Biology subjects

farimani, A. B.

Publications and source records attributed to farimani, A. B..

3 recordsLinked to original sources

bioRxiv2025

Recapitulating apicobasal tissue polarity in extracellular matrix incorporated airway organoids

The airway epithelium is a dynamic barrier that interfaces with the external environment and internal matrix along its apicobasal axis. To recapitulate this tissue arrangement in an organoid format, we present the decellularized ExtraCellular Matrix-incorporated Apical-out Airway Organoid (dECM-AoAO) that integrates basolateral matrix cues through incorporation of human lung dECM microparticles, while maintaining direct apical exposure to the exterior. Compared to the ECM-free AoAO, dECM incorporation effectively diversifies lineage distribution that better recapitulates native epithelial composition. Harnessing dECM-AoAO locomotion powered by its outward-facing ciliary beating, we developed an experimental and computational pipeline for swarm analysis of organoid group motility as a functional readout of ciliary function. Lastly, dECM-AoAO withstood cryopreservation and revival with sustained viability, lineage composition, and ciliary function, enabling future scalability and broad distribution. Together, this work establishes dECM-AoAO as a more physiologically relevant model system for investigating epithelial-ECM crosstalk during airway homeostasis, pathogenesis, and injury responses.

cell biology↗

bioRxiv2025

Large Language Model Agent for Modular Task Execution in Drug Discovery

We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline. By combining LLM reasoning with domain-specific tools, the framework performs biomedical data retrieval, domain-specific question answering, molecular generation, property prediction, property-aware molecular refinement, and 3D protein-ligand structure generation. In a case study targeting BCL-2 in lymphocytic leukemia, the agent autonomously retrieved relevant biomolecular information--including FASTA sequences, SMILES representations, and literature--and answered mechanistic questions with improved contextual accuracy over standard LLMs. It then generated chemically diverse seed molecules and predicted 67 ADMET-related properties, which guided iterative molecular refinement. Across two refinement rounds, the number of molecules with QED > 0.6 increased from 34 to 55, and those passing at least four out of five empirical drug-likeness rules rose from 29 to 52, within a pool of 194 molecules. The framework also employed Boltz-2 to generate 3D protein-ligand complexes and provide rapid binding affinity estimates for candidate compounds. These results demonstrate that the approach effectively supports molecular screening, prioritization, and structure evaluation. Its modular design enables flexible integration of evolving tools and models, providing a scalable foundation for AI-assisted therapeutic discovery.

pharmacology and toxicology↗

bioRxiv2023

A Machine Learning Method to Characterize Conformational Changes of Amino Acids in Proteins

Amino acid dynamics are significant in determining the overall function, structure, stability, and activity of proteins. However, atomic-level descriptions of the structural features of proteins are limited by the current resolutions of experimental and computational techniques. In this study, we developed a machine learning (ML) framework for characterizing the individual aminoacids dynamic in a protein and compute its contribution to the overall function of proteins. This framewor identifies specific types of angular features in amino acids, such as bimodal-switch residues. It can assist in the analysis of various protein characteristics and provide valuable insights into the dynamic behavior of individual amino acids within a protein structure. We found that there is a strong correlation between a specific type of bimodal-switch residues and the global features in proteins. This knowledge can help us to identify key residues that are strongly correlated to the overall function of the protein.

bioengineering↗