Peroxisomes are important membrane-bound organelles that are involved in a multitude of metabolic processes. Peroxisomal biogenesis factor 3 (Pex3) is a crucial peroxisomal membrane protein (PMP) that plays a vital role in many of these processes through the specific recruitment of several binding partners. In the yeast Hansenula polymorpha , Pex3 recruits PMP import receptor Pex19, the receptor for newly synthesized PMPs; autophagy-related protein 30 (Atg30), a protein involved in the selective breakdown of peroxisomes (pexophagy); and inheritance of peroxisomes protein 1 (Inp1), a protein important for peroxisome retention in mother cells. Here, we show that the overexpression of any of these proteins affects peroxisomal processes, with the level of overexpression being the primary determinant of competition. This finding is confirmed by our analysis based on the crystal structure model of the H. polymorpha Pex3–Pex19 complex and AlphaFold2 predictions. It shows that the interaction regions of Pex3 with these proteins overlap. These results provide a crucial insight into the complex role of Pex3 in regulating different processes in peroxisome biology.

Mycobacteria have up to five distinct type VII secretion pathways that play diverse roles in the cell ranging from iron uptake to virulence. To date, high-resolution structures of a hexameric ring-like pore complex have only been determined for closely related mycobacterial ESX-5 systems. The most significant difference between them is the arrangement and flexibility of the inner transmembrane helices of the EccC 5 subunit within the central pore, leading to either a closed or to a semi-open conformation of the pore. In this work, we probed the functional roles of several central pore-forming residues in mediating secretion and demonstrate their crucial role in ESX-5 substrate translocation efficiency. Structural characterization of an ESX-5 variant with a conserved proline (P73A) surprisingly revealed rigidification of the pore-forming helices. In summary, our data demonstrate that maintaining the conformational flexibility of the central ESX-5 pore is essential for substrate secretion. Our findings reveal how the rigid structural ESX-5 scaffold with separate sections – facing the periplasm, the inner mycobacterial cell wall membrane and facing the cytosol – provides a complex framework for a high level of pore dynamics, restricted to the inner EccC 5 subunit pore-forming helices. Their proper integration is required to generate a functional secretion system. The high level of conservation of several pore-forming residues across different type VII secretion systems indicates our findings to be general valid for these systems.

SUMMARY:AlphaPulldown2 streamlines protein structural modeling by automating workflows, improving code adaptability, and optimizing data management for large-scale applications. It introduces an automated Snakemake pipeline, compressed data storage, support for additional modeling backends like UniFold and AlphaLink2, and a range of other improvements. These upgrades make AlphaPulldown2 a versatile platform for predicting both binary interactions and complex multi-unit assemblies. AVAILABILITY AND IMPLEMENTATION:AlphaPulldown2 is freely available at https://github.com/KosinskiLab/AlphaPulldown.

Peroxisomes are important membrane-bound organelles that are involved in a multitude of metabolic processes. Peroxisomal biogenesis factor 3 (Pex3) is a crucial peroxisomal membrane protein (PMP) that plays a vital role in many of these processes through the specific recruitment of several binding partners. In the yeast Hansenula polymorpha, Pex3 recruits PMP import receptor Pex19, the receptor for newly synthesized PMPs; autophagy-related protein 30 (Atg30), a protein involved in the selective breakdown of peroxisomes (pexophagy); and inheritance of peroxisomes protein 1 (Inp1), a protein important for peroxisome retention in mother cells. Here, we show that the overexpression of any of these proteins affects peroxisomal processes, with the level of overexpression being the primary determinant of competition. This finding is confirmed by our analysis based on the crystal structure model of the H. polymorpha Pex3-Pex19 complex and AlphaFold2 predictions. It shows that the interaction regions of Pex3 with these proteins overlap. These results provide a crucial insight into the complex role of Pex3 in regulating different processes in peroxisome biology.

Peroxisomes are essential cellular organelles that enable the sequestered execution of a broad range of metabolic processes. Due to the lack of an internal protein synthesis machinery, they entirely depend on the import of target proteins to carry out their functions within peroxisomes. While the process of cargo/receptor recognition is well understood, knowledge about the molecular mechanisms of the subsequent translocation steps, including cargo release and receptor recycling, is lacking behind. Here, we provide structural and functional evidence on the role of Pex8 in these processes. First, we show that Pex8 in yeast is essential for peroxisomal cargo translocation, irrespective of the mechanism of receptor/cargo recognition. Next, we reveal that Pex8 binds through an irregular twelvefold HEAT repeat array to a short three-helical bundle within the otherwise unfolded N-terminal domain of the Pex5 receptor. Impairing this interaction abolishes peroxisomal protein translocation. It is complemented by a secondary autonomous Pex8 cargo-like interaction site with the C-terminal domain of Pex5, thus generating a bipartite interaction between the two proteins. Our data support a model in which Pex5/Pex8 complex formation allows assembly with the peroxisomal Pex2/Pex10/Pex12 E3-ubiquitin ligase complex to initiate recycling of the receptor. In summary, our findings provide in-depth insight into the transition from cargo release into peroxisomes to receptor recycling, which is essential to uncover the overall process of the peroxisomal cargo translocation. ### Competing Interest Statement The authors have declared no competing interest.

MOTIVATION:The structural characterization of protein-protein interactions is a key step in understanding the functions of living cells. Here, I show that AlphaFold3 often fails to predict protein complexes that are either weak or dependent on the presence of a cofactor that is not included in a prediction. RESULTS:To address this problem, I developed gapTrick, an AlphaFold2-based approach that uses multimeric templates to improve prediction reliability. I demonstrate that gapTrick improves predictions of weak and incomplete complexes based on low-accuracy templates, such as individual protein models that have been rigid-body fitted into cryo-EM reconstructions. I also show that gapTrick identifies residue-residue interactions with high precision. These interaction predictions are a very strong indicator of model correctness. The approach can aid in the interpretation of challenging experimental structures and the computational identification of protein-protein interactions. AVAILABILITY AND IMPLEMENTATION:The gapTrick source code is available at https://github.com/gchojnowski/gapTrick and requires only a standard AlphaFold2 installation to run. The repository also provides a Colab notebook that can be used to run gapTrick without installing it on the user's computer.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Sequence-register shifts remain one of the most elusive errors in experimental macromolecular models. They may affect model interpretation and propagate to newly built models from older structures. In a recent publication, it was shown that register shifts in cryo-EM models of proteins can be detected using a systematic reassignment of short model fragments to the target sequence. Here, it is shown that the same approach can be used to detect register shifts in crystal structure models using standard, model-bias-corrected electron-density maps (2mFo - DFc). Five register-shift errors in models deposited in the PDB detected using this method are described in detail.

Thrombospondin type-1 domain-containing 7A (THSD7A) is a large extracellular protein that is found in podocyte foot processes of the kidney glomerulus. It has been established as a causative autoantigen in membranous nephropathy. Amongst the predicted 21 thrombospondin repeat domains of its extracellular segment, the highest frequency of autoimmune response has been associated with the two N-terminal domains. Here, we show that antibodies against this THSD7A segment in mice induce typical clinical and morphological signs of membranous nephropathy. The high-resolution structure of these two domains reveals a non-canonical thrombospondin repeat fold that is distinct from the established type 1 thrombospondin repeat. As it shares a conserved disulfide pattern with the canonical fold, we refer to these domains d1 and d2 as type 1A thrombospondin repeats. Both domains comprise a seven layered CC-W-PP-R-W-QQ-CC pattern, which is only partly shared by other THSD7A thrombospondin repeat domains. The two domains form a well-defined V-shaped tandem arrangement. Our findings provide crucial insight into specific structural features of these two domains that are distinct from other regions of THSD7A and hence could cause the high level of antigenicity found for these two domains.

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.

In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.

Summary The Artificial Intelligence-based structure prediction program AlphaFold-Multimer enabled structural modelling of protein complexes with unprecedented accuracy. Increasingly, AlphaFold-Multimer is also used to discover new protein-protein interactions. Here, we present AlphaPulldown , a Python package that streamlines protein-protein interaction screens and high-throughput modelling of higher-order oligomers using AlphaFold-Multimer. It provides a convenient command line interface, a variety of confidence scores, and a graphical analysis tool. Availability and implementation AlphaPulldown is freely available at https://www.embl-hamburg.de/AlphaPulldown .

Oxalyl-CoA synthetase from Saccharomyces cerevisiae is one of the most abundant peroxisomal proteins in yeast and hence has become a model to study peroxisomal translocation. It contains a C-terminal Peroxisome Targeting Signal 1, which however is partly dispensable, suggesting additional receptor bindings sites. To unravel any additional features that may contribute to its capacity to be recognized as peroxisomal target, we determined its assembly and overall architecture by an integrated structural biology approach, including X-ray crystallography, single particle cryo-electron microscopy and small angle X-ray scattering. Surprisingly, it assembles into mixture of concentration-dependent dimers, tetramers and hexamers by dimer self-association. Hexameric particles form an unprecedented asymmetric horseshoe-like arrangement, which considerably differs from symmetric hexameric assembly found in many other protein structures. A single mutation within the self-association interface is sufficient to abolish any higher-level oligomerization, resulting in a homogenous dimeric assembly. The small C-terminal domain of yeast Oxalyl-CoA synthetase is connected by a partly flexible hinge with the large N-terminal domain, which provides the sole basis for oligomeric assembly. Our data provide a basis to mechanistically study peroxisomal translocation of this target.

ABSTRACTSequence assignment is a key step of the model building process in both cryogenic electron microscopy (cryo-EM) and macromolecular crystallography (MX). If the assignment fails, it can result in difficult to identify errors affecting the interpretation of a model. There are many model validation strategies that help experimentalists in this step of protein model building, but they are virtually non-existent for nucleic acids. Here I present doubleHelix – a comprehensive method for assignment, identification, and validation of nucleic acid sequences in structures determined using cryo-EM and MX. The method combines a neural network classifier of nucleobase identities and a sequence-independent secondary structure assignment approach. I show that the presented method can successfully assist model building at lower resolutions, where visual map interpretation is very difficult. Moreover, I present examples of sequence assignment errors detected using doubleHelix in cryo-EM and MX structures of ribosomes deposited in the Protein Data Bank, which escaped the scrutiny of available model-validation approaches.The doubleHelix program source code is available under BSD-3 license athttps://gitlab.com/gchojnowski/doublehelix.

Structure determination is a key step in the functional characterization of many non-coding RNA molecules. High-resolution RNA 3D structure determination efforts, however, are not keeping up with the pace of discovery of new non-coding RNA sequences. This increases the importance of computational approaches and low-resolution experimental data, such as from the small-angle X-ray scattering experiments. We present RNA Masonry, a computer program and a web service for a fully automated modeling of RNA 3D structures. It assemblies RNA fragments into geometrically plausible models that meet user-provided secondary structure constraints, restraints on tertiary contacts, and small-angle X-ray scattering data. We illustrate the method description with detailed benchmarks and its application to structural studies of viral RNAs with SAXS restraints.

Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org).

The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register shifts remain one of the most difficult to identify and correct. Here, checkMySequence, a fast, fully automated and parameter-free method for detecting register shifts in protein models built into cryo-EM maps, is introduced. It is shown that the method can assist model building in cases where poorer map resolution hinders visual interpretation. It is also shown that checkMySequence could have helped to avoid a widely discussed sequence-register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community. The software is freely available at https://gitlab.com/gchojnowski/checkmysequence.

The resistance of bacteria to β-lactam antibiotics is primarily caused by the production of β-lactamases. Here, novel crystal structures of the native β-lactamase TEM-171 and two complexes with the widely used inhibitor tazobactam are presented, alongside complementary data from UV spectroscopy and fluorescence quenching. The six chemically identical β-lactamase molecules in the crystallographic asymmetric unit displayed different degrees of disorder. The tazobactam intermediate was covalently bound to the catalytic Ser70 in the trans-enamine configuration. While the conformation of tazobactam in the first complex resembled that in published β-lactamase-tazobactam structures, in the second complex, which was obtained after longer soaking of the native crystals in the inhibitor solution, a new and previously unreported tazobactam conformation was observed. It is proposed that the two complexes correspond to different stages along the deacylation path of the acyl-enzyme intermediate. The results provide a novel structural basis for the rational design of new β-lactamase inhibitors.