Biology subjects

Bassot, C.

Publications and source records attributed to Bassot, C..

6 recordsLinked to original sources

bioRxiv2026

Balancing spatial resolution and proteome depth in LC-MS based spatialproteomics

Spatial proteomics aims to resolve protein composition within intact tissues, yet extraction-based liquid chromatography-mass spectrometry (LC-MS) workflows face an inherent trade-off: smaller sampling units increase spatial specificity, whereas larger sampling units provide greater proteome depth and robustness. As analytical sensitivity improves, sampling-unit size therefore becomes a key experimental design parameter. Current extraction-based LC-MS workflows typically rely on laser capture microdissection (LCM), where sample recovery and scalability can become limiting at low input. Spatially resolved laser-activated cell sorting (SLACS) offers an alternative tissue-isolation strategy based on single-pulse near-infrared laser activation. Here, we use SLACS to systematically examine the resolution-sensitivity trade-off across sampling units ranging from single-cell-equivalent to larger low-input tissue regions. Few-cell sampling retained substantial proteomic information relative to larger regions while increasing spatial specificity. Applied to the mouse somatosensory cortex, SLACS generated deep, layer-resolved proteomic profiles from regions corresponding to approximately 60 cells and preserved major layer-specific molecular patterns at inputs as low as approximately 6 cells. These results highlight sampling-unit size as an important experimental design parameter in extraction-based spatial proteomics and support few-cell sampling as a practical compromise between spatial specificity, proteome depth and robustness.

biochemistry↗

bioRxiv2024

Ribosomes modulate transcriptome abundance via generalized frameshift and out-of-frame mRNA decay.

Cells need to adapt their transcriptome to quickly match cellular needs in changing environments. mRNA abundance can be controlled by altering both its synthesis and decay. Here we show how, in response to poor nutritional conditions, the bulk of the S. cerevisiae transcriptome undergoes -1 ribosome frameshifts and experiences an accelerated out-of-frame co-translational mRNA decay. Using RNA metabolic labelling, we demonstrate that in poor nutritional conditions, NMD-dependent degradation represents at least one third of the total mRNA decay. We further characterize this mechanism and identify low codon optimality as a key factor for ribosomes to induce out-of-frame mRNA decay. Finally, we show that this phenomenon is conserved from bacteria to humans. Our work provides evidence for a direct regulatory feedback mechanism coupling protein demand with the control of mRNA abundance to limit cellular growth and expands the functional role of mRNA quality control.

molecular biology↗

bioRxiv2021

Limits and potential of combined folding and docking using PconsDock.

In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSA). In CASP14, the best groups predicted the structure of most proteins with impressive accuracy. The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein-protein interfaces. However, most of the earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This paper introduces a fold-and-dock method, PconsDock, based on predicted residue-residue distances with trRosetta. PconsDock can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein-protein docking yielded limited success. However, using alternative methods for MSA generating allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. However, no a priori structural information for the individual proteins is needed. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. PconsDocck contributed to the best model for one of the CASP14 oligomeric targets, H1065.

bioinformatics↗

bioRxiv2020

The evolutionary history of topological variations in the CPA/AT superfamily

CPA/AT transporters consist of two structurally and evolutionarily related inverted repeat units, each of them with one core and one scaffold subdomain. During evolution, these families have undergone substantial changes in structure, topology and function. Central to the function of the transporters is the existence of two non-canonical helices that are involved in the transport process. In different families, two different types of these helices have been identified, reentrant and broken. Here, we use an integrated topology annotation method to identify novel topologies in the families. It combines topology prediction, similarity to families with known structure, and the difference in positively charged residues present in inside and outside loops in alternative topological models. We identified families with diverse topologies containing broken or reentrant helix. We classified all families based on 3 distinct evolutionary groups that each share a structurally similar C-terminal repeat unit newly termed as "Fold-types". Using the evolutionary relationship between families we propose topological transitions including, a transition between broken and reentrant helices, complete change of orientation, changes in the number of scaffold helices and even in some rare cases, losses of core helices. The evolutionary history of the repeat units shows gene duplication and repeat shuffling events to result in these extensive topology variations. The novel structure-based classification, together with supporting structural models and other information, is presented in a searchable database, CPAfold (cpafold.bioinfo.se). Our comprehensive study of topology variations within the CPA superfamily provides better insight about their structure and evolution.

bioinformatics↗

bioRxiv2020

Identification of a novel fold type in CPA/AT transporters by ab-initio structure prediction

Members of the CPA/AT transporter superfamily show significant structural variability. All previously known members consist of an inverted duplicated repeat unit that folds into two separate domains, the core and the scaffold domain. Crucial for its transporting function, the central helix in the core domain is a noncanonical transmembrane helix, which can either be in the form of a broken helix or a reentrant helix. Here, we expand the structural knowledge of the CPA/AT family by using contact-prediction-based protein modelling. We show that the N-terminal domains of the Pfam families; PSE (Cons_hypoth698 PF03601), Lysine exporter (PF03956) and LrgB (PF04172) families have a previously unseen reentrant-helix-reentrant fold. The close homology between PSE and the Sodium-citrate symporter (2HCT) suggests that the new fold originates from the truncation of an ancestral reentrant protein, caused by the loss of the C-terminal reentrant helix. To compensate for the lost reentrant helix one external loop moves into the membrane to form the second reentrant helix, highlighting the adaptability of the CPA/AT transporters. This study also demonstrates that the most recent deep-learning-based modelling methods have become a useful tool to gain biologically relevant structural, evolutionary and functional insights about protein families.

bioinformatics↗

bioRxiv2019

Accurate contact-based modelling of repeat proteins predicts the structure of Curlin and SPW repeats.

Repeat proteins are an abundant class in eukaryotic proteomes. They are involved in many eukaryotic specific functions, including signalling. For many of these families, the structure is not known. Recently, it has been shown that the structure of many protein families can be predicted by using contact predictions from direct coupling analysis and deep learning. However, their unique sequence features present in repeat proteins is a challenge for contact predictions DCA-methods. Here, we show that using the deep learning-based PconsC4 is more effective for predicting both intra and interunit contacts among a comprehensive set of repeat proteins. In a benchmark dataset of 819 repeat proteins about one third can be correctly modelled and among 51 PFAM families lacking a protein structure, we produce models of five families with estimated high accuracy.\n\nAuthor SummaryRepeat proteins are widespread among organisms and particularly abundant in eukaryotic proteomes. Their primary sequence present repetition in the amino acid sequences that origin structures with repeated folds/domains. Although the repeated units are easy to be recognized in primary sequence, often structure information are missing. Here we used contact prediction for predicting the structure of repeats protein directly from their primary sequences. We benchmark our method on a dataset comprehensive of all the known repeated structures. We evaluate the contact predictions and the obtained models set for different classes of proteins and different lengths of the target, and we benchmark the quality assessment of the models on repeats proteins. Finally, we applied the methods on the repeat PFAM families missing of resolved structures, five of them modelled with high accuracy.

bioinformatics↗