In Vitro Evolution Reveals Noncationic Protein–RNA Interaction Mediated by Metal Ions

RNA–peptide/protein interactions have been of utmost importance to life since its earliest forms, reaching even before the last universal common ancestor (LUCA). However, the ancient molecular mechanisms behind this key biological interaction remain enigmatic because extant RNA–protein interactions rely heavily on positively charged and aromatic amino acids that were absent (or heavily under-represented) in the early pre-LUCA evolutionary period. Here, an RNA-binding variant of the ribosomal uL11 C-terminal domain was selected from an approximately 10¹⁰ library of partially randomized sequences, all composed of ten prebiotically plausible canonical amino acids. The selected variant binds to the cognate RNA with a similar overall affinity although it is less structured in the unbound form than the wild-type protein domain. The variant complex association and dissociation are both slower than for the wild-type, implying different mechanistic processes involved. The profile of the wild-type and mutant complex stabilities along with molecular dynamics simulations uncovers qualitative differences in the interaction modes. In the absence of positively charged and aromatic residues, the mutant uL11 domain uses ion bridging (K⁺/Mg²⁺) interactions between the RNA sugar-phosphate backbone and glutamic acid residues as an alternative source of stabilization. This study presents experimental support to provide a new perspective on how early protein–RNA interactions evolved, where the lack of aromatic/basic residues may have been compensated by acidic residues plus metal ions.     

Match_Motif: A rapid computational tool to assist in protein–protein interaction design

In order to generate protein assemblies with a desired function, the rational design of protein–protein binding interfaces is of significant interest. Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein–protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from https://www.groups.ph.tum.de/t38/downloads/.     

Protein–protein interaction prediction methods: from docking-based to AI-based approaches

Protein–protein interactions (PPIs), such as protein–protein inhibitor, antibody–antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody–antigen interactions, offering both historical and forward-looking perspectives.     

Computational Assessment of Protein–protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.     

Substrate Affinity Versus Catalytic Efficiency: Ancestral Sequence Reconstruction of tRNA Nucleotidyltransferases Solves an Enzyme Puzzle

In tRNA maturation, CCA-addition by tRNA nucleotidyltransferase is a unique and highly accurate reaction. While the mechanism of nucleotide selection and polymerization is well understood, it remains a mystery why bacterial and eukaryotic enzymes exhibit an unexpected and surprisingly low tRNA substrate affinity while they efficiently catalyze the CCA-addition. To get insights into the evolution of this high-fidelity RNA synthesis, the reconstruction and characterization of ancestral enzymes is a versatile tool. Here, we investigate a reconstructed candidate of a 2 billion years old CCA-adding enzyme from Gammaproteobacteria and compare it to the corresponding modern enzyme of Escherichia coli. We show that the ancestral candidate catalyzes an error-free CCA-addition, but has a much higher tRNA affinity compared with the extant enzyme. The consequence of this increased substrate binding is an enhanced reverse reaction, where the enzyme removes the CCA end from the mature tRNA. As a result, the ancestral candidate exhibits a lower catalytic efficiency in vitro as well as in vivo. Furthermore, the efficient tRNA interaction leads to a processive polymerization, while the extant enzyme catalyzes nucleotide addition in a distributive way. Thus, the modern enzymes increased their polymerization efficiency by lowering the binding affinity to tRNA, so that CCA synthesis is efficiently promoted due to a reduced reverse reaction. Hence, the puzzling and at a first glance contradicting and detrimental weak substrate interaction represents a distinct activity enhancement in the evolution of CCA-adding enzymes.     

PPICurator: A Tool for Extracting Comprehensive Protein–Protein Interaction Information

Protein–protein interaction extraction through biological literature curation is widely employed for proteome analysis. There is a strong need for a tool that can assist researchers in extracting comprehensive PPI information through literature curation, which is critical in research on protein, for example, construction of protein interaction network, identification of protein signaling pathway, and discovery of meaningful protein interaction. However, most of current tools can only extract PPI relations. None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. To address these issues, this paper proposes PPICurator, a novel tool for extracting comprehensive PPI information with a variety of logic and syntax features based on a new support vector machine classifier. PPICurator provides a friendly web‐based user interface. It is a platform that automates the extraction of comprehensive PPI information through literature, including PPI relations, as well as their confidential scores, interaction directions, effects, and functional annotations. Thus, PPICurator is more comprehensive than state‐of‐the‐art tools. Moreover, it outperforms state‐of‐the‐art tools in the accuracy of PPI relation extraction measured by F‐score and recall on the widely used open datasets. PPICurator is available at https://ppicurator.hupo.org.cn .     

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.     

Open Modification Searching of SARS-CoV-2–Human Protein Interaction Data Reveals Novel Viral Modification Sites

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus 2019 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus–host protein–protein interactions through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications. We have reanalyzed public affinity purification–mass spectrometry data using open modification searching to investigate the presence of post-translational modifications in the context of the SARS-CoV-2 virus–host protein–protein interaction network. Based on an over twofold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus–host interactions for alternative viruses, as well as previously unknown protein interactions. In addition, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.     

Sulfotyrosine residues: Interaction specificity determinants for extracellular protein–protein interactions

Tyrosine sulfation, a post-translational modification, can determine and often enhance protein–protein interaction specificity. Sulfotyrosyl residues (sTyrs) are formed by the enzyme tyrosyl-protein sulfotransferase during protein maturation in the Golgi apparatus and most often occur singly or as a cluster within a six-residue span. With both negative charge and aromatic character, sTyr facilitates numerous atomic contacts as visualized in binding interface structural models, thus there is no discernible binding site consensus. Found exclusively in secreted proteins, in this review, we discuss the four broad sequence contexts in which sTyr has been observed: first, a solitary sTyr has been shown to be critical for diverse high-affinity interactions, such as between peptide hormones and their receptors, in both plants and animals. Second, sTyr clusters within structurally flexible anionic segments are essential for a variety of cellular processes, including coreceptor binding to the HIV-1 envelope spike protein during virus entry, chemokine interactions with receptors, and leukocyte rolling cell adhesion. Third, a subcategory of sTyr clusters is found in conserved acidic sequences termed hirudin-like motifs that enable proteins to interact with thrombin; consequently, many proven and potential therapeutic proteins derived from blood-consuming invertebrates depend on sTyrs for their activity. Finally, several proteins that interact with collagen or similar proteins contain one or more sTyrs within an acidic residue array. Refined methods to direct sTyr incorporation in peptides synthesized both in vitro and in vivo, together with continued advances in mass spectrometry and affinity detection, promise to accelerate discoveries of sTyr occurrence and function.     

Iron-regulated assembly of the cytosolic iron–sulfur cluster biogenesis machinery

The cytosolic iron–sulfur (Fe-S) cluster assembly (CIA) pathway delivers Fe-S clusters to nuclear and cytosolic Fe-S proteins involved in essential cellular functions. Although the delivery process is regulated by the availability of iron and oxygen, it remains unclear how CIA components orchestrate the cluster transfer under varying cellular environments. Here, we utilized a targeted proteomics assay for monitoring CIA factors and substrates to characterize the CIA machinery. We find that nucleotide-binding protein 1 (NUBP1/NBP35), cytosolic iron–sulfur assembly component 3 (CIAO3/NARFL), and CIA substrates associate with nucleotide-binding protein 2 (NUBP2/CFD1), a component of the CIA scaffold complex. NUBP2 also weakly associates with the CIA targeting complex (MMS19, CIAO1, and CIAO2B) indicating the possible existence of a higher order complex. Interactions between CIAO3 and the CIA scaffold complex are strengthened upon iron supplementation or low oxygen tension, while iron chelation and reactive oxygen species weaken CIAO3 interactions with CIA components. We further demonstrate that CIAO3 mutants defective in Fe-S cluster binding fail to integrate into the higher order complexes. However, these mutants exhibit stronger associations with CIA substrates under conditions in which the association with the CIA targeting complex is reduced suggesting that CIAO3 and CIA substrates may associate in complexes independently of the CIA targeting complex. Together, our data suggest that CIA components potentially form a metabolon whose assembly is regulated by environmental cues and requires Fe-S cluster incorporation in CIAO3. These findings provide additional evidence that the CIA pathway adapts to changes in cellular environment through complex reorganization.     

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.     

Interactions between semaphorins and plexin–neuropilin receptor complexes in the membranes of live cells

Signaling of semaphorin ligands via their plexin–neuropilin receptors is involved in tissue patterning in the developing embryo. These proteins play roles in cell migration and adhesion but are also important in disease etiology, including in cancer angiogenesis and metastasis. While some structures of the soluble domains of these receptors have been determined, the conformations of the full-length receptor complexes are just beginning to be elucidated, especially within the context of the plasma membrane. Pulsed-interleaved excitation fluorescence cross-correlation spectroscopy allows direct insight into the formation of protein–protein interactions in the membranes of live cells. Here, we investigated the homodimerization of neuropilin-1 (Nrp1), plexin A2, plexin A4, and plexin D1 using pulsed-interleaved excitation fluorescence cross-correlation spectroscopy. Consistent with previous studies, we found that Nrp1, plexin A2, and plexin A4 are present as dimers in the absence of exogenous ligand. Plexin D1, on the other hand, was monomeric under similar conditions, which had not been previously reported. We also found that plexin A2 and A4 assemble into a heteromeric complex. Stimulation with semaphorin 3A or semaphorin 3C neither disrupts nor enhances the dimerization of the receptors when expressed alone, suggesting that activation involves a conformational change rather than a shift in the monomer–dimer equilibrium. However, upon stimulation with semaphorin 3C, plexin D1 and Nrp1 form a heteromeric complex. This analysis of interactions provides a complementary approach to the existing structural and biochemical data that will aid in the development of new therapeutic strategies to target these receptors in cancer.     

Beta turn propensity and a model polymer scaling exponent identify intrinsically disordered phase-separating proteins

The complex cellular milieu can spontaneously demix, or phase separate, in a process controlled in part by intrinsically disordered (ID) proteins. A protein''s propensity to phase separate is thought to be driven by a preference for protein–protein over protein–solvent interactions. The hydrodynamic size of monomeric proteins, as quantified by the polymer scaling exponent (v), is driven by a similar balance. We hypothesized that mean v, as predicted by protein sequence, would be smaller for proteins with a strong propensity to phase separate. To test this hypothesis, we analyzed protein databases containing subsets of proteins that are folded, disordered, or disordered and known to spontaneously phase separate. We find that the phase-separating disordered proteins, on average, had lower calculated values of v compared with their non-phase-separating counterparts. Moreover, these proteins had a higher sequence-predicted propensity for β-turns. Using a simple, surface area-based model, we propose a physical mechanism for this difference: transient β-turn structures reduce the desolvation penalty of forming a protein-rich phase and increase exposure of atoms involved in π/sp² valence electron interactions. By this mechanism, β-turns could act as energetically favored nucleation points, which may explain the increased propensity for turns in ID regions (IDRs) utilized biologically for phase separation. Phase-separating IDRs, non-phase-separating IDRs, and folded regions could be distinguished by combining v and β-turn propensity. Finally, we propose a new algorithm, ParSe (partition sequence), for predicting phase-separating protein regions, and which is able to accurately identify folded, disordered, and phase-separating protein regions based on the primary sequence.     

CeLINC,a fluorescence-based protein–protein interaction assay in Caenorhabditis elegans

Interactions among proteins are fundamental for life and determining whether two particular proteins physically interact can be essential for fully understanding a protein’s function. We present Caenorhabditis elegans light-induced coclustering (CeLINC), an optical binary protein–protein interaction assay to determine whether two proteins interact in vivo. Based on CRY2/CIB1 light-dependent oligomerization, CeLINC can rapidly and unambiguously identify protein–protein interactions between pairs of fluorescently tagged proteins. A fluorescently tagged bait protein is captured using a nanobody directed against the fluorescent protein (GFP or mCherry) and brought into artificial clusters within the cell. Colocalization of a fluorescently tagged prey protein in the cluster indicates a protein interaction. We tested the system with an array of positive and negative reference protein pairs. Assay performance was extremely robust with no false positives detected in the negative reference pairs. We then used the system to test for interactions among apical and basolateral polarity regulators. We confirmed interactions seen between PAR-6, PKC-3, and PAR-3, but observed no physical interactions among the basolateral Scribble module proteins LET-413, DLG-1, and LGL-1. We have generated a plasmid toolkit that allows use of custom promoters or CRY2 variants to promote flexibility of the system. The CeLINC assay is a powerful and rapid technique that can be widely applied in C. elegans due to the universal plasmids that can be used with existing fluorescently tagged strains without need for additional cloning or genetic modification of the genome.     

Rewiring Ancient Residue Interaction Networks Drove the Evolution of Specificity in Steroid Receptors

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Ancestral Sequence Reconstruction of a Cytochrome P450 Family Involved in Chemical Defense Reveals the Functional Evolution of a Promiscuous,Xenobiotic-Metabolizing Enzyme in Vertebrates

The cytochrome P450 family 1 enzymes (CYP1s) are a diverse family of hemoprotein monooxygenases, which metabolize many xenobiotics including numerous environmental carcinogens. However, their historical function and evolution remain largely unstudied. Here we investigate CYP1 evolution via the reconstruction and characterization of the vertebrate CYP1 ancestors. Younger ancestors and extant forms generally demonstrated higher activity toward typical CYP1 xenobiotic and steroid substrates than older ancestors, suggesting significant diversification away from the original CYP1 function. Caffeine metabolism appears to be a recently evolved trait of the CYP1A subfamily, observed in the mammalian CYP1A lineage, and may parallel the recent evolution of caffeine synthesis in multiple separate plant species. Likewise, the aryl hydrocarbon receptor agonist, 6-formylindolo[3,2-b]carbazole (FICZ) was metabolized to a greater extent by certain younger ancestors and extant forms, suggesting that activity toward FICZ increased in specific CYP1 evolutionary branches, a process that may have occurred in parallel to the exploitation of land where UV-exposure was higher than in aquatic environments. As observed with previous reconstructions of P450 enzymes, thermostability correlated with evolutionary age; the oldest ancestor was up to 35 °C more thermostable than the extant forms, with a ¹⁰T₅₀ (temperature at which 50% of the hemoprotein remains intact after 10 min) of 71 °C. This robustness may have facilitated evolutionary diversification of the CYP1s by buffering the destabilizing effects of mutations that conferred novel functions, a phenomenon which may also be useful in exploiting the catalytic versatility of these ancestral enzymes for commercial application as biocatalysts.