Biology subjects

Omidi, A.

Publications and source records attributed to Omidi, A..

3 recordsLinked to original sources

bioRxiv2026

De Novo Design of Protein Switches with Diffusion-Based Ensemble Sampling

Protein switches are proteins that can respond to biochemical stimuli by rearranging their structural elements, essential for cells to transduce signals. The de novo design of such proteins requires amino acid sequences whose energy landscapes support multiple stimulus-dependent conformations, yet most current de novo protein design pipelines are optimized for single stable structures. Existing multi-state inverse-folding methods can design sequences compatible with multiple backbones, but they assume that suitable backbone ensembles are already available, often requiring expert knowledge. We introduce Diff-Switch, a framework for sampling switch-like backbone ensembles from pretrained protein diffusion models. Given a reference backbone structure and domain decomposition, our method preserves local domain geometry while encouraging diversity in global domain arrangements along user-specified collective variables, inspired by metadynamics. We implement this objective through a controlled diffusion sampler with reward-tilting for local similarity between the ensemble members and history-dependent bias in collective-variable space to avoid repeated sampling of the same global arrangement. The resulting ensembles provide candidate conformational states for downstream multi-state inverse folding. Across our evaluation set of 20 diverse proteins, using conformations from these generated ensembles improves the success rate of finding switch-compatible sequences over baseline sampling. We further apply the method to a real-world protein switch design task and characterize the resulting designs.

bioinformatics↗

bioRxiv2024

Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases

To deliver clinically relevant insights from large patient cohorts profiled with single-cell technologies, a key challenge is to relate sample-level and single-cell measurements. We present MultiMIL, a deep learning framework that applies attention-based multiple-instance learning for phenotype prediction and cell state identification. We applied MultiMIL to peripheral blood mononuclear cells from COVID-19 patients, the Human Lung Cell Atlas, and a spatial proteomics breast cancer dataset, demonstrating how our model can be utilized to find phenotype-associated cell states, learn phenotype-informed sample representations, and expand disease signatures.

bioinformatics↗

bioRxiv2020

Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories

Rare variants are collectively numerous and may underlie a considerable proportion of complex disease risk. However, identifying genuine rare variant associations is challenging due to small effect sizes, presence of technical artefacts, and heterogeneity in population structure. We hypothesized that rare variant burden over a large number of genes can be combined into predictive rare variant genetic risk score (RVGRS). We propose a novel method (RV-EXCALIBER) that leverages summary-level data from a large public exome sequencing database (gnomAD) as controls and robustly calibrates rare variant burden to account for the aforementioned biases. A RVGRS was found to strongly associate with coronary artery disease (CAD) in European and South Asian populations. Calibrated RVGRS capture the aggregate effect of rare variants through a polygenic model of inheritance, identifies 1.5% of the population with substantial risk of early CAD, and confers risk even when adjusting for known Mendelian CAD genes, clinical risk factors, and common variant gene scores.

genetics↗