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Biology subjects

Edich, M.

Publications and source records attributed to Edich, M..

2 recordsLinked to original sources

Unlocking the Secrets of NSP3: AlphaFold2-assisted Domain Determination in SARS-CoV-2 Protein

Non-structural protein 3 (nsp3) is crucial for the SARS-CoV-2 infection cycle. It is the largest protein of the virus, consisting of roughly 2000 residues, and a major drug target. However, due to its size, disordered regions, and transmembrane domains, the atomic structure of the whole protein has not yet been established. Only 10 out of its 16 domains were individually determined in experiments. Here, we demonstrate how structural bioinformatics, AI-based fold prediction, and traditional experiments complement each other and can shed light on the makeup of this important protein, both in SARS-CoV-2 and related viruses. Our method can be generalized for other multi-domain proteins, so we describe it in detail. Our prediction-based approach reveals a previously undescribed folded domain, which we could confirm experimentally. Our research also suggests a potential function of the nidovirus-wide conserved domain Y1: This domain may be involved in the assembly of nsp3, nsp4, and nsp6 into the hexameric pore, which was discovered by electron tomography and exports RNA into the cytosol. The Y1-hexamer, however, could not be expressed and purified on its own. We also provide a revised domain segmentation and nomenclature of nsp3 domains based on a compilation of previous research and our own findings.

molecular biology↗

The impact of AlphaFold on experimental structure solution

AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, posttranslational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning based fold prediction are a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead.

bioinformatics↗