A methodological approach for the analysis of ecosystem services from the local communities' perspective.

In this paper, we developed an innovative and plural methodology for a socio-cultural assessment of ecosystem services (ES). This methodology was performed using diverse and interdependent tools applied within the framework of ethnoecology and post-normal science, with the aim of identifying ES from the perspective of local communities that inhabit different socio-ecosystems, highlighting the relevance of Indigenous and Local Knowledge (ILK). As examples of how this methodology works, we analyzed a multiple case study performed in three peasant communities of the Dry Chaco eco-region, Argentina. We identified ES in all the categories and their fundamental contributions to the particular way of life in this area. The method is flexible enough to be used in other socio-ecosystems with different environmental and social features.

Identification of novel molecular determinants of co-receptor usage in HIV-1 subtype F V3 envelope sequences.

HIV-1 determinants of coreceptor usage within the gp120 V3 loop have been broadly studied over the past years. This information has led to the development of state-of the-art bioinformatic tools that are useful to predict co-receptor usage based on the V3 loop sequence mainly of subtypes B, C and A. However, these methods show a poor performance for subtype F V3 loops, which are found in an increasing number of HIV-1 strains worldwide. In the present work we investigated determinants of viral tropisms in the understudied subtype F by looking at genotypic and structural information of coreceptor:V3 loop interactions in a novel group of 40 subtype F V3 loops obtained from HIV-1 strains phenotypically characterized either as syncytium inducing or non-syncytium inducing by the MT-2 assay. We provide novel information about estimated interactions energies between a set of V3 loops with known tropism in subtype F, that allowed us to improve predictions of the coreceptor usage for this subtype. Understanding genetic and structural features underlying HIV coreceptor usage across different subtypes is relevant for the rational design of preventive and therapeutic strategies aimed at limiting the HIV-1 epidemic worldwide.

Aromatic clusters in protein-protein and protein-drug complexes.

Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein-drug and protein-protein interactions. They are relevant for both protein stability and molecular recognition processes due to their natural occurrence in aromatic aminoacids (Trp, Phe, Tyr and His) as well as in designed drugs since they are believed to contribute to optimizing both affinity and specificity of drug-like molecules. Despite the mentioned relevance, the impact of aromatic clusters on protein-protein and protein-drug complexes is still poorly characterized, especially in those that go beyond a dimer. In this work, we studied protein-drug and protein-protein complexes and systematically analyzed the presence and structure of their aromatic clusters. Our results show that aromatic clusters are highly prevalent in both protein-protein and protein-drug complexes, and suggest that protein-protein aromatic clusters have idealized interactions, probably because they were optimized by evolution, as compared to protein-drug clusters that were manually designed. Interestingly, the configuration, solvent accessibility and secondary structure of aromatic residues in protein-drug complexes shed light on the relation between these properties and compound affinity, allowing researchers to better design new molecules.

TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes.

The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/ .

T-Cell Receptor Cognate Target Prediction Based on Paired α and ß Chain Sequence and Structural CDR Loop Similarities.

T-cell receptors (TCR) mediate immune responses recognizing peptides in complex with major histocompatibility complexes (pMHC) displayed on the surface of cells. Resolving the challenge of predicting the cognate pMHC target of a TCR would benefit many applications in the field of immunology, including vaccine design/discovery and the development of immunotherapies. Here, we developed a model for prediction of TCR targets based on similarity to a database of TCRs with known targets. Benchmarking the model on a large set of TCRs with known target, we demonstrated how the predictive performance is increased (i) by focusing on CDRs rather than the full length TCR protein sequences, (ii) by incorporating information from paired α and ß chains, and (iii) integrating information for all 6 CDR loops rather than just CDR3. Finally, we show how integration of the structure of CDR loops, as obtained through homology modeling, boosts the predictive power of the model, in particular in situations where no high-similarity TCRs are available for the query. These findings demonstrate that TCRs that bind to the same target also share, to a very high degree, sequence, and structural features. This observation has profound impact for future development of prediction models for TCR-pMHC interactions and for the use of such models for the rational design of T cell based therapies.

An integrative, multi-omics approach towards the prioritization of Klebsiella pneumoniae drug targets.

Klebsiella pneumoniae (Kp) is a globally disseminated opportunistic pathogen that can cause life-threatening infections. It has been found as the culprit of many infection outbreaks in hospital environments, being particularly aggressive towards newborns and adults under intensive care. Many Kp strains produce extended-spectrum ß-lactamases, enzymes that promote resistance against antibiotics used to fight these infections. The presence of other resistance determinants leading to multidrug-resistance also limit therapeutic options, and the use of 'last-resort' drugs, such as polymyxins, is not uncommon. The global emergence and spread of resistant strains underline the need for novel antimicrobials against Kp and related bacterial pathogens. To tackle this great challenge, we generated multiple layers of 'omics' data related to Kp and prioritized proteins that could serve as attractive targets for antimicrobial development. Genomics, transcriptomics, structuromic and metabolic information were integrated in order to prioritize candidate targets, and this data compendium is freely available as a web server. Twenty-nine proteins with desirable characteristics from a drug development perspective were shortlisted, which participate in important processes such as lipid synthesis, cofactor production, and core metabolism. Collectively, our results point towards novel targets for the control of Kp and related bacterial pathogens.

Identification of the cognate peptide-MHC target of T cell receptors using molecular modeling and force field scoring.

Interactions of T cell receptors (TCR) to peptides in complex with MHC (p:MHC) are key features that mediate cellular immune responses. While MHC binding is required for a peptide to be presented to T cells, not all MHC binders are immunogenic. The interaction of a TCR to the p:MHC complex holds a key, but currently poorly comprehended, component for our understanding of this variation in the immunogenicity of MHC binding peptides. Here, we demonstrate that identification of the cognate target of a TCR from a set of p:MHC complexes to a high degree is achievable using simple force-field energy terms. Building a benchmark of TCR:p:MHC complexes where epitopes and non-epitopes are modelled using state-of-the-art molecular modelling tools, scoring p:MHC to a given TCR using force-fields, optimized in a cross-validation setup to evaluate TCR inter atomic interactions involved with each p:MHC, we demonstrate that this approach can successfully be used to distinguish between epitopes and non-epitopes. A detailed analysis of the performance of this force-field-based approach demonstrate that its predictive performance depend on the ability to both accurately predict the binding of the peptide to the MHC and model the TCR:p:MHC complex structure. In summary, we conclude that it is possible to identify the TCR cognate target among different candidate peptides by using a force-field based model, and believe this works could lay the foundation for future work within prediction of TCR:p:MHC interactions.

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens.

Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request.

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis.

Current Tuberculosis treatment is long and expensive, faces the increasing burden of MDR/XDR strains and lack of effective treatment against latent form, resulting in an urgent need of new anti-TB drugs. Key to TB biology is its capacity to fight the host's RNOS mediated attack. RNOS are known to display a concentration dependent mycobactericidal activity, which leads to the following hypothesis "if we know which proteins are targeted by RNOS and kill TB, we we might be able to inhibit them with drugs resulting in a synergistic bactericidal effect". Based on this idea, we performed an Mtb metabolic network whole proteome analysis of potential RNOS sensitive and relevant targets which includes target druggability and essentiality criteria. Our results, available at http://tuberq.proteinq.com.ar yield new potential TB targets, like I3PS, while also providing and updated view of previous proposals becoming an important tool for researchers looking for new ways of killing TB.

An integrated structural proteomics approach along the druggable genome of Corynebacterium pseudotuberculosis species for putative druggable targets.

BACKGROUND: The bacterium Corynebacterium pseudotuberculosis (Cp) causes caseous lymphadenitis (CLA), mastitis, ulcerative lymphangitis, and oedema in a number of hosts, comprising ruminants, thereby intimidating economic and dairy industries worldwide. So far there is no effective drug or vaccine available against Cp. Previously, a pan-genomic analysis was performed for both biovar equi and biovar ovis and a Pathogenicity Islands (PAIS) analysis within the strains highlighted a large set of proteins that could be relevant therapeutic targets for controlling the onset of CLA. In the present work, a structural druggability analysis pipeline was accomplished along 15 previously sequenced Cp strains from both biovar equi and biovar ovis. METHODS AND RESULTS: We computed the whole modelome of a reference strain Cp1002 (NCBI Accession: NC_017300.1) and then the homology models of proteins, of 14 different Cp strains, with high identity (≥ 85%) to the reference strain were also done. Druggability score of all proteins pockets was calculated and only those targets that have a highly druggable (HD) pocket in all strains were kept, a set of 58 proteins. Finally, this information was merged with the previous PAIS analysis giving two possible highly relevant targets to conduct drug discovery projects. Also, off-targeting information against host organisms, including Homo sapiens and a further analysis for protein essentiality provided a final set of 31 druggable, essential and non-host homologous targets, tabulated in table S4, additional file 1. Out of 31 globally druggable targets, 9 targets have already been reported in other pathogenic microorganisms, 3 of them (3-isopropylmalate dehydratase small subunit, 50S ribosomal protein L30, Chromosomal replication initiator protein DnaA) in C. pseudotuberculosis. CONCLUSION: Overall we provide valuable information of possible targets against C. pseudotuberculosis where some of these targets have already been reported in other microorganisms for drug discovery projects, also discarding targets that might be physiologically relevant but are not amenable for drug binding. We propose that the constructed in silico dataset might serve as a guidance for the scientific community to have a better understanding while selecting putative therapeutic protein candidates as druggable ones as effective measures against C. pseudotuberculosis.

Protein topology determines cysteine oxidation fate: the case of sulfenyl amide formation among protein families.

Cysteine residues have a rich chemistry and play a critical role in the catalytic activity of a plethora of enzymes. However, cysteines are susceptible to oxidation by Reactive Oxygen and Nitrogen Species, leading to a loss of their catalytic function. Therefore, cysteine oxidation is emerging as a relevant physiological regulatory mechanism. Formation of a cyclic sulfenyl amide residue at the active site of redox-regulated proteins has been proposed as a protection mechanism against irreversible oxidation as the sulfenyl amide intermediate has been identified in several proteins. However, how and why only some specific cysteine residues in particular proteins react to form this intermediate is still unknown. In the present work using in-silico based tools, we have identified a constrained conformation that accelerates sulfenyl amide formation. By means of combined MD and QM/MM calculation we show that this conformation positions the NH backbone towards the sulfenic acid and promotes the reaction to yield the sulfenyl amide intermediate, in one step with the concomitant release of a water molecule. Moreover, in a large subset of the proteins we found a conserved beta sheet-loop-helix motif, which is present across different protein folds, that is key for sulfenyl amide production as it promotes the previous formation of sulfenic acid. For catalytic activity, in several cases, proteins need the Cysteine to be in the cysteinate form, i.e. a low pKa Cys. We found that the conserved motif stabilizes the cysteinate by hydrogen bonding to several NH backbone moieties. As cysteinate is also more reactive toward ROS we propose that the sheet-loop-helix motif and the constraint conformation have been selected by evolution for proteins that need a reactive Cys protected from irreversible oxidation. Our results also highlight how fold conservation can be correlated to redox chemistry regulation of protein function.

Exposing the secrets of two well-known Lactobacillus casei phages, J-1 and PL-1, by genomic and structural analysis.

Bacteriophage J-1 was isolated in 1965 from an abnormal fermentation of Yakult using Lactobacillus casei strain Shirota, and a related phage, PL-1, was subsequently recovered from a strain resistant to J-1. Complete genome sequencing shows that J-1 and PL-1 are almost identical, but PL-1 has a deletion of 1.9 kbp relative to J-1, resulting in the loss of four predicted gene products involved in immunity regulation. The structural proteins were identified by mass spectrometry analysis. Similarly to phage A2, two capsid proteins are generated by a translational frameshift and undergo proteolytic processing. The structure of gene product 16 (gp16), a putative tail protein, was modeled based on the crystal structure of baseplate distal tail proteins (Dit) that form the baseplate hub in other Siphoviridae. However, two regions of the C terminus of gp16 could not be modeled using this template. The first region accounts for the differences between J-1 and PL-1 gp16 and showed sequence similarity to carbohydrate-binding modules (CBMs). J-1 and PL-1 GFP-gp16 fusions bind specifically to Lactobacillus casei/paracasei cells, and the addition of l-rhamnose inhibits binding. J-1 gp16 exhibited a higher affinity than PL-1 gp16 for cell walls of L. casei ATCC 27139 in phage adsorption inhibition assays, in agreement with differential adsorption kinetics observed for both phages in this strain. The data presented here provide insights into how Lactobacillus phages interact with their hosts at the first steps of infection.

TuberQ: a Mycobacterium tuberculosis protein druggability database.

In 2012 an estimated 8.6 million people developed tuberculosis (TB) and 1.3 million died from the disease [including 320 000 deaths among human immunodeficiency virus (HIV)-positive people]. There is an urgent need for new anti-TB drugs owing to the following: the fact that current treatments have severe side effects, the increasing emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb), the negative drug-drug interactions with certain HIV (or other disease) treatments and the ineffectiveness against dormant Mtb. In this context we present here the TuberQ database, a novel resource for all researchers working in the field of drug development in TB. The main feature of TuberQ is to provide a druggability analysis of Mtb proteins in a consistent and effective manner, contributing to a better selection of potential drug targets for screening campaigns and the analysis of targets for structure-based drug design projects. The structural druggability analysis is combined with features related to the characteristics of putative inhibitor binding pockets and with functional and biological data of proteins. The structural analysis is performed on all available unique Mtb structures and high-quality structural homology-based models. This information is shown in an interactive manner, depicting the protein structure, the pockets and the associated characteristics for each protein. TuberQ also provides information about gene essentiality information, as determined from whole cell-based knockout experiments, and expression information obtained from microarray experiments done in different stress-related conditions. We hope that TuberQ will be a powerful tool for researchers working in TB and eventually will lead to the identification of novel putative targets and progresses in therapeutic activities. Database URL: http://tuberq.proteinq.com.ar/

Draft Genome Sequence of the Polyextremophilic Exiguobacterium sp. Strain S17, Isolated from Hyperarsenic Lakes in the Argentinian Puna.

Exiguobacterium sp. strain S17 is a moderately halotolerant, arsenic-resistant bacterium that was isolated from Laguna Socompa stromatolites in the Argentinian Puna. The draft genome sequence suggests potent enzyme candidates that are essential for survival under multiple environmental extreme conditions, such as high levels of UV radiation, elevated salinity, and the presence of critical arsenic concentrations.

BeEP Server: Using evolutionary information for quality assessment of protein structure models.

The BeEP Server (http://www.embnet.qb.fcen.uba.ar/embnet/beep.php) is an online resource aimed to help in the endgame of protein structure prediction. It is able to rank submitted structural models of a protein through an explicit use of evolutionary information, a criterion differing from structural or energetic considerations commonly used in other assessment programs. The idea behind BeEP (Best Evolutionary Pattern) is to benefit from the substitution pattern derived from structural constraints present in a set of homologous proteins adopting a given protein conformation. The BeEP method uses a model of protein evolution that takes into account the structure of a protein to build site-specific substitution matrices. The suitability of these substitution matrices is assessed through maximum likelihood calculations from which position-specific and global scores can be derived. These scores estimate how well the structural constraints derived from each structural model are represented in a sequence alignment of homologous proteins. Our assessment on a subset of proteins from the Critical Assessment of techniques for protein Structure Prediction (CASP) experiment has shown that BeEP is capable of discriminating the models and selecting one or more native-like structures. Moreover, BeEP is not explicitly parameterized to find structural similarities between models and given targets, potentially helping to explore the conformational ensemble of the native state.

1H, 15N and 13C chemical shift assignments of the BA42 protein of the psychrophilic bacteria Bizionia argentinensis sp. nov.

BA42 is a protein belonging to the psychrophilic bacteria Bizionia argentinensis sp. nov. Bioinformatics analysis showed that it presents significant sequence identity with a Pfam A family, DUF 477, found both in eukarya and eubacteria but of unknown function in all these organisms. Here, we report the NMR spectra assignment of this 145 amino acid protein. These data will allow performing NMR structural studies with the aim of using the three-dimensional structure as relevant information in order to determine the function of this family of proteins.

Draft genome sequence of Bizionia argentinensis, isolated from Antarctic surface water.

A psychrotolerant marine bacterial strain, designated JUB59(T), was isolated from Antarctic surface seawater and classified as a new species of the genus Bizionia. Here, we present the first draft genome sequence for this genus, which suggests interesting features such as UV resistance, hydrolytic exoenzymes, and nitrogen metabolism.

Aromatic-aromatic interactions in proteins: beyond the dimer.

Aromatic residues are key widespread elements of protein structures and have been shown to be important for structure stability, folding, protein-protein recognition, and ligand binding. The interactions of pairs of aromatic residues (aromatic dimers) have been extensively studied in protein structures. Isolated aromatic molecules tend to form higher order clusters, like trimers, tetramers, and pentamers, that adopt particular well-defined structures. Taking this into account, we have surveyed protein structures deposited in the Protein Data Bank in order to find clusters of aromatic residues in proteins larger than dimers and characterized them. Our results show that larger clusters are found in one of every two unique proteins crystallized so far, that the clusters are built adopting the same trimer motifs found for benzene clusters in vacuum, and that they are clearly nonlocal brining primary structure distant sites together. We extensively analyze the trimers and tetramers conformations and found two main cluster types: a symmetric cluster and an extended ladder. Finally, using calmodulin as a test case, we show aromatic clsuters possible role in folding and protein-protein interactions. All together, our study highlights the relevance of aromatic clusters beyond the dimer in protein function, stability, and ligand recognition.