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Biology subjects

La Fleur, A.

Publications and source records attributed to La Fleur, A..

3 recordsLinked to original sources

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

synthetic biology↗

Interpreting Neural Networks for Biological Sequences by Learning Stochastic Masks

Sequence-based neural networks can learn to make accurate predictions from large biological datasets, but model interpretation remains challenging. Many existing feature attribution methods are optimized for continuous rather than discrete input patterns and assess individual feature importance in isolation, making them ill-suited for interpreting non-linear interactions in molecular sequences. Building on work in computer vision and natural language processing, we developed an approach based on deep generative modeling - Scrambler networks - wherein the most salient sequence positions are identified with learned input masks. Scramblers learn to generate Position-Specific Scoring Matrices (PSSMs) where unimportant nucleotides or residues are scrambled by raising their entropy. We apply Scramblers to interpret the effects of genetic variants, uncover non-linear interactions between cis-regulatory elements, explain binding specificity for protein-protein interactions, and identify structural determinants of de novo designed proteins. We show that interpretation based on a generative model allows for efficient attribution across large datasets and results in high-quality explanations, often outperforming state-of-the-art methods.

genomics↗

A comprehensive logic-based model of the human immune system to study the dynamics responses to mono- and coinfections

The immune system is a complex and dynamic network, crucial for combating infections and maintaining health. Developing a comprehensive digital twin of the immune system requires incorporating essential cellular components and their interactions. This study presents the first blueprint for an immune system digital twin, consisting of a comprehensive and simulatable mechanistic model. It integrates 51 innate and adaptive immune cells, 37 secretory factors, and 11 disease conditions, providing the foundation for developing a multi-scale model. The cellular-level model demonstrates its potential in characterizing immune responses to various single and combinatorial disease conditions. By making the model available in easy-to-use formats directly in the Cell Collective platform, the community can easily and further expand it. This blueprint represents a significant step towards developing general-purpose immune digital twins, with far-reaching implications for the future of digital twin technology in life sciences and healthcare, advancing patient care, and accelerating precision medicine.

systems biology↗