Biology subjects

Thorn, A.

Publications and source records attributed to Thorn, A..

7 recordsLinked to original sources

bioRxiv2025

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↗

bioRxiv2023

Actin-microtubule crosstalk imparts stiffness to the contractile ring in fission yeast.

Actin-microtubule interactions are critical for cell division yet how these networks of polymers mutually influence their mechanical properties and functions in live cells remains unknown. In fission yeast, the post-anaphase array (PAA) of microtubules assembles in the plane of the contractile ring and its assembly relies on the Myp2p-dependent recruitment of Mto1p, a component of equatorial microtubule organizing centers (eMTOCs). The general organization of this array of microtubule and the impact on their physical attachment to the contractile ring remain unclear. We found that Myp2p facilitates the recruitment of Mto1p to the inner face of the contractile ring where the eMTOCs polymerize microtubules without their direct interaction. The PAA microtubules form a dynamic polygon of Ase1p crosslinked microtubules inside the contractile ring. The specific loss of PAA microtubules affects the mechanical properties of the contractile ring of actin by lowering its stiffness. This change in the mechanical properties of the ring has no measurable impact on cytokinesis or on the anchoring of the ring. Our work proposes that the PAA microtubules exploit the contractile ring for their assembly and function during cell division while the contractile ring may receive no benefit from these interactions.

cell biology↗

bioRxiv2023

Microtubule competition and cell growth recenter the nucleus after anaphase in fission yeast.

Cells actively position their nucleus based on their activity. In fission yeast, microtubule-dependent nuclear centering is critical for symmetrical cell division. After spindle disassembly at the end of anaphase, the nucleus recenters over a ~90 min period, approximately half of the duration of the cell cycle. Live cell and simulation experiments support the cooperation of two distinct mechanisms in the slow recentering of the nucleus. First, a push-push mechanism acts from spindle disassembly to septation and involves the opposing actions of the mitotic Spindle Pole Body microtubules that push the nucleus away from the ends of the cell while post-anaphase array of microtubules basket the nucleus and limit its migration toward the division plane. Second, a slow-and-grow mechanism finalizes nuclear centering in the newborn cell. In this mechanism, microtubule competition stalls the nucleus while asymmetric cell growth slowly centers it. Our work underlines how intrinsic properties of microtubules differently impact nuclear positioning according to microtubule network organization and cell size.

cell biology↗

bioRxiv2022

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↗

bioRxiv2021

Detecting ice artefacts in processed macromolecular diffraction data with machine learning

Contamination with diffraction from ice crystals can negatively affect, or even impede macromolecular structure determination and therefore, detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed, it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX webserver and CCP4-distributed software. SynopsisA program utilizing artificial learning and convolutional neural networks, named Helcaraxe, has been developed which can detect ice crystal artefacts in processed macromolecular diffraction data with unprecedented accuracy.

biophysics↗

bioRxiv2020

Making the invisible enemy visible

During the COVID-19 pandemic, structural biologists rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 204 previously solved structures from SARS-CoV-1, 548 structures covering 16 of the SARS-CoV-2 viral proteins have been released in a span of only 6 months. These structural models serve as the basis for research to understand how the virus hijacks human cells, for structure-based drug design, and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and may be even more common among these structures, which were solved quickly and under immense pressure. The Coronavirus Structural Task Force has responded to this challenge by rapidly categorizing, evaluating and reviewing all of these experimental protein structures in order to help downstream users and original authors. In addition, the Task Force provided improved models for key structures online, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge and others.

molecular biology↗

bioRxiv2019

Automated interpretation of Cryo-EM density maps with convolutional neural networks

In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (Cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Here, we demonstrate the potential of convolutional neural networks for the annotation of Cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate protein secondary structure elements as well as RNA/DNA. It can be straightforwardly applied to annotate newly reconstructed maps to support domain placement or to supply a starting point for main-chain placement. Due to its high recall and precision rates of 95.1% and 80.3%, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.

molecular biology↗