Biology subjects

Secker, C.

Publications and source records attributed to Secker, C..

7 recordsLinked to original sources

bioRxiv2026

HAP40 functions as a proteostasis regulator by controlling huntingtin interactions and its release into the extracellular space

Huntingtin-associated protein 40 (HAP40) forms a stable protein complex with huntingtin (HTT). Its cellular function and how HAP40 loss influences mutant HTT (mHTT) abundance and pathobiology are currently unclear. Here, using diverse cellular models and OMICs methods, we demonstrate that HAP40 is an obligate interaction partner of full-length HTT and through its binding controls the abundance of HTT-associated proteins, indicating that it functions as HTT interaction regulatory unit. Also, loss of HAP40 in mHTT-expressing striatal cells impairs autophagosome-lysosome flux, triggers massive transcriptional dysregulation, including the activation of the CLEAR network, demonstrating that it functions as proteostasis regulator that acts on quality control pathways. Finally, mHTT-expressing cells lacking HAP40 showed increased secretion of mHTT through the ER-to-Golgi route, indicating that striatal cells reduce intracellular mHTT-induced proteotoxicity through activation of secretory pathways. Together, these results establish HAP40 as a critical proteostasis regulator that through controlling HTT interactions maintains cellular homeostasis.

cell biology↗

bioRxiv2026

Evolutionary exploration of drug-like chemical space utilizing generative AI and virtual screening

The identification of suitable lead molecules in the vast chemical space is a critical and challenging task in drug discovery campaigns. Recently, it has been demonstrated that large-scale virtual screening provides a powerful approach to accelerate the identification of novel drug candidates by screening ever increasing virtual ligand libraries, which have reached magnitudes of > 1020 compounds. However, this desirable increase in potentially bioactive molecules poses a new challenge as enumerating and virtually screening such huge compound libraries is computationally prohibitive. Consequently, advanced approaches to navigate ultra-large chemical spaces and to identify suitable candidate molecules therein are urgently needed. Here, we present an evolutionary algorithm framework using molecular generative AI, reaction-based substructure searching, and iterative model fine-tuning for a targeted and efficient exploration of chemical fragment spaces. Combining this approach with large-scale virtual screening we are able to identify target-specific candidate molecules within the commercially available Enamine REAL Space ([~]1015). We demonstrate the applicability of the approach by successfully identifying and biochemically validating pH-specific ligands of the {micro}-opioid receptor. Our results demonstrate that integrating generative AI with evolutionary algorithms provides a promising route to explore ultra-large chemical spaces for the discovery of novel, synthetically accessible lead molecules.

bioinformatics↗

bioRxiv2023

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

systems biology↗

bioRxiv2023

VirtualFlow 2.0 - The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules

Identifying potent lead molecules for specific targets remains a major bottleneck in drug discovery. As structural information about proteins becomes increasingly available, ultra-large virtual screenings (ULVSs) which computationally evaluate billions of molecules offer a powerful way to accelerate early-stage drug discovery. Here, we introduce AdaptiveFlow, an open-source platform designed to make ULVSs more accessible, scalable, and efficient. AdaptiveFlow provides free access to a screening-ready version of the Enamine REAL Space (1), the largest library of ready-to-dock, drug-like molecules, containing 69 billion compounds that we prepared using the ligand preparation module of the platform. A key innovation of the platform is its use of a multi-dimensional grid of molecular properties, which helps researchers explore and prioritize chemical space more effectively and reduce the computational costs by a factor of approximately 1000. This grid forms the basis of a new method for identifying promising regions of chemical space, enabling systematic exploration and prioritization of compound libraries. An optional active learning component can further accelerate this process by adaptively steering the search toward molecules most likely to bind a given target. To support a broad range of applications, AdaptiveFlow is compatible with over 1,500 docking protocols. The platform achieves near-linear scaling on up to 5.6 million CPUs in the AWS Cloud, setting a new benchmark for large-scale cloud computing in drug discovery. Using this approach, we identified nanomolar inhibitors of two disease-relevant targets: ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1 (PARP-1) (2, 3). By leveraging newly solved crystal structures of FSP1 in complex with NAD+, FAD, and coenzyme Q1, we validated these hits experimentally and determined the co-crystal structures of FSP1 bound to small-molecule inhibitors, enabling insights into inhibitor binding mechanisms previously unknown. With its high scalability, flexibility, and open accessibility, AdaptiveFlow offers a powerful new resource for discovering and optimizing drug candidates at an unprecedented scale and speed.

bioinformatics↗

bioRxiv2022

Expanding the Huntingtons disease research toolbox; validated huntingtin subdomain constructs for biochemical and structural investigation of the huntingtin protein

Huntingtons disease is characterised by CAG expansion in the huntingtin gene above a critical threshold of ~35 repeats, resulting in polyglutamine expansion of the huntingtin protein (HTT). The biological role of wildtype HTT and the associated mechanisms of disease pathology caused by expanded HTT remain incompletely understood, in part, due to challenges characterising interactions between HTT and putative binding partners. Here we describe a biochemical toolkit of rationally designed, high-quality recombinant HTT subdomains; one spanning the N-terminal HEAT and bridge domains (NTD) and the second spanning the C-terminal HEAT domain (CTD). Using biophysical methods and cryo-electron microscopy, we show these smaller subdomains are natively folded and can associate to reconstitute a functional full-length HTT structure capable of forming a near native-like complex with 40 kDa HTT-associated protein (HAP40). We report biotin-tagged variants of these subdomains, as well as full-length HTT, that permit immobilisation of each protein for quantitative biophysical assays without impacting protein quality. We demonstrate the CTD alone can form a stable complex when co-expressed with HAP40, which can be structurally resolved. The CTD-HAP40 complex binds the NTD, with a dissociation constant of approximately 10 nM as measured by bio-layer interferometry. We validate the interaction between the CTD and HAP40 using a luciferase two-hybrid assay and use subdomain constructs to demonstrate their respective stabilization of HAP40 in cells. These open-source biochemical tools will enable the wider HD community to study fundamental HTT biology, discover new macromolecular or small-molecule binding partners and map interaction sites across this very large protein.

biochemistry↗

bioRxiv2021

A quantitative mapping approach to identify direct interactions within complexomes

Complementary methods are required to fully characterize all protein complexes, or the complexome, of a cell. Affinity purification coupled to mass-spectrometry (AP-MS) can identify the composition of complexes at proteome-scale. However, information on direct contacts between subunits is often lacking. In contrast, solving the 3D structure of protein complexes can provide this information, but structural biology techniques are not yet scalable for systematic, proteome-wide efforts. Here, we optimally combine two orthogonal high-throughput binary interaction assays, LuTHy and N2H, and demonstrate that their quantitative readouts can be used to differentiate direct interactions from indirect associations within multiprotein complexes. We also show that LuTHy allows accurate distance measurements between proteins in live cells and apply these findings to study the impact of the polyglutamine expansion mutation on the structurally unresolved N-terminal domain of Huntingtin. Thus, we present a new framework based on quantitative interaction assays to complement structural biology and AP-MS techniques, which should help to provide first-approximation contact maps of multiprotein complexes at proteome-scale. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/457734v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1cc7cf1org.highwire.dtl.DTLVardef@1eed9borg.highwire.dtl.DTLVardef@8197b2org.highwire.dtl.DTLVardef@e5cb8_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2019

SURF1 mutations causative of Leigh syndrome impair human neurogenesis

Mutations in the mitochondrial complex IV assembly factor SURF1 represent a major cause of Leigh syndrome (LS), a rare fatal neurological disorder. SURF1-deficient animals have failed to recapitulate the neuronal pathology of human LS, hindering our understanding of the disease mechanisms. We generated induced pluripotent stem cells from LS patients carrying homozygous SURF1 mutations (SURF1 iPS) and performed biallelic correction via CRISPR/Cas9. In contrast to corrected cells, SURF1 iPS showed impaired neuronal differentiation. Aberrant bioenergetics in SURF1 iPS occurred already in neural progenitor cells (NPCs), disrupting their neurogenic potency. Cerebral organoids from SURF1 iPS were smaller and recapitulated the neurogenesis defects. Our data imply that SURF1 mutations cause a failure in the development of maturing neurons. Using NPC function as an interventional target, we identified SURF1 gene augmentation as a potential strategy for restoring neurogenesis in LS patients carrying SURF1 mutations.

neuroscience↗