Biology subjects

Barati farimani, A.

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

3 recordsLinked to original sources

bioRxiv2022

Activity Map and Transition Pathways of G Protein Coupled Receptor Revealed by Machine Learning

Approximately, one-third of all FDA-approved drugs target G protein-coupled receptors (GPCRs). However, more knowledge of protein structure-activity correlation is required to improve the efficacy of the drugs targeting GPCRs. In this study, we developed a machine learning (ML) model to predict activation state and activity level of the receptors with high prediction accuracy. Furthermore, we applied this model to thousands of molecular dynamics trajectories to correlate residue-level conformational changes of a GPCR to its activity level. Finally, the most probable transition pathway between activation states of a receptor can be identified by using the state-activity information. In addition, with this model, we can associate the contribution of each amino acid to the activation process. Using this method we will be able to design drugs that mainly target principal amino acids driving the transition between activation states of GPCRs. Our advanced method is generalizable to all GPCR classes and provides mechanistic insight into the activation mechanism in the receptors.

biophysics↗

bioRxiv2022

Engineering Rotating Apical-Out Airway Organoid for Assessing Respiratory Cilia Motility

Motile cilia project from the airway apical surface and directly interface with inhaled external environment. Due to cilias nanoscale dimension and high beating frequency, quantitative assessment of their motility remains a sophisticated task. Here we described a robust approach for reproducible engineering of apical-out airway organoid (AOAO) of defined size. Propelled by exterior-facing cilia beating, the mature AOAO exhibited stable rotational motion when surrounded by Matrigel. We developed a computational framework leveraging computer vision algorithms to quantify AOAO rotation and validated its correlation with direct measurement of cilia motility. We further established the feasibility of using AOAO rotation to recapitulate and measure defective cilia motility caused by chemotherapy-induced toxicity and by CCDC39 mutations in cells from primary ciliary dyskinesia patient. We expect our rotating AOAO model and the associated computational pipeline to offer a generalizable framework to expediate modeling of and therapeutic development for genetic and environmental ciliopathies.

bioengineering↗

bioRxiv2020

Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction

Viral pandemics are emerging as a serious global threat to public health, like the recent outbreak of COVID-19. Viruses, especially those belonging to a large family of +ssRNA viruses, have a high possibility of mutating by inserting, deleting, or substituting one or multiple genome segments. It is of great importance for human health worldwide to predict the possible virus mutations, which can effectively avoid the potential second outbreak. In this work, we develop a GAN-based multi-class protein sequence generative model, named ProteinSeqGAN. Given the viral species, the generator is modeled on RNNs to predict the corresponding antigen epitope sequences synthesized by viral genomes. Additionally, a Graphical Protein Autoencoder (GProAE) built upon VAE is proposed to featurize proteins bioinformatically. GProAE, as a multi-class discriminator, also learns to evaluate the goodness of protein sequences and predict the corresponding viral species. Further experiments show that our ProteinSeqGAN model can generate valid antigen protein sequences from both bioinformatics and statistics perspectives, which can be promising predictions of virus mutations.

bioinformatics↗