Biology subjects

Doffini, V.

Publications and source records attributed to Doffini, V..

4 recordsLinked to original sources

bioRxiv2026

Probing the scalability of ultra stable catch bond complexes

The catch bond complex between serine-aspartate repeat protein G (SdrG) from Staphylococcus epidermidis and the beta chain of fibrinogen (Fg{beta}) exhibits two distinct rupture populations when dissociated under tensile force. Such complexes present exciting possibilities for developing dynamic biomaterials due to their unique response to shear force. However, the environmental responsiveness of this complex and its influence on adhesion behaviour in multi-valent systems remain underexplored. Using AFM-single molecule force spectroscopy (AFM-SMFS) and spinning disk adhesion (SDA) assays, we examined how protein orientation, mutations, and environment influence the stability and scaling behaviour of this catch bond system. Our findings confirmed that anchor point location (i.e., the direction from which the protein is pulled) strongly influences catch bond behaviour, while an S338H mutation in the binding domain destabilised the interaction in both single-molecule and multi-valent adhesion assays. This research examines how catch bond behaviour translates from the nanoscale to microscale using single molecule and multi-valent cell adhesion measurements and provides a toolkit for exploiting catch bonds towards macroscale material applications.

biophysics↗

bioRxiv2024

Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening

High-throughput screening (HTS) combined with deep mutational scanning (DMS) and next-generation DNA sequencing (NGS) have great potential to accelerate discovery and optimization of biological therapeutics. Typical workflows involve generation of a mutagenized variant library, screening/selection of variants based on phenotypic fitness, and comprehensive analysis of binned variant populations by NGS. However, in such cases, the HTS data are subject to interval censoring, where each fitness value is calculated based on the assignment of variants to bins. Such censoring leads to increased uncertainty, which can impact data accuracy and, consequently, the performance of machine learning (ML) algorithms tasked with predicting sequence-fitness pairings. Here, we investigated the impact of interval censoring on data quality and ML performance in biological HTS experiments. We theoretically analyzed the impact of data censoring and propose a dimensionless number, the Ratio of Discretization (RD), to assist in optimizing HTS parameters such as the bin width and the sampling size. This approach can be used to minimize errors in fitness prediction by ML and to improve the reliability of these methods. These findings are not limited to biological HTS techniques and can be applied to other systems where interval censoring is an advantageous measurement strategy.

bioinformatics↗

bioRxiv2023

Iterative Machine Learning for Classification and Discovery of Single-molecule Unfolding Trajectories from Force Spectroscopy Data

We report the application of machine learning techniques to accelerate classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) where a user classifies a small number of repeatable unfolding patterns encoded as image data, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multi-domain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm, and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis, and generate new insights in protein biophysics.

biophysics↗

bioRxiv2023

Dissecting the Biophysical Origins of Activity-Stability Tradeoffs in D-amino Acid Oxidase with Enzyme Proximity-Seq

Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we developed Enzyme Proximity Sequencing (EP-Seq), a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We used EP-Seq to analyze how 6,399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase (DAOx) from R.gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. EP-Seq can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.

bioengineering↗