Lu Wang

The Wang group uses theoretical and computational tools to study the structure, dynamics and spectroscopy of condensed phase systems. They have recently designed a theoretical framework that accurately and efficiently models the linear and two-dimensional infrared spectroscopy of nucleic acids. This advancement harnesses machine learning techniques to predict transition dipole moments of the light-absorbing chromophores with high precision. Accompanying the rapid development of experimental spectroscopy in this field, their approach will elucidate the molecular origin of the observed spectral features and guide the design of new experiments to probe the three-dimensional fold, conformational dynamics and functions of nucleic acids.

The Wang group has also conducted a comprehensive statistical analysis of the Protein Data Bank, uncovering the prevalence of short hydrogen bonds in proteins, protein-ligand complexes and nucleic acids.  Leveraging the structural, chemical and sequence features identified from this analysis, they have developed the Machine Learning Assisted Prediction of Short Hydrogen Bonds (MAPSHB) models to effectively predict the formation of short hydrogen bonds between amino acids and between amino acids and ligands. To faciliate accessibility to the models, the Wang group has created a web server ( https://wanggroup.rutgers.edu/mapshb-model/the-mapshb-model) that allows users to upload protein structures and obtain predictions of potential short hydrogen bonds. This resource offers essential insights for predicting and refining protein structures, and enhances our understanding of protein interactions and stability.