The antimicrobial compound xantholysin defines a new group of Pseudomonas cyclic lipopeptides

The rhizosphere isolate Pseudomonas putida BW11M1 produces a mixture of cyclic lipopeptide congeners, designated xantholysins. Properties of the major compound xantholysin A, shared with several other Pseudomonas lipopeptides, include antifungal activity and toxicity to Gram-positive bacteria, a supportive role in biofilm formation, and facilitation of surface colonization through swarming. Atypical is the lipopeptide’s capacity to inhibit some Gram-negative bacteria, including several xanthomonads. The lipotetradecadepsipeptides are assembled by XtlA, XtlB and XtlC, three co-linearly operating non-ribosomal peptide synthetases (NRPSs) displaying similarity in modular architecture with the entolysin-producing enzymes of the entomopathogenic Pseudomonas entomophila L48. A shifted serine-incorporating unit in the eight-module enzyme XtlB elongating the central peptide moiety not only generates an amino acid sequence differing at several equivalent positions from entolysin, but also directs xantholysin’s macrocyclization into an octacyclic structure, distinct from the pentacyclic closure in entolysin. Relaxed fatty acid specificity during lipoinitiation by XtlA (acylation with 3-hydroxydodec-5-enoate instead of 3-hydroxydecanoate) and for incorporation of the ultimate amino acid by XtlC (valine instead of isoleucine) account for the production of the minor structural variants xantholysin C and B, respectively. Remarkably, the genetic backbones of the xantholysin and entolysin NRPS systems also bear pronounced phylogenetic similarity to those of the P. putida strains PCL1445 and RW10S2, albeit generating the seemingly structurally unrelated cyclic lipopeptides putisolvin (undecapeptide containing a cyclotetrapeptide) and WLIP (nonapeptide containing a cycloheptapeptide), respectively. This similarity includes the linked genes encoding the cognate LuxR-family regulator and tripartite export system components in addition to individual modules of the NRPS enzymes, and probably reflects a common evolutionary origin. Phylogenetic scrutiny of the modules used for selective amino acid activation by these synthetases indicates that bacteria such as pseudomonads recruit and reshuffle individual biosynthetic units and blocks thereof to engineer reorganized or novel NRPS assembly lines for diversified synthesis of lipopeptides

PepShell : visualization of conformational proteomics data

Proteins are dynamic molecules; they undergo crucial conformational changes induced by post-translational modifications and by binding of cofactors or other molecules. The characterization of these conformational changes and their relation to protein function is a central goal of structural biology. Unfortunately, most conventional methods to obtain structural information do not provide information on protein dynamics. Therefore, mass spectrometry-based approaches, such as limited proteolysis, hydrogen-deuterium exchange, and stable-isotope labeling, are frequently used to characterize protein conformation and dynamics, yet the interpretation of these data can be cumbersome and time consuming. Here, we present PepShell, a tool that allows interactive data analysis of mass spectrometry-based conformational proteomics studies by visualization of the identified peptides both at the sequence and structure levels. Moreover, PepShell allows the comparison of experiments under different conditions, including different proteolysis times or binding of the protein to different substrates or inhibitors

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others

Recent progress in low-carbon binders

The development of low-carbon binders has been recognized as a means of reducing the carbon footprint of the Portland cement industry, in response to growing global concerns over CO2 emissions from the construction sector. This paper reviews recent progress in the three most attractive low-carbon binders: alkali-activated, carbonate, and belite-ye'elimite-based binders. Alkali-activated binders/materials were reviewed at the past two ICCC congresses, so this paper focuses on some key developments of alkali-activated binders/materials since the last keynote paper was published in 2015. Recent progress on carbonate and belite-ye'elimite-based binders are also reviewed and discussed, as they are attracting more and more attention as essential alternative low-carbon cementitious materials. These classes of binders have a clear role to play in providing a sustainable future for global construction, as part of the available toolkit of cements

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

In silico methods for predictive toxicological and pharmacological modelling

This thesis is concerned with the development and improvement of in silico toxicological models and will capitalise on the latest advances in predictive toxicological models at all levels of the toxicological QSAR pipeline. A review of data from in vitro and in vivo assays used for the prediction of mutagenicity and carcinogenicity is presented, collating over 10,000 molecules. The results of this review supported the subsequent development of an in silico model to predict the in vivo carcinogenicity assay. The overall model found 69.3% accuracy and 0.700 ROC AUC. This work was followed with an investigation into the use of quantum mechanical methods to predict lipophilicity (LogP). Solvated free energies in water and in 1-octanol were calculated using the M06-2X hybrid density functional and the def2-SVP basis set. The resulting model performed well in the SAMPL6 LogP Prediction Challenge where the model was placed seventh overall and notably superior to conventional methods. Finally, a larger investigation was conducted with skin sensitisation as the prediction target. Skin sensitisation is a toxicological outcome with scarce data available for the assays used in its prediction. The use of quantum mechanical calculations in this study enabled direct quantitative characterisation of electronic effects highly relevant to the skin sensitisation adverse outcome pathway. Ames mutagenicity models were also used for predicting skin sensitisation due to the importance of electrophilicity in both mechanisms of toxicity. Implicit solvation was incorporated into quantum molecular descriptor calculations as it was relevant to skin sensitisation mechanisms. The predictive performance achieved in the study was superior to that produced by the in vitro local lymph node assays for predicting human outcomes. These studies have generated insight into in silico chemical toxicity prediction methods and validate the use of state of the art computational methodologies

Low-cost quantum mechanical descriptors for data efficient skin sensitization QSAR models

Quantitative Structure Activity Relationship modelling methodologies need to incorporate relevant mechanistic information to have high predictive performance and validity. Electrophilic reactivity is a common mechanistic feature of skin sensitization endpoints which could be concisely characterized with electronic descriptors which is key to enabling the modelling of small datasets in this domain. However, quantum mechanical methodologies have previously featured high computational costs which would exclude the use of large datasets. Consequently, we investigate the use of electronic descriptors calculated using the Hartree Fock with 3 corrections (Hf-3c) method, a low-cost ab initio methodology that has higher chemical accuracy than previous semiempirical methodologies for modelling in vitro skin sensitization assay outcomes. We also model the Ames assay as a surrogate for determining skin sensitization outcomes. The quantum chemical descriptors calculated using the Hf-3c method with conductor-like polarizable continuum model (CPCM) implicit solvation found improved QSAR model performance for the in vitro Ames (n = 6049, 0.770 AUC), KeratinoSens (n = 164, 0.763 AUC), and Direct Peptide Reactivity Assay (n = 122, 0.750 AUC) datasets, with their combination producing high predictive performance for unseen in vivo Local Lymph Node Assay (n = 86, 0.789 AUC) and Human Repeated Insult Patch Test (n = 86, 0.791 AUC) assay toxicant outcomes