Structure of the ultra-high-affinity colicin E2 DNase--Im2 complex

Evolutionary paths that link orthogonal pairs of binding proteins

Some protein binding pairs exhibit extreme specificities that functionally insulate them from homologs. Such pairs evolve mostly by accumulating single-point mutations, and mutants are selected if their affinity exceeds the threshold required for function1-4. Thus, homologous and high-specificity binding pairs bring to light an evolutionary conundrum: how does a new specificity evolve while maintaining the required affinity in each intermediate5,6? Until now, a fully functional single-mutation path that connects two orthogonal pairs has only been described where the pairs were mutationally close thus enabling experimental enumeration of all intermediates2. We present an atomistic and graph-theoretical framework for discovering low molecular strain single-mutation paths that connect two extant pairs, enabling enumeration beyond experimental capability. We apply it to two orthogonal bacterial colicin endonuclease-immunity pairs separated by 17 interface mutations7. We were not able to find a strain-free and functional path in the sequence space defined by the two extant pairs. But including mutations that bridge amino acids that cannot be exchanged through single-nucleotide mutations led us to a strain-free 19-mutation trajectory that is completely viable in vivo. Our experiments show that the specificity switch is remarkably abrupt, resulting from only one radical mutation on each partner. Furthermore, each of the critical specificity-switch mutations increases fitness, demonstrating that functional divergence could be driven by positive Darwinian selection. These results reveal how even radical functional changes in an epistatic fitness landscape may evolve.

Comparison of Kill Switch Toxins in Plant-Beneficial Pseudomonas fluorescens Reveals Drivers of Lethality, Stability, and Escape

Kill switches provide a biocontainment strategy in which unwanted growth of an engineered microorganism is prevented by expression of a toxin gene. A major challenge in kill switch engineering is balancing evolutionary stability with robust cell killing activity in application relevant host strains. Understanding host-specific containment dynamics and modes of failure helps to develop potent yet stable kill switches. To guide the design of robust kill switches in the agriculturally relevant strain Pseudomonas fluorescens SBW25, we present a comparison of lethality, stability, and genetic escape of eight different toxic effectors in the presence of their cognate inactivators (i.e., toxin-antitoxin modules, polymorphic exotoxin-immunity systems, restriction endonuclease-methyltransferase pair). We find that cell killing capacity and evolutionary stability are inversely correlated and dependent on the level of protection provided by the inactivator gene. Decreasing the proteolytic stability of the inactivator protein can increase cell killing capacity, but at the cost of long-term circuit stability. By comparing toxins within the same genetic context, we determine that modes of genetic escape increase with circuit complexity and are driven by toxin activity, the protective capacity of the inactivator, and the presence of mutation-prone sequences within the circuit. Collectively, the results of our study reveal that circuit complexity, toxin choice, inactivator stability, and DNA sequence design are powerful drivers of kill switch stability and valuable targets for optimization of biocontainment systems.

Affinity of disordered protein complexes is modulated by entropy-energy reinforcement

Intrinsically disordered proteins (IDPs), which are very common and essential to many biological activities, sometimes function via interaction with another IDP and form a fuzzy complex, which can be highly stable. It is unclear what the biophysical forces are that govern their thermodynamics and specificity, which are essential for de novo fuzzy complex design. Here, we explored the fuzzy complex formed between ProTα and H1, which are oppositely charged IDPs, by swapping the charges between them, generating variants that have either greater polyampholytic or polyelectrolytic nature as well as different charge patterns. Charge swapping and shuffling dramatically change the affinity of the fuzzy complex, which is contributed to by both enthalpy and entropy, where the latter is dominated by counterion release.

The association between two intrinsically disordered proteins (IDPs) may produce a fuzzy complex characterized by a high binding affinity, similar to that found in the ultrastable complexes formed between two well-structured proteins. Here, using coarse-grained simulations, we quantified the biophysical forces driving the formation of such fuzzy complexes. We found that the high-affinity complex formed between the highly and oppositely charged H1 and ProTα proteins is sensitive to electrostatic interactions. We investigated 52 variants of the complex by swapping charges between the two oppositely charged proteins to produce sequences whose negatively or positively charged residue content was more homogeneous or heterogenous (i.e., polyelectrolytic or polyampholytic, having higher or lower absolute net charges, respectively) than the wild type. We also changed the distributions of oppositely charged residues within each participating sequence to produce variants in which the charges were segregated or well mixed. Both types of changes significantly affect binding affinity in fuzzy complexes, which is governed by both enthalpy and entropy. The formation of H1–ProTa is supported by an increase in configurational entropy and by entropy due to counterion release. The latter can be twice as large as the former, illustrating the dominance of counterion entropy in modulating the binding thermodynamics. Complexes formed between proteins with greater absolute net charges are more stable, both enthalpically and entropically, indicating that enthalpy and entropy have a mutually reinforcing effect. The sensitivity of the thermodynamics of the complex to net charge and the charge pattern within each of the binding constituents may provide a means to achieve binding specificity between IDPs.

LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction

Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.

Structural design principles for specific ultra-high affinity interactions between colicins/pyocins and immunity proteins

The interactions of the antibiotic proteins colicins/pyocins with immunity proteins is a seminal model system for studying protein–protein interactions and specificity. Yet, a precise and quantitative determination of which structural elements and residues determine their binding affinity and specificity is still lacking. Here, we used comparative structure-based energy calculations to map residues that substantially contribute to interactions across native and engineered complexes of colicins/pyocins and immunity proteins. We show that the immunity protein α1–α2 motif is a unique structurally-dissimilar element that restricts interaction specificity towards all colicins/pyocins, in both engineered and native complexes. This motif combines with a diverse and extensive array of electrostatic/polar interactions that enable the exquisite specificity that characterizes these interactions while achieving ultra-high affinity. Surprisingly, the divergence of these contributing colicin residues is reciprocal to residue conservation in immunity proteins. The structurally-dissimilar immunity protein α1–α2 motif is recognized by divergent colicins similarly, while the conserved immunity protein α3 helix interacts with diverse colicin residues. Electrostatics thus plays a key role in setting interaction specificity across all colicins and immunity proteins. Our analysis and resulting residue-level maps illuminate the molecular basis for these protein–protein interactions, with implications for drug development and rational engineering of these interfaces.

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45

Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.

Performance of human and server prediction in CAPRI rounds 38-45

CAPRI challenges offer a variety of blind tests for protein-protein interaction prediction. In CAPRI Rounds 38-45, we generated a set of putative binding modes for each target with an FFT-based docking algorithm, and then scored and ranked these binding modes with a proprietary scoring function, ITScorePP. We have also developed a novel web server, Rebipp. The algorithm utilizes information retrieval to identify relevant biological information to significantly reduce the search space for a particular protein. In parallel, we have also constructed a GPU-based docking server, MDockPP, for protein-protein complex structure prediction. Here, the performance of our protocol in CAPRI rounds 38-45 is reported, which include 16 docking and scoring targets. Among them, three targets contain multiple interfaces: Targets 124, 125, and 136 have 2, 4, and 3 interfaces, respectively. In the predictor experiments, we predicted correct binding modes for nine targets, including one high-accuracy interface, six medium-accuracy binding modes, and six acceptable-accuracy binding modes. For the docking server prediction experiments, we predicted correct binding modes for eight targets, including one high-accuracy, three medium-accuracy, and five acceptable-accuracy binding modes.

HK97 gp74 Possesses an α-Helical Insertion in the ββα Fold That Affects Its Metal Binding, cos Site Digestion, and In Vivo Activities

The last gene in the genome of the bacteriophage HK97 encodes gp74, an HNH endonuclease. HNH motifs contain two conserved His residues and an invariant Asn residue, and they adopt a ββα structure. gp74 is essential for phage head morphogenesis, likely because gp74 enhances the specific endonuclease activity of the HK97 terminase complex. Notably, the ability of gp74 to enhance the terminase-mediated cleavage of the phage cos site requires an intact HNH motif in gp74. Mutation of H82, the conserved metal-binding His residue in the HNH motif, to Ala abrogates gp74-mediated stimulation of terminase activity. Here, we present nuclear magnetic resonance (NMR) studies demonstrating that gp74 contains an α-helical insertion in the Ω-loop, which connects the two β-strands of the ββα fold, and a disordered C-terminal tail. NMR data indicate that the Ω-loop insert makes contacts to the ββα fold and influences the ability of gp74 to bind divalent metal ions. Further, the Ω-loop insert and C-terminal tail contribute to gp74-mediated DNA digestion and to gp74 activity in phage morphogenesis. The data presented here enrich our molecular-level understanding of how HNH endonucleases enhance terminase-mediated digestion of the cos site and contribute to the phage replication cycle.IMPORTANCE This study demonstrates that residues outside the canonical ββα fold, namely, the Ω-loop α-helical insert and a disordered C-terminal tail, regulate the activity of the HNH endonuclease gp74. The increased divalent metal ion binding when the Ω-loop insert is removed compared to reduced cos site digestion and phage formation indicates that the Ω-loop insert plays multiple regulatory roles. The data presented here provide insights into the molecular basis of the involvement of HNH proteins in phage DNA packing.

Coarse-grained and atomic resolution biomolecular docking with the ATTRACT approach

The ATTRACT protein-protein docking program has been employed to predict protein-protein complex structures in CAPRI rounds 38-45. For 11 out of 16 targets acceptable or better quality solutions have been submitted (~70%). It includes also several cases of peptide-protein docking and the successful prediction of the geometry of carbohydrate-protein interactions. The option of combining rigid body minimization and simultaneous optimization in collective degrees of freedom based on elastic network modes was employed and systematically evaluated. Application to a large benchmark set indicates a modest improvement in docking performance compared to rigid docking. Possible further improvements of the docking approach in particular at the scoring and the flexible refinement steps are discussed.

Ultrahigh specificity in a network of computationally designed protein-interaction pairs

Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity.

The molecular basis of ultrahigh specificity in protein-protein interactions remains obscure. The authors present a computational method to design atomically accurate new pairs exhibiting >100,000-fold specificity switches, generating a large and complex interaction network.

The Inclusion of Water Molecules in Residue Interaction Networks Identifies Additional Central Residues

The relevance of water molecules for the recognition and the interaction of biomolecules is widely appreciated. In this paper we address the role that water molecules associated to protein complexes play for the functional relevance of residues by considering their residue interaction networks (RINs). These are commonly defined on the basis of the amino acid composition of the proteins themselves, disregarding the solvation state of the protein. We determine properties of the RINs of two protein complexes, colicin E2/Im2 and barnase/barstar, with and without associated water molecules, using a previously developed methodology and its associated application RINspector. We find that the inclusion of water molecules in RINs leads to an increase in the number of central residues which adds a novel mechanism to the relevance of water molecules for protein function.

A Colicin M-Type Bacteriocin from Pseudomonas aeruginosa Targeting the HxuC Heme Receptor Requires a Novel Immunity Partner

Pyocins are bacteriocins secreted by Pseudomonas aeruginosa, and they assist in the colonization of different niches. A major subset of these antibacterial proteins adopt a modular organization characteristic of polymorphic toxins. They include a receptor-binding domain, a segment enabling membrane passage, and a toxin module at the carboxy terminus, which eventually kills the target cells. To protect themselves from their own products, bacteriocin-producing strains express an immunity gene concomitantly with the bacteriocin. We show here that a pyocin equipped with a phylogenetically distinct ColM toxin domain, PaeM4, mediates antagonism against a large set of P. aeruginosa isolates. Immunity to PaeM4 is provided by the inner membrane protein PmiC, which is equipped with a transmembrane topology not previously described for the ColM family. Given that strains lacking a pmiC gene are killed by PaeM4, the presence of such an immunity partner likely is a key criterion for escaping cellular death mediated by PaeM4. The presence of a TonB box in PaeM4 and enhanced bacteriocin activity under iron-poor conditions strongly suggested the targeting of a TonB-dependent receptor. Evaluation of PaeM4 activities against TonB-dependent receptor knockout mutants in P. aeruginosa PAO1 revealed that the heme receptor HxuC (PA1302) serves as a PaeM4 target at the cellular surface. Because other ColM-type pyocins may target the ferrichrome receptor FiuA, our results illustrate the versatility in target recognition conferred by the polymorphic nature of ColM-type bacteriocins.IMPORTANCE The antimicrobial armamentarium of a bacterium is a major asset for colonizing competitive environments. Bacteriocins comprise a subset of these compounds. Pyocins are an example of such antibacterial proteins produced by Pseudomonas aeruginosa, killing other P. aeruginosa strains. A large group of these molecules show a modular protein architecture that includes a receptor-binding domain for initial target cell attachment and a killer domain. In this study, we have shown that a novel modular pyocin (PaeM4) that kills target bacteria via interference with peptidoglycan assembly takes advantage of the HxuC heme receptor. Cells can protect themselves from killing by the presence of a dedicated immunity partner, an integral inner membrane protein that adopts a transmembrane topology distinct from that of proteins currently known to provide immunity against such toxin activity. Understanding the receptors with which pyocins interact and how immunity to pyocins is achieved is a pivotal step toward the rational design of bacteriocin cocktails for the treatment of P. aeruginosa infections.

Estimating Interprotein Pairwise Interaction Energies in Cell Lysates from a Single Native Mass Spectrum

A powerful method to determine the energetic coupling between amino acids is double mutant cycle analysis. In this method, two residues are mutated separately and in combination and the energetic effects of the mutations are determined. A deviation of the effect of the double mutation from the sum of effects of the single mutations indicates that the two residues are interacting directly or indirectly. Here, we show that double mutant cycle analysis by native mass spectrometry can be carried out for interactions in crude Escherichia coli cell extracts, thereby obviating the need for protein purification and generating binding isotherms. Our results indicate that intermolecular hydrogen bond strengths are not affected by the more crowded conditions in cell lysates.

Complex microbial systems across different levels of description

Complex biological systems offer a variety of interesting phenomena at the different physical scales. With increasing abstraction, details of the microscopic scales can often be extrapolated to average or typical macroscopic properties. However, emergent properties and cross-scale interactions can impede naïve abstractions and necessitate comprehensive investigations of these complex systems. In this review paper, we focus on microbial communities, and first, summarize a general hierarchy of relevant scales and description levels to understand these complex systems: (1) genetic networks, (2) single cells, (3) populations, and (4) emergent multi-cellular properties. Second, we employ two illustrating examples, microbial competition and biofilm formation, to elucidate how cross-scale interactions and emergent properties enrich the observed multi-cellular behavior in these systems. Finally, we conclude with pointing out the necessity of multi-scale investigations to understand complex biological systems and discuss recent investigations.

CsrA and its regulators control the time-point of ColicinE2 release in Escherichia coli

The bacterial SOS response is a cellular reaction to DNA damage, that, among other actions, triggers the expression of colicin - toxic bacteriocins in Escherichia coli that are released to kill close relatives competing for resources. However, it is largely unknown, how the complex network regulating toxin expression controls the time-point of toxin release to prevent premature release of inefficient protein concentrations. Here, we study how different regulatory mechanisms affect production and release of the bacteriocin ColicinE2 in Escherichia coli. Combining experimental and theoretical approaches, we demonstrate that the global carbon storage regulator CsrA controls the duration of the delay between toxin production and release and emphasize the importance of CsrA sequestering elements for the timing of ColicinE2 release. In particular, we show that ssDNA originating from rolling-circle replication of the toxin-producing plasmid represents a yet unknown additional CsrA sequestering element, which is essential in the ColicinE2-producing strain to enable toxin release by reducing the amount of free CsrA molecules in the bacterial cell. Taken together, our findings show that CsrA times ColicinE2 release and reveal a dual function for CsrA as an ssDNA and mRNA-binding protein, introducing ssDNA as an important post-transcriptional gene regulatory element.

Systematic classification of the His-Me finger superfamily

The His-Me finger endonucleases, also known as HNH or ββα-metal endonucleases, form a large and diverse protein superfamily. The His-Me finger domain can be found in proteins that play an essential role in cells, including genome maintenance, intron homing, host defense and target offense. Its overall structural compactness and non-specificity make it a perfectly-tailored pathogenic module that participates on both sides of inter- and intra-organismal competition. An extremely low sequence similarity across the superfamily makes it difficult to identify and classify new His-Me fingers. Using state-of-the-art distant homology detection methods, we provide an updated and systematic classification of His-Me finger proteins. In this work, we identified over 100 000 proteins and clustered them into 38 groups, of which three groups are new and cannot be found in any existing public domain database of protein families. Based on an analysis of sequences, structures, domain architectures, and genomic contexts, we provide a careful functional annotation of the poorly characterized members of this superfamily. Our results may inspire further experimental investigations that should address the predicted activity and clarify the potential substrates, to provide more detailed insights into the fundamental biological roles of these proteins.

Modeling oblong proteins and water-mediated interfaces with RosettaDock in CAPRI rounds 28-35

The 28th-35th rounds of the Critical Assessment of PRotein Interactions (CAPRI) served as a practical benchmark for our RosettaDock protein-protein docking protocols, highlighting strengths and weaknesses of the approach. We achieved acceptable or better quality models in three out of 11 targets. For the two α-repeat protein-green fluorescent protein (αrep-GFP) complexes, we used a novel ellipsoidal partial-global docking method (Ellipsoidal Dock) to generate models with 2.2 Å/1.5 Å interface RMSD, capturing 49%/42% of the native contacts, for the 7-/5-repeat αrep complexes. For the DNase-immunity protein complex, we used a new predictor of hydrogen-bonding networks, HBNet with Bridging Waters, to place individual water models at the complex interface; models were generated with 1.8 Å interface RMSD and 12% native water contacts recovered. The targets for which RosettaDock failed to create an acceptable model were typically difficult in general, as six had no acceptable models submitted by any CAPRI predictor. The UCH-L5-RPN13 and UCH-L5-INO80G de-ubiquitinating enzyme-inhibitor complexes comprised inhibitors undergoing significant structural changes upon binding, with the partners being highly interwoven in the docked complexes. Our failure to predict the nucleosome-enzyme complex in Target 95 was largely due to tight constraints we placed on our model based on sparse biochemical data suggesting two specific cross-interface interactions, preventing the correct structure from being sampled. While RosettaDock's three successes show that it is a state-of-the-art docking method, the difficulties with highly flexible and multi-domain complexes highlight the need for better flexible docking and domain-assembly methods. Proteins 2017; 85:479-486. © 2016 Wiley Periodicals, Inc.

Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions

Protein-protein interactions are either through direct contacts between two binding partners or mediated by structural waters. Both direct contacts and water-mediated interactions are crucial to the formation of a protein-protein complex. During the recent CAPRI rounds, a novel parallel searching strategy for predicting water-mediated interactions is introduced into our protein-protein docking method, MDockPP. Briefly, a FFT-based docking algorithm is employed in generating putative binding modes, and an iteratively derived statistical potential-based scoring function, ITScorePP, in conjunction with biological information is used to assess and rank the binding modes. Up to 10 binding modes are selected as the initial protein-protein complex structures for MD simulations in explicit solvent. Water molecules near the interface are clustered based on the snapshots extracted from independent equilibrated trajectories. Then, protein-ligand docking is employed for a parallel search for water molecules near the protein-protein interface. The water molecules generated by ligand docking and the clustered water molecules generated by MD simulations are merged, referred to as the predicted structural water molecules. Here, we report the performance of this protocol for CAPRI rounds 28-29 and 31-35 containing 20 valid docking targets and 11 scoring targets. In the docking experiments, we predicted correct binding modes for nine targets, including one high-accuracy, two medium-accuracy, and six acceptable predictions. Regarding the two targets for the prediction of water-mediated interactions, we achieved models ranked as "excellent" in accordance with the CAPRI evaluation criteria; one of these two targets is considered as a difficult target for structural water prediction. Proteins 2017; 85:424-434. © 2016 Wiley Periodicals, Inc.

Hierarchical Post-transcriptional Regulation of Colicin E2 Expression in Escherichia coli

Post-transcriptional regulation of gene expression plays a crucial role in many bacterial pathways. In particular, the translation of mRNA can be regulated by trans-acting, small, non-coding RNAs (sRNAs) or mRNA-binding proteins, each of which has been successfully treated theoretically using two-component models. An important system that includes a combination of these modes of post-transcriptional regulation is the Colicin E2 system. DNA damage, by triggering the SOS response, leads to the heterogeneous expression of the Colicin E2 operon including the cea gene encoding the toxin colicin E2, and the cel gene that codes for the induction of cell lysis and release of colicin. Although previous studies have uncovered the system's basic regulatory interactions, its dynamical behavior is still unknown. Here, we develop a simple, yet comprehensive, mathematical model of the colicin E2 regulatory network, and study its dynamics. Its post-transcriptional regulation can be reduced to three hierarchically ordered components: the mRNA including the cel gene, the mRNA-binding protein CsrA, and an effective sRNA that regulates CsrA. We demonstrate that the stationary state of this system exhibits a pronounced threshold in the abundance of free mRNA. As post-transcriptional regulation is known to be noisy, we performed a detailed stochastic analysis, and found fluctuations to be largest at production rates close to the threshold. The magnitude of fluctuations can be tuned by the rate of production of the sRNA. To study the dynamics in response to an SOS signal, we incorporated the LexA-RecA SOS response network into our model. We found that CsrA regulation filtered out short-lived activation peaks and caused a delay in lysis gene expression for prolonged SOS signals, which is also seen in experiments. Moreover, we showed that a stochastic SOS signal creates a broad lysis time distribution. Our model thus theoretically describes Colicin E2 expression dynamics in detail and reveals the importance of the specific regulatory components for the timing of toxin release.

On the ability of molecular dynamics simulation and continuum electrostatics to treat interfacial water molecules in protein-protein complexes

Interfacial waters are increasingly appreciated as playing a key role in protein-protein interactions. We report on a study of the prediction of interfacial water positions by both Molecular Dynamics and explicit solvent-continuum electrostatics based on the Dipolar Poisson-Boltzmann Langevin (DPBL) model, for three test cases: (i) the barnase/barstar complex (ii) the complex between the DNase domain of colicin E2 and its cognate Im2 immunity protein and (iii) the highly unusual anti-freeze protein Maxi which contains a large number of waters in its interior. We characterize the waters at the interface and in the core of the Maxi protein by the statistics of correctly predicted positions with respect to crystallographic water positions in the PDB files as well as the dynamic measures of diffusion constants and position lifetimes. Our approach provides a methodology for the evaluation of predicted interfacial water positions through an investigation of water-mediated inter-chain contacts. While our results show satisfactory behaviour for molecular dynamics simulation, they also highlight the need for improvement of continuum methods.

Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI

The ATTRACT coarse-grained docking approach in combination with various types of atomistic, flexible refinement methods has been applied to predict protein-protein and peptide-protein complexes in CAPRI rounds 28-36. For a large fraction of CAPRI targets (12 out of 18), at least one model of acceptable or better quality was generated, corresponding to a success rate of 67%. In particular, for several peptide-protein complexes excellent predictions were achieved. In several cases, a combination of template-based modeling and extensive molecular dynamics-based refinement yielded medium and even high quality solutions. In one particularly challenging case, the structure of an ubiquitylation enzyme bound to the nucleosome was correctly predicted as a set of acceptable quality solutions. Based on the experience with the CAPRI targets, new interface refinement approaches and methods for ab-initio peptide-protein docking have been developed. Failures and possible improvements of the docking method with respect to scoring and protein flexibility will also be discussed. Proteins 2017; 85:391-398. © 2016 Wiley Periodicals, Inc.

Performance of ZDOCK and IRAD in CAPRI rounds 28-34

We report the performance of our protein-protein docking pipeline, including the ZDOCK rigid-body docking algorithm, on 19 targets in CAPRI rounds 28-34. Following the docking step, we reranked the ZDOCK predictions using the IRAD scoring function, pruned redundant predictions, performed energy landscape analysis, and utilized our interface prediction approach RCF. In addition, we applied constraints to the search space based on biological information that we culled from the literature, which increased the chance of making a correct prediction. For all but two targets we were able to find and apply biological information and we found the information to be highly accurate, indicating that effective incorporation of biological information is an important component for protein-protein docking. Proteins 2017; 85:408-416. © 2016 Wiley Periodicals, Inc.

Molecular docking as a popular tool in drug design, an in silico travel

New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism-or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein-protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.

Diversification of β-Augmentation Interactions between CDI Toxin/Immunity Proteins

Contact-dependent growth inhibition (CDI) is a widespread mechanism of inter-bacterial competition mediated by the CdiB/CdiA family of two-partner secretion proteins. CdiA effectors carry diverse C-terminal toxin domains (CdiA-CT), which are delivered into neighboring target cells to inhibit growth. CDI(+) bacteria also produce CdiI immunity proteins that bind specifically to cognate CdiA-CT toxins and protect the cell from auto-inhibition. Here, we compare the structures of homologous CdiA-CT/CdiI complexes from Escherichia coli EC869 and Yersinia pseudotuberculosis YPIII to explore the evolution of CDI toxin/immunity protein interactions. Both complexes share an unusual β-augmentation interaction, in which the toxin domain extends a β-hairpin into the immunity protein to complete a six-stranded anti-parallel sheet. However, the specific contacts differ substantially between the two complexes. The EC869 β-hairpin interacts mainly through direct H-bond and ion-pair interactions, whereas the YPIII β-hairpin pocket contains more hydrophobic contacts and a network of bridging water molecules. In accord with these differences, we find that each CdiI protein only protects target bacteria from its cognate CdiA-CT toxin. The compact β-hairpin binding pocket within the immunity protein represents a tractable system for the rationale design of small molecules to block CdiA-CT/CdiI complex formation. We synthesized a macrocyclic peptide mimic of the β-hairpin from EC869 toxin and solved its structure in complex with cognate immunity protein. These latter studies suggest that small molecules could potentially be used to disrupt CDI toxin/immunity complexes.

Structures of the Ultra-High-Affinity Protein-Protein Complexes of Pyocins S2 and AP41 and Their Cognate Immunity Proteins from Pseudomonas aeruginosa

How ultra-high-affinity protein-protein interactions retain high specificity is still poorly understood. The interaction between colicin DNase domains and their inhibitory immunity (Im) proteins is an ultra-high-affinity interaction that is essential for the neutralisation of endogenous DNase catalytic activity and for protection against exogenous DNase bacteriocins. The colicin DNase-Im interaction is a model system for the study of high-affinity protein-protein interactions. However, despite the fact that closely related colicin-like bacteriocins are widely produced by Gram-negative bacteria, this interaction has only been studied using colicins from Escherichia coli. In this work, we present the first crystal structures of two pyocin DNase-Im complexes from Pseudomonas aeruginosa, pyocin S2 DNase-ImS2 and pyocin AP41 DNase-ImAP41. These structures represent divergent DNase-Im subfamilies and are important in extending our understanding of protein-protein interactions for this important class of high-affinity protein complex. A key finding of this work is that mutations within the immunity protein binding energy hotspot, helix III, are tolerated by complementary substitutions at the DNase-Immunity protein binding interface. Im helix III is strictly conserved in colicins where an Asp forms polar interactions with the DNase backbone. ImAP41 contains an Asp-to-Gly substitution in helix III and our structures show the role of a co-evolved substitution where Pro in DNase loop 4 occupies the volume vacated and removes the unfulfilled hydrogen bond. We observe the co-evolved mutations in other DNase-Immunity pairs that appear to underpin the split of this family into two distinct groups.

Amount of colicin release in Escherichia coli is regulated by lysis gene expression of the colicin E2 operon

The production of bacteriocins in response to worsening environmental conditions is one means of bacteria to outcompete other microorganisms. Colicins, one class of bacteriocins in Escherichia coli, are effective against closely related Enterobacteriaceae. Current research focuses on production, release and uptake of these toxins by bacteria. However, little is known about the quantitative aspects of these dynamic processes. Here, we quantitatively study expression dynamics of the Colicin E2 operon in E. coli on a single cell level using fluorescence time-lapse microscopy. DNA damage, triggering SOS response leads to the heterogeneous expression of this operon including the cea gene encoding the toxin, Colicin E2, and the cel gene coding for the induction of cell lysis and subsequent colicin release. Advancing previous whole population investigations, our time-lapse experiments reveal that at low exogenous stress levels all cells eventually respond after a given time (heterogeneous timing). This heterogeneous timing is lost at high stress levels, at which a synchronized stress response of all cells 60 min after induction via stress can be observed. We further demonstrate, that the amount of colicin released is dependent on cel (lysis) gene expression, independent of the applied exogenous stress level. A heterogeneous response in combination with heterogeneous timing can be biologically significant. It might enable a bacterial population to endure low stress levels, while at high stress levels an immediate and synchronized population wide response can give single surviving cells of the own species the chance to take over the bacterial community after the stress has ceased.

A "fuzzy"-logic language for encoding multiple physical traits in biomolecules

To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics.

The targets of CAPRI rounds 20-27

Eight CAPRI prediction rounds with a total of 15 targets were held in the years 2010-2012. Only five of the targets were protein assemblies comparable with those of earlier CAPRI rounds. In one target, the solvent positions at the interface had to be predicted; another was a protein-polysaccharide complex. The remainders were designed complexes issued from protein engineering experiments, and the prediction concerned either their structure or the binding affinity of the designed ligand. Affinity prediction was a new experiment in CAPRI, and a challenge for its participants. It pushed the community into developing novel procedures and score functions that will improve the performance of docking methods, help designing binders, and yield better structure-based estimates of the binding free energy of natural assemblies.

Blind prediction of interfacial water positions in CAPRI

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

Inclusion of the orientational entropic effect and low-resolution experimental information for protein-protein docking in Critical Assessment of PRedicted Interactions (CAPRI)

Inclusion of entropy is important and challenging for protein-protein binding prediction. Here, we present a statistical mechanics-based approach to empirically consider the effect of orientational entropy. Specifically, we globally sample the possible binding orientations based on a simple shape-complementarity scoring function using an FFT-type docking method. Then, for each generated orientation, we calculate the probability through the partition function of the ensemble of accessible states, which are assumed to be represented by the set of nearby binding modes. For each mode, the interaction energy is calculated using our ITScorePP scoring function that was developed in our laboratory based on principles of statistical mechanics. Using the above protocol, we present the results of our participation in Rounds 22-27 of the Critical Assessment of PRedicted Interactions (CAPRI) experiment for 10 targets (T46-T58). Additional experimental information, such as low-resolution small-angle X-ray scattering data, was used when available. In the prediction (or docking) experiments of the 10 target complexes, we achieved correct binding modes for six targets: one with high accuracy (T47), two with medium accuracy (T48 and T57), and three with acceptable accuracy (T49, T50, and T58). In the scoring experiments of seven target complexes, we obtained correct binding modes for six targets: one with high accuracy (T47), two with medium accuracy (T49 and T50), and three with acceptable accuracy (T46, T51, and T53).

Using the concept of transient complex for affinity predictions in CAPRI rounds 20-27 and beyond

Predictions of protein-protein binders and binding affinities have traditionally focused on features pertaining to the native complexes. In developing a computational method for predicting protein-protein association rate constants, we introduced the concept of transient complex after mapping the interaction energy surface. The transient complex is located at the outer boundary of the bound-state energy well, having near-native separation and relative orientation between the subunits but not yet formed most of the short-range native interactions. We found that the width of the binding funnel and the electrostatic interaction energy of the transient complex are among the features predictive of binders and binding affinities. These ideas were very promising for the five affinity-related targets (T43-45, 55, and 56) of CAPRI rounds 20-27. For T43, we ranked the single crystallographic complex as number 1 and were one of only two groups that clearly identified that complex as a true binder; for T44, we ranked the only design with measurable binding affinity as number 4. For the nine docking targets, continuing on our success in previous CAPRI rounds, we produced 10 medium-quality models for T47 and acceptable models for T48 and T49. We conclude that the interaction energy landscape and the transient complex in particular will complement existing features in leading to better prediction of binding affinities.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.

How good is automated protein docking?

The protein docking server ClusPro has been participating in critical assessment of prediction of interactions (CAPRI) since its introduction in 2004. This article evaluates the performance of ClusPro 2.0 for targets 46-58 in Rounds 22-27 of CAPRI. The analysis leads to a number of important observations. First, ClusPro reliably yields acceptable or medium accuracy models for targets of moderate difficulty that have also been successfully predicted by other groups, and fails only for targets that have few acceptable models submitted. Second, the quality of automated docking by ClusPro is very close to that of the best human predictor groups, including our own submissions. This is very important, because servers have to submit results within 48 h and the predictions should be reproducible, whereas human predictors have several weeks and can use any type of information. Third, while we refined the ClusPro results for manual submission by running computationally costly Monte Carlo minimization simulations, we observed significant improvement in accuracy only for two of the six complexes correctly predicted by ClusPro. Fourth, new developments, not seen in previous rounds of CAPRI, are that the top ranked model provided by ClusPro was acceptable or better quality for all these six targets, and that the top ranked model was also the highest quality for five of the six, confirming that ranking models based on cluster size can reliably identify the best near-native conformations.

Data-driven models for protein interaction and design

We describe methods and results for four new types of challenge in the Critical Assessment of PRedicted Interactions (CAPRI). Two new challenges asked predictors to create models related to protein interface design. The first of these was to distinguish binding interfaces from designed nonbinding interfaces. The second was to predict the effects of all single-point mutations on hemagglutinin binding to two small designed proteins. Two additional challenges asked predictors to submit high-resolution structures for interface-bound crystallographic waters and for binding heparin to a putative glycosylase.

Expanding the frontiers of protein-protein modeling: from docking and scoring to binding affinity predictions and other challenges

In addition to protein-protein docking, this CAPRI edition included new challenges, like protein-water and protein-sugar interactions, or the prediction of binding affinities and ΔΔG changes upon mutation. Regarding the standard protein-protein docking cases, our approach, mostly based on the pyDock scheme, submitted correct models as predictors and as scorers for 67% and 57% of the evaluated targets, respectively. In this edition, available information on known interface residues hardly made any difference for our predictions. In one of the targets, the inclusion of available experimental small-angle X-ray scattering (SAXS) data using our pyDockSAXS approach slightly improved the predictions. In addition to the standard protein-protein docking assessment, new challenges were proposed. One of the new problems was predicting the position of the interface water molecules, for which we submitted models with 20% and 43% of the water-mediated native contacts predicted as predictors and scorers, respectively. Another new problem was the prediction of protein-carbohydrate binding, where our submitted model was very close to being acceptable. A set of targets were related to the prediction of binding affinities, in which our pyDock scheme was able to discriminate between natural and designed complexes with area under the curve = 83%. It was also proposed to estimate the effect of point mutations on binding affinity. Our approach, based on machine learning methods, showed high rates of correctly classified mutations for all cases. The overall results were highly rewarding, and show that the field is ready to move forward and face new interesting challenges in interactomics.

Flexible docking and refinement with a coarse-grained protein model using ATTRACT

A coarse-grained (CG) protein model implemented in the ATTRACT protein-protein docking program has been employed to predict protein-protein complex structures in CAPRI Rounds 22-27. For six targets, acceptable or better quality solutions have been submitted corresponding to ~60% of all targets. For one target, promising results on the prediction of the hydration structure at the protein-protein interface have been achieved. New approaches for the rapid flexible refinement have been developed based on a combination of atomistic representation of the bonded geometry and a CG description of nonbonded interactions. Possible further improvements of the docking approach in particular at the scoring and the flexible refinement steps are discussed.

Improved flexible refinement of protein docking in CAPRI rounds 22-27

Since the fourth evaluation for critical assessment of prediction of interactions (CAPRI), we have made improvements in three major areas in our refinement approach, namely the treatment of conformational flexibility, the binding free energy model, and the search algorithm. First, we incorporated backbone flexibility into our previous approach, which only optimized rigid backbone poses with limited side-chain flexibility. Here, we formulated and solved the conformational search as a hierarchical optimization problem (involving rigid-body poses, backbone flexibility, and side-chain flexibility). Second, we used continuum electrostatic calculations to include solvation effects in the binding free energy model. Finally, we eliminated sloppy modes (directions in which the free energy is essentially constant) to improve the efficiency of the search. With these improvements, we produced correct predictions for 6 of the 10 latest CAPRI targets, including one high, three medium, and two acceptable accuracy predictions. Compared to our previous performance in CAPRI, substantial improvements have been made for targets requiring homology modeling.

Defining the limits of homology modeling in information-driven protein docking

Information-driven docking is currently one of the most successful approaches to obtain structural models of protein interactions as demonstrated in the latest round of CAPRI. While various experimental and computational techniques can be used to retrieve information about the binding mode, the availability of three-dimensional structures of the interacting partners remains a limiting factor. Fortunately, the wealth of structural information gathered by large-scale initiatives allows for homology-based modeling of a significant fraction of the protein universe. Defining the limits of information-driven docking based on such homology models is therefore highly relevant. Here we show, using previous CAPRI targets, that out of a variety of measures, the global sequence identity between template and target is a simple but reliable predictor of the achievable quality of the docking models. This indicates that a well-defined overall fold is critical for the interaction. Furthermore, the quality of the data at our disposal to characterize the interaction plays a determinant role in the success of the docking. Given reliable interface information we can obtain acceptable predictions even at low global sequence identity. These results, which define the boundaries between trustworthy and unreliable predictions, should guide both experts and nonexperts in defining the limits of what is achievable by docking. This is highly relevant considering that the fraction of the interactome amenable for docking is only bound to grow as the number of experimentally solved structures increases.

Atypical cohesin-dockerin complex responsible for cell surface attachment of cellulosomal components: binding fidelity, promiscuity, and structural buttresses

The rumen bacterium Ruminococcus flavefaciens produces a highly organized multienzyme cellulosome complex that plays a key role in the degradation of plant cell wall polysaccharides, notably cellulose. The R. flavefaciens cellulosomal system is anchored to the bacterial cell wall through a relatively small ScaE scaffoldin subunit, which bears a single type IIIe cohesin responsible for the attachment of two major dockerin-containing scaffoldin proteins, ScaB and the cellulose-binding protein CttA. Although ScaB recruits the catalytic machinery onto the complex, CttA mediates attachment of the bacterial substrate via its two putative carbohydrate-binding modules. In an effort to understand the structural basis for assembly and cell surface attachment of the cellulosome in R. flavefaciens, we determined the crystal structure of the high affinity complex (Kd = 20.83 nM) between the cohesin module of ScaE (CohE) and its cognate X-dockerin (XDoc) modular dyad from CttA at 1.97-Å resolution. The structure reveals an atypical calcium-binding loop containing a 13-residue insert. The results further pinpoint two charged specificity-related residues on the surface of the cohesin module that are responsible for specific versus promiscuous cross-strain binding of the dockerin module. In addition, a combined functional role for the three enigmatic dockerin inserts was established whereby these extraneous segments serve as structural buttresses that reinforce the stalklike conformation of the X-module, thus segregating its tethered complement of cellulosomal components from the cell surface. The novel structure of the RfCohE-XDoc complex sheds light on divergent dockerin structure and function and provides insight into the specificity features of the type IIIe cohesin-dockerin interaction.

Regulating colicin synthesis to cope with stress and lethality of colicin production

Colicins are plasmid-encoded bacteriocins active against Escherichia coli and closely related species of Enterobacteriaceae. They promote microbial diversity and genetic diversity in E. coli populations. Colicin synthesis is characteristically repressed by the LexA protein, the key regulator of the SOS response. As colicins are released by cell lysis, generally two LexA dimers binding to two overlapping SOS boxes control untimely expression. Nevertheless, genetic organization of the colicin clusters, additional transcription regulators as well as post-transcriptional mechanisms involving translational efficiency of the lysis and activity genes fine-tune colicin expression and protect against lethality of colicin production.

Pathways of colicin import: utilization of BtuB, OmpF porin and the TolC drug-export protein

Pathway I. Group A nuclease colicins parasitize and bind tightly (Kd ≤ 10−9 M) to the vitamin B12 receptor on which they diffuse laterally in the OM (outer membrane) and use their long (≥100 Å; 1 Å=0.1 nm) receptor-binding domain as a ‘fishing pole’ to locate the OmpF porin channel for translocation. Crystal structures of OmpF imply that a disordered N-terminal segment of the colicin T-domain initiates insertion. Pathway II. Colicin N does not possess a ‘fishing pole’ receptor-binding domain. Instead, it uses OmpF as the Omp (outer membrane protein) for reception and translocation, processes in which LPS (lipopolysaccharide) may also serve. Keio collection experiments defined the LPS core that is used. Pathway III. Colicin E1 utilizes the drug-export protein TolC for import. CD spectra and thermal-melting analysis predict: (i) N-terminal translocation (T) and central receptor (BtuB) -binding (R) domains are predominantly α-helical; and (ii) helical coiled-coil conformation of the R-domain is similar to that of colicins E3 and Ia. Recombinant colicin peptides spanning the N-terminal translocation domain defined TolC-binding site(s). The N-terminal 40-residue segment lacks the ordered secondary structure. Peptide 41–190 is helical (78%), co-elutes with TolC and occluded TolC channels. Driven by a trans-negative potential, peptides 82–140 and 141–190 occluded TolC channels. The use of TolC for colicin E1 import implies that the interaction of this colicin with the other Tol proteins does not occur in the periplasmic space, but rather through Tol domains in the cytoplasmic membrane, thus explaining colicin E1 cytotoxicity towards a strain in which a 234 residue periplasmic TolA segment is deleted.

The crystal structure of the lipid II-degrading bacteriocin syringacin M suggests unexpected evolutionary relationships between colicin M-like bacteriocins

Colicin-like bacteriocins show potential as next generation antibiotics with clinical and agricultural applications. Key to these potential applications is their high potency and species specificity that enables a single pathogenic species to be targeted with minimal disturbance of the wider microbial community. Here we present the structure and function of the colicin M-like bacteriocin, syringacin M from Pseudomonas syringae pv. tomato DC3000. Syringacin M kills susceptible cells through a highly specific phosphatase activity that targets lipid II, ultimately inhibiting peptidoglycan synthesis. Comparison of the structures of syringacin M and colicin M reveals that, in addition to the expected similarity between the homologous C-terminal catalytic domains, the receptor binding domains of these proteins, which share no discernible sequence homology, share a striking structural similarity. This indicates that the generation of the novel receptor binding and species specificities of these bacteriocins has been driven by diversifying selection rather than diversifying recombination as suggested previously. Additionally, the structure of syringacin M reveals the presence of an active site calcium ion that is coordinated by a conserved aspartic acid side chain and is essential for catalytic activity. We show that mutation of this residue to alanine inactivates syringacin M and that the metal ion is absent from the structure of the mutant protein. Consistent with the presence of Ca(2+) in the active site, we show that syringacin M activity is supported by Ca(2+), along with Mg(2+) and Mn(2+), and the protein is catalytically inactive in the absence of these ions.