HotPatch: a statistical approach to finding biologically relevant features on protein surfaces

ANN based prediction of ligand binding sites outside deep cavities to facilitate drug designing

The ever-changing environmental conditions and pollution are the prime reasons for the onset of several emerging and re-merging diseases. This demands the faster designing of new drugs to curb the deadly diseases in less waiting time to cure the animals and humans. Drug molecules interact with only protein surface on specific locations termed as ligand binding sites (LBS). Therefore, the knowledge of LBS is required for rational drug designing. Existing geometrical LBS prediction methods rely on search of cavities based on the fact that 83% of the LBS found in deep cavities, however, these methods usually fail where LBS localize outside deep cavities. To overcome this challenge, the present work provides an artificial neural network (ANN) based method to predict LBS outside deep cavities in animal proteins including human to facilitate drug designing. In the present work a feed-forward backpropagation neural network was trained by utilizing 38 structural, atomic, physiochemical, and evolutionary discriminant features of LBS and non-LBS residues localized in the extracted roughest patch on protein surface. The performance of this ANN based prediction method was found 76% better for those proteins where cavity subspace (extracted by MetaPocket 2.0, a consensus method) failed to predict LBS due to their localization outside the deep cavities. The prediction of LBS outside deep cavities will facilitate in drug designing for the proteins where it is not possible due to lack of LBS information as the geometrical LBS prediction methods rely on extraction of deep cavities.

Predicting Protein-Protein Interfaces that Bind Intrinsically Disordered Protein Regions

A long-standing goal in biology is the complete annotation of function and structure on all protein-protein interactions, a large fraction of which is mediated by intrinsically disordered protein regions (IDRs). However, knowledge derived from experimental structures of such protein complexes is disproportionately small due, in part, to challenges in studying interactions of IDRs. Here, we introduce IDRBind, a computational method that by combining gradient boosted trees and conditional random field models predicts binding sites of IDRs with performance approaching state-of-the-art globular interface predictions, making it suitable for proteome-wide applications. Although designed and trained with a focus on molecular recognition features, which are long interaction-mediating-elements in IDRs, IDRBind also predicts the binding sites of short peptides more accurately than existing specialized predictors. Consistent with IDRBind's specificity, a comparison of protein interface categories uncovered uniform trends in multiple physicochemical properties, positioning molecular recognition feature interfaces between peptide and globular interfaces.

Structure-Related Differences between Cytochrome Oxidase I Proteins in a Stable Heteroplasmic Mitochondrial System

Many bivalve species have two types of mitochondrial DNA passed independently through the female line (F genome) and male line (M genome). Here we study the cytochrome oxidase I protein in such bivalve species and provide evidence for differences between the F and M proteins in amino acid property values, particularly relating to hydrophobicity and helicity. The magnitude of these differences varies between different regions of the protein and the change from the ancestor is most marked in the M protein. The observed changes occur in parallel and in the same direction in the different species studied. Two possible causes are considered, first relaxation of purifying selection with drift and second positive selection. These may operate in different ways in different regions of the protein. Many different amino acid substitutions contribute in a small way to the observed variation, but substitutions involving alanine and serine have a quantitatively large effect. Some of these substitutions are potential targets for phosphorylation and some are close to residues of functional importance in the catalytic mechanism. We propose that the observed changes in the F and M proteins might contribute to functional differences between them relating to ATP production and mitochondrial membrane potential with implications for sperm function.

Computational design of membrane proteins using RosettaMembrane

Computational membrane protein design is challenging due to the small number of high-resolution structures available to elucidate the physical basis of membrane protein structure, multiple functionally important conformational states, and a limited number of high-throughput biophysical assays to monitor function. However, structural determination of membrane proteins has made tremendous progress in the past years. Concurrently the field of soluble computational design has made impressive inroads. These developments allow us to tackle the formidable challenge of designing functional membrane proteins. Herein, Rosetta is benchmarked for membrane protein design. We evaluate strategies to cope with the often reduced quality of experimental membrane protein structures. Further, we test the usage of symmetry in design protocols, which is particularly important as many membrane proteins exist as homo-oligomers. We compare a soluble scoring function with a scoring function optimized for membrane proteins, RosettaMembrane. Both scoring functions recovered around half of the native sequence when completely redesigning membrane proteins. However, RosettaMembrane recovered the most native-like amino acid property composition. While leucine was overrepresented in the inner and outer-hydrophobic regions of RosettaMembrane designs, it resulted in a native-like surface hydrophobicity indicating that it is currently the best option for designing membrane proteins with Rosetta.

IntPred: a structure-based predictor of protein-protein interaction sites

Motivation

Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods.

Results

On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent.

Availability and implementation

IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Protein surface roughness accounts for binding free energy of Plasmepsin II-ligand complexes

The calculation of absolute binding affinities for protein-inhibitor complexes remains as one of the main challenges in computational structure-based ligand design. The present work explored the calculations of surface fractal dimension (as a measure of surface roughness) and the relationship with experimental binding free energies of Plasmepsin II complexes. Plasmepsin II is an attractive target for novel therapeutic compounds to treat malaria. However, the structural flexibility of this enzyme is a drawback when searching for specific inhibitors. Concerning that, we performed separate explicitly solvated molecular dynamics simulations using the available high-resolution crystal structures of different Plasmepsin II complexes. Molecular dynamics simulations allowed a better approximation to systems dynamics and, therefore, a more reliable estimation of surface roughness. This constitutes a novel approximation in order to obtain more realistic values of fractal dimension, because previous works considered only x-ray structures. Binding site fractal dimension was calculated considering the ensemble of structures generated at different simulation times. A linear relationship between binding site fractal dimension and experimental binding free energies of the complexes was observed within 20 ns. Previous studies of the subject did not uncover this relationship. Regression model, coined FD model, was built to estimate binding free energies from binding site fractal dimension values. Leave-one-out cross-validation showed that our model reproduced accurately the absolute binding free energies for our training set (R²  = 0.76; <|error|> =0.55 kcal/mol; SD_error  = 0.19 kcal/mol). The fact that such a simple model may be applied raises some questions that are addressed in the article.

High-throughput Screening of Small Molecule Inhibitors of the Streptococcus Quorum-sensing Signal Pathway

The main components of the quorum-sensing system are expected to be favorable targets for drug development to combat various chronic infectious diseases. ComA of Streptococcus is an ATP-binding cassette transporter containing a peptidase domain (PEP), which is essential for the quorum-sensing signal production. Using high-throughput screening, we found a potent small molecule that suppressed the S. mutans quorum-sensing pathway through inhibition of PEP activity. The compound effectively attenuated the biofilm formation and competence development of S. mutans without inhibiting cell growth. The kinetic and structural studies with this molecule and a related compound unexpectedly revealed an allosteric site of PEP. This relatively hydrophobic site is thought to undergo large structural changes during the catalytic process. These compounds inhibit PEP activity by binding to and suppressing the structural changes of this site. These results showed that PEP is a good target for inhibitors of the Streptococcus quorum-sensing system.

Prediction of protein-protein interaction sites by means of ensemble learning and weighted feature descriptor

Background

Reliable prediction of protein–protein interaction sites is an important goal in the field of bioinformatics. Many computational methods have been explored for the large-scale prediction of protein–protein interaction sites based on various data types, including protein sequence, structural and genomic data. Although much progress has been achieved in recent years, the problem has not yet been satisfactorily solved.

Results

In this work, we presented an efficient approach that uses ensemble learning algorithm with weighted feature descriptor (EL-WFD) to predict protein–protein interaction sites. Moreover, weighted feature descriptor was designed to describe the distance influence of neighboring residues on interaction sites. The results on two dataset (Hetero and Homo), show that the proposed method yields a satisfactory accuracy with 83.8 % recall and 96.3 % precision on the Hetero dataset and 84.2 % recall and 96.3 % precision on the Homo dataset, respectively. In both datasets, our method tend to obtain high Mathews correlation coefficient compared with state-of-the-art technique random forest method.

Conclusions

The experimental results show that the EL-WFD method is quite effective in predicting protein–protein interaction sites. The novel weighted feature descriptor was proved to be promising in discovering interaction sites. Overall, the proposed method can be considered as a new powerful tool for predicting protein–protein interaction sites with excellence performance.

Logistic regression models to predict solvent accessible residues using sequence- and homology-based qualitative and quantitative descriptors applied to a domain-complete X-ray structure learning set

A working example of relative solvent accessibility (RSA) prediction for proteins is presented. Novel logistic regression models with various qualitative descriptors that include amino acid type and quantitative descriptors that include 20- and six-term sequence entropy have been built and validated. A domain-complete learning set of over 1300 proteins is used to fit initial models with various sequence homology descriptors as well as query residue qualitative descriptors. Homology descriptors are derived from BLASTp sequence alignments, whereas the RSA values are determined directly from the crystal structure. The logistic regression models are fitted using dichotomous responses indicating buried or accessible solvent, with binary classifications obtained from the RSA values. The fitted models determine binary predictions of residue solvent accessibility with accuracies comparable to other less computationally intensive methods using the standard RSA threshold criteria 20 and 25% as solvent accessible. When an additional non-homology descriptor describing Lobanov-Galzitskaya residue disorder propensity is included, incremental improvements in accuracy are achieved with 25% threshold accuracies of 76.12 and 74.79% for the Manesh-215 and CASP(8+9) test sets, respectively. Moreover, the described software and the accompanying learning and validation sets allow students and researchers to explore the utility of RSA prediction with simple, physically intuitive models in any number of related applications.

Structural basis of Sorcin-mediated calcium-dependent signal transduction

Sorcin is an essential penta-EF hand calcium binding protein, able to confer the multi-drug resistance phenotype to drug-sensitive cancer cells and to reduce Endoplasmic Reticulum stress and cell death. Sorcin silencing blocks cell cycle progression in mitosis and induces cell death by triggering apoptosis. Sorcin participates in the modulation of calcium homeostasis and in calcium-dependent cell signalling in normal and cancer cells. The molecular basis of Sorcin action is yet unknown. The X-ray structures of Sorcin in the apo (apoSor) and in calcium bound form (CaSor) reveal the structural basis of Sorcin action: calcium binding to the EF1-3 hands promotes a large conformational change, involving a movement of the long D-helix joining the EF1-EF2 sub-domain to EF3 and the opening of EF1. This movement promotes the exposure of a hydrophobic pocket, which can accommodate in CaSor the portion of its N-terminal domain displaying the consensus binding motif identified by phage display experiments. This domain inhibits the interaction of sorcin with PDCD6, a protein that carries the Sorcin consensus motif, co-localizes with Sorcin in the perinuclear region of the cell and in the midbody and is involved in the onset of apoptosis.

An in silico approach towards the identification of novel inhibitors of the TLR-4 signaling pathway

Precise functioning and fine-tuning of Toll-like receptor 4 (TLR4) signaling is a critical requirement for the smooth functioning of the innate immune system, since aberrant TLR4 activation causes excessive production of pro-inflammatory cytokines and interferons. This can result in life threatening conditions such as septic shock and other inflammatory disorders. The TRIF-related adaptor molecule (TRAM) adaptor protein is unique to the TLR4 signaling pathway and abrogation of TRAM-mediated TLR4 signaling is a promising strategy for developing therapeutics aimed at disrupting TRAM interactions with other components of the TLR4 signaling complex. The VIPER motif from the vaccinia virus-producing protein, A46 has been reported to disrupt TRAM-TLR4 interactions. We have exploited this information, in combination with homology modeling and docking approaches, to identify a potential binding site on TRAM lined by the BB loop and αC helix. Virtual screening of commercially available small molecules targeting the binding site enabled to short-list 12 small molecules to abrogate TRAM-mediated TLR4 signaling. Molecular dynamics and molecular mechanics calculations have been performed for the analysis of these receptor-ligand interactions.

Fluorescence spectroscopic analysis of the structure and dynamics of Bacillus subtilis lipase A governing its activity profile under alkaline conditions

Because of their vast diversity of substrate specificity and reaction conditions, lipases are versatile materials for biocatalysis. Lipase A from Bacillus subtilis (BSLA) is the smallest lipase yet discovered. It has the typical α/β hydrolase fold but lacks a lid covering the substrate cleft. In this study, the pH-dependence of the activity, stability, structure, and dynamics of BSLA was investigated by fluorescence spectroscopy. By use of a fluorogenic substrate it was revealed that the optimum pH for BSLA activity is 8.5 whereas thermodynamic and kinetic stability are maximum at pH 10. The origin of this behavior was clarified by investigation of ANS (8-anilino-1-naphthalenesulfonic acid) binding and fluorescence quenching of the two single tryptophan mutants W31F and W42F. Variations in segmental dynamics were investigated by use of time-resolved fluorescence anisotropy. This analysis showed that the activity maximum is governed by high surface hydrophobicity and high segmental mobility of surface loops whereas the stability optimum is a result of low segmental mobility and surface hydrophobicity.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

Ontogeny of recognition specificity and functionality for the broadly neutralizing anti-HIV antibody 4E10

The process of antibody ontogeny typically improves affinity, on-rate, and thermostability, narrows polyspecificity, and rigidifies the combining site to the conformer optimal for binding from the broader ensemble accessible to the precursor. However, many broadly-neutralizing anti-HIV antibodies incorporate unusual structural elements and recognition specificities or properties that often lead to autoreactivity. The ontogeny of 4E10, an autoreactive antibody with unexpected combining site flexibility, was delineated through structural and biophysical comparisons of the mature antibody with multiple potential precursors. 4E10 gained affinity primarily by off-rate enhancement through a small number of mutations to a highly conserved recognition surface. Controverting the conventional paradigm, the combining site gained flexibility and autoreactivity during ontogeny, while losing thermostability, though polyspecificity was unaffected. Details of the recognition mechanism, including inferred global effects due to 4E10 binding, suggest that neutralization by 4E10 may involve mechanisms beyond simply binding, also requiring the ability of the antibody to induce conformational changes distant from its binding site. 4E10 is, therefore, unlikely to be re-elicited by conventional vaccination strategies.

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor

Background

Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement.

Results

The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions.

Conclusion

Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.

Structure-based design of small-molecule protein–protein interaction modulators: the story so far

As the pivotal role of protein–protein interactions in cell growth, transcriptional activity, intracellular trafficking, signal transduction and pathological conditions has been assessed, experimental and in silico strategies have been developed to design protein–protein interaction modulators. State-of-the-art structure-based design methods, mainly pharmacophore modeling and docking, which have succeeded in the identification of enzyme inhibitors, receptor agonists and antagonists, and new tools specifically conceived to target protein–protein interfaces (e.g., hot-spot and druggable pocket prediction methods) have been applied in the search for small-molecule protein–protein interaction modulators. Many successful applications of structure-based design approaches that, despite the challenge of targeting protein–protein interfaces with small molecules, have led to the identification of micromolar and submicromolar hits are reviewed here.

Discovery of small molecule inhibitors targeting the SUMO–SIM interaction using a protein interface consensus approach

We present a virtual screening approach incorporating the consensus of protein interactions that led to the discovery of non-peptidic inhibitors.

Solution structure and interface-driven self-assembly of NC2, a new member of the Class II hydrophobin proteins

Hydrophobins are fungal proteins that self-assemble spontaneously to form amphipathic monolayers at hydrophobic:hydrophilic interfaces. Hydrophobin assemblies facilitate fungal transitions between wet and dry environments and interactions with plant and animal hosts. NC2 is a previously uncharacterized hydrophobin from Neurospora crassa. It is a highly surface active protein and is able to form protein layers on a water:air interface that stabilize air bubbles. On a hydrophobic substrate, NC2 forms layers consisting of an ordered network of protein molecules, which dramatically decrease the water contact angle. The solution structure and dynamics of NC2 have been determined using nuclear magnetic resonance spectroscopy. The structure of this protein displays the same core fold as observed in other hydrophobin structures determined to date, including the Class II hydrophobins HFBI and HFBII from Trichoderma reesei, but certain features illuminate the structural differences between Classes I and II hydrophobins and also highlight the variations between structures of Class II hydrophobin family members. The unique properties of hydrophobins have attracted much attention for biotechnology applications. The insights obtained through determining the structure, biophysical properties and assembly characteristics of NC2 will facilitate the development of hydrophobin-based applications.

Rapid identification of ligand-binding sites by using an assignment-free NMR approach

In this study, we developed an assignment-free approach for rapid identification of ligand-binding sites in target proteins by using NMR. With a sophisticated cell-free stable isotope-labeling procedure that introduces (15)N- or (13)C-labels to specific atoms of target proteins, this approach requires only a single series of ligand titrations with labeled targets. Using titration data, ligand-binding sites in the target protein can be identified without time-consuming assignment procedures. We demonstrated the feasibility of this approach by using structurally well-characterized interactions between mitogen-activated protein (MAP) kinase p38α and its inhibitor 2-amino-3-benzyloxypyridine. Furthermore, we confirmed the recently proposed fatty acid binding to p38α and confirmed the fatty acid-binding site in the MAP kinase insert region.

Autoreactivity and exceptional CDR plasticity (but not unusual polyspecificity) hinder elicitation of the anti-HIV antibody 4E10

The broadly-neutralizing anti-HIV antibody 4E10 recognizes an epitope in the membrane-proximal external region of the HIV envelope protein gp41. Previous attempts to elicit 4E10 by vaccination with envelope-derived or reverse-engineered immunogens have failed. It was presumed that the ontogeny of 4E10-equivalent responses was blocked by inherent autoreactivity and exceptional polyreactivity. We generated 4E10 heavy-chain knock-in mice, which displayed significant B cell dysregulation, consistent with recognition of autoantigen/s by 4E10 and the presumption that tolerance mechanisms may hinder the elicitation of 4E10 or 4E10-equivalent responses. Previously proposed candidate 4E10 autoantigens include the mitochondrial lipid cardiolipin and a nuclear splicing factor, 3B3. However, using carefully-controlled assays, 4E10 bound only weakly to cardiolipin-containing liposomes, but also bound negatively-charged, non-cardiolipin-containing liposomes comparably poorly. 4E10/liposome binding was predominantly mediated by electrostatic interactions rather than presumed hydrophobic interactions. The crystal structure of 4E10 free of bound ligands showed a dramatic restructuring of the combining site, occluding the HIV epitope binding site and revealing profound flexibility, but creating an electropositive pocket consistent with non-specific binding of phospholipid headgroups. These results strongly suggested that antigens other than cardiolipin mediate 4E10 autoreactivity. Using a synthetic peptide library spanning the human proteome, we determined that 4E10 displays limited and focused, but unexceptional, polyspecificity. We also identified a novel autoepitope shared by three ER-resident inositol trisphosphate receptors, validated through binding studies and immunohistochemistry. Tissue staining with 4E10 demonstrated reactivity consistent with the type 1 inositol trisphosphate receptor as the most likely candidate autoantigen, but is inconsistent with splicing factor 3B3. These results demonstrate that 4E10 recognition of liposomes competes with MPER recognition and that HIV antigen and autoepitope recognition may be distinct enough to permit eliciting 4E10-like antibodies, evading autoimmunity through directed engineering. However, 4E10 combining site flexibility, exceptional for a highly-matured antibody, may preclude eliciting 4E10 by conventional immunization strategies.

Fractal nature of protein surface roughness: a note on quantification of change of surface roughness in active sites, before and after binding

Year 2010 marked the 25th year since we came to know that roughness of a protein surface has fractal symmetry. Ever since the publication of Lewis and Rees' paper, hundreds of works from a spectrum of perspectives have established that fractal dimension (FD) can be considered as a reliable marker that describes roughness of protein surface objectively. In this article, we introduce readers to the fundamentals of fractals and present categorical biophysical and geometrical reasons as to why FD-based constructs can describe protein surface roughness more accurately. We then review the commonality (and the lack of it) between numerous approaches that have attempted to investigate protein surface with fractal measures, before exploring the patterns in the results that they have produced. Apart from presenting the genealogy of approaches and results, we present an analysis that quantifies the difference in surface roughness in stretches of protein surface containing the active site, before and after binding to ligands, to underline the utility of FD-based measures further. It has been found that surface stretches containing the active site, in general, undergo a significant increment in its roughness after binding. After presenting the entire repertoire of FD-based surface roughness studies, we talk about two yet-unexplored problems where application of FD-based techniques can help in deciphering underlying patterns of surface interactions. Finally, we list the limitations of FD-based constructs and put down several precautions that one must take while working with them.

An integrated fragment based screening approach for the discovery of small molecule modulators of the VWF-GPIbα interaction

An integrated approach comprising STD NMR screening, pharmacophore based analogue selection and a bioassay is presented for the discovery of a stabilizer of the clinically relevant VWF-GPIbα protein-protein interaction.

Structural differences between thermophilic and mesophilic membrane proteins

The evolutionary adaptations of thermophilic water-soluble proteins required for maintaining stability at high temperature have been extensively investigated. Little is known about the adaptations in membrane proteins, however. Here, we compare many properties of mesophilic and thermophilic membrane protein structures, including side-chain burial, packing, hydrogen bonding, transmembrane kinks, loop lengths, hydrophobicity, and other sequence features. Most of these properties are quite similar between mesophiles and thermophiles although we observe a slight increase in side-chain burial and possibly a slight decrease in the frequency of transmembrane kinks in thermophilic membrane protein structures. The most striking difference is the increased hydrophobicity of thermophilic transmembrane helices, possibly reflecting more stringent hydrophobicity requirements for membrane partitioning at high temperature. In agreement with prior work examining transmembrane sequences, we find that thermophiles have an increase in small residues (Gly, Ala, Ser, and Val) and a strong suppression of Cys. We also find a relative dearth of most strongly polar residues (Asp, Asn, Glu, Gln, and Arg). These results suggest that in thermophiles, there is significant evolutionary pressure to offload destabilizing polar amino acids, to decrease the entropy cost of side chain burial, and to eliminate thermally sensitive amino acids.

Parkinson's disease-associated mutations in DJ-1 modulate its dimerization in living cells

Mutations in the protein DJ-1 cause recessive forms of early onset familial Parkinson’s disease (PD). To date, most of the causative mutations studied destabilize formation of DJ-1 homodimers, which appears to be closely linked to its normal function in oxidative stress and other cellular processes. Despite the importance of understanding the dimerization dynamics of this protein, this aspect of DJ-1 biology has not previously been directly studied in living cells. Here, we use bimolecular fluorescence complementation to study DJ-1 dimerization and find not only that DJ-1 forms homodimers in living cells but that most PD causative DJ-1 mutations disrupt this process, including the L166P, M26I, L10P, and P158∆ mutations. Interestingly, the E64D mutant form of DJ-1 retains the ability to form homodimers. However, while wild-type DJ-1 dimers are stabilized under oxidative stress conditions, we find that the E64D mutation blocks this stabilization. Furthermore, our data show that the E64D mutation potentiates the formation of aggresomes containing DJ-1. We also observe that while the widely studied L166P mutation prevents DJ-1 from forming homodimers or heterodimers with wild-type protein, the mutant protein is able to partially disrupt formation of wild-type homodimers. In summary, by investigating DJ-1 dimerization in living cells, we have uncovered several novel properties of PD causative mutations in DJ-1, which may ultimately provide novel insight into PD pathogenesis and possible therapeutic options.

Zinc-mediated allosteric inhibition of caspase-6

Zinc and caspase-6 have independently been implicated in several neurodegenerative disorders. Depletion of zinc intracellularly leads to apoptosis by an unknown mechanism. Zinc inhibits cysteine proteases, including the apoptotic caspases, leading to the hypothesis that zinc-mediated inhibition of caspase-6 might contribute to its regulation in a neurodegenerative context. Using inductively coupled plasma optical emission spectroscopy, we observed that caspase-6 binds one zinc per monomer, under the same conditions where the zinc leads to complete loss of enzymatic activity. To understand the molecular details of zinc binding and inhibition, we performed an anomalous diffraction experiment above the zinc edge. The anomalous difference maps showed strong 5σ peaks, indicating the presence of one zinc/monomer bound at an exosite distal from the active site. Zinc was not observed bound to the active site. The zinc in the exosite was liganded by Lys-36, Glu-244, and His-287 with a water molecule serving as the fourth ligand, forming a distorted tetrahedral ligation sphere. This exosite appears to be unique to caspase-6, as the residues involved in zinc binding were not conserved across the caspase family. Our data suggest that binding of zinc at the exosite is the primary route of inhibition, potentially locking caspase-6 into the inactive helical conformation.

Structural analysis of hypothetical proteins from Helicobacter pylori: an approach to estimate functions of unknown or hypothetical proteins

Helicobacter pylori (H. pylori) have a unique ability to survive in extreme acidic environments and to colonize the gastric mucosa. It can cause diverse gastric diseases such as peptic ulcers, chronic gastritis, mucosa-associated lymphoid tissue (MALT) lymphoma, gastric cancer, etc. Based on genomic research of H. pylori, over 1600 genes have been functionally identified so far. However, H. pylori possess some genes that are uncharacterized since: (i) the gene sequences are quite new; (ii) the function of genes have not been characterized in any other bacterial systems; and (iii) sometimes, the protein that is classified into a known protein based on the sequence homology shows some functional ambiguity, which raises questions about the function of the protein produced in H. pylori. Thus, there are still a lot of genes to be biologically or biochemically characterized to understand the whole picture of gene functions in the bacteria. In this regard, knowledge on the 3D structure of a protein, especially unknown or hypothetical protein, is frequently useful to elucidate the structure-function relationship of the uncharacterized gene product. That is, a structural comparison with known proteins provides valuable information to help predict the cellular functions of hypothetical proteins. Here, we show the 3D structures of some hypothetical proteins determined by NMR spectroscopy and X-ray crystallography as a part of the structural genomics of H. pylori. In addition, we show some successful approaches of elucidating the function of unknown proteins based on their structural information.

Tandem SAM domain structure of human Caskin1: a presynaptic, self-assembling scaffold for CASK

The synaptic scaffolding proteins CASK and Caskin1 are part of the fibrous mesh of proteins that organize the active zones of neural synapses. CASK binds to a region of Caskin1 called the CASK interaction domain (CID). Adjacent to the CID, Caskin1 contains two tandem sterile α motif (SAM) domains. Many SAM domains form polymers so they are good candidates for forming the fibrous structures seen in the active zone. We show here that the SAM domains of Caskin1 form a new type of SAM helical polymer. The Caskin1 polymer interface exhibits a remarkable segregation of charged residues, resulting in a high sensitivity to ionic strength in vitro. The Caskin1 polymers can be decorated with CASK proteins, illustrating how these proteins may work together to organize the cytomatrix in active zones.

Identification of a small molecule that modulates platelet glycoprotein Ib-von Willebrand factor interaction

The von Willebrand factor (VWF) A1-glycoprotein (GP) Ibα interaction is of major importance during thrombosis mainly at sites of high shear stress. Inhibitors of this interaction prevent platelet-dependent thrombus formation in vivo, without major bleeding complications. However, the size and/or protein nature of the inhibitors currently in development limit oral bioavailability and clinical development. We therefore aimed to search for a small molecule protein-protein interaction inhibitor interfering with the VWF-GPIbα binding. After determination of putative small molecule binding pockets on the surface of VWF-A1 and GPIbα using site-finding algorithms and molecular dynamics, high throughput molecular docking was performed on both binding partners. A selection of compounds showing good in silico docking scores into the predicted pockets was retained for testing their in vitro effect on VWF-GPIbα complex formation, by which we identified a compound that surprisingly stimulated the VWF-GPIbα binding in a ristocetin cofactor ELISA and increased platelet adhesion in whole blood to collagen under arterial shear rate but in contrast inhibited ristocetin-induced platelet aggregation. The selected compound adhering to the predicted binding partner GPIbα could be confirmed by saturation transfer difference NMR spectroscopy. We thus clearly identified a small molecule that modulates VWF-GPIbα binding and that will now serve as a starting point for further studies and chemical modifications to fully characterize the interaction and to manipulate specific activity of the compound.

Protein docking prediction using predicted protein-protein interface

Background

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.

Results

We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.

Conclusion

We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Conformational basis for substrate recruitment in protein tyrosine phosphatase 10D

The coordinated activity of protein tyrosine phosphatases (PTPs) is crucial for the initiation, modulation, and termination of diverse cellular processes. The catalytic activity of this protein depends on a nucleophilic cysteine at the active site that mediates the hydrolysis of the incoming phosphotyrosine substrate. While the role of conserved residues in the catalytic mechanism of PTPs has been extensively examined, the diversity in the mechanisms of substrate recognition and modulation of catalytic activity suggests that other, less conserved sequence and structural features could contribute to this process. Here we describe the crystal structures of Drosophila melanogaster PTP10D in the apo form as well as in a complex with a substrate peptide and an inhibitor. These studies reveal the role of aromatic ring stacking interactions at the boundary of the active site of PTPs in mediating substrate recruitment. We note that phenylalanine 76, of the so-called KNRY loop, is crucial for orienting the phosphotyrosine residue toward the nucleophilic cysteine. Mutation of phenylalanine 76 to leucine results in a 60-fold decrease in the catalytic efficiency of the enzyme. Fluorescence measurements with a competitive inhibitor, p-nitrocatechol sulfate, suggest that Phe76 also influences the formation of the enzyme-substrate intermediate. The structural and biochemical data for PTP10D thus highlight the role of relatively less conserved residues in PTP domains in both substrate recruitment and modulation of reaction kinetics.

Prediction of protein-protein interaction sites using patch-based residue characterization

Identifying protein-protein interaction sites provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Using a patch-based model for residue characterization, we trained random forest classifiers for residue-based interface prediction, which was followed by a clustering procedure to produce patches for patch-based interface prediction. For residue-based interface prediction, our method achieves a specificity rate of 0.7 and a sensitivity rate of 0.78. For patch-based interface prediction, a success rate of 0.80 is achieved. Based on same datasets, we also compare it with several published methods. The results show that our method is a successful predictor for residue-based and patch-based interface prediction.

Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics

A target is druggable if it can be modulated in vivo by a drug-like molecule. The general properties of oral drugs are summarized by the 'rule of 5' which specifies parameters related to size and lipophilicity. Structure-based target druggability assessment consists of predicting ligand-binding sites on the protein that are complementary to these drug-like properties. Automated identification of ligand-binding sites can use geometrical considerations alone or include specific physicochemical properties of the protein surface. Features of a pocket's size and shape, together with measures of its hydrophobicity, are most informative in identifying suitable drug-binding pockets. The recent availability of several validation sets of druggable versus undruggable targets has helped fuel the development of more elaborate methods.

Zinc binding drives sheet formation by the SAM domain of diacylglycerol kinase δ

The diacylglycerol kinase (DGK) family of enzymes plays critical roles in lipid signaling pathways by converting diacylglycerol to phosphatidic acid, thereby downregulating signaling by the former and upregulating signaling by the latter second messenger. Ten DGK family isozymes have been identified to date, which possess different interaction motifs imparting distinct temporal and spatial control of DGK activity to each isozyme. Two DGK family members, δ and η, contain a sterile alpha motif (SAM) domain. The SAM domain of DGKδ1 forms helical polymers that are important for retaining the enzyme in cytoplasmic puncta, thereby inhibiting activity at the plasma membrane until pathway activation. Because zinc was found to be important for stabilizing the similar SAM polymers of the scaffolding protein Shank-3, we investigated the potential role of zinc in DGKδ SAM domain (DGKδSAM) assembly. We find that DGKδSAM binds zinc at multiple sites, driving the organization of the DGKδSAM into large sheets of polymers. Moreover, a mutant DGKδ containing a SAM domain refractory to zinc binding diminishes the formation of cytoplasmic puncta, shows partially impaired regulation of transport to the plasma membrane, and lacks the ability to inhibit the formation of CopII coated vesicles. These results suggest that zinc may play an important role in the assembly and physiology of the DGKδ isozyme.

Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library

A new approach to predicting the ligand-binding sites of proteins was developed, using protein-ligand docking computation. In this method, many compounds in a random library are docked onto the whole protein surface. We assumed that the true ligand-binding site would exhibit stronger affinity to the compounds in the random library than the other sites, even if the random library did not include the ligand corresponding to the true binding site. We also assumed that the affinity of the true ligand-binding site would be correlated to the docking scores of the compounds in the random library, if the ligand-binding site was correctly predicted. We call this method the molecular-docking binding-site finding (MolSite) method. The MolSite method was applied to 89 known protein-ligand complex structures extracted from the Protein Data Bank, and it predicted the correct binding sites with about 80-99% accuracy, when only the single top-ranked site was adopted. In addition, the average docking score was weakly correlated to the experimental protein-ligand binding free energy, with a correlation coefficient of 0.44.

Crystal structure of a bacterial phosphoglucomutase, an enzyme involved in the virulence of multiple human pathogens

The crystal structure of the enzyme phosphoglucomutase from Salmonella typhimurium (StPGM) is reported at 1.7 A resolution. This is the first high-resolution structural characterization of a bacterial protein from this large enzyme family, which has a central role in metabolism and is also important to bacterial virulence and infectivity. A comparison of the active site of StPGM with that of other phosphoglucomutases reveals conserved residues that are likely involved in catalysis and ligand binding for the entire enzyme family. An alternate crystal form of StPGM and normal mode analysis give insights into conformational changes of the C-terminal domain that occur upon ligand binding. A novel observation from the StPGM structure is an apparent dimer in the asymmetric unit of the crystal, mediated largely through contacts in an N-terminal helix. Analytical ultracentrifugation and small-angle X-ray scattering confirm that StPGM forms a dimer in solution. Multiple sequence alignments and phylogenetic studies show that a distinct subset of bacterial PGMs share the signature dimerization helix, while other bacterial and eukaryotic PGMs are likely monomers. These structural, biochemical, and bioinformatic studies of StPGM provide insights into the large α-D-phosphohexomutase enzyme superfamily to which it belongs, and are also relevant to the design of inhibitors specific to the bacterial PGMs.

A point mutation in the amino terminus of TLR7 abolishes signaling without affecting ligand binding

TLR7 is the mammalian receptor for ssRNA and some nucleotide-like small molecules. We have generated a mouse by N-nitrose-N'-ethyl urea mutagenesis in which threonine 68 of TLR7 was substituted with isoleucine. Cells bearing this mutant TLR7 lost the sensitivity to the small-molecule TLR7 agonist resiquimod, hence the name TLR7(rsq1). In this work, we report the characterization of this mutant protein. Similar to the wild-type counterpart, TLR7(rsq1) localizes to the endoplasmic reticulum and is expressed at normal levels in both primary cells and reconstituted 293T cells. In addition to small-molecule TLR7 agonists, TLR7(rsq1) fails to be activated by ssRNA. Whole-transcriptome analysis demonstrates that TLR7 is the exclusive and indispensable receptor for both classes of ligands, consistent with the fact that both ligands induce highly similar transcriptional signatures in TLR7(wt/wt) splenocytes. Thus, TLR7(rsq1) is a bona fide phenocopy of the TLR7 null mouse. Because TLR7(rsq1) binds to ssRNA, our studies imply that the N-terminal portion of TLR7 triggers a yet to be identified event on TLR7. TLR7(rsq1) mice might represent a valuable tool to help elucidate novel aspects of TLR7 biology.

Characterization of the relationship between integrase, excisionase and antirepressor activities associated with a superinfecting Shiga toxin encoding bacteriophage

Shigatoxigenic Escherichia coli emerged as new food borne pathogens in the early 1980s, primarily driven by the dispersal of Shiga toxin-encoding lambdoid bacteriophages. At least some of these Stx phages display superinfection phenotypes, which differ significantly from lambda phage itself, driving through in situ recombination further phage evolution, increasing host range and potentially increasing the host's pathogenic profile. Here, increasing levels of Stx phage Φ24(B) integrase expression in multiple lysogen cultures are demonstrated along with apparently negligible repression of integrase expression by the cognate CI repressor. The Φ24(B) int transcription start site and promoter region were identified and found to differ from in silico predictions. The unidirectional activity of this integrase was determined in an in situ, inducible tri-partite reaction. This indicated that Φ24(B) must encode a novel directionality factor that is controlling excision events during prophage induction. This excisionase was subsequently identified and characterized through complementation experiments. In addition, the previous proposal that a putative antirepressor was responsible for the lack of immunity to superinfection through inactivation of CI has been revisited and a new hypothesis involving the role of this protein in promoting efficient induction of the Φ24(B) prophage is proposed.

A highly conserved protein of unknown function in Sinorhizobium meliloti affects sRNA regulation similar to Hfq

The SMc01113/YbeY protein, belonging to the UPF0054 family, is highly conserved in nearly every bacterium. However, the function of these proteins still remains elusive. Our results show that SMc01113/YbeY proteins share structural similarities with the MID domain of the Argonaute (AGO) proteins, and might similarly bind to a small-RNA (sRNA) seed, making a special interaction with the phosphate on the 5′-side of the seed, suggesting they may form a component of the bacterial sRNA pathway. Indeed, eliminating SMc01113/YbeY expression in Sinorhizobium meliloti produces symbiotic and physiological phenotypes strikingly similar to those of the hfq mutant. Hfq, an RNA chaperone, is central to bacterial sRNA-pathway. We evaluated the expression of 13 target genes in the smc01113 and hfq mutants. Further, we predicted the sRNAs that may potentially target these genes, and evaluated the accumulation of nine sRNAs in WT and smc01113 and hfq mutants. Similar to hfq, smc01113 regulates the accumulation of sRNAs as well as the target mRNAs. AGOs are central components of the eukaryotic sRNA machinery and conceptual parallels between the prokaryotic and eukaryotic sRNA pathways have long been drawn. Our study provides the first line of evidence for such conceptual parallels. Furthermore, our investigation gives insights into the sRNA-mediated regulation of stress adaptation in S. meliloti.

Gadd45a is an RNA binding protein and is localized in nuclear speckles

Background

The Gadd45 proteins play important roles in growth control, maintenance of genomic stability, DNA repair, and apoptosis. Recently, Gadd45 proteins have also been implicated in epigenetic gene regulation by promoting active DNA demethylation. Gadd45 proteins have sequence homology with the L7Ae/L30e/S12e RNA binding superfamily of ribosomal proteins, which raises the question if they may interact directly with nucleic acids.

Principal Findings

Here we show that Gadd45a binds RNA but not single- or double stranded DNA or methylated DNA in vitro. Sucrose density gradient centrifugation experiments demonstrate that Gadd45a is present in high molecular weight particles, which are RNase sensitive. Gadd45a displays RNase-sensitive colocalization in nuclear speckles with the RNA helicase p68 and the RNA binding protein SC35. A K45A point mutation defective in RNA binding was still active in DNA demethylation. This suggests that RNA binding is not absolutely essential for demethylation of an artificial substrate. A point mutation at G39 impared RNA binding, nuclear speckle localization and DNA demethylation, emphasizing its relevance for Gadd45a function.

Significance

The results implicate RNA in Gadd45a function and suggest that Gadd45a is associated with a ribonucleoprotein particle.

The transcriptional co-activator LEDGF/p75 displays a dynamic scan-and-lock mechanism for chromatin tethering

Nearly all cellular and disease related functions of the transcriptional co-activator lens epithelium-derived growth factor (LEDGF/p75) involve tethering of interaction partners to chromatin via its conserved integrase binding domain (IBD), but little is known about the mechanism of in vivo chromatin binding and tethering. In this work we studied LEDGF/p75 in real-time in living HeLa cells combining different quantitative fluorescence techniques: spot fluorescence recovery after photobleaching (sFRAP) and half-nucleus fluorescence recovery after photobleaching (hnFRAP), continuous photobleaching, fluorescence correlation spectroscopy (FCS) and an improved FCS method to study diffusion dependence of chromatin binding, tunable focus FCS. LEDGF/p75 moves about in nuclei of living cells in a chromatin hopping/scanning mode typical for transcription factors. The PWWP domain of LEDGF/p75 is necessary, but not sufficient for in vivo chromatin binding. After interaction with HIV-1 integrase via its IBD, a general protein–protein interaction motif, kinetics of LEDGF/p75 shift to 75-fold larger affinity for chromatin. The PWWP is crucial for locking the complex on chromatin. We propose a scan-and-lock model for LEDGF/p75, unifying paradoxical notions of transcriptional co-activation and lentiviral integration targeting.

Protein pockets: inventory, shape, and comparison

The shape of the protein surface dictates what interactions are possible with other macromolecules, but defining discrete pockets or possible interaction sites remains difficult. First, there is the problem of defining the extent of the pocket. Second, one has to characterize the shape of each pocket. Third, one needs to make quantitative comparisons between pockets on different proteins. An elegant solution to these problems is to sort all surface and solvent points by travel depth and then collect a hierarchical tree of pockets. The connectivity of the tree is determined via the deepest saddle points between each pair of neighboring pockets. The resulting pocket surfaces tessellate the entire protein surface, producing a complete inventory of pockets. This method of identifying pockets also allows one to easily compute important shape metrics, including the problematic pocket volume, surface area, and mouth size. Pockets are also annotated with their lining residue lists and polarity and with other residue-based properties. Using this tree and the various shape metrics pockets can be merged, grouped, or filtered for further analysis. Since this method includes the entire surface, it guarantees that any pocket of interest will be found among the output pockets, unlike all previous methods of pocket identification. The resulting hierarchy of pockets is easy to visualize and aids users in higher level analysis. Comparison of pockets is done by using the shape metrics, avoiding the complex shape alignment problem. Example applications show that the method facilitates pocket comparison along mutational or time-dependent series. Pockets from families of proteins can be examined using multiple pocket tree alignments to see how ligand binding sites or how other pockets have changed with evolution. Our method is called CLIPPERS for complete liberal inventory of protein pockets elucidating and reporting on shape.

Evolution: a guide to perturb protein function and networks

Protein interactions give rise to networks that control cell fate in health and disease; selective means to probe these interactions are therefore of wide interest. We discuss here Evolutionary Tracing (ET), a comparative method to identify protein functional sites and to guide experiments that selectively block, recode, or mimic their amino acid determinants. These studies suggest, in principle, a scalable approach to perturb individual links in protein networks.

Molecular surface mesh generation by filtering electron density map

Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Structure of bacteriophage T4 endonuclease II mutant E118A, a tetrameric GIY-YIG enzyme

Coliphage T4 endonuclease II (EndoII), encoded by gene denA, is a small (16 kDa, 136 aa) enzyme belonging to the GIY-YIG family of endonucleases, which lacks a C-terminal domain corresponding to that providing most of the binding energy in the structurally characterized GIY-YIG endonucleases, I-TevI and UvrC. In vivo, it is involved in degradation of host DNA, permitting scavenging of host-derived nucleotides for phage DNA synthesis. EndoII primarily catalyzes single-stranded nicking of DNA; 5- to 10-fold less frequently double-stranded breaks are produced. The Glu118Ala mutant of EndoII was crystallized in space group P2(1) with four monomers in the asymmetric unit. The fold of the EndoII monomer is similar to that of the catalytic domains of UvrC and I-TevI. In contrast to these enzymes, EndoII forms a striking X-shaped tetrameric structure composed as a dimer of dimers, with a protruding hairpin domain not present in UvrC or I-TevI providing most of the dimerization and tetramerization interfaces. A bound phosphate ion in one of the four active sites of EndoII likely mimics the scissile phosphate in a true substrate complex. In silico docking experiments showed that a protruding loop containing a nuclease-associated modular domain 3 element is likely to be involved in substrate binding, as well as residues forming a separate nucleic acid binding surface adjacent to the active site. The positioning of these sites within the EndoII primary dimer suggests that the substrate would bind to a primary EndoII dimer diagonally over the active sites, requiring significant distortion of the enzyme or the substrate DNA, or both, for simultaneous nicking of both DNA strands. The scarcity of potential nucleic acid binding residues between the active sites indicates that EndoII may bind its substrate inefficiently across the two sites in the dimer, offering a plausible explanation for the catalytic preponderance of single-strand nicks. Mutations analyzed in earlier functional studies are discussed in their structural context.

Crystal structure of the peptidase domain of Streptococcus ComA, a bifunctional ATP-binding cassette transporter involved in the quorum-sensing pathway

ComA of Streptococcus is a member of the bacteriocin-associated ATP-binding cassette transporter family and is postulated to be responsible for both the processing of the propeptide ComC and secretion of the mature quorum-sensing signal. The 150-amino acid peptidase domain (PEP) of ComA specifically recognizes an extended region of ComC that is 15 amino acids in length. It has been proposed that an amphipathic alpha-helix formed by the N-terminal leader region of ComC, as well as the Gly-Gly motif at the cleavage site, is critical for the PEP-ComC interaction. To elucidate the substrate recognition mechanism, we determined the three-dimensional crystal structure of Streptococcus mutans PEP and then constructed models for the PEP.ComC complexes. PEP had an overall structure similar to the papain-like cysteine proteases as has long been predicted. The active site was located at the bottom of a narrow cleft, which is suitable for binding the Gly-Gly motif. Together with the results from mutational experiments, a shallow hydrophobic concave surface of PEP was proposed as a site that accommodates the N-terminal helix of ComC. This dual mode of substrate recognition would provide the small PEP domain with an extremely high substrate specificity.