{"id":23407,"date":"2022-07-21T09:26:08","date_gmt":"2022-07-21T08:26:08","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=23407"},"modified":"2022-08-07T12:33:15","modified_gmt":"2022-08-07T11:33:15","slug":"analysing-role-nmr-spectroscopy-integrated-structural-biology","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/analysing-role-nmr-spectroscopy-integrated-structural-biology\/23407\/","title":{"rendered":"Analysing the role of NMR spectroscopy in integrated structural biology"},"content":{"rendered":"

Dr Bj\u00f6rn M Burmann from the University of Gothenburg<\/a> outlines the central role of NMR spectroscopy in modern structural biology.<\/h2>\n

The central dogma that structure and dynamics guide function of the macromolecular world for machines and instruments also applies to the micromolecular world, as the protein structure and its inherent dynamical properties are directly linked to its respective function within the cellular context. Therefore, to understand the functional repertoire of important proteins<\/a> and other biomolecules, such as nucleic acids, lipids, etc., a detailed understanding of this crucial interplay is mandatory to be able to address diseases rooted on protein dysfunction and misfolding.<\/p>\n

Methods for examining protein structure<\/h3>\n

To be able to achieve a high-resolution three-dimensional structure of a macromolecule, like a protein, researchers have, in general, three experimental options: X-ray crystallography, cryo-electron microscopy (cryo-EM) and nuclear magnetic resonance (NMR) spectroscopy. X-ray crystallography was, for the last decade, a very powerful and highly successful technique, with the main drawback that the protein under study needed to be crystallised, which caused (especially for proteins containing large flexible parts) severe issues impairing their structural characterisation in many cases.<\/p>\n

However, cryo-EM has lately become the method of choice for the study of larger protein complexes, as it needs significantly less material and the proteins do not need to form a crystal. Nevertheless, both of these highly efficient methods provide only a single static picture of a protein with limited information of its inherent movements, the so-called protein dynamics. In recent decades, it has become more apparent that a view of a protein as a static entity is rather wrong, and that one key feature is their inherent dynamics and structural adaptions.<\/p>\n

Besides these experimental approaches, in the last one to two years the latest in silico<\/em> protein structure prediction approaches made headlines as they came close to fulfilling the classical dogma posed by Afinsen almost 50 years ago that the \u201camino acid sequence determines the most stable three-dimensional fold of a given protein\u201d and thus the functional form.1<\/sup> The algorithms AlphaFold from DeepMind,2,3<\/sup> as well as RoseTTAFold by David Baker\u2019s lab,4,5<\/sup> come very close to fulfilling this promise, providing researchers with a myriad of previously unavailable structural information guiding current experimental designs. Several comparative studies already indicated that, in the majority of the tested cases, the now available computational predictions match well with the available experimental data.6,7<\/sup><\/p>\n

This, of course, poses the interesting question of whether the above-mentioned experimental methods might turn obsolete in the near future. Besides the general fact that in silico<\/em> predictions will always need experimental verification, there are two main crucial aspects these methodologies currently cannot address:<\/p>\n