Assessment of protein-protein interfaces in cryo-EM derived assemblies

PPIFold: a tool for analysis of protein-protein interaction from AlphaPullDown

Motivation

Protein structure and protein-protein interaction (PPI) predictions based on coevolution have transformed structural biology, but managing pre-processing and post-processing can be complex and time-consuming, making these tools less accessible.

Results

Here, we introduce PPIFold, a pipeline built on the AlphaPulldown Python package, designed to automate file handling and streamline the generation of outputs, facilitating the interpretation of PPI prediction results. The pipeline was validated on the bacterial Type 4 Secretion System nanomachine, demonstrating its effectiveness in simplifying PPI analysis and enhancing accessibility for researchers.

Availability and implementation

PPIFold is implemented as a pip package and available at: https://github.com/Qrouger/PPIFold.

AlphaPulldown2-a general pipeline for high-throughput structural modeling

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.

Nuclear pore permeability and fluid flow are modulated by its dilation state

Changing environmental conditions necessitate rapid adaptation of cytoplasmic and nuclear volumes. We use the slime mold Dictyostelium discoideum, known for its ability to tolerate extreme changes in osmolarity, to assess which role nuclear pore complexes (NPCs) play in achieving nuclear volume adaptation and relieving mechanical stress. We capitalize on the unique properties of D. discoideum to quantify fluid flow across NPCs. D. discoideum has an elaborate NPC structure in situ. Its dilation state affects NPC permeability for nucleocytosolic flow. Based on mathematical concepts adapted from hydrodynamics, we conceptualize this phenomenon as porous flow across NPCs, which is distinct from canonically characterized modes of nucleocytoplasmic transport because of its dependence on pressure. Viral NPC blockage decreased nucleocytosolic flow. Our results may be relevant for any biological conditions that entail rapid nuclear size adaptation, including metastasizing cancer cells, migrating cells, or differentiating tissues.

A ribosome-associating chaperone mediates GTP-driven vectorial folding of nascent eEF1A

Eukaryotic translation elongation factor 1A (eEF1A) is a highly abundant, multi-domain GTPase. Post-translational steps essential for eEF1A biogenesis are carried out by bespoke chaperones but co-translational mechanisms tailored to eEF1A folding remain unexplored. Here, we use AlphaPulldown to identify Ypl225w (also known as Chp1, Chaperone 1 for eEF1A) as a conserved yeast protein predicted to stabilize the N-terminal, GTP-binding (G) domain of eEF1A against its misfolding propensity, as predicted by computational simulations and validated by microscopy analysis of ypl225wΔ cells. Proteomics and biochemical reconstitution reveal that Ypl225w functions as a co-translational chaperone by forming dual interactions with the eEF1A G domain nascent chain and the UBA domain of ribosome-bound nascent polypeptide-associated complex (NAC). Lastly, we show that Ypl225w primes eEF1A nascent chains for binding to GTP as part of a folding mechanism tightly coupled to chaperone recycling. Our work shows that an ATP-independent chaperone can drive vectorial folding of nascent chains by co-opting G protein nucleotide binding.

Glucose-6-phosphate dehydrogenase and its 3D structures from crystallography and electron cryo-microscopy

Glucose-6-phosphate dehydrogenase (G6PD) is the first enzyme in the pentose phosphate pathway. It has been extensively studied by biochemical and structural techniques. 13 X-ray crystal structures and five electron cryo-microscopy structures in the PDB are focused on in this topical review. Two F420-dependent glucose-6-phosphate dehydrogenase (FGD) structures are also reported. The significant differences between human and parasite G6PDs can be exploited to find selective drugs against infections such as malaria and leishmaniasis. Furthermore, G6PD is a prognostic marker in several cancer types and is also considered to be a tumour target. On the other hand, FGD is considered to be a target against Mycobacterium tuberculosis and possesses a high biotechnological potential in biocatalysis and bioremediation.

AlphaCRV: a pipeline for identifying accurate binder topologies in mass-modeling with AlphaFold

Motivation

The speed and accuracy of deep learning-based structure prediction algorithms make it now possible to perform in silico "pull-downs" to identify protein-protein interactions on a proteome-wide scale. However, on such a large scale, existing scoring algorithms are often insufficient to discriminate biologically relevant interactions from false positives.

Results

Here, we introduce AlphaCRV, a Python package that helps identify correct interactors in a one-against-many AlphaFold screen by clustering, ranking, and visualizing conserved binding topologies, based on protein sequence and fold.

Availability and implementation

AlphaCRV is a Python package for Linux, freely available at https://github.com/strubelab/AlphaCRV.

An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction

In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.

Global Interactome Mapping Reveals Pro-tumorigenic Interactions of NF-κB in Breast Cancer

NF-κB pathway is involved in inflammation; however, recent data shows its role also in cancer development and progression, including metastasis. To understand the role of NF-κB interactome dynamics in cancer, we study the complexity of breast cancer interactome in luminal A breast cancer model and its rearrangement associated with NF-κB modulation. Liquid chromatography-mass spectrometry measurement of 160 size-exclusion chromatography fractions identifies 5460 protein groups. Seven thousand five hundred sixty eight interactions among these proteins have been reconstructed by PrInCE algorithm, of which 2564 have been validated in independent datasets. NF-κB modulation leads to rearrangement of protein complexes involved in NF-κB signaling and immune response, cell cycle regulation, and DNA replication. Central NF-κB transcription regulator RELA co-elutes with interactors of NF-κB activator PRMT5, and these complexes are confirmed by AlphaPulldown prediction. A complementary immunoprecipitation experiment recapitulates RELA interactions with other NF-κB factors, associating NF-κB inhibition with lower binding of NF-κB activators to RELA. This study describes a network of pro-tumorigenic protein interactions and their rearrangement upon NF-κB inhibition with potential therapeutic implications in tumors with high NF-κB activity.

RNA to Rule Them All: Critical Steps in Lassa Virus Ribonucleoparticle Assembly and Recruitment

Lassa virus is a negative-strand RNA virus with only four structural proteins that causes periodic outbreaks in West Africa. The nucleoprotein (NP) encapsidates the viral genome, forming ribonucleoprotein complexes (RNPs) together with the viral RNA and the L protein. RNPs must be continuously restructured during viral genome replication and transcription. The Z protein is important for membrane recruitment of RNPs, viral particle assembly, and budding and has also been shown to interact with the L protein. However, the interaction of NP, viral RNA, and Z is poorly understood. Here, we characterize the interactions between Lassa virus NP, Z, and RNA using structural mass spectrometry. We identify the presence of RNA as the driver for the disassembly of ring-like NP trimers, a storage form, into monomers to subsequently form higher order RNA-bound NP assemblies. We locate the interaction site of Z and NP and demonstrate that while NP binds Z independently of the presence of RNA, this interaction is pH-dependent. These data improve our understanding of RNP assembly, recruitment, and release in Lassa virus.

Molecular basis of TASL recruitment by the peptide/histidine transporter 1, PHT1

PHT1 is a histidine /oligopeptide transporter with an essential role in Toll-like receptor innate immune responses. It can act as a receptor by recruiting the adaptor protein TASL which leads to type I interferon production via IRF5. Persistent stimulation of this signalling pathway is known to be involved in the pathogenesis of systemic lupus erythematosus (SLE). Understanding how PHT1 recruits TASL at the molecular level, is therefore clinically important for the development of therapeutics against SLE and other autoimmune diseases. Here we present the Cryo-EM structure of PHT1 stabilized in the outward-open conformation. By combining biochemical and structural modeling techniques we propose a model of the PHT1-TASL complex, in which the first 16 N-terminal TASL residues fold into a helical structure that bind in the central cavity of the inward-open conformation of PHT1. This work provides critical insights into the molecular basis of PHT1/TASL mediated type I interferon production.

Analysis of protein-protein interface with incorporating low-frequency molecular interactions in molecular dynamics simulation

Protein-protein interactions are vital for various biological processes such as immune reaction, signal transduction, and viral infection. Molecular Dynamics (MD) simulation is a powerful tool for analyzing non-covalent interactions between two protein molecules. In general, MD simulation studies on the protein-protein interface have focused on the analysis of major and frequent molecular interactions. In this study, we demonstrate that minor interactions with low-frequency need to be incorporated to analyze the molecular interactions in the protein-protein interface more efficiently using the complex of SARS-CoV2-RBD and ACE2 receptor as a model system. It was observed that the dominance of interactions in the MD-simulated structures didn't directly correlate with the interactions in the experimentally determined structure. The interactions from the experimentally determined structure could be reproduced better in the ensemble of MD simulated structures by including the less frequent interactions compared to the norm of choosing only highly frequent interactions. Residue Interaction Networks (RINs) analysis also showed that the critical residues in the protein-protein interface could be more efficiently identified by incorporating low-frequency interactions in MD simulation. It is expected that the approach proposed in this study can be a new way of studying protein-protein interaction through MD simulation.

AlphaPulldown-a python package for protein-protein interaction screens using AlphaFold-Multimer

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 (PPIs). Here, we present AlphaPulldown, a Python package that streamlines PPI 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.

SUPPLEMENTARY INFORMATION

Supplementary note is available at Bioinformatics online.

Overview and applications of map and model validation tools in the CCP-EM software suite

Cryogenic electron microscopy (cryo-EM) has recently been established as a powerful technique for solving macromolecular structures. Although the best resolutions achievable are improving, a significant majority of data are still resolved at resolutions worse than 3 Å, where it is non-trivial to build or fit atomic models. The map reconstructions and atomic models derived from the maps are also prone to errors accumulated through the different stages of data processing. Here, we highlight the need to evaluate both model geometry and fit to data at different resolutions. Assessment of cryo-EM structures from SARS-CoV-2 highlights a bias towards optimising the model geometry to agree with the most common conformations, compared to the agreement with data. We present the CoVal web service which provides multiple validation metrics to reflect the quality of atomic models derived from cryo-EM data of structures from SARS-CoV-2. We demonstrate that further refinement can lead to improvement of the agreement with data without the loss of geometric quality. We also discuss the recent CCP-EM developments aimed at addressing some of the current shortcomings.

Cryo-electron Microscopy of Adeno-associated Virus

Adeno-associated virus (AAV) has a single-stranded DNA genome encapsidated in a small icosahedrally symmetric protein shell with 60 subunits. AAV is the leading delivery vector in emerging gene therapy treatments for inherited disorders, so its structure and molecular interactions with human hosts are of intense interest. A wide array of electron microscopic approaches have been used to visualize the virus and its complexes, depending on the scientific question, technology available, and amenability of the sample. Approaches range from subvolume tomographic analyses of complexes with large and flexible host proteins to detailed analysis of atomic interactions within the virus and with small ligands at resolutions as high as 1.6 Å. Analyses have led to the reclassification of glycan receptors as attachment factors, to structures with a new-found receptor protein, to identification of the epitopes of antibodies, and a new understanding of possible neutralization mechanisms. AAV is now well-enough characterized that it has also become a model system for EM methods development. Heralding a new era, cryo-EM is now also being deployed as an analytic tool in the process development and production quality control of high value pharmaceutical biologics, namely AAV vectors.

Cryo-EM reveals mechanisms of angiotensin I-converting enzyme allostery and dimerization

Hypertension (high blood pressure) is a major risk factor for cardiovascular disease, which is the leading cause of death worldwide. The somatic isoform of angiotensin I‐converting enzyme (sACE) plays a critical role in blood pressure regulation, and ACE inhibitors are thus widely used to treat hypertension and cardiovascular disease. Our current understanding of sACE structure, dynamics, function, and inhibition has been limited because truncated, minimally glycosylated forms of sACE are typically used for X‐ray crystallography and molecular dynamics simulations. Here, we report the first cryo‐EM structures of full‐length, glycosylated, soluble sACE (sACE^S1211). Both monomeric and dimeric forms of the highly flexible apo enzyme were reconstructed from a single dataset. The N‐ and C‐terminal domains of monomeric sACE^S1211 were resolved at 3.7 and 4.1 Å, respectively, while the interacting N‐terminal domains responsible for dimer formation were resolved at 3.8 Å. Mechanisms are proposed for intradomain hinging, cooperativity, and homodimerization. Furthermore, the observation that both domains were in the open conformation has implications for the design of sACE modulators.

AI-based structure prediction empowers integrative structural analysis of human nuclear pores

INTRODUCTION The eukaryotic nucleus pro-tects the genome and is enclosed by the two membranes of the nuclear envelope. Nuclear pore complexes (NPCs) perforate the nuclear envelope to facilitate nucleocytoplasmic transport. With a molecular weight of ∼120 MDa, the human NPC is one of the larg-est protein complexes. Its ~1000 proteins are taken in multiple copies from a set of about 30 distinct nucleoporins (NUPs). They can be roughly categorized into two classes. Scaf-fold NUPs contain folded domains and form a cylindrical scaffold architecture around a central channel. Intrinsically disordered NUPs line the scaffold and extend into the central channel, where they interact with cargo complexes. The NPC architecture is highly dynamic. It responds to changes in nuclear envelope tension with conforma-tional breathing that manifests in dilation and constriction movements. Elucidating the scaffold architecture, ultimately at atomic resolution, will be important for gaining a more precise understanding of NPC function and dynamics but imposes a substantial chal-lenge for structural biologists. RATIONALE Considerable progress has been made toward this goal by a joint effort in the field. A synergistic combination of complementary approaches has turned out to be critical. In situ structural biology techniques were used to reveal the overall layout of the NPC scaffold that defines the spatial reference for molecular modeling. High-resolution structures of many NUPs were determined in vitro. Proteomic analysis and extensive biochemical work unraveled the interaction network of NUPs. Integra-tive modeling has been used to combine the different types of data, resulting in a rough outline of the NPC scaffold. Previous struc-tural models of the human NPC, however, were patchy and limited in accuracy owing to several challenges: (i) Many of the high-resolution structures of individual NUPs have been solved from distantly related species and, consequently, do not comprehensively cover their human counterparts. (ii) The scaf-fold is interconnected by a set of intrinsically disordered linker NUPs that are not straight-forwardly accessible to common structural biology techniques. (iii) The NPC scaffold intimately embraces the fused inner and outer nuclear membranes in a distinctive topol-ogy and cannot be studied in isolation. (iv) The conformational dynamics of scaffold NUPs limits the resolution achievable in structure determination. RESULTS In this study, we used artificial intelligence (AI)-based prediction to generate an exten-sive repertoire of structural models of human NUPs and their subcomplexes. The resulting models cover various domains and interfaces that so far remained structurally uncharac-terized. Benchmarking against previous and unpublished x-ray and cryo-electron micros-copy structures revealed unprecedented accu-racy. We obtained well-resolved cryo-electron tomographic maps of both the constricted and dilated conformational states of the hu-man NPC. Using integrative modeling, we fit-ted the structural models of individual NUPs into the cryo-electron microscopy maps. We explicitly included several linker NUPs and traced their trajectory through the NPC scaf-fold. We elucidated in great detail how mem-brane-associated and transmembrane NUPs are distributed across the fusion topology of both nuclear membranes. The resulting architectural model increases the structural coverage of the human NPC scaffold by about twofold. We extensively validated our model against both earlier and new experimental data. The completeness of our model has enabled microsecond-long coarse-grained molecular dynamics simulations of the NPC scaffold within an explicit membrane en-vironment and solvent. These simulations reveal that the NPC scaffold prevents the constriction of the otherwise stable double-membrane fusion pore to small diameters in the absence of membrane tension. CONCLUSION Our 70-MDa atomically re-solved model covers >90% of the human NPC scaffold. It captures conforma-tional changes that occur during dilation and constriction. It also reveals the precise anchoring sites for intrinsically disordered NUPs, the identification of which is a prerequisite for a complete and dy-namic model of the NPC. Our study exempli-fies how AI-based structure prediction may accelerate the elucidation of subcellular ar-chitecture at atomic resolution. [Figure: see text].

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.

Interface refinement of low- to medium-resolution Cryo-EM complexes using HADDOCK2.4

A wide range of cellular processes requires the formation of multimeric protein complexes. The rise of cryo-electron microscopy (cryo-EM) has enabled the structural characterization of these protein assemblies. The density maps produced can, however, still suffer from limited resolution, impeding the process of resolving structures at atomic resolution. In order to solve this issue, monomers can be fitted into low- to medium-resolution maps. Unfortunately, the models produced frequently contain atomic clashes at the protein-protein interfaces (PPIs), as intermolecular interactions are typically not considered during monomer fitting. Here, we present a refinement approach based on HADDOCK2.4 to remove intermolecular clashes and optimize PPIs. A dataset of 14 cryo-EM complexes was used to test eight protocols. The best-performing protocol, consisting of a semi-flexible simulated annealing refinement with centroid restraints on the monomers, was able to decrease intermolecular atomic clashes by 98% without significantly deteriorating the quality of the cryo-EM density fit.

Atomic model validation using the CCP-EM software suite

Recently, there has been a dramatic improvement in the quality and quantity of data derived using cryogenic electron microscopy (cryo-EM). This is also associated with a large increase in the number of atomic models built. Although the best resolutions that are achievable are improving, often the local resolution is variable, and a significant majority of data are still resolved at resolutions worse than 3 Å. Model building and refinement is often challenging at these resolutions, and hence atomic model validation becomes even more crucial to identify less reliable regions of the model. Here, a graphical user interface for atomic model validation, implemented in the CCP-EM software suite, is presented. It is aimed to develop this into a platform where users can access multiple complementary validation metrics that work across a range of resolutions and obtain a summary of evaluations. Based on the validation estimates from atomic models associated with cryo-EM structures from SARS-CoV-2, it was observed that models typically favor adopting the most common conformations over fitting the observations when compared with the model agreement with data. At low resolutions, the stereochemical quality may be favored over data fit, but care should be taken to ensure that the model agrees with the data in terms of resolvable features. It is demonstrated that further re-refinement can lead to improvement of the agreement with data without the loss of geometric quality. This also highlights the need for improved resolution-dependent weight optimization in model refinement and an effective test for overfitting that would help to guide the refinement process.