Post-Structural-Biology – An idea whose time has come

Date

May 22, 2024

Time

1:00 PM - 2:00 PM

Speaker

Robert Konrat

Affiliation

University of Vienna, Austria

Language

en

Main Topic

Biologie

Host

Stephan Grill

Description

Artificial intelligence (AI) techniques led to impressive advancements in many disciplines and are transforming science and societies. In structural biology the accurate predictions of protein structure from amino-acid sequences are clearly disruptive and have become invaluable tools for structural biology research. However, despite their undisputable usefulness they do have fundamental limitations in providing insights into, for example, the determinants of protein complex formation, protein conformational switching and resulting functional dynamics, let alone an adequate understanding of intrinsically disordered proteins (IDPs), amongst other aspects of protein biology. However, these AI-inherent limitations underscore the significance and future relevance of nuclear magnetic resonance (NMR) as a crucial experimental tool in filling these crucial knowledge gaps. IDPs are challenging the established structural biology structure-function paradigm and thus mandate suitable theoretical framework and concepts to properly address the subtle interdependence between protein structure and dynamics. In the lecture it will be described how the combination of NMR and novel computational protein sequence analysis tools can be used to provide a more comprehensive view of IDPs and how they can simultaneously maintain modularity, robustness, and adaptability, as essential features for their biological functionalities. Additionally, NMR avoids the fundamental shortcomings of conventional Structure-Based-Drug-Design (static snapshot, lack of dynamics, no direct and quantitative information about intermolecular interactions) by providing access to site-specific information about structural dynamics and the chemical environment in solution. In the lecture it will be outlined how previous technical limitations can be overcome thanks to the advent of sophisticated isotope-labeling strategies and by applying meaningful NMR experiments. The combination of these techniques with AI-based approaches for protein structure predictions enables the consideration of the entire conformational state distribution of protein-ligand complexes and to effectively harness enthalpy-entropy compensatory effects, with considerable impact on future drug design strategies.

Last modified: May 23, 2024, 7:39:26 AM