male fertility in Drosophila and we now show that EMC is also required for these important biological processes. Interestingly, Xport-A is essential for the biogenesis of both Rh1 and TRP, raising the possibility that disruption of Rh1 and TRP biogenesis in EMC loss of function mutations is secondary to the Xport-A defect. We show that EMC is required for Xport-A TMD membrane insertion and increasing the hydrophobicity of Xport-A TMD rendered its membrane insertion to become EMC-independent. Moreover, these EMC-independent Xport-A mutants rescued Rh1 and TRP biogenesis in EMC mutants. Our data establish that EMC can impact the biogenesis of polytopic membrane proteins indirectly, by controlling the biogenesis and membrane insertion of an essential protein co-factor.

cell biology↗

bioRxiv2018

Whole-genome sequencing of parvoviruses from wild and domestic animals in Brazil provides new insights into parvovirus distribution and diversity

AbstractParvoviruses (family Parvoviridae) are small, single-stranded DNA viruses. Many parvoviral pathogens of medical, veterinary and ecological importance have been identified. In this study, we used high-throughput sequencing (HTS) to investigate the diversity of parvoviruses infecting wild and domestic animals in Brazil. We identified 21 parvovirus sequences (including twelve nearly complete genomes and nine partial genomes) in samples derived from rodents, bats, opossums, birds and cattle in Pernambuco, Sao Paulo, Parana and Rio Grande do Sul states. These sequences were investigated using phylogenetic and distance-based approaches, and were thereby classified into eight parvovirus species (six of which have not been described previously), representing six distinct genera in the subfamily Parvovirinae. Our findings extend the known biogeographic range of previously characterized parvovirus species, and the known host range of three parvovirus genera (Dependovirus, Aveparvovirus, and Tetraparvovirus). Moreover, our investigation provides a window into the ecological dynamics of parvovirus infections in vertebrates, revealing that many parvovirus genera contain well-defined sub-lineages that circulate widely throughout the world within particular taxonomic groups of hosts.

microbiology↗