Key research themes
1. How can hierarchical domain partitioning enhance protein structure elucidation beyond traditional secondary and tertiary annotations?
This research area investigates methods to decompose protein 3D structures into hierarchically organized structural units, such as subdomains and protein units (PUs), complementing classical domain and secondary structure definitions. Understanding proteins at multiple hierarchical levels is crucial for characterizing folding, functionality, and evolution, providing richer insights than single-level annotations.
2. How can advanced computational methods improve the reliability of molecular structure elucidation from spectral data?
This theme examines computational strategies and expert systems that leverage spectroscopic data (notably 1D and 2D NMR, MS, IR) to automate or assist in elucidating molecular structures. Emphasis is on methods addressing complex molecules, integrating diverse spectral inputs, and reducing human error or time expenditure while improving confidence in the identified structures.
3. What roles do molecular representations and computational analysis of structural parameters play in refining macromolecular and small molecule crystal structures?
This theme addresses computational tools and models for interpreting, validating, and refining structural data of macromolecules and small molecules from crystallographic or spectroscopic experiments. It includes coarse-grained geometric models, software parsers, crystallographic quality assessments, and computational approaches to exploit structural data for improved understanding and prediction of molecular architecture.