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Tuberous sclerosis complex (TSC) is an inherited neurocutaneous disorder caused by mutations in TSC1 or TSC2 genes, with patients often exhibiting neurodevelopmental (ND) manifestations termed TSC-associated neuropsychiatric disorders (TAND) including autism spectrum disorder (ASD). The hamartin-tuberin (TSC1-TSC2) protein complex inactivates mechanistic target of rapamycin complex 1 (mTORC1) signaling, leading to increased protein synthesis via inactivation of translational repressor eIF4E-binding proteins (4E-BPs). In TSC1-null neural progenitor cells (NPCs), we previously reported early ND phenotypic changes, including increased proliferation/altered neurite outgrowth, which were unaffected by mTORC1-inhibitor rapamycin. Here, using polysome-profiling to quantify translational efficiencies at a transcriptome-wide level, we observed numerous TSC1-dependent alterations in NPCs, largely recapitulated in post-mortem brains from ASD donors. Although rapamycin partially reversed TSC1-associated alterations, most neural activity/synaptic- or ASD-related genes remained insensitive but were inhibited by third-generation bi-steric, mTORC1-selective inhibitor RMC-6272, which also reversed altered ND phenotypes. Together these data reveal potential implications for treatment of TAND.

A definitive transcriptome atlas for the non-coding expressed elements of the members of the Mycobacterium tuberculosis complex (MTBC) does not exist. Incomplete lists of non-coding transcripts can be obtained for some of the reference genomes (e.g., M. tuberculosis H37Rv) but to what extent these transcripts have homologues in closely related species or even strains is not clear. This has implications for the analysis of transcriptomic data; non-coding parts of the transcriptome are often ignored in the absence of formal, reliable annotation. Here, we review the state of our knowledge of non-coding RNAs in pathogenic mycobacteria, emphasizing the disparities in the information included in commonly used databases. We then proceed to review ways of combining computational solutions for predicting the non-coding transcriptome with experiments that can help refine and confirm these predictions.

A whole genome co-expression network was created usingMycobacterium tuberculosistranscriptomic data from publicly available RNA-sequencing experiments covering a wide variety of experimental conditions. The network includes expressed regions with no formal annotation, including putative short RNAs and untranslated regions of expressed transcripts, along with the protein-coding genes. These unannotated expressed transcripts were among the best-connected members of the module sub-networks, making up more than half of the ‘hub’ elements in modules that include protein-coding genes known to be part of regulatory systems involved in stress response and host adaptation. This dataset provides a valuable resource for investigating the role of non-coding RNA, and conserved hypothetical proteins, in transcriptomic remodelling. Based on their connections to genes with known functional groupings and correlations with replicated host conditions, predicted expressed transcripts can be screened as suitable candidates for further experimental validation.

Members of the Mycobacterium tuberculosis complex (MTBC) show distinct host adaptations, preferences and phenotypes despite being >99% identical at the nucleic acid level. Previous studies have explored gene expression changes between the members, however few studies have probed differences in gene essentiality. To better understand the functional impacts of the nucleic acid differences between Mycobacterium bovis and Mycobacterium tuberculosis we used the Mycomar T7 phagemid delivery system to generate whole genome transposon libraries in laboratory strains of both species and compared the essentiality status of genes during growth under identical in vitro conditions. Libraries contained insertions in 54% of possible TA sites in M. bovis and 40% of those present in M. tuberculosis, achieving similar saturation levels to those previously reported for the MTBC. The distributions of essentiality across the functional categories were similar in both species. 527 genes were found to be essential in M. bovis whereas 477 genes were essential in M. tuberculosis and 370 essential genes were common in both species. CRISPRi was successfully utilised in both species to determine the impacts of silencing genes including wag31, a gene involved in peptidoglycan synthesis and Rv2182c/Mb2204c, a gene involved in glycerophospholipid metabolism. We observed species specific differences in the response to gene silencing, with the inhibition of expression of Mb2204c in M. bovis showing significantly less growth impact than silencing its ortholog (Rv2182c) in M. tuberculosis. Given that glycerophospholipid metabolism is a validated pathway for antimicrobials, our observations suggest that target vulnerability in the animal adapted lineages cannot be assumed to be the same as the human counterpart. This is of relevance for zoonotic tuberculosis as it implies that the development of antimicrobials targeting the human adapted lineage might not necessarily be effective against the animal adapted lineage. The generation of a transposon library and the first reported utilisation of CRISPRi in M. bovis will enable the use of these tools to further probe the genetic basis of survival under disease relevant conditions.

A definitive transcriptome atlas for the non-coding expressed elements of pathogenic mycobacteria does not exist. Incomplete lists of non-coding transcripts can be obtained for some of the reference genomes (e.g. Mycobacterium tuberculosis H37Rv) but to what extent these transcripts have homologues in closely related species or even strains is not clear. This has implications for the analysis of transcriptomic data; non-coding parts of the transcriptome are often ignored in the absence of formal, reliable annotation. Here, we review the state of our knowledge of non-coding RNAs in pathogenic mycobacteria, emphasising the disparities in the information included in commonly used databases. We then proceed to review ways of combining computational solutions for predicting the non- coding transcriptome with experiments that can help refine and confirm these predictions.

RCUK funded OA Mycobacterium tuberculosis (MTb) is the causative agent of pulmonary tuberculosis (TB). MTb colonizes the human lung, often entering a non-replicating state before progressing to life-threatening active infections. Transcriptional reprogramming is essential for TB pathogenesis. In vitro, Cmr (a member of the CRP/FNR super-family of transcription regulators) bound at a single DNA site to act as a dual regulator of cmr transcription and an activator of the divergent rv1676 gene. Transcriptional profiling and DNA-binding assays suggested that Cmr directly represses dosR expression. The DosR regulon is thought to be involved in establishing latent tuberculosis infections in response to hypoxia and nitric oxide. Accordingly, DNA-binding by Cmr was severely impaired by nitrosation. A cmr mutant was better able to survive a nitrosative stress challenge but was attenuated in a mouse aerosol infection model. The complemented mutant exhibited a ∼2-fold increase in cmr expression, which led to increased sensitivity to nitrosative stress. This, and the inability to restore wild-type behaviour in the infection model, suggests that precise regulation of the cmr locus, which is associated with Region of Difference 150 in hypervirulent Beijing strains of Mtb, is important for TB pathogenesis.

We present flexiMAP (flexible Modeling of Alternative PolyAdenylation), a new beta-regression-based method implemented in R, for discovering differential alternative polyadenylation events in standard RNA-seq data. We show, using both simulated and real data, that flexiMAP exhibits a good balance between specificity and sensitivity and compares favourably to existing methods, especially at low fold changes. In addition, the tests on simulated data reveal some hitherto unrecognised caveats of existing methods. Importantly, flexiMAP allows modeling of multiple known covariates that often confound the results of RNA-seq data analysis. This repository contains scripts and all data required to reproduce data in the manuscript. _simulationData zip files contain extensive set of simulations which should serve as a useful resource for the development of similar methods in the future. Additionally, it contains 3’ sequencing data (PolyA-seq) and RNA-seq data from the Human Brain Reference and the Universal Human Reference MAQC samples (Bullard et al., 2010) that was used in the manuscript., {"references":["Krzysztof J. Szkop, David S. Moss and Irene Nobeli flexiMAP: A regression-based method for discovering differential alternative polyadenylation events in standard RNA-seq data"]}

I huff and puff my way up the moderate slope. Even by my own abysmal standards, this is a poor run. In the past hour, I have been overtaken by both an octogenarian and a mum pushing her toddlers in a buggy. Yet I am smiling. I am a happy runner, despite my utter mediocrity at this sport. But at work, happiness had become elusive. After a relatively relaxed Ph.D. and postdoc, I had been thrilled when I landed a tenured job. But as I worked to establish myself as a group leader, I began to feel intense pressure to be more competitive and publish more. Recently, as I wondered why I felt so discontented at my job, I realized that I could apply some lessons from running to my research. ![Figure][1] ILLUSTRATION: ROBERT NEUBECKER > “Not being able to sprint makes me a better endurance runner.” Lesson one: In the right race, your weakness can become your strength. For more than 30 years I equated running to speed and so, lacking the fast-twitch muscles of a sprinter, I chose not to run at all. But once I finally started running, I learned that not being able to sprint makes me a better endurance runner. Similarly, I often perceived my inability to focus on a single research topic as a barrier to success as an academic scientist. But I've found that my desire to branch out to different fields helps me make connections across disciplines and see my work in new ways, which has led to unexpected and exciting insights. Lesson two: Choose the right pace for your race. Last Christmas, a silver-haired gentleman helped me beat my (lamentable) 5K personal best by whispering, “Go at your pace, not theirs!” when I got stuck behind slower runners. I now realize that this would have been excellent advice early in my career. Some Ph.D. students push themselves too hard and burn out, but I had the opposite problem. I was happy to trundle along at the slowest pace I could get away with—but it ultimately held me back. Even though I like my slow-but-steady pace, I still needed to learn to push myself rather than drag my feet. Lesson three: An honest race is the only race worth running. I invariably finish in the bottom quartile of the local 5K run. All I would have to do to move up a couple of hundred places is take a shortcut through the fields. Yet I don't. Nobody does. Scientists are usually like that, too—but not always. Principal investigators are pressured to keep their spot in the fast lane, postdocs are chasing the elusive permanent contract, and students are keen to make their mark. Several of my publications would have had a much easier ride through the reviewing system had I been slightly less honest about our findings. The temptation to cheat to get an advantage can be great. However, and this is something that is often overlooked, an advantage is only useful if you are, in fact, engaged in a competition. This brings me to the last lesson, which is the most important of all. Lesson four: There really is no race. For me, running isn't about being faster than other runners. Likewise, my goal in research is not to “beat” my colleagues. Mark Rowlands, a philosopher, academic, and runner, argues that running makes us happy because it is a form of play and as such has intrinsic value. I don't run just to eat more peanut butter or to save money on psychotherapy (although these are strong motivating factors in my case). I run because doing so offers a glimpse of life's real value. I now think this is the secret to being happy in research, too. I don't do research only to get invited to conferences, see my name in print, or be promoted. Like running, research is a game with its own intrinsic value. Playing this game of discovery gives me enough joy to keep me going. Do I recommend an academic career in the slow lane? It doesn't work for everybody. Letting go of ambition in academia is a bit like leaving your GPS watch at home when heading out for a run. Scientists are ambitious; they want to be the hares leading the race ahead. But the tortoise's secret is that there is a lot of fun to be had at the back of the pack. [1]: pending:yes

The success of Mycobacterium tuberculosis relies on the ability to switch between active growth and non-replicating persistence, associated with latent TB infection. Resuscitation promoting factors (Rpfs) are essential for the transition between these states. Rpf expression is tightly regulated as these enzymes are able to degrade the cell wall, and hence potentially lethal to the bacterium itself. We have identified a regulatory element in the 5′ untranslated region (UTR) of rpfB. We demonstrate that this element is a transcriptionally regulated RNA switch/riboswitch candidate, which appears to be restricted to pathogenic mycobacteria, suggesting a role in virulence. We have used translation start site mapping to re-annotate the RpfB start codon and identified and validated a ribosome binding site that is likely to be targeted by an rpfB antisense RNA. Finally, we show that rpfB is co-transcribed with ksgA and ispE downstream. ksgA encodes a universally conserved methyltransferase involved in ribosome maturation and ispE encodes an essential kinase involved in cell wall synthesis. This arrangement implies co-regulation of resuscitation, cell wall synthesis and ribosome maturation via the RNA switch.

Voltage-gated sodium channels are important targets for the development of pharmaceutical drugs, because mutations in different human sodium channel isoforms have causal relationships with a range of neurological and cardiovascular diseases. In this study, functional electrophysiological studies show that the prokaryotic sodium channel from Magnetococcus marinus (NavMs) binds and is inhibited by eukaryotic sodium channel blockers in a manner similar to the human Nav1.1 channel, despite millions of years of divergent evolution between the two types of channels. Crystal complexes of the NavMs pore with several brominated blocker compounds depict a common antagonist binding site in the cavity, adjacent to lipid-facing fenestrations proposed to be the portals for drug entry. In silico docking studies indicate the full extent of the blocker binding site, and electrophysiology studies of NavMs channels with mutations at adjacent residues validate the location. These results suggest that the NavMs channel can be a valuable tool for screening and rational design of human drugs.

An experimentally determined structure for human CYP2J2-a member of the cytochrome P450 family with significant and diverse roles across a number of tissues-does not yet exist. Our understanding of how CYP2J2 accommodates its cognate substrates and how it might be inhibited by other ligands thus relies on our ability to computationally predict such interactions using modelling techniques. In this study we present a computational investigation of the binding of arachidonic acid (AA) to CYP2J2 using homology modelling, induced fit docking (IFD) and molecular dynamics (MD) simulations. Our study reveals a catalytically competent binding mode for AA that is distinct from a recently published study that followed a different computational pipeline. Our proposed binding mode for AA is supported by crystal structures of complexes of related enzymes to inhibitors, and evolutionary conservation of a residue whose role appears essential for placing AA in the right site for catalysis. Graphical Abstract Arachidonic acid docked in the active site of CYP2J2 assumes a catalytically competent binding mode stabilised by hydrogen bonds to Arg117.

Objectives The aim of this study was to comprehensively evaluate the antibacterial activity and MurE inhibition of a set of N-methyl-2-alkenyl-4-quinolones found to inhibit the growth of fast-growing mycobacteria. Methods Using the spot culture growth inhibition assay, MICs were determined for Mycobacterium tuberculosis H37Rv, Mycobacterium bovis BCG and Mycobacterium smegmatis mc2155. MICs were determined for Mycobacterium fortuitum, Mycobacterium phlei, methicillin-resistant Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa using microplate dilution assays. Inhibition of M. tuberculosis MurE ligase activity was determined both by colorimetric and HPLC methods. Computational modelling and binding prediction of the quinolones in the MurE structure was performed using Glide. Kinetic experiments were conducted for understanding possible competitive relations of the quinolones with the endogenous substrates of MurE ligase. Results The novel synthetic N-methyl-2-alkenyl-4-quinolones were found to be growth inhibitors of M. tuberculosis and rapid-growing mycobacteria as well as methicillin-resistant S. aureus, while showing no inhibition for E. coli and P. aeruginosa. The quinolones were found to be inhibitory to MurE ligase of M. tuberculosis in the micromolar range (IC50 ∼40–200 μM) when assayed either spectroscopically or by HPLC. Computational docking of the quinolones on the published M. tuberculosis MurE crystal structure suggested that the uracil recognition site is a probable binding site for the quinolones. Conclusions N-methyl-2-alkenyl-4-quinolones are inhibitors of mycobacterial and staphylococcal growth, and show MurE ligase inhibition. Therefore, they are considered as a starting point for the development of increased affinity MurE activity disruptors.

Onset of the lytic phase in the KSHV life cycle is accompanied by the rapid, global degradation of host (and viral) mRNA transcripts in a process termed host shutoff. Key to this destruction is the virally encoded alkaline exonuclease SOX. While SOX has been shown to possess an intrinsic RNase activity and a potential consensus sequence for endonucleolytic cleavage identified, the structures of the RNA substrates targeted remained unclear. Based on an analysis of three reported target transcripts, we were able to identify common structures and confirm that these are indeed degraded by SOX in vitro as well as predict the presence of such elements in the KSHV pre-microRNA transcript K12-2. From these studies, we were able to determine the crystal structure of SOX productively bound to a 31 nucleotide K12-2 fragment. This complex not only reveals the structural determinants required for RNA recognition and degradation but, together with biochemical and biophysical studies, reveals distinct roles for residues implicated in host shutoff. Our results further confirm that SOX and the host exoribonuclease Xrn1 act in concert to elicit the rapid degradation of mRNA substrates observed in vivo, and that the activities of the two ribonucleases are co-ordinated.

The study of a collection of metabolites as a whole (metabolome), as opposed to isolated small molecules, is a fast-growing field promising to take us one step further towards understanding cell biology, and relating the genetic capabilities of an organism to its observed phenotype. The new sciences of metabolomics and metabonomics can exploit a variety of existing experimental and computational methods, but they also require new technology that can deal with both the amount and the diversity of the data relating to the rich world of metabolites. More specifically, the collaboration between bioinformaticians and chemoinformaticians promises to advance our view of cognate molecules, by shedding light on their atomic structure and properties. Modelling of the interactions of metabolites with other entities in the cell, and eventually complete modelling of reaction pathways will be essential for analysis of the experimental data, and prediction of an organism's response to environmental challenges.

In a cell, there are many possibilities for cross interactions between enzymes and small molecules, arising from the similarities in the structures of the metabolites and the flexibility in binding of protein active sites. Despite this promiscuity, the cognate partners must be able to recognize each other in vivo, for the cell to function efficiently. This study examines the basis of this selectivity in recognition using standard docking calculations and finds significant improvement when proteins and ligands are cross-docked. We find that cognate molecules rarely form the most stable complexes and that specificity may be driven either by recognition of the substrate by the enzyme or the recognition of the enzyme by the substrate. Despite limitations of the in silico methods, especially the scoring functions, these calculations highlight the need to consider cross reactions in the cell and suggest that localization and compartmentalization must be important factors in the evolution of complex cells. However, the inherent promiscuity of these interactions can also benefit an organism, by facilitating the evolution of new functions from old ones. The results also suggest that high-throughput screening should involve not just a panel of small molecules, but also a panel of proteins to test for cross-reactivity.

The Escherichia coli metabolome has been characterised using the two-dimensional structures of 745 metabolites, obtained from the EcoCyc and KEGG databases. Physicochemical properties of the metabolome have been calculated to provide an overview of this set of cognate ligands. A library of fragments commonly found among these molecules has been employed to reveal the main constituents of metabolites, and to assist a broad classification of the metabolome into biochemically relevant classes. Fragment-based fingerprints reveal the metabolome as a continuum in the two-dimensional structural space, where clusters of molecules sharing similar scaffolds can be identified, but are generally overlapping. Nucleotide, carbohydrate and amino acid-like molecules are the most prominent, but at high levels of similarity, a more detailed classification is possible. Classification schemes for the metabolome are a promising tool for understanding the chemical diversity of the metabolome. When used in conjunction with existing classifications of the proteome, they can help to elucidate the binding preferences and promiscuity of proteins and their cognate substrates.

This work describes a method for predicting DNA binding function from structure using 3-dimensional templates. Proteins that bind DNA using small contiguous helix-turn-helix (HTH) motifs comprise a significant number of all DNA-binding proteins. A structural template library of seven HTH motifs has been created from non-homologous DNA-binding proteins in the Protein Data Bank. The templates were used to scan complete protein structures using an algorithm that calculated the root mean squared deviation (rmsd) for the optimal superposition of each template on each structure, based on C(alpha) backbone coordinates. Distributions of rmsd values for known HTH-containing proteins (true hits) and non-HTH proteins (false hits) were calculated. A threshold value of 1.6 A rmsd was selected that gave a true hit rate of 88.4% and a false positive rate of 0.7%. The false positive rate was further reduced to 0.5% by introducing an accessible surface area threshold value of 990 A2 per HTH motif. The template library and the validated thresholds were used to make predictions for target proteins from a structural genomics project.

In this review we highlight the importance of defining the untranslated parts of transcripts, and present a number of computational approaches for the discovery and quantification of alternative transcription start and poly-adenylation events in high-throughput transcriptomic data. The fate of eukaryotic transcripts is closely linked to their untranslated regions, which are determined by the position at which transcription starts and ends at a genomic locus. Although the extent of alternative transcription starts and alternative poly-adenylation sites has been revealed by sequencing methods focused on the ends of transcripts, the application of these methods is not yet widely adopted by the community. We suggest that computational methods applied to standard high-throughput technologies are a useful, albeit less accurate, alternative to the expertise-demanding 5' and 3' sequencing and they are the only option for analysing legacy transcriptomic data. We review these methods here, focusing on technical challenges and arguing for the need to include better normalization of the data and more appropriate statistical models of the expected variation in the signal.

The molecular recognition and discrimination of adenine and guanine ligand moieties in complexes with proteins have been studied using empirical observations on carefully selected crystal structures. The distribution of protein folds that bind these purines has been found to differ significantly from that across the whole PDB, but the most populated architectures and folds are also the most common in three genomes from the three different domains of life. The protein environments around the two nucleic acid bases were significantly different, in terms of the propensities of amino acid residues to be in the binding site, as well as their propensities to form hydrogen bonds to the bases. Plots of the distribution of protein atoms around the two purines clearly show different clustering of hydrogen bond donors and acceptors opposite complimentary acceptors and donors in the rings, with hydrophobic areas below and above the rings. However, the clustering pattern is fuzzy, reflecting the variety of ways that proteins have evolved to recognise the same molecular moiety. Furthermore, an analysis of the conservation of residues in the protein chains binding guanine shows that residues in contact with the base are in general better conserved than the rest of the chain.

The hydrogen-bonded nature of short O–H−···O contacts has been investigated by comparing the components of the intermolecular interaction energy in the O–H···O contacts present in the H2O···H2O, H2O···(OH−) and (OH−)···(OH−) model dimers, and then in the more chemically significant H2C2O4···H2C2O4, H2C2O4···(HC2O4−) and (HC2O4−)···(HC2O4−) dimers, oriented as in the crystal of KHC2O4. The energy components were computed by means of intermolecular perturbation theory (IMPT). The inter-anion contacts are found to be repulsive due to the dominant repulsive character of the electrostatic term, the strongest terms in all dimers analyzed in this work. The same term is attractive in the neutral O–H···O and even stronger in the charge assisted O–H···O− contacts, whose hydrogen-bonded character is well known.

A model intermolecular potential for oxalic acid is derived from the properties of the isolated molecule. Various methods of obtaining an isotropic atom−atom repulsion potential from the overlap of...

The overlap of the monomer charge distributions is used to derive atom-atom intermolecular repulsion models for N ··· H and N ··· O to model the hydrogen bonds formed between methylcyanide/methanol and pyridine/methanol. The use of the overlap approximation allows the repulsion contribution to be assigned to specific atomic pairs, and the anisotropic form to be derived analytically. The resulting models are tested against intermolecular perturbation theory calculations of the exchange—repulsion surface, in the regions sampled by these N ··· H—O hydrogen bonds in molecular crystal structures. Such models readily reproduce the repulsion surface within a 5% rms error, with the main error being attributable to the overlap approximation. The anisotropy of the atom-atom repulsion in these hydrogen bonds is small and the expansion converges slowly. However, the methodology shows that considering the overlap is a useful method of assigning the repulsion between organic molecules into atomic contributions and esti...

The directionality and relative strengths of hydrogen bonds to monocyclic aromatic heterocycles were investigated using crystal structure data and theoretical calculations. Surveys of the Cambridge Structural Database for hydrogen bonds between C(sp3)(SINGLE BOND)O(SINGLE BOND)H and aromatic fragments containing one or more nitrogen and/or oxygen heteroatoms showed that hydrogen bonds to nitrogen atoms are much more abundant than to oxygen. Distinct preferred orientations were also revealed in these surveys. Theoretical calculations were performed on the interaction of methanol with pyridine, pyrimidine, pyrazine, pyridazine, oxazole, isoxazole, 1,2,4-oxadiazole, and furan as models for the heterocyclic fragments. The intermolecular potential surface was thoroughly scanned using a model potential that accurately described the electrostatic forces (derived from distributed multipole analysis) with empirical parameters for the repulsion and dispersion terms. Minima on this surface agreed well with the observed orientations in the data base and they were typically deeper for nitrogen than for oxygen acceptors, although the hydrogen bond strength and geometry was influenced by other heteroatoms in the ring. These results were confirmed by highly accurate intermolecular perturbation theory calculations, which also estimated the deviations from hydrogen bonding in the traditional nitrogen lone pair direction that could occur with negligible reduction in the interaction energy. © 1997 John Wiley & Sons, Inc. J Comput Chem18: 2060–2074, 1997

The difference in the hydrogen bond acceptor strengths of phenol and anisole in their planar and perpendicular conformation has been studied to assess the effect of conjugation with a π system on the acceptor strength of an oxygen atom. Intermolecular perturbation theory calculations were used, together with an analysis of hydrogen bond formation in molecular crystal structures. Anisoles and phenols form hydrogen bonds which are intermediate in strength between those of furan and tetrahydrofuran, and are a few kJ/mol stronger for the perpendicular conformations than the planar conformations. This can be partly attributed to the change in the oxygen charge distribution with conformation.

The search for druggable pockets on the surface of a protein is often performed on a single conformer, treated as a rigid body. Transient druggable pockets may be missed in this approach. Here, we describe a methodology for systematic in silico analysis of surface clefts across multiple conformers of the metastable protein α(1)-antitrypsin (A1AT). Pathological mutations disturb the conformational landscape of A1AT, triggering polymerisation that leads to emphysema and hepatic cirrhosis. Computational screens for small molecule inhibitors of polymerisation have generally focused on one major druggable site visible in all crystal structures of native A1AT. In an alternative approach, we scan all surface clefts observed in crystal structures of A1AT and in 100 computationally produced conformers, mimicking the native solution ensemble. We assess the persistence, variability and druggability of these pockets. Finally, we employ molecular docking using publicly available libraries of small molecules to explore scaffold preferences for each site. Our approach identifies a number of novel target sites for drug design. In particular one transient site shows favourable characteristics for druggability due to high enclosure and hydrophobicity. Hits against this and other druggable sites achieve docking scores corresponding to a K(d) in the µM-nM range, comparing favourably with a recently identified promising lead. Preliminary ThermoFluor studies support the docking predictions. In conclusion, our strategy shows considerable promise compared with the conventional single pocket/single conformer approach to in silico screening. Our best-scoring ligands warrant further experimental investigation.

Native, metastable serpins inherently tend to undergo stabilizing conformational transitions in mechanisms of health (e.g., enzyme inhibition) and disease (serpinopathies). This intrinsic tendency is modifiable by ligand binding, thus structure-based drug design is an attractive strategy in the serpinopathies. This can be viewed as a labor-intensive approach, and historically, its intellectual attractiveness has been tempered by relatively limited success in development of drugs reaching clinical practice. However, the increasing availability of a range of powerful experimental systems and higher-throughput techniques is causing academic and early-stage industrial pharmaceutical approaches to converge. In this review, we outline the different systems and techniques that are bridging the gap between what have traditionally been considered distinct disciplines. The individual methods are not serpin-specific. Indeed, many have only recently been applied to serpins, and thus investigators in other fields may have greater experience of their use to date. However, by presenting examples from our work and that of other investigators in the serpin field, we highlight how techniques with potential for automation and scaling can be combined to address a range of context-specific challenges in targeting the serpinopathies.

Book synopsis: A breakthrough guide employing knowledge that unites cheminformatics and bioinformatics as innovation for the future Bridging the gap between cheminformatics and bioinformatics for the first time, Computational Approaches in Cheminformatics and Bioinformatics provides insight on how to blend these two sciences for progressive research benefits. It describes the development and evolution of these fields, how chemical information may be used for biological relations and vice versa, the implications of these new connections, and foreseeable developments in the future. Using algorithms and domains as workflow tools, this revolutionary text drives bioinformaticians to consider chemical structure, and similarly, encourages cheminformaticians to consider large biological systems such as protein targets and networks.

A new 1.8 Å resolution structure of α1-antitrypsin demonstrates structural variability within an allosteric site in the molecule., The intrinsic propensity of α1-antitrypsin to undergo conformational transitions from its metastable native state to hyperstable forms provides a motive force for its antiprotease function. However, aberrant conformational change can also occur via an intermolecular linkage that results in polymerization. This has both loss-of-function and gain-of-function effects that lead to deficiency of the protein in human circulation, emphysema and hepatic cirrhosis. One of the most promising therapeutic strategies being developed to treat this disease targets small molecules to an allosteric site in the α1-antitrypsin molecule. Partial filling of this site impedes polymerization without abolishing function. Drug development can be improved by optimizing data on the structure and dynamics of this site. A new 1.8 Å resolution structure of α1-antitrypsin demonstrates structural variability within this site, with associated fluctuations in its upper and lower entrance grooves and ligand-binding characteristics around the innermost stable enclosed hydrophobic recess. These data will allow a broader selection of chemotypes and derivatives to be tested in silico and in vitro when screening and developing compounds to modulate conformational change to block the pathological mechanism while preserving function.

Our main objective was to compile a data set of high-quality protein-fragment complexes and make it publicly available. Once assembled, the data set was challenged using docking procedures to address the following questions: (i) Can molecular docking correctly reproduce the experimentally solved structures? (ii) How thorough must the sampling be to replicate the experimental data? (iii) Can commonly used scoring functions discriminate between the native pose and other energy minima? The data set, named SERAPhiC (Selected Fragment Protein Complexes), is publicly available in a ready-to-dock format ( http://www.iit.it/en/drug-discovery-and-development/seraphic.html ). It offers computational medicinal chemists a reliable test set for both in silico protocol assessment and software development.

Molecular recognition between proteins and their interacting partners underlies the biochemistry of living organisms. Specificity in this recognition is thought to be essential, whereas promiscuity is often associated with unwanted side effects, poor catalytic properties and errors in biological function. Recent experimental evidence suggests that promiscuity, not only in interactions but also in the actual function of proteins, is not as rare as was previously thought. This has implications not only for our fundamental understanding of molecular recognition and how protein function has evolved over time but also in the realm of biotechnology. Understanding protein promiscuity is becoming increasingly important not only to optimize protein engineering applications in areas as diverse as synthetic biology and metagenomics but also to lower attrition rates in drug discovery programs, identify drug interaction surfaces less susceptible to escape mutations and potentiate the power of polypharmacology.

Protein ligand docking has recently been investigated as a tool for protein function identification, with some success in identifying both known and unknown substrates of proteins. However, identifying a protein's substrate when cross-docking a large number of enzymes and their cognate ligands remains a challenge. To explore a more limited yet practically important and timely problem in more detail, we have used docking for identifying the substrates of a single protein family with remarkable substrate diversity, the short-chain dehydrogenases/reductases. We examine different protocols for identifying candidate substrates for 27 short-chain dehydrogenase/reductase proteins of known catalytic function. We present the results of docking > 900 metabolites from the human metabolome to each of these proteins together with their known cognate substrates and products, and we investigate the ability of docking to (a) reproduce a viable binding mode for the substrate and (b) to rank the substrate highly amongst the dataset of other metabolites. In addition, we examine whether our docking results provide information about the nature of the substrate, based on the best-scoring metabolites in the dataset. We compare two different docking methods and two alternative scoring functions for one of the docking methods, and we attempt to rationalise both successes and failures. Finally, we introduce a new protocol, whereby we dock only a set of representative structures (medoids) to each of the proteins, in the hope of characterising each binding site in terms of its ligand preferences, with a reduced computational cost. We compare the results from this protocol with our original docking experiments, and we find that although the rank of the representatives correlates well with the mean rank of the clusters to which they belong, a simple structure-based clustering is too naïve for the purpose of substrate identification. Many clusters comprise ligands with widely varying affinities for the same protein; hence important candidates can be missed if a single representative is used.

PROCOGNATE is a database of protein cognate ligands for the domains in enzyme structures as described by CATH, SCOP and Pfam, and is available as an interactive website or a flat file. This article gives an overview of the database and its generation and presents a new website front end, as well as recent increased coverage in our dataset via inclusion of Pfam domains. We also describe navigation of the website and its features. The current version (1.3) of PROCOGNATE covers 4123, 4536, 5876 structures and 377, 326, 695 superfamilies/families in CATH, SCOP and Pfam, respectively. PROCOGNATE can be accessed at: http://www.ebi.ac.uk/thornton-srv/databases/procognate/

Here, we present an automatic assignment of potential cognate ligands to domains of enzymes in the CATH and SCOP protein domain classifications on the basis of structural data available in the wwPDB. This procedure involves two steps; firstly, we assign the binding of particular ligands to particular domains; secondly, we compare the chemical similarity of the PDB ligands to ligands in KEGG in order to assign cognate ligands. We find that use of the Enzyme Commission (EC) numbers is necessary to enable efficient and accurate cognate ligand assignment. The PROCOGNATE database currently has cognate ligand mapping for 3277 (4118) protein structures and 351 (302) superfamilies, as described by the CATH and (SCOP) databases, respectively. We find that just under half of all ligands are only and always bound by a single domain, with 16% bound by more than one domain and the remainder of the ligands showing a variety of binding modes. This finding has implications for domain recombination and the evolution of new protein functions. Domain architecture or context is also found to affect substrate specificity of particular domains, and we discuss example cases. The most popular PDB ligands are all found to be generic components of crystallisation buffers, highlighting the non-cognate ligand problem inherent in the PDB. In contrast, the most popular cognate ligands are all found to be universal cellular currencies of reducing power and energy such as NADH, FADH2 and ATP, respectively, reflecting the fact that the vast majority of enzymatic reactions utilise one of these popular co-factors. These ligands all share a common adenine ribonucleotide moiety, suggesting that many different domain superfamilies have converged to bind this chemical framework.

As enzymes evolve and diverge from common ancestor sequences, they often keep their overall reaction chemistry but specialise in the binding of different cognate ligands. This study borrows methods for the computational assessment of 2D similarity of small molecules from the field of chemoinformatics, to examine the extent of structure conservation of cognate ligands binding to similar proteins. Proteins from 87 structural superfamilies from Escherichia coli form the core dataset, which is extended using homologues with functional assignments from any organism. We find that correlation of the substrate similarity with protein similarity (measured by either sequence-based or structure-based scores) can only be clearly established for very similar proteins. At low sequence identities, the superfamily to which a protein belongs can give helpful clues to its function, and more importantly, the confidence attached to such clues is superfamily-dependent. Our data indicate that only a few superfamilies show great substrate diversity, and that most exhibit conservation of at least part of the structural scaffold of the substrate.