Protein-protein interactions in bacteria: a promising and challenging avenue towards the discovery of new 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.

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. In this chapter, we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction.The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 (angiotensin converting enzyme 2) receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

Design of novel anti-quorum sensing peptides targeting LuxO to combat Vibrio cholerae pathogenesis

Vibrio cholerae, the Gram-negative bacterium abruptly colonizes the human intestine causing diarrhea, employing quorum sensing (QS) system as the major survival technique for mediating biofilm formation, virulence, competence, etc. Two distinct QS systems coordinated by the auto-inducer molecules, cholera-specific CqsA/S system and universal LuxS/PQ system, operate in parallel converging into a common phosphorelay pathway involving LuxU and LuxO. The master regulatory proteins of the QS system, AphA and HapR regulate the biofilm formation based on cell density, whose expression is controlled by the global response regulator LuxO. At low cell density, activated LuxO indirectly represses HapR, favoring the virulence cascade expression. Rather at high cell densities, LuxO represses AphA expression subsequently blocking the expression of virulence factors. Hence, targeting LuxO would be a promising strategy to downregulate the virulence pathway and modulate the QS system. With this insight, the present study has been designed to intrude the interaction between LuxU and LuxO through in silico design of novel peptides and validation of these peptides through molecular simulations. QS peptides were designed using QSPred server by altering the template sequence representing the LuxU-LuxO interaction, among which PEP8 exhibited better interaction and stability with the target protein LuxO. These in silico designed novel peptides would serve as potential target-specific molecules that would inhibit the LuxU-LuxO interaction and modulate the QS system to restrict Vibrio cholerae pathogenesis. However, further in vitro validations would substantiate the efficacy of these novel QS peptides.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00172-2.

The DarT/DarG Toxin-Antitoxin ADP-Ribosylation System as a Novel Target for a Rational Design of Innovative Antimicrobial Strategies

The chemical modification of cellular macromolecules by the transfer of ADP-ribose unit(s), known as ADP-ribosylation, is an ancient homeostatic and stress response control system. Highly conserved across the evolution, ADP-ribosyltransferases and ADP-ribosylhydrolases control ADP-ribosylation signalling and cellular responses. In addition to proteins, both prokaryotic and eukaryotic transferases can covalently link ADP-ribosylation to different conformations of nucleic acids, thus highlighting the evolutionary conservation of archaic stress response mechanisms. Here, we report several structural and functional aspects of DNA ADP-ribosylation modification controlled by the prototype DarT and DarG pair, which show ADP-ribosyltransferase and hydrolase activity, respectively. DarT/DarG is a toxin-antitoxin system conserved in many bacterial pathogens, for example in Mycobacterium tuberculosis, which regulates two clinically important processes for human health, namely, growth control and the anti-phage response. The chemical modulation of the DarT/DarG system by selective inhibitors may thus represent an exciting strategy to tackle resistance to current antimicrobial therapies.

Computer-Aided Drug Design: An Update

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

Analysis of Host–Bacteria Protein Interactions Reveals Conserved Domains and Motifs That Mediate Fundamental Infection Pathways

Adhesion and colonization of host cells by pathogenic bacteria depend on protein–protein interactions (PPIs). These interactions are interesting from the pharmacological point of view since new molecules that inhibit host-pathogen PPIs would act as new antimicrobials. Most of these interactions are discovered using high-throughput methods that may display a high false positive rate. The absence of curation of these databases can make the available data unreliable. To address this issue, a comprehensive filtering process was developed to obtain a reliable list of domains and motifs that participate in PPIs between bacteria and human cells. From a structural point of view, our analysis revealed that human proteins involved in the interactions are rich in alpha helix and disordered regions and poorer in beta structure. Disordered regions in human proteins harbor short sequence motifs that are specifically recognized by certain domains in pathogenic proteins. The most relevant domain–domain interactions were validated by AlphaFold, showing that a proper analysis of host-pathogen PPI databases can reveal structural conserved patterns. Domain–motif interactions, on the contrary, were more difficult to validate, since unstructured regions were involved, where AlphaFold could not make a good prediction. Moreover, these interactions are also likely accommodated by post-translational modifications, especially phosphorylation, which can potentially occur in 25–50% of host proteins. Hence, while common structural patterns are involved in host–pathogen PPIs and can be retrieved from available databases, more information is required to properly infer the full interactome. By resolving these issues, and in combination with new prediction tools like Alphafold, new classes of antimicrobials could be discovered from a more detailed understanding of these interactions.

Macromolecular Structure Assembly as a Novel Antibiotic Target

This review discusses the inhibition of macromolecular structure formation as a novel and under-investigated drug target. The disruption of cell wall structures by penicillin-binding protein interactions is one potential target. Inhibition of DNA polymerase III assembly by novel drugs is a second target that should be investigated. RNA polymerase protein structural interactions are a third potential target. Finally, disruption of ribosomal subunit biogenesis represents a fourth important target that can be further investigated. Methods to examine these possibilities are discussed.

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.

Protein Interaction Network Reconstruction with a Structural Gated Attention Deep Model by Incorporating Network Structure Information

Protein-protein interactions (PPIs) provide a physical basis of molecular communications for a wide range of biological processes in living cells. Establishing the PPI network has become a fundamental but essential task for a better understanding of biological events and disease pathogenesis. Although many machine learning algorithms have been employed to predict PPIs, with only protein sequence information as the training features, these models suffer from low robustness and prediction accuracy. In this study, a new deep-learning-based framework named the Structural Gated Attention Deep (SGAD) model was proposed to improve the performance of PPI network reconstruction (PINR). The improved predictive performances were achieved by augmenting multiple protein sequence descriptors, the topological features and information flow of the PPI network, which were further implemented with a gating mechanism to improve its robustness to noise. On 11 independent test data sets and one combined data set, SGAD yielded area under the curve values of approximately 0.83-0.93, outperforming other models. Furthermore, the SGAD ensemble can learn more characteristics information on protein pairs through a two-layer neural network, serving as a powerful tool in the exploration of PPI biological space.

Protein-Protein Interface Topology as a Predictor of Secondary Structure and Molecular Function Using Convolutional Deep Learning

To power the specific recognition and binding of protein partners into functional complexes, a wealth of information about the structure and function of the partners is necessarily encoded into the global shape of protein-protein interfaces and their local topological features. To identify whether this is the case, this study uses convolutional deep learning methods (typically leveraged for 2D image recognition) on 3D voxel representations of protein-protein interfaces colored by burial depth. A novel two-stage network fed with voxelizations of each interface at two distinct resolutions achieves balance between performance and computational cost. From the shape of the interfaces, the network tries to predict the presence of secondary structure motifs at the interface and the molecular function of the corresponding complex. Secondary structure and certain classes of function are found to be very well predicted, validating the hypothesis that interface shape is a conveyor of higher-level information. Interface patterns triggering the recognition of specific classes are also identified and described.

Modulators of protein-protein interactions as antimicrobial agents

Protein-Protein interactions (PPIs) are involved in a myriad of cellular processes in all living organisms and the modulation of PPIs is already under investigation for the development of new drugs targeting cancers, autoimmune diseases and viruses. PPIs are also involved in the regulation of vital functions in bacteria and, therefore, targeting bacterial PPIs offers an attractive strategy for the development of antibiotics with novel modes of action. The latter are urgently needed to tackle multidrug-resistant and multidrug-tolerant bacteria. In this review, we describe recent developments in the modulation of PPIs in pathogenic bacteria for antibiotic development, including advanced small molecule and peptide inhibitors acting on bacterial PPIs involved in division, replication and transcription, outer membrane protein biogenesis, with an additional focus on toxin-antitoxin systems as upcoming drug targets.

Evolution in non-peptide α-helix mimetics on the road to effective protein-protein interaction modulators

Modulation of interactome networks, essentially protein-protein interactions (PPIs), might represent valuable therapeutic approaches to different pathological conditions. Since a high percentage of PPIs are mediated by α-helical structures at the interacting surface, the development of compounds able to reproduce the amino acid side-chain organization of α-helices (e.g. stabilized α-helix peptides and β-derivatives, proteomimetics, and α-helix small-molecule mimetics) focuses the attention of different research groups. This appraisal describes the recent progress in the non-peptide α-helix mimetics field, which has evolved from single-face to multi-face reproducing compounds and from oligomeric to monomeric scaffolds able to bear different substituents in similar spatial dispositions as the side-chains in canonical helices. Grouped by chemical structures, the review contemplates terphenyl-like molecules, oligobenzamides and heterocyclic analogues, benzamide-amino acid conjugates and non-oligomeric small-molecules mimetics, among others, and their effectiveness to stabilize/disrupt therapeutically relevant PPIs. The X-ray structures of a couple of oligomeric peptidomimetics and of some small-molecules complexed with the MDM2 protein, as well as the state of the art on their development in clinical trials, are also remarked. The discovery of a continuously increasing number of new disease-relevant PPIs could offer future opportunities for these and other forthcoming α-helix mimetics.

MrpH, a new class of metal-binding adhesin, requires zinc to mediate biofilm formation

Proteus mirabilis, a Gram-negative uropathogen, is a major causative agent in catheter-associated urinary tract infections (CAUTI). Mannose-resistant Proteus-like fimbriae (MR/P) are crucially important for P. mirabilis infectivity and are required for biofilm formation and auto-aggregation, as well as for bladder and kidney colonization. Here, the X-ray crystal structure of the MR/P tip adhesin, MrpH, is reported. The structure has a fold not previously described and contains a transition metal center with Zn²⁺ coordinated by three conserved histidine residues and a ligand. Using biofilm assays, chelation, metal complementation, and site-directed mutagenesis of the three histidines, we show that an intact metal binding site occupied by zinc is essential for MR/P fimbria-mediated biofilm formation, and furthermore, that P. mirabilis biofilm formation is reversible in a zinc-dependent manner. Zinc is also required for MR/P-dependent agglutination of erythrocytes, and mutation of the metal binding site renders P. mirabilis unfit in a mouse model of UTI. The studies presented here provide important clues as to the mechanism of MR/P-mediated biofilm formation and serve as a starting point for identifying the physiological MR/P fimbrial receptor.

Many bacteria use fimbriae to adhere to surfaces, and this function is often essential for pathogens to gain a foothold in the host. In this study, we examine the major virulence-associated fimbrial protein, MrpH, of the bacterial urinary tract pathogen Proteus mirabilis. This species is particularly known for causing catheter-associated urinary tract infections, in which it forms damaging urinary stones and crystalline biofilms that can block the flow of urine through indwelling catheters. MrpH resides at the tip of mannose-resistant Proteus-like (MR/P) fimbriae and is required for MR/P-dependent adherence to surfaces. Although MR/P belongs to a well-known class of adhesive fimbriae encoded by the chaperone-usher pathway, we found that MrpH has a dramatically different structure compared with other tip-located adhesins in this family. Unexpectedly, MrpH was found to bind a zinc cation, which we show is essential for MR/P-mediated biofilm formation and adherence to red blood cells. Furthermore, MR/P-mediated adherence can be modified by controlling zinc levels. These findings have the potential to aid development of better anti-biofilm urinary catheters or other methods to prevent P. mirabilis infection of the urinary tract.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Protein-protein complexes as targets for drug discovery against infectious diseases

Antibiotics are therapeutic agents against bacterial infections, however, the emergence of multiple and extremely drug-resistant microbes (Multi-Drug Resistant and Extremely Drug-Resistant) are compromising the effectiveness of the currently available treatment options. The drug resistance is not a novel crisis, the current pace of drug discovery has failed to compete with the growth of MDR and XDR pathogenic strains and therefore, it is highly central to find out novel antimicrobial drugs with unique mechanisms of action which may reduce the burden of MDR and XDR pathogenic strains. Protein-protein interactions (PPIs) are involved in a countless of the physiological and cellular phenomena and have become an attractive target to treat the diseases. Therefore, targeting PPIs in infectious agents may offer a completely novel strategy of intervention to develop anti-infective drugs that may combat the ever-increasing rate of drug resistant strains. This chapter describes how small molecule candidate inhibitors that are capable of disrupting the PPIs in pathogenic microbes and it could be an alternative lead discovery strategy to obtain novel antibiotics. Over the last three decades, there has been increasing efforts focused on the manipulation of PPIs in order to develop novel therapeutic interventions. The diversity and complexity of such a complex and highly dynamic systems pose many challenges in targeting PPIs by drug-like molecules with necessary selectivity and potency. Traditional and novel drug discovery strategies have provided tools for designing and assessing PPI inhibitors against infectious diseases.

Natural products discovery and potential for new antibiotics

Microbial natural products have been one of the most important sources for the discovery of potential new antibiotics. However, the decline in the number of new chemical scaffolds discovered and the rediscovery problem of old known molecules has become a limitation for discovery programs developed by an industry confronted by a lack of incentives and a broken economic model. In contrast, the emergence of multidrug resistance in key pathogens has continued to progress and this issue is compounded by a lack of new antibiotics in development to address most of the difficult to treat infections. Advances in genome mining have confirmed the richness of biosynthetic gene clusters (BGCs) in the majority of microbial sources, and this suggests that an untapped chemical diversity is waiting to be discovered. The development of new genome engineering and synthetic biology tools, and the implementation of comparative omic approaches is fostering the development of new integrated culture-based strategies and genomic-driven approaches aimed at delivering new chemical classes of antibiotics.

Combination of SAXS and Protein Painting Discloses the Three-Dimensional Organization of the Bacterial Cysteine Synthase Complex, a Potential Target for Enhancers of Antibiotic Action

The formation of multienzymatic complexes allows for the fine tuning of many aspects of enzymatic functions, such as efficiency, localization, stability, and moonlighting. Here, we investigated, in solution, the structure of bacterial cysteine synthase (CS) complex. CS is formed by serine acetyltransferase (CysE) and O-acetylserine sulfhydrylase isozyme A (CysK), the enzymes that catalyze the last two steps of cysteine biosynthesis in bacteria. CysK and CysE have been proposed as potential targets for antibiotics, since cysteine and related metabolites are intimately linked to protection of bacterial cells against redox damage and to antibiotic resistance. We applied a combined approach of small-angle X-ray scattering (SAXS) spectroscopy and protein painting to obtain a model for the solution structure of CS. Protein painting allowed the identification of protein–protein interaction hotspots that were then used as constrains to model the CS quaternary assembly inside the SAXS envelope. We demonstrate that the active site entrance of CysK is involved in complex formation, as suggested by site-directed mutagenesis and functional studies. Furthermore, complex formation involves a conformational change in one CysK subunit that is likely transmitted through the dimer interface to the other subunit, with a regulatory effect. Finally, SAXS data indicate that only one active site of CysK is involved in direct interaction with CysE and unambiguously unveil the quaternary arrangement of CS.

A Single Tool to Monitor Multiple Protein-Protein Interactions of the Escherichia coli Acyl Carrier Protein

Protein-protein interactions are ubiquitous to all domains of life and have gained recent interest as drug targets. However, many current methods to study protein-protein interactions can be costly and are low-throughput. Here, we demonstrate a solvatochromic tool based on the natural post-translational modification of the Escherichia coli acyl carrier protein (EcACP) used to visualize protein-protein interactions between EcACP and 13 different partner enzymes from several biosynthetic pathways. We use this tool to confirm proposed interactions between EcACP and both catalytic and regulatory proteins. We also show the utility of this method toward detecting allosteric changes to partner enzyme structure and the validation of active site inhibitors. We anticipate the future adaptation of this assay into a high-throughput screen for antibiotic discovery.