Key research themes
1. How can multivariate statistical techniques improve the quantification and classification of molecular conformations, particularly in chemical and biomolecular data?
This theme encompasses the development and application of multivariate statistical methods such as principal component analysis (PCA), principal component regression (PCR), partial least squares (PLS), support vector machine (SVM), and clustering algorithms to analyze complex, high-dimensional chemical and biomolecular data. Such methods address challenges arising from multicollinearity, the presence of many variables relative to few observations, and correlated variables, which are common in chemometrics and molecular conformation analysis. The aim is to optimize classification, regression, model calibration, and conformational state identification by effectively reducing dimensionality and integrating variance information.
2. What computational and statistical strategies enable accurate reweighting and interpretation of large molecular conformational ensembles against experimental data?
This theme focuses on methodological innovations in reconciling large-scale computational ensembles of molecular conformations, such as those derived from molecular dynamics (MD) simulations, with experimental measurements. Key challenges include efficiently reweighting tens of thousands of conformers to fit experimental data while preserving maximum entropy, thereby quantifying the information content of the measured parameters and improving structural models of flexible biomolecules. These approaches combine convex optimization, maximum entropy principles, and Bayesian inference to yield deterministic, robust, and scalable solutions for ensemble refinement.
3. How can rigorous computational chemistry techniques predict the molecular effects of genetic variants and protein-nucleic acid interactions linked with disease?
This theme investigates advanced computational modeling methods ranging from quantum mechanics, molecular dynamics, to statistical potentials to study the molecular consequences of genetic variations, particularly missense mutations, and protein-RNA binding affinities. The research highlights the integration of multiphysics approaches to predict changes in stability, conformational dynamics, binding interactions, and electrostatics of biological macromolecules affected by disease-related variants. Emphasis is placed on quantifying binding free energies from simulation fluctuations, elucidating the physical underpinnings of pathogenicity, and guiding precision medicine through computational biophysics.