Biology subjects

Mollaei, P.

Publications and source records attributed to Mollaei, P..

2 recordsLinked to original sources

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↗

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↗