Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models

The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.     

A new hydrogen-bonding potential for the design of protein-RNA interactions predicts specific contacts and discriminates decoys

RNA-binding proteins play many essential roles in the regulation of gene expression in the cell. Despite the significant increase in the number of structures for RNA–protein complexes in the last few years, the molecular basis of specificity remains unclear even for the best-studied protein families. We have developed a distance and orientation-dependent hydrogen-bonding potential based on the statistical analysis of hydrogen-bonding geometries that are observed in high-resolution crystal structures of protein–DNA and protein–RNA complexes. We observe very strong geometrical preferences that reflect significant energetic constraints on the relative placement of hydrogen-bonding atom pairs at protein–nucleic acid interfaces. A scoring function based on the hydrogen-bonding potential discriminates native protein–RNA structures from incorrectly docked decoys with remarkable predictive power. By incorporating the new hydrogen-bonding potential into a physical model of protein–RNA interfaces with full atom representation, we were able to recover native amino acids at protein–RNA interfaces.     

An ultra‐high affinity protein–protein interface displaying sequence‐robustness

Protein–protein interactions are crucial in biology and play roles in for example, the immune system, signaling pathways, and enzyme regulation. Ultra‐high affinity interactions (K _d <0.1 nM) occur in these systems, however, structures and energetics behind stability of ultra‐high affinity protein–protein complexes are not well understood. Regulation of the starch debranching barley limit dextrinase (LD) and its endogenous cereal type inhibitor (LDI) exemplifies an ultra‐high affinity complex (K _d of 42 pM). In this study the LD–LDI complex is investigated to unveil how robust the ultra‐high affinity is to LDI sequence variation at the protein–protein interface and whether alternative sequences can retain the ultra‐high binding affinity. The interface of LD–LDI was engineered using computational protein redesign aiming at identifying LDI variants predicted to retain ultra‐high binding affinity. These variants present a very diverse set of mutations going beyond conservative and alanine substitutions typically used to probe interfaces. Surface plasmon resonance analysis of the LDI variants revealed that high affinity of LD–LDI requires interactions of several residues at the rim of the protein interface, unlike the classical hotspot arrangement where key residues are found at the center of the interface. Notably, substitution of interface residues in LDI, including amino acids with functional groups different from the wild‐type, could occur without loss of affinity. This demonstrates that ultra‐high binding affinity can be conferred without hotspot residues, thus making complexes more robust to mutational drift in evolution. The present mutational analysis also demonstrates how energetic coupling can emerge between residues at large distances at the interface.     

Improving integrative 3D modeling into low‐ to medium‐resolution electron microscopy structures with evolutionary couplings

Electron microscopy (EM) continues to provide near‐atomic resolution structures for well‐behaved proteins and protein complexes. Unfortunately, structures of some complexes are limited to low‐ to medium‐resolution due to biochemical or conformational heterogeneity. Thus, the application of unbiased systematic methods for fitting individual structures into EM maps is important. A method that employs co‐evolutionary information obtained solely from sequence data could prove invaluable for quick, confident localization of subunits within these structures. Here, we incorporate the co‐evolution of intermolecular amino acids as a new type of distance restraint in the integrative modeling platform in order to build three‐dimensional models of atomic structures into EM maps ranging from 10–14 Å in resolution. We validate this method using four complexes of known structure, where we highlight the conservation of intermolecular couplings despite dynamic conformational changes using the BAM complex. Finally, we use this method to assemble the subunits of the bacterial holo‐translocon into a model that agrees with previous biochemical data. The use of evolutionary couplings in integrative modeling improves systematic, unbiased fitting of atomic models into medium‐ to low‐resolution EM maps, providing additional information to integrative models lacking in spatial data.     

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody's interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody–antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs.     

Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)‐based approaches have allowed unbiased mapping of these disease‐mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system‐level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS‐based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.     

Complementing machine learning‐based structure predictions with native mass spectrometry

The advent of machine learning‐based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user‐provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time‐effective tool that provides information on post‐translational modifications, ligand interactions, conformational changes, and higher‐order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.     

Architecture of the active post‐translational Sec translocon

In eukaryotes, most secretory and membrane proteins are targeted by an N‐terminal signal sequence to the endoplasmic reticulum, where the trimeric Sec61 complex serves as protein‐conducting channel (PCC). In the post‐translational mode, fully synthesized proteins are recognized by a specialized channel additionally containing the Sec62, Sec63, Sec71, and Sec72 subunits. Recent structures of this Sec complex in the idle state revealed the overall architecture in a pre‐opened state. Here, we present a cryo‐EM structure of the yeast Sec complex bound to a substrate, and a crystal structure of the Sec62 cytosolic domain. The signal sequence is inserted into the lateral gate of Sec61α similar to previous structures, yet, with the gate adopting an even more open conformation. The signal sequence is flanked by two Sec62 transmembrane helices, the cytoplasmic N‐terminal domain of Sec62 is more rigidly positioned, and the plug domain is relocated. We crystallized the Sec62 domain and mapped its interaction with the C‐terminus of Sec63. Together, we obtained a near‐complete and integrated model of the active Sec complex.     

Interactomes of SARS‐CoV‐2 and human coronaviruses reveal host factors potentially affecting pathogenesis

Host–virus protein–protein interactions play key roles in the life cycle of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). We conducted a comprehensive interactome study between the virus and host cells using tandem affinity purification and proximity‐labeling strategies and identified 437 human proteins as the high‐confidence interacting proteins. Further characterization of these interactions and comparison to other large‐scale study of cellular responses to SARS‐CoV‐2 infection elucidated how distinct SARS‐CoV‐2 viral proteins participate in its life cycle. With these data mining, we discovered potential drug targets for the treatment of COVID‐19. The interactomes of two key SARS‐CoV‐2‐encoded viral proteins, NSP1 and N, were compared with the interactomes of their counterparts in other human coronaviruses. These comparisons not only revealed common host pathways these viruses manipulate for their survival, but also showed divergent protein–protein interactions that may explain differences in disease pathology. This comprehensive interactome of SARS‐CoV‐2 provides valuable resources for the understanding and treating of this disease.     

Genome‐scale metabolic modeling reveals key features of a minimal gene set

Mesoplasma florum, a fast‐growing near‐minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein‐coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species‐specific constraints to produce the functional iJL208 genome‐scale model (GEM) of metabolism. Genome‐wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI‐syn3.0 and its parent JCVI‐syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping‐stone toward model‐driven whole‐genome engineering.     

Purification of MAP–kinase protein complexes and identification of candidate components by XL–TAP–MS

The purification of low-abundance protein complexes and detection of in vivo protein–protein interactions in complex biological samples remains a challenging task. Here, we devised crosslinking and tandem affinity purification coupled to mass spectrometry (XL–TAP–MS), a quantitative proteomics approach for analyzing tandem affinity-purified, crosslinked protein complexes from plant tissues. We exemplarily applied XL–TAP–MS to study the MKK2–Mitogen-activated protein kinase (MPK4) signaling module in Arabidopsis thaliana. A tandem affinity tag consisting of an in vivo-biotinylated protein domain flanked by two hexahistidine sequences was adopted to allow for the affinity-based isolation of formaldehyde–crosslinked protein complexes under fully denaturing conditions. Combined with ¹⁵N stable isotopic labeling and tandem MS we captured and identified a total of 107 MKK2–MPK4 module-interacting proteins. Consistent with the role of the MPK signaling module in plant immunity, many of the module-interacting proteins are involved in the biotic and abiotic stress response of Arabidopsis. Validation of binary protein–protein interactions by in planta split-luciferase assays and in vitro kinase assays disclosed several direct phosphorylation targets of MPK4. Together, the XL–TAP–MS approach purifies low abundance protein complexes from biological samples and discovers previously unknown protein–protein interactions.

XL–TAP–MS: a novel technique that allows purification of crosslinked, low abundant protein complexes from plant tissues under denatured conditions and detection of in vivo protein–protein interactions.     

Assessment and significance of protein–protein interactions during development of protein biopharmaceuticals

Early development of protein biotherapeutics using recombinant DNA technology involved progress in the areas of cloning, screening, expression and recovery/purification. As the biotechnology industry matured, resulting in marketed products, a greater emphasis was placed on development of formulations and delivery systems requiring a better understanding of the chemical and physical properties of newly developed protein drugs. Biophysical techniques such as analytical ultracentrifugation, dynamic and static light scattering, and circular dichroism were used to study protein–protein interactions during various stages of development of protein therapeutics. These studies included investigation of protein self-association in many of the early development projects including analysis of highly glycosylated proteins expressed in mammalian CHO cell cultures. Assessment of protein–protein interactions during development of an IgG1 monoclonal antibody that binds to IgE were important in understanding the pharmacokinetics and dosing for this important biotherapeutic used to treat severe allergic IgE-mediated asthma. These studies were extended to the investigation of monoclonal antibody–antigen interactions in human serum using the fluorescent detection system of the analytical ultracentrifuge. Analysis by sedimentation velocity analytical ultracentrifugation was also used to investigate competitive binding to monoclonal antibody targets. Recent development of high concentration protein formulations for subcutaneous administration of therapeutics posed challenges, which resulted in the use of dynamic and static light scattering, and preparative analytical ultracentrifugation to understand the self-association and rheological properties of concentrated monoclonal antibody solutions.     

NMR hawk‐eyed view of AlphaFold2 structures

The prediction of the three‐dimensional (3D) structure of proteins from the amino acid sequence made a stunning breakthrough reaching atomic accuracy. Using the neural network‐based method AlphaFold2, 3D structures of almost the entire human proteome have been predicted and made available (https://www.alphafold.ebi.ac.uk). To gain insight into how well AlphaFold2 structures represent the conformation of proteins in solution, I here compare the AlphaFold2 structures of selected small proteins with their 3D structures that were determined by nuclear magnetic resonance (NMR) spectroscopy. Proteins were selected for which the 3D solution structures were determined on the basis of a very large number of distance restraints and residual dipolar couplings and are thus some of the best‐resolved solution structures of proteins to date. The quality of the backbone conformation of the AlphaFold2 structures is assessed by fitting a large set of experimental residual dipolar couplings (RDCs). The analysis shows that experimental RDCs fit extremely well to the AlphaFold2 structures predicted for GB3, DinI, and ubiquitin. In the case of GB3, the accuracy of the AlphaFold2 structure even surpasses that of a 1.1 Å crystal structure. Fitting of experimental RDCs furthermore allows identification of AlphaFold2 structures that are best representative of the protein''s conformation in solution as seen for the EF hands of the N‐terminal domain of Ca²⁺‐ligated calmodulin. Taken together, the analysis shows that structures predicted by AlphaFold2 can be highly representative of the solution conformation of proteins. The combination of AlphaFold2 structures with RDCs promises to be a powerful approach to study structural changes in proteins.     

Systematic evaluation of antibody-mediated siRNA delivery using an industrial platform of THIOMAB–siRNA conjugates

Delivery of siRNA is a key hurdle to realizing the therapeutic promise of RNAi. By targeting internalizing cell surface antigens, antibody–siRNA complexes provide a possible solution. However, initial reports of antibody–siRNA complexes relied on non-specific charged interactions and have not been broadly applicable. To assess and improve this delivery method, we built on an industrial platform of therapeutic antibodies called THIOMABs, engineered to enable precise covalent coupling of siRNAs. We report that such coupling generates monomeric antibody–siRNA conjugates (ARCs) that retain antibody and siRNA activities. To broadly assess this technology, we generated a battery of THIOMABs against seven targets that use multiple internalization routes, enabling systematic manipulation of multiple parameters that impact delivery. We identify ARCs that induce targeted silencing in vitro and extend tests to target prostate carcinoma cells following systemic administration in mouse models. However, optimal silencing was restricted to specific conditions and only observed using a subset of ARCs. Trafficking studies point to ARC entrapment in endocytic compartments as a limiting factor, independent of the route of antigen internalization. Our broad characterization of multiple parameters using therapeutic-grade conjugate technology provides a thorough assessment of this delivery technology, highlighting both examples of success as well as remaining challenges.     

A structural inventory of native ribosomal ABCE1‐43S pre‐initiation complexes

In eukaryotic translation, termination and ribosome recycling phases are linked to subsequent initiation of a new round of translation by persistence of several factors at ribosomal sub‐complexes. These comprise/include the large eIF3 complex, eIF3j (Hcr1 in yeast) and the ATP‐binding cassette protein ABCE1 (Rli1 in yeast). The ATPase is mainly active as a recycling factor, but it can remain bound to the dissociated 40S subunit until formation of the next 43S pre‐initiation complexes. However, its functional role and native architectural context remains largely enigmatic. Here, we present an architectural inventory of native yeast and human ABCE1‐containing pre‐initiation complexes by cryo‐EM. We found that ABCE1 was mostly associated with early 43S, but also with later 48S phases of initiation. It adopted a novel hybrid conformation of its nucleotide‐binding domains, while interacting with the N‐terminus of eIF3j. Further, eIF3j occupied the mRNA entry channel via its ultimate C‐terminus providing a structural explanation for its antagonistic role with respect to mRNA binding. Overall, the native human samples provide a near‐complete molecular picture of the architecture and sophisticated interaction network of the 43S‐bound eIF3 complex and the eIF2 ternary complex containing the initiator tRNA.     

A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method

Protein-RNA interactions play important roles in many biological processes. Given the high cost and technique difficulties in experimental methods, computationally predicting the binding complexes from individual protein and RNA structures is pressingly needed, in which a reliable scoring function is one of the critical components. Here, we have developed a knowledge-based scoring function, referred to as ITScore-PR, for protein-RNA binding mode prediction by using a statistical mechanics-based iterative method. The pairwise distance-dependent atomic interaction potentials of ITScore-PR were derived from experimentally determined protein–RNA complex structures. For validation, we have compared ITScore-PR with 10 other scoring methods on four diverse test sets. For bound docking, ITScore-PR achieved a success rate of up to 86% if the top prediction was considered and up to 94% if the top 10 predictions were considered, respectively. For truly unbound docking, the respective success rates of ITScore-PR were up to 24 and 46%. ITScore-PR can be used stand-alone or easily implemented in other docking programs for protein–RNA recognition.     

Frequent Assembly of Chimeric Complexes in the Protein Interaction Network of an Interspecies Yeast Hybrid

Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein–protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein–protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein–protein interactions in the hybrid, for instance, in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteomes.     

Computational design of small molecular modulators of protein–protein interactions with a novel thermodynamic cycle: Allosteric inhibitors of HIV‐1 integrase

Targeting protein–protein interactions for therapeutic development involves designing small molecules to either disrupt or enhance a known PPI. For this purpose, it is necessary to compute reliably the effect of chemical modifications of small molecules on the protein–protein association free energy. Here we present results obtained using a novel thermodynamic free energy cycle, for the rational design of allosteric inhibitors of HIV‐1 integrase (ALLINI) that act specifically in the early stage of the infection cycle. The new compounds can serve as molecular probes to dissect the multifunctional mechanisms of ALLINIs to inform the discovery of new allosteric inhibitors. The free energy protocol developed here can be more broadly applied to study quantitatively the effects of small molecules on modulating the strengths of protein–protein interactions.