Biology subjects

Trinquier, J.

Publications and source records attributed to Trinquier, J..

4 recordsLinked to original sources

bioRxiv2026

Viability of engineered AAVs via protein language models

Capsid engineering has greatly improved the performance of recombinant AAV vectors used for gene therapy. One commonly used strategy is the insertion of a short, 7-mer, peptide into surface-exposed loops to modify receptor interactions and enhance cell entry. While effective in receptor retargeting and improved transduction, these insertions might destabilize the capsid protein, hinder assembly, and thus limit production. While previous attempts have used deep mutational scanning and AI to predict which insertions are viable, there is lack in understanding the structural consequences of these peptide insertions at the amino-acid level. Here we combined experiments, deep sequencing and large protein language models to gain insight on the impact of 7-mer insertions on the VR-VIII region. We first characterize the biochemical properties of viable insertions, thus identifying which residues are well tolerated, and which should instead be avoided. We then focus on the nearby context of those insertions, by studying the effect of the linkers, either for highly diverse libraries or for individual variants known for their efficiency. Next, we study the broader context, by extending our analysis to the whole capsid sequence, and identifying regions that can tolerate insertions without long-ranged structural deformations that could affect capsid functionality. We conclude with a cross-serotype comparison and a viability analysis of tens of previously engineered variants. Our work showcases how AI can uncover structure-function rules governing the success of engineered AAV capsids.

bioinformatics↗

bioRxiv2026

Promfusion: a synthetic fusion promoter enabling enhanced and balanced photoreceptor transgene expression

Achieving efficient and balanced transgene expression in both rods and cones remains a major challenge in retinal gene therapy. Current promoters either lack specificity or fail to provide sufficient cellular coverage and expression level. To address this limitation, we developed and evaluated two fusion promoters, Pikali and Nocchu, by combining PR1.7, a cone-specific promoter and GRK1, a promoter most active in rods. Here, we show that Pikali and Nocchu outperform their parental promoters, driving broader and more balanced GFP expression in rods and cones of human iPSC-derived retinal organoids. These constructs achieved transduction in 30% to 45% of photoreceptors, with higher expression levels than GRK1 and broader cellular coverage than PR1.7. Our findings establish Pikali and Nocchu as excellent candidates for retinal gene therapy, overcoming the limitations of existing promoters. By combining specificity, efficiency, and extensive photoreceptor targeting, these fusion constructs represent a novel and promising strategy for next-generation gene therapy vectors, addressing inherited retinal dystrophies and advancing clinical translation.

genetics↗

bioRxiv2025

SoftAlign: End-to-end protein structures alignment

With the recent breakthrough of highly accurate structure prediction methods, there has been a rapid growth of available protein structures. Efficient methods are needed to infer structural similarity within these datasets. We present an end-to-end alignment method, called SoftAlign, which takes as input the 3D coordinates of a protein pair and outputs a structural alignment. In addition to the traditional Smith-Waterman alignment method, we introduce a modified softmax alignment that shows very promising results for structure similarity detection. We demonstrate that the SoftAlign model is able to recapitulate TM-align alignments while running faster, and it is more accurate than Foldseek on alignment and classification tasks. Although SoftAlign is not the fastest method available, it is highly precise and can be used effectively with other prefilters. In addition to developing an end-to-end structural aligner, our main contribution is the introduction and analysis of a pseudo-alignment method based on softmax, which can be used with other architectures, even those not based on structural information. The code for SoftAlign is available at https://github.com/jtrinquier/SoftAlign.

bioinformatics↗

bioRxiv2021

Efficient generative modeling of protein sequences using simple autoregressive models

Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally extremely efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost. Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Using these models, we can easily estimate both the model probability of a given sequence, and the size of the functional sequence space related to a specific protein family. In the case of response regulators, we find a huge number of ca. 1068 sequences, which nevertheless constitute only the astronomically small fraction 10-80 of all amino-acid sequences of the same length. These findings illustrate the potential and the difficulty in exploring sequence space via generative sequence models.

bioinformatics↗