Biology subjects

Thiyagalingam, J.

Publications and source records attributed to Thiyagalingam, J..

6 recordsLinked to original sources

bioRxiv2026

BioMeme: biological response memory for virtual cells

Virtual cells predict responses to unmeasured perturbations, but neural models absorb measurements into weights, require retraining and cannot cite supporting experiments. Here we introduce biological response memory, replacing a learned state-to-response layer with a memory of measured experiments. BioMeme predicts next-dose responses in a new cell type from measured lower-dose states, recalling similarly responding compounds in that cell and the same compound in a reference cell line. Each prediction is an exact weighted sum of named measurements, and memory updates with one linear solve. In all nine preregistered comparisons across three screens, memory predicted response direction better than the learners it replaced. In U2OS, BioMeme outperformed input-matched ridge, adapted chemCPA and a neural layer in memory's place, and matched a full-panel network reading reference responses directly (error within 3.4%). Transfer was compound-specific, lowering error by 21.2% against at most 1.8% for wrong compounds, and replicated in reserved MCF7 data. With 20 references, fitting took seconds on one CPU thread, at least 420 times faster than neural tuning and fitting. Adding its prediction to one measured well cut that well's error by 27-31%. Biological response memory establishes a design principle for virtual cells any laboratory can update, inspect and run.

bioinformatics↗

bioRxiv2026

DPI-score: A deep learning-based metric for assessing protein-protein interfaces in cryo-EM derived assemblies

Advances in cryoEM have led to a surge in high-resolution structures; however, model building and refinement at resolutions (>=3 [A]) and in regions with variable local resolution remain challenging, making validation essential. CryoEM derived assemblies often contain extensive protein-protein interfaces, however, most existing validation metrics focus on density fit or overall geometry without directly assessing interface quality. To address this, we present DPI-Score, a deep learning-based metric for assessing protein-protein interfaces in cryoEM derived complexes. The method uses only raw structural coordinates of interface atoms, without requiring engineered features, and achieves 87.53% validation accuracy. DPI-Score was applied to 29,120 interfaces from 6,011 fitted entries with resolutions worse than 3 [A] in the Electron Microscopy Data Bank. Here, we show that DPI-Score provides complementary information to existing validation metrics and can identify interface errors in modelled assemblies that are not detected by density-based scores alone.

bioinformatics↗

bioRxiv2025

Automated filtering of particle images in single particle cryoEM

Continued exponential growth in the number of structures resolved by single particle cryoEM, as seen in the last decade, requires ever more effective data analysis workflows. Datasets are rarely homogeneous, demanding a multistep procedure for discarding outliers. Since individual particles are very noisy, either 2D or 3D averages are normally used for discrimination. This becomes challenging when the 2D classes themselves are heterogeneous, leading to selection of contaminants or discarding useful rare views/poses. The 3D model-based discrimination requires trustworthy 3D maps and a correct assignment of Euler angles, which in turn depends on the quality of the initial data and might not be available at the very early stages of the analysis. We propose a novel deep-learning approach for improving quality of single particle datasets. The two-stage procedure consists of denoising single particle images using Variational AutoEncoder framework followed by particle quality filtering based on the score inferred for every particle by Domain Adaptation Neural Network trained on a large data set of categorised 2D averages. This approach allows an automated scoring of noisy raw images using data patterns learned from the high signal-to-noise ratio, externally derived 2D classes. Consequently, a higher quality data set enters computationally expensive steps of the data analysis, reducing the need for protracted and expensive calculations. Importantly, our method does not require any prior knowledge about the data or existence of a 3D model, making it universally applicable. Tests on publicly available datasets demonstrated that our approach largely outperformed 2D class-based particle discrimination. Smaller subsets of the top-scoring particles selected with our method were required to obtain the author-reported 3D model resolution. When applied to the user data in the automated on-the-fly data processing pipeline, the method rescued 30% of cases, which otherwise would not reach confidence threshold required for making decision to proceed to the 3D model refinement. It also led to general improvements in the quality of the 3D models for many datasets which were selected for the high-resolution processing.

biophysics↗

bioRxiv2024

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the Single Particle Analysis (SPA) workflow into a reasonably standardised procedure. Significant investment of development time have been made particularly in sample preparation, microscope data collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practises developed at national facilities has also begun to establish a degree of standardisation to data structures coming from the SPA workflow. There is opportunity to capitalise on this moment in the fields maturation, to capture metadata from SPA experiments and correlate this with experimental outcomes, which is presented here in a set of programmes called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as for defining methods for metadata capture to assist with archiving of cryoEM SPA data. We also envisage this tool to be useful to microscope operators and facilities looking to rapidly generate reports on SPA data collection and screening sessions. SynopsisEMinsight is a Python-based tool for systematically mining metadata from single particle analysis cryoEM experiments. The capture and analysis of metadata facilitates assessment of instrument performance, provides concise reporting of experiment performance and sample quality by analysing preprocessing results, and gathers metadata for deposition. We envisage this approach to benefit the microscope operator, facility managers, database developers and users.

biophysics↗

bioRxiv2023

Single-cell phenomics reveals behavioural and mechanical heterogeneities underpinning collective migration during mouse anterior patterning

Distal Visceral Endoderm (DVE) cells show a stereotypic unidirectional migration essential for correct orientation of the anterior-posterior axis. They migrate within a simple epithelium, the Visceral Endoderm (VE). It is unknown how DVE cells negotiate their way amongst the surrounding VE cells, what determines the bounds of DVE migration within the VE, and the relative contributions of different cell behaviours to this migration. To address these questions, we used lightsheet microscopy to generate a multi-embryo, singlecell resolution, longitudinal dataset of cell behaviour and morphology. We developed a machine learning based pipeline to segment cells and a data-informed systematic computational framework to extract and compare select morphological, behavioural and molecular parameters of all VE cells in a unified coordinate space. Unbiased clustering of this single-cell phenomic dataset reveals considerable patterned phenotypic heterogeneity within the VE and a previously unknown sub-grouping within the DVE. While migrating, DVE cells retain regular morphology, do not exchange neighbours and are crowded, all hallmarks of the jammed state. In contrast, VE cells immediately ahead of them deform and undergo neighbour exchange. We show that DVE cells are characterised by higher levels of apical F-actin and elevated tension relative to the VE cells immediately ahead of them through which they migrate, but stop migrating upon reaching a region of the VE with matching elevated tension. Lefty1 mutants, known to show abnormal over-migration of DVE cells, show disruption to this patterned tension in the VE. Our findings provide novel insights into the control of cell behaviour during the remodelling of curved epithelia, indicating that the collective migration of sub-sets of cells can be circumscribed by modulating the mechanical properties of surrounding cells and that migrating cells in this context remain as a jammed solid flock, with surrounding cells facilitating their movement by becoming unjammed. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/534937v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@10a7153org.highwire.dtl.DTLVardef@963a1forg.highwire.dtl.DTLVardef@c1fcd0org.highwire.dtl.DTLVardef@1bcdded_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2020

Assessment of protein-protein interfaces in cryo-EM derived assemblies

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for the map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we have assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed PI-score, a density-independent machine learning-based metric, trained using protein-protein interfaces features in high-resolution crystal structures. Using PI-score, we were able to identify errors at interfaces in the PDB-deposited cryo-EM structures (including SARS-CoV-2 complexes) and in the models submitted for cryo-EM targets in CASP13 and the EM model challenge. Some of the identified errors, especially at medium-to-low resolution structures, were not captured by density-based assessment scores. Our method can therefore provide a powerful complementary assessment tool for the increasing number of complexes solved by cryo-EM.

bioinformatics↗