The binary protein-protein interaction landscape of Escherichia coli

Protein interactions in human pathogens revealed through deep learning

Identification of bacterial protein-protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseTTAFold2-Lite, a rapid deep learning model that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1,923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer-membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

Experimental characterization and prediction of Escherichia coli host cell proteome retention during preparative chromatography

Purification of recombinantly produced biopharmaceuticals involves removal of host cell material, such as host cell proteins (HCPs). For lysates of the common expression host Escherichia coli (E. coli) over 1500 unique proteins can be identified. Currently, understanding the behavior of individual HCPs for purification operations, such as preparative chromatography, is limited. Therefore, we aim to elucidate the elution behavior of individual HCPs from E. coli strain BLR(DE3) during chromatography. Understanding this complex mixture and knowing the chromatographic behavior of each individual HCP improves the ability for rational purification process design. Specifically, linear gradient experiments were performed using ion exchange (IEX) and hydrophobic interaction chromatography, coupled with mass spectrometry-based proteomics to map the retention of individual HCPs. We combined knowledge of protein location, function, and interaction available in literature to identify trends in elution behavior. Additionally, quantitative structure-property relationship models were trained relating the protein 3D structure to elution behavior during IEX. For the complete data set a model with a cross-validated R2 of 0.55 was constructed, that could be improved to a R2 of 0.70 by considering only monomeric proteins. Ultimately this study is a significant step toward greater process understanding.

Pairing interacting protein sequences using masked language modeling

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.

ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Essential and virulence-related protein interactions of pathogens revealed through deep learning

Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Revolutionizing protein-protein interaction prediction with deep learning

Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning methods, coupled with the vast genomic sequence data, have significantly boosted the accuracy of predicting protein structures and modeling protein complexes, approaching levels comparable to experimental techniques. Herein, we review the latest advances in the computational methods for modeling 3D protein complexes and the prediction of protein interaction partners, emphasizing the application of deep learning methods deriving from coevolution analysis. The review also highlights biomedical applications of PPI prediction and outlines challenges in the field.

Enhancing coevolutionary signals in protein-protein interaction prediction through clade-wise alignment integration

Protein-protein interactions (PPIs) play essential roles in most biological processes. The binding interfaces between interacting proteins impose evolutionary constraints that have successfully been employed to predict PPIs from multiple sequence alignments (MSAs). To construct MSAs, critical choices have to be made: how to ensure the reliable identification of orthologs, and how to optimally balance the need for large alignments versus sufficient alignment quality. Here, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed under distinct clades in the tree of life. Coevolutionary signals are searched separately within these clades, and are only subsequently integrated using machine learning techniques. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. Given the recent successes of AlphaFold in predicting direct PPIs at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates-thus reducing false positives as well as computation time.

The Quantitative Biotinylproteomics Studies Reveal a WInd-Related Kinase 1 (Raf-Like Kinase 36) Functioning as an Early Signaling Component in Wind-Induced Thigmomorphogenesis and Gravitropism

Wind is one of the most prevalent environmental forces entraining plants to develop various mechano-responses, collectively called thigmomorphogenesis. Largely unknown is how plants transduce these versatile wind force signals downstream to nuclear events and to the development of thigmomorphogenic phenotype or anemotropic response. To identify molecular components at the early steps of the wind force signaling, two mechanical signaling-related phosphoproteins, identified from our previous phosphoproteomic study of Arabidopsis touch response, mitogen-activated protein kinase kinase 1 (MKK1) and 2 (MKK2), were selected for performing in planta TurboID (ID)-based quantitative proximity-labeling (PL) proteomics. This quantitative biotinylproteomics was separately performed on MKK1-ID and MKK2-ID transgenic plants, respectively, using the genetically engineered TurboID biotin ligase expression transgenics as a universal control. This unique PTM proteomics successfully identified 11 and 71 MKK1 and MKK2 putative interactors, respectively. Biotin occupancy ratio (BOR) was found to be an alternative parameter to measure the extent of proximity and specificity between the proximal target proteins and the bait fusion protein. Bioinformatics analysis of these biotinylprotein data also found that TurboID biotin ligase favorably labels the loop region of target proteins. A WInd-Related Kinase 1 (WIRK1), previously known as rapidly accelerated fibrosarcoma (Raf)-like kinase 36 (RAF36), was found to be a putative common interactor for both MKK1 and MKK2 and preferentially interacts with MKK2. Further molecular biology studies of the Arabidopsis RAF36 kinase found that it plays a role in wind regulation of the touch-responsive TCH3 and CML38 gene expression and the phosphorylation of a touch-regulated PATL3 phosphoprotein. Measurement of leaf morphology and shoot gravitropic response of wirk1 (raf36) mutant revealed that the WIRK1 gene is involved in both wind-triggered rosette thigmomorphogenesis and gravitropism of Arabidopsis stems, suggesting that the WIRK1 (RAF36) protein probably functioning upstream of both MKK1 and MKK2 and that it may serve as the crosstalk point among multiple mechano-signal transduction pathways mediating both wind mechano-response and gravitropism.

A journey into the regulatory secrets of the de novo purine nucleotide biosynthesis

De novo purine nucleotide biosynthesis (DNPNB) consists of sequential reactions that are majorly conserved in living organisms. Several regulation events take place to maintain physiological concentrations of adenylate and guanylate nucleotides in cells and to fine-tune the production of purine nucleotides in response to changing cellular demands. Recent years have seen a renewed interest in the DNPNB enzymes, with some being highlighted as promising targets for therapeutic molecules. Herein, a review of two newly revealed modes of regulation of the DNPNB pathway has been carried out: i) the unprecedent allosteric regulation of one of the limiting enzymes of the pathway named inosine 5'-monophosphate dehydrogenase (IMPDH), and ii) the supramolecular assembly of DNPNB enzymes. Moreover, recent advances that revealed the therapeutic potential of DNPNB enzymes in bacteria could open the road for the pharmacological development of novel antibiotics.

Structural assembly of the bacterial essential interactome

The study of protein interactions in living organisms is fundamental for understanding biological processes and central metabolic pathways. Yet, our knowledge of the bacterial interactome remains limited. Here, we combined gene deletion mutant analysis with deep-learning protein folding using AlphaFold2 to predict the core bacterial essential interactome. We predicted and modeled 1402 interactions between essential proteins in bacteria and generated 146 high-accuracy models. Our analysis reveals previously unknown details about the assembly mechanisms of these complexes, highlighting the importance of specific structural features in their stability and function. Our work provides a framework for predicting the essential interactomes of bacteria and highlight the potential of deep-learning algorithms in advancing our understanding of the complex biology of living organisms. Also, the results presented here offer a promising approach to identify novel antibiotic targets.

Tetrameric UvrD Helicase Is Located at the E. Coli Replisome due to Frequent Replication Blocks

DNA replication in all organisms must overcome nucleoprotein blocks to complete genome duplication. Accessory replicative helicases in Escherichia coli, Rep and UvrD, help remove these blocks and aid the re-initiation of replication. Mechanistic details of Rep function have emerged from recent live cell studies; however, the division of UvrD functions between its activities in DNA repair and role as an accessory helicase remain unclear in live cells. By integrating super-resolved single-molecule fluorescence microscopy with biochemical analysis, we find that UvrD self-associates into tetrameric assemblies and, unlike Rep, is not recruited to a specific replisome protein despite being found at approximately 80% of replication forks. Instead, its colocation with forks is likely due to the very high frequency of replication blocks composed of DNA-bound proteins, including RNA polymerase and factors involved in repairing DNA damage. Deleting rep and DNA repair factor genes mutS and uvrA, and inhibiting transcription through RNA polymerase mutation and antibiotic inhibition, indicates that the level of UvrD at the fork is dependent on UvrD's function. Our findings show that UvrD is recruited to sites of nucleoprotein blocks via different mechanisms to Rep and plays a multi-faceted role in ensuring successful DNA replication.

Bioinformatics prediction and screening of viral mimicry candidates through integrating known and predicted DMI data

Domain-motif interactions (DMIs) represent transient bonds formed when a Short Linear Motif (SLiM) engages a globular domain via a compact contact interface. Understanding the mechanics of DMIs is critical for maintaining diverse regulatory processes and deciphering how various viruses hijack host cellular machinery. However, identifying DMIs through traditional in vitro and in vivo experiments is challenging due to their degenerate nature and small contact areas. Predictions often carry a high rate of false positives, necessitating rigorous in-silico validation before embarking on experimental work. This study assessed the binding energy changes in predicted SLiM instances through in-silico peptide exchange experiment, elucidating how they interact with known 3D DMI complexes. We identified a subset of potential mimicry candidates that exhibited effective binding affinities with native DMI structures, suggesting their potential to be true mimicry candidates. The identified viral SLiMs can be potential targets in developing therapeutics, opening new opportunities for innovative treatments that can be finely tuned to address the complex molecular underpinnings of various diseases. To gain a comprehensive understanding of identified DMIs, it is imperative to conduct further validation through experimental approaches.

The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

The bacterium Candidatus Liberibacter asiaticus (CLas) causes citrus Huanglongbing disease. Our understanding of the pathogenicity and biology of this microorganism remains limited because CLas has not yet been cultivated in artificial media. Its genome is relatively small and encodes approximately 1136 proteins, of which 415 have unknown functions. Here, we use a high-throughput yeast-two-hybrid (Y2H) screen to identify interactions between CLas proteins, thus providing insights into their potential functions. We identify 4245 interactions between 542 proteins, after screening 916 bait and 936 prey proteins. The false positive rate of the Y2H assay is estimated to be 2.9%. Pull-down assays for nine protein-protein interactions (PPIs) likely involved in flagellar function support the robustness of the Y2H results. The average number of PPIs per node in the CLas interactome is 15.6, which is higher than the numbers previously reported for interactomes of free-living bacteria, suggesting that CLas genome reduction has been accompanied by increased protein multi-functionality. We propose potential functions for 171 uncharacterized proteins, based on the PPI results, guilt-by-association analyses, and comparison with data from other bacterial species. We identify 40 hub-node proteins, including quinone oxidoreductase and LysR, which are known to protect other bacteria against oxidative stress and might be important for CLas survival in the phloem. We expect our PPI database to facilitate research on CLas biology and pathogenicity mechanisms.

Dynamic remodeling of Escherichia coli interactome in response to environmental perturbations

Proteins play an essential role in the vital biological processes governing cellular functions. Most proteins function as members of macromolecular machines, with the network of interacting proteins revealing the molecular mechanisms driving the formation of these complexes. Profiling the physiology-driven remodeling of these interactions within different contexts constitutes a crucial component to achieving a comprehensive systems-level understanding of interactome dynamics. Here, we apply co-fractionation mass spectrometry and computational modeling to quantify and profile the interactions of ∼2000 proteins in the bacterium Escherichia coli cultured under 10 distinct culture conditions. The resulting quantitative co-elution patterns revealed large-scale condition-dependent interaction remodeling among protein complexes involved in diverse biochemical pathways in response to the unique environmental challenges. The network-level analysis highlighted interactome-wide biophysical properties and structural patterns governing interaction remodeling. Our results provide evidence of the local and global plasticity of the E. coli interactome along with a rigorous generalizable framework to define protein interaction specificity. We provide an accompanying interactive web application to facilitate the exploration of these rewired networks.

Co-evolution at protein-protein interfaces guides inference of stoichiometry of oligomeric protein complexes by de novo structure prediction

The quaternary structure with specific stoichiometry is pivotal to the specific function of protein complexes. However, determining the structure of many protein complexes experimentally remains a major bottleneck. Structural bioinformatics approaches, such as the deep learning algorithm Alphafold2-multimer (AF2-multimer), leverage the co-evolution of amino acids and sequence-structure relationships for accurate de novo structure and contact prediction. Pseudo-likelihood maximization direct coupling analysis (plmDCA) has been used to detect co-evolving residue pairs by statistical modeling. Here, we provide evidence that combining both methods can be used for de novo prediction of the quaternary structure and stoichiometry of a protein complex. We achieve this by augmenting the existing AF2-multimer confidence metrics with an interpretable score to identify the complex with an optimal fraction of native contacts of co-evolving residue pairs at intermolecular interfaces. We use this strategy to predict the quaternary structure and non-trivial stoichiometries of Bacillus subtilis spore germination protein complexes with unknown structures. Co-evolution at intermolecular interfaces may therefore synergize with AI-based de novo quaternary structure prediction of structurally uncharacterized bacterial protein complexes.

How different viruses perturb host cellular machinery via short linear motifs

The virus interacts with its hosts by developing protein-protein interactions. Most viruses employ protein interactions to imitate the host protein: A viral protein with the same amino acid sequence or structure as the host protein attaches to the host protein's binding partner and interferes with the host protein's pathways. Being opportunistic, viruses have evolved to manipulate host cellular mechanisms by mimicking short linear motifs. In this review, we shed light on the current understanding of mimicry via short linear motifs and focus on viral mimicry by genetically different viral subtypes by providing recent examples of mimicry evidence and how high-throughput methods can be a reliable source to study SLiM-mediated viral mimicry.

ZEPPI: proteome-scale sequence-based evaluation of protein-protein interaction models

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence co-evolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. ZEPPI can be implemented on a proteome-wide scale as evidenced by calculations on millions of structural models of dimeric complexes in the E. coli and human interactomes found in the PrePPI database. A number of examples that illustrate how these tools can yield novel functional hypotheses are provided.

Progress toward Proteome-Wide Photo-Cross-Linking to Enable Residue-Level Visualization of Protein Structures and Networks In Vivo

Cross-linking mass spectrometry (XL-MS) is emerging as a method at the crossroads of structural and cellular biology, uniquely capable of identifying protein-protein interactions with residue-level resolution and on the proteome-wide scale. With the development of cross-linkers that can form linkages inside cells and easily cleave during fragmentation on the mass spectrometer (MS-cleavable cross-links), it has become increasingly facile to identify contacts between any two proteins in complex samples, including in live cells or tissues. Photo-cross-linkers possess the advantages of high temporal resolution and high reactivity, thereby engaging all residue-types (rather than just lysine); nevertheless, photo-cross-linkers have not enjoyed widespread use and are yet to be employed for proteome-wide studies because their products are challenging to identify. Here, we demonstrate the synthesis and application of two heterobifunctional photo-cross-linkers that feature diazirines and N-hydroxy-succinimidyl carbamate groups, the latter of which unveil doubly fissile MS-cleavable linkages upon acyl transfer to protein targets. Moreover, these cross-linkers demonstrate high water-solubility and cell-permeability. Using these compounds, we demonstrate the feasibility of proteome-wide photo-cross-linking in cellulo. These studies elucidate a small portion of Escherichia coli's interaction network, albeit with residue-level resolution. With further optimization, these methods will enable the detection of protein quinary interaction networks in their native environment at residue-level resolution, and we expect that they will prove useful toward the effort to explore the molecular sociology of the cell.

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

Bacterial Subcellular Architecture, Structural Epistasis, and Antibiotic Resistance

Epistasis refers to the way in which genetic interactions between some genetic loci affect phenotypes and fitness. In this study, we propose the concept of "structural epistasis" to emphasize the role of the variable physical interactions between molecules located in particular spaces inside the bacterial cell in the emergence of novel phenotypes. The architecture of the bacterial cell (typically Gram-negative), which consists of concentrical layers of membranes, particles, and molecules with differing configurations and densities (from the outer membrane to the nucleoid) determines and is in turn determined by the cell shape and size, depending on the growth phases, exposure to toxic conditions, stress responses, and the bacterial environment. Antibiotics change the bacterial cell's internal molecular topology, producing unexpected interactions among molecules. In contrast, changes in shape and size may alter antibiotic action. The mechanisms of antibiotic resistance (and their vectors, as mobile genetic elements) also influence molecular connectivity in the bacterial cell and can produce unexpected phenotypes, influencing the action of other antimicrobial agents.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

Protein complexes in cells by AI-assisted structural proteomics

Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry and co-fractionation mass spectrometry (CoFrac-MS) to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold-Multimer and, after controlling for the false-positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein-protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria.

Gene transferability and sociality do not correlate with gene connectivity

The connectivity of a gene, defined as the number of interactions a gene's product has with other genes' products, is a key characteristic of a gene. In prokaryotes, the complexity hypothesis predicts that genes which undergo more frequent horizontal transfer will be less connected than genes which are only very rarely transferred. We tested the role of horizontal gene transfer, and other potentially important factors, by examining the connectivity of chromosomal and plasmid genes, across 134 diverse prokaryotic species. We found that (i) genes on plasmids were less connected than genes on chromosomes; (ii) connectivity of plasmid genes was not correlated with plasmid mobility; and (iii) the sociality of genes (cooperative or private) was not correlated with gene connectivity.

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally derived databases. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.

Development of a Method Combining Peptidiscs and Proteomics to Identify, Stabilize, and Purify a Detergent-Sensitive Membrane Protein Assembly

The peptidisc membrane mimetic enables global reconstitution of the bacterial membrane proteome into water-soluble detergent-free particles, termed peptidisc libraries. We present here a method that combines peptidisc libraries and chromosomal-level gene tagging technology with affinity purification and mass spectrometry (AP/MS) to stabilize and identify fragile membrane protein complexes that exist at native expression levels. This method circumvents common artifacts caused by bait protein overproduction and protein complex dissociation due to lengthy exposure to detergents during protein isolation. Using the Escherichia coli Sec system as a case study, we identify an expanded version of the translocon, termed the HMD complex, consisting of nine different integral membrane subunits. This complex is stable in peptidiscs but dissociates in detergents. Guided by this native-level proteomic information, we design and validate a procedure that enables purification of the HMD complex with minimal protein dissociation. These results highlight the utility of peptidiscs and AP/MS to discover and stabilize fragile membrane protein assemblies. Data are available via ProteomeXchange with identifier PXD032315.

Protein-protein interaction network of E. coli K-12 has significant high-dimensional cavities: New insights from algebraic topological studies

As a model system, Escherichia coli (E. coli) has been used to study various life processes. A dramatic paradigm shift has occurred in recent years, with the study of single proteins moving towards the study of dynamically-interacting proteins, especially protein-protein interaction (PPI) networks. However, despite the importance of PPI networks, little is known about the intrinsic nature of the network structure, especially high-dimensional topological properties . By introducing general hypergeometric distribution, here we reconstruct a statistically-reliable combined PPI network of E. coli (E.coli-PPI-Network) from several datasets. Unlike traditional graph analysis, algebraic topology was introduced to analyze the topological structures of the E.coli-PPI-Network, including high-dimensional cavities and cycles. Random networks with the same node and edge number (RandomNet), or scale-free networks with the same degree distribution (RandomNet-SameDD) were produced as controls. We discovered that the E.coli-PPI-Network had special algebraic typological structures, exhibiting more high-dimensional cavities and cycles, compared to RandomNets or, importantly, RandomNet-SameDD. Based on these results, we defined degree of involved q dimensional cycles of proteins (q-DC_protein ) in the network, a novel concept which relies on the integral structure of the network and is different from traditional node degree or hubs. Finally, top proteins ranked by their 1-DC_protein were identified. In conclusion, by introducing mathematical and computer technologies, we discovered novel algebraic topological properties of the E.coli-PPI-Network, which has special high-dimensional cavities and cycles, and thereby revealed certain intrinsic rules of information flow underlining bacteria biology.

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families. Does successful inference mainly rely on correlations from structural contacts or from phylogeny, or both? Do these two types of signal combine constructively or hinder each other? To address these questions, we generate and analyze synthetic data produced using a minimal model that allows us to control the amounts of structural constraints and phylogeny. We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI. Furthermore, signal from phylogeny can rescue partner inference when signal from contacts becomes less informative, including in the realistic case where inter-protein contacts are restricted to a small subset of sites. We also demonstrate that DCA-inferred couplings between non-contact pairs of sites improve partner inference in the presence of strong phylogeny, while deteriorating it otherwise. Moreover, restricting to non-contact pairs of sites preserves inference performance in the presence of strong phylogeny. In a natural data set, as well as in realistic synthetic data based on it, we find that non-contact pairs of sites contribute positively to partner inference performance, and that restricting to them preserves performance, evidencing an important role of phylogeny.

The role of nitrogen-responsive regulators in controlling inorganic polyphosphate synthesis in Escherichia coli

Inorganic polyphosphate (polyP) is synthesized by bacteria under stressful environmental conditions and acts by a variety of mechanisms to promote cell survival. While the kinase that synthesizes polyP (PPK, encoded by the ppk gene) is well known, ppk transcription is not activated by environmental stress and little is understood about how environmental stress signals lead to polyP accumulation. Previous work has shown that the transcriptional regulators DksA, RpoN (σ54) and RpoE (σ24) positively regulate polyP production, but not ppk transcription, in Escherichia coli. In this work, we examine the role of the alternative sigma factor RpoN and nitrogen starvation stress response pathways in controlling polyP synthesis. We show that the RpoN enhancer binding proteins GlnG and GlrR impact polyP production, and uncover a new role for the nitrogen phosphotransferase regulator PtsN (EIIANtr) as a positive regulator of polyP production, acting upstream of DksA, downstream of RpoN and apparently independently of RpoE. However, neither these regulatory proteins nor common nitrogen metabolites appear to act directly on PPK, and the precise mechanism(s) by which polyP production is modulated after stress remain(s) unclear. Unexpectedly, we also found that the genes that impact polyP production vary depending on the composition of the rich media in which the cells were grown before exposure to polyP-inducing stress. These results constitute progress towards deciphering the regulatory networks driving polyP production under stress, and highlight the remarkable complexity of this regulation and its connections to a broad range of stress-sensing pathways.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Improved prediction of protein-protein interactions using AlphaFold2

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.

Predicting the structure of protein complexes is extremely difficult. Here, authors apply AlphaFold2 with optimized multiple sequence alignments to model complexes of interacting proteins, enabling prediction of both if and how proteins interact with state-of-art accuracy.

ATP-Binding Cassette Transporters: Snap-on Complexes?

ATP-binding cassette (ABC) transporters are one of the largest families of membrane proteins in prokaryotic organisms. Much is now understood about the structure of these transporters and many reviews have been written on that subject. In contrast, less has been written on the assembly of ABC transporter complexes and this will be a major focus of this book chapter. The complexes are formed from two cytoplasmic subunits that are highly conserved (in terms of their primary and three-dimensional structures) across the whole family. These ATP-binding subunits give rise to the name of the family. They must assemble with two transmembrane subunits that will typically form the permease component of the transporter. The transmembrane subunits have been found to be surprisingly diverse in structure when the whole family is examined, with seven distinct folds identified so far. Hence nucleotide-binding subunits appear to have been bolted on to a variety of transmembrane platforms during evolution, leading to a greater variety in function. Furthermore, many importers within the family utilise a further external substrate-binding component to trap scarce substrates and deliver them to the correct permease components. In this chapter, we will discuss whether assembly of the various ABC transporter subunits occurs with high fidelity within the crowded cellular environment and whether promiscuity in assembly of transmembrane and cytoplasmic components can occur. We also discuss the new AlphaFold protein structure prediction tool which predicts a new type of transmembrane domain fold within the ABC transporters that is associated with cation exporters of bacteria and plants.

OUP accepted manuscript

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

Multifaceted impact of a nucleoside monophosphate kinase on 5'-end-dependent mRNA degradation in bacteria

A key pathway for mRNA degradation in bacterial cells begins with conversion of the initial 5'-terminal triphosphate to a monophosphate, a modification that renders transcripts more vulnerable to attack by ribonucleases whose affinity for monophosphorylated 5' ends potentiates their catalytic efficacy. In Escherichia coli, the only proteins known to be important for controlling degradation via this pathway are the RNA pyrophosphohydrolase RppH, its heteromeric partner DapF, and the 5'-monophosphate-assisted endonucleases RNase E and RNase G. We have now identified the metabolic enzyme cytidylate kinase as another protein that affects rates of 5'-end-dependent mRNA degradation in E. coli. It does so by utilizing two distinct mechanisms to influence the 5'-terminal phosphorylation state of RNA, each dependent on the catalytic activity of cytidylate kinase and not its mere presence in cells. First, this enzyme acts in conjunction with DapF to stimulate the conversion of 5' triphosphates to monophosphates by RppH. In addition, it suppresses the direct synthesis of monophosphorylated transcripts that begin with cytidine by reducing the cellular concentration of cytidine monophosphate, thereby disfavoring the 5'-terminal incorporation of this nucleotide by RNA polymerase during transcription initiation. Together, these findings suggest dual signaling pathways by which nucleotide metabolism can impact mRNA degradation in bacteria.

Computationally Reconstructed Interactome of Bradyrhizobium diazoefficiens USDA110 Reveals Novel Functional Modules and Protein Hubs for Symbiotic Nitrogen Fixation

Symbiotic nitrogen fixation is an important part of the nitrogen biogeochemical cycles and the main nitrogen source of the biosphere. As a classical model system for symbiotic nitrogen fixation, rhizobium-legume systems have been studied elaborately for decades. Details about the molecular mechanisms of the communication and coordination between rhizobia and host plants is becoming clearer. For more systematic insights, there is an increasing demand for new studies integrating multiomics information. Here, we present a comprehensive computational framework integrating the reconstructed protein interactome of B. diazoefficiens USDA110 with its transcriptome and proteome data to study the complex protein-protein interaction (PPI) network involved in the symbiosis system. We reconstructed the interactome of B. diazoefficiens USDA110 by computational approaches. Based on the comparison of interactomes between B. diazoefficiens USDA110 and other rhizobia, we inferred that the slow growth of B. diazoefficiens USDA110 may be due to the requirement of more protein modifications, and we further identified 36 conserved functional PPI modules. Integrated with transcriptome and proteome data, interactomes representing free-living cell and symbiotic nitrogen-fixing (SNF) bacteroid were obtained. Based on the SNF interactome, a core-sub-PPI-network for symbiotic nitrogen fixation was determined and nine novel functional modules and eleven key protein hubs playing key roles in symbiosis were identified. The reconstructed interactome of B. diazoefficiens USDA110 may serve as a valuable reference for studying the mechanism underlying the SNF system of rhizobia and legumes.

Unraveling Protein Interactions between the Temperate Virus Bam35 and Its Bacillus Host Using an Integrative Yeast Two Hybrid-High Throughput Sequencing Approach

Bacillus virus Bam35 is the model Betatectivirus and member of the family Tectiviridae, which is composed of tailless, icosahedral, and membrane-containing bacteriophages. Interest in these viruses has greatly increased in recent years as they are thought to be an evolutionary link between diverse groups of prokaryotic and eukaryotic viruses. Additionally, betatectiviruses infect bacteria of the Bacillus cereus group, which are known for their applications in industry and notorious since it contains many pathogens. Here, we present the first protein–protein interactions (PPIs) network for a tectivirus–host system by studying the Bam35–Bacillus thuringiensis model using a novel approach that integrates the traditional yeast two-hybrid system and high-throughput sequencing (Y2H-HTS). We generated and thoroughly analyzed a genomic library of Bam35′s host B. thuringiensis HER1410 and screened interactions with all the viral proteins using different combinations of bait–prey couples. Initial analysis of the raw data enabled the identification of over 4000 candidate interactions, which were sequentially filtered to produce 182 high-confidence interactions that were defined as part of the core virus–host interactome. Overall, host metabolism proteins and peptidases were particularly enriched within the detected interactions, distinguishing this host–phage system from the other reported host–phage PPIs. Our approach also suggested biological roles for several Bam35 proteins of unknown function, including the membrane structural protein P25, which may be a viral hub with a role in host membrane modification during viral particle morphogenesis. This work resulted in a better understanding of the Bam35–B. thuringiensis interaction at the molecular level and holds great potential for the generalization of the Y2H-HTS approach for other virus–host models.

In vivo evaluation of the interaction between the Escherichia coli IGP synthase subunits using the Bacterial Two-Hybrid system

Histidine biosynthesis is one of the most characterized metabolic routes for its antiquity and its central role in cellular metabolism; indeed, it represents a cross-road between nitrogen metabolism and de novo synthesis of purines. This interconnection is due to the activity of imidazole glycerol phosphate synthase, a heterodimeric enzyme constituted by the products of two his genes, hisH and hisF, encoding a glutamine amidotransferase and a cyclase, respectively. Despite their interaction was suggested by several in vitro experiments, their in vivo complex formation has not been demonstrated. On the contrary, the analysis of the entire Escherichia coli interactome performed using the yeast two hybrid system did not suggest the in vivo interaction of the two IGP synthase subunits. The aim of this study was to demonstrate the interaction of the two proteins using the Bacterial Adenylate Cyclase Two-Hybrid (BACTH) system. Data obtained demonstrated the in vivo interaction occurring between the proteins encoded by the E. coli hisH and hisF genes; this finding might also open the way to pharmaceutical applications through the design of selective drugs toward this enzyme.

iBRET Screen of the ABCD1 Peroxisomal Network and Mutation-Induced Network Perturbations

Mapping the network of proteins provides a powerful means to investigate the function of disease genes and to unravel the molecular basis of phenotypes. We present an automated informatics-aided and bioluminescence resonance energy transfer-based approach (iBRET) enabling high-confidence detection of protein-protein interactions in living mammalian cells. A screen of the ABCD1 protein, which is affected in X-linked adrenoleukodystrophy (X-ALD), against an organelle library of peroxisomal proteins demonstrated applicability of iBRET for large-scale experiments. We identified novel protein-protein interactions for ABCD1 (with ALDH3A2, DAO, ECI2, FAR1, PEX10, PEX13, PEX5, PXMP2, and PIPOX), mapped its position within the peroxisomal protein-protein interaction network, and determined that pathogenic missense variants in ABCD1 alter the interaction with selected binding partners. These findings provide mechanistic insights into pathophysiology of X-ALD and may foster the identification of new disease modifiers.

Using yeast two-hybrid system and molecular dynamics simulation to detect venom protein-protein interactions

•

The venom protein-protein interactions in snake venom remain largely unknown.

•

Y2H coupled with MD simulations was used to detect venom protein interactions.

•

Venom PLA₂s interact with themselves and Lys49 PLA₂ interacts with venom CRISP.

Proteins and peptides are major components of snake venom. Venom protein transcriptomes and proteomes of many snake species have been reported; however, snake venom complexity (i.e., the venom protein-protein interactions, PPIs) remains largely unknown. To detect the venom protein interactions, we used the most common snake venom component, phospholipase A₂s (PLA₂s) as a “bait” to identify the interactions between PLA₂s and 14 of the most common proteins in Western diamondback rattlesnake (Crotalus atrox) venom by using yeast two-hybrid (Y2H) analysis, a technique used to detect PPIs. As a result, we identified PLA₂s interacting with themselves, and lysing-49 PLA₂ (Lys49 PLA₂) interacting with venom cysteine-rich secretory protein (CRISP). To reveal the complex structure of Lys49 PLA₂-CRISP interaction at the structural level, we first built the three-dimensional (3D) structures of Lys49 PLA₂ and CRISP by a widely used computational program-MODELLER. The binding modes of Lys49 PLA₂-CRISP interaction were then predicted through three different docking programs including ClusPro, ZDOCK and HADDOCK. Furthermore, the most likely complex structure of Lys49 PLA₂-CRISP was inferred by molecular dynamic (MD) simulations with GROMACS software. The techniques used and results obtained from this study strengthen the understanding of snake venom protein interactions and pave the way for the study of animal venom complexity.

Prediction of Human-Plasmodium vivax Protein Associations From Heterogeneous Network Structures Based on Machine-Learning Approach

Malaria caused by Plasmodium vivax can lead to severe morbidity and death. In addition, resistance has been reported to existing drugs in treating this malaria. Therefore, the identification of new human proteins associated with malaria is urgently needed for the development of additional drugs. In this study, we established an analysis framework to predict human-P. vivax protein associations using network topological profiles from a heterogeneous network structure of human and P. vivax, machine-learning techniques and statistical analysis. Novel associations were predicted and ranked to determine the importance of human proteins associated with malaria. With the best-ranking score, 411 human proteins were identified as promising proteins. Their regulations and functions were statistically analyzed, which led to the identification of proteins involved in the regulation of membrane and vesicle formation, and proteasome complexes as potential targets for the treatment of P. vivax malaria. In conclusion, by integrating related data, our analysis was efficient in identifying potential targets providing an insight into human-parasite protein associations. Furthermore, generalizing this model could allow researchers to gain further insights into other diseases and enhance the field of biomedical science.

ZapG (YhcB/DUF1043), a novel cell division protein in gamma-proteobacteria linking the Z-ring to septal peptidoglycan synthesis

YhcB, a poorly understood protein conserved across gamma-proteobacteria, contains a domain of unknown function (DUF1043) and an N-terminal transmembrane domain. Here, we used an integrated approach including X-ray crystallography, genetics, and molecular biology to investigate the function and structure of YhcB. The Escherichia coli yhcB KO strain does not grow at 45 °C and is hypersensitive to cell wall–acting antibiotics, even in the stationary phase. The deletion of yhcB leads to filamentation, abnormal FtsZ ring formation, and aberrant septum development. The Z-ring is essential for the positioning of the septa and the initiation of cell division. We found that YhcB interacts with proteins of the divisome (e.g., FtsI, FtsQ) and elongasome (e.g., RodZ, RodA). Seven of these interactions are also conserved in Yersinia pestis and/or Vibrio cholerae. Furthermore, we mapped the amino acid residues likely involved in the interactions of YhcB with FtsI and RodZ. The 2.8 Å crystal structure of the cytosolic domain of Haemophilus ducreyi YhcB shows a unique tetrameric α-helical coiled-coil structure likely to be involved in linking the Z-ring to the septal peptidoglycan-synthesizing complexes. In summary, YhcB is a conserved and conditionally essential protein that plays a role in cell division and consequently affects envelope biogenesis. Based on these findings, we propose to rename YhcB to ZapG (Z-ring-associated protein G). This study will serve as a starting point for future studies on this protein family and on how cells transit from exponential to stationary survival.

The Protein Interactome of Glycolysis in Escherichia coli

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

Integrating Multimeric Threading With High-throughput Experiments for Structural Interactome of Escherichia coli

Genome-wide protein-protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a relatively high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline, Threpp, to address both problems. Starting from a pair of monomer sequences, Threpp first threads both sequences through a complex structure library, where the alignment score is combined with HTE data using a naïve Bayesian classifier model to predict the likelihood of two chains to interact with each other. Next, quaternary complex structures of the identified PPIs are constructed by reassembling monomeric alignments with dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks show a scale-free cluster size distribution, consistent with previous studies, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5, and 39 showed a close consistency with the later released experimental structures with an average TM-score = 0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.

A survey on computational models for predicting protein-protein interactions

Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suffer from the disadvantages of being time-consuming and labor-intensive. The results obtained are often not robust and considerably uncertain. Due recently to advances in high-throughput technologies, a large amount of proteomics data has been collected and this presents a significant opportunity and also a challenge to develop computational models to predict protein-protein interactions (PPIs) based on these data. In this paper, we present a comprehensive survey of the recent efforts that have been made towards the development of effective computational models for PPI prediction. The survey introduces the algorithms that can be used to learn computational models for predicting PPIs, and it classifies these models into different categories. To understand their relative merits, the paper discusses different validation schemes and metrics to evaluate the prediction performance. Biological databases that are commonly used in different experiments for performance comparison are also described and their use in a series of extensive experiments to compare different prediction models are discussed. Finally, we present some open issues in PPI prediction for future work. We explain how the performance of PPI prediction can be improved if these issues are effectively tackled.

Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences

Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.

Our understanding of the residue-level details of protein interactions remains incomplete. Here, the authors show sequence coevolution can be used to infer interacting proteins with residue-level details, including predicting 467 interactions de novo in the Escherichia coli cell envelope proteome.

Functions of Essential Genes and a Scale-Free Protein Interaction Network Revealed by Structure-Based Function and Interaction Prediction for a Minimal Genome

When the JCVI-syn3.0 genome was designed and implemented in 2016 as the minimal genome of a free-living organism, approximately one-third of the 438 protein-coding genes had no known function. Subsequent refinement into JCVI-syn3A led to inclusion of 16 additional protein-coding genes, including several unknown functions, resulting in an improved growth phenotype. Here, we seek to unveil the biological roles and protein-protein interaction (PPI) networks for these poorly characterized proteins using state-of-the-art deep learning contact-assisted structure prediction, followed by structure-based annotation of functions and PPI predictions. Our pipeline is able to confidently assign functions for many previously unannotated proteins such as putative vitamin transporters, which suggest the importance of nutrient uptake even in a minimized genome. Remarkably, despite the artificial selection of genes in the minimal syn3 genome, our reconstructed PPI network still shows a power law distribution of node degrees typical of naturally evolved bacterial PPI networks. Making use of our framework for combined structure/function/interaction modeling, we are able to identify both fundamental aspects of network biology that are retained in a minimal proteome and additional essential functions not yet recognized among the poorly annotated components of the syn3.0 and syn3A proteomes.

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

BACKGROUND

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.

METHODS

On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).

RESULTS

We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).

CONCLUSION

The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.

Internetwork connectivity of molecular networks across species of life

Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene and protein-protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN-PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.