In vitro prototyping and rapid optimization of biosynthetic enzymes for cell design.

The design and optimization of biosynthetic pathways for industrially relevant, non-model organisms is challenging due to transformation idiosyncrasies, reduced numbers of validated genetic parts and a lack of high-throughput workflows. Here we describe a platform for in vitro prototyping and rapid optimization of biosynthetic enzymes (iPROBE) to accelerate this process. In iPROBE, cell lysates are enriched with biosynthetic enzymes by cell-free protein synthesis and then metabolic pathways are assembled in a mix-and-match fashion to assess pathway performance. We demonstrate iPROBE by screening 54 different cell-free pathways for 3-hydroxybutyrate production and optimizing a six-step butanol pathway across 205 permutations using data-driven design. Observing a strong correlation (r = 0.79) between cell-free and cellular performance, we then scaled up our highest-performing pathway, which improved in vivo 3-HB production in Clostridium by 20-fold to 14.63 ± 0.48 g l-1. We expect iPROBE to accelerate design-build-test cycles for industrial biotechnology.

Network measures for chemical library design.

In this overview, we examine recent developments in network approaches to drug design. A brief overview of networks is followed by a discussion of how chemical similarity networks and their properties address challenges in drug design. Multiple methods used to assess or enhance chemical diversity for early-stage drug discovery are discussed, as well as methods that can be used for drug repositioning and ligand polypharmacology.

Outcome of antegrade continent enema (ACE) procedures in children and young adults.

OBJECTIVE: Intractable incontinence affects a large number of children and young adults in the US. The goal of this study is to evaluate the long-term outcomes of surgical access for administration of antegrade continence enemas (ACE) in affected children and young adults. METHODS: Patients who underwent surgical procedure to enable administration of ACE from 1994 to 2011 were retrospectively reviewed. Data collected included patient demographics, primary diagnosis, surgical technique, conduit used, complications, follow-up duration, and social continence. RESULTS: Sixty eighty patients underwent surgery to enable ACE; mean follow up was 61 months. Enteral conduit (EC) was performed in 19 patients, tube cecostomy catheters (CC) in 49. Meningomyelocele was diagnosed in 60% of patients. Mean age was 11 (1.67-53) years. Complications included tube dislodgement (43%), granulation tissue (46%), site infection (13%), leakage (32%), break in the tube (6%) and tract stenosis (6%). Complete social continence was achieved in 68%, partial continence was achieved in 29%, and no benefit was achieved in 3% of patients. The rate of complications and incontinence resolution following CC was 78% and 66%, and following EC 89% and 74%. The differences were not statistically significant. CC patients developed granulation tissue more frequently (53%) and leaks of fecal material less frequently (20%) compared to EC patients (26% and 53%) (p < 0.05 and < 0.01). Although children 7 years or younger developed more overall complications (94%) than older patients (69%; p < 0.05), there was not a significant difference in the frequency of any one complication or in the rate of continence, between the two groups. Multivariate analysis showed that EC is three times more likely to be complicated by fecal leakage. CC patients are at greater risk to develop granulation tissue (p < 0.05). CONCLUSIONS: Most patients achieved social continence and improved hygiene with the aid of ACE. Younger children also benefited greatly from institution of ACE. CC was associated with fewer major complications such as leak of fecal contents than EC but required regular tube changes.

Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers.

Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle-filled polymers is demonstrated. A key component of this "Materials Genomics" approach is the development and use of Materials Quantitative Structure-Property Relationship (MQSPR) models trained on atomic-level features of nanofiller and polymer constituents and used to predict the polar and dispersive components of their surface energies. Surface energy differences are then correlated with the nanofiller dispersion morphology and filler/matrix interface properties and integrated into a numerical analysis approach that allows the prediction of thermomechanical properties of the spherical nanofilled polymer composites. Systematic experimental studies of silica nanoparticles modified with three different surface chemistries in polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethyl methacrylate) (PEMA) and poly(2-vinyl pyridine) (P2VP) are used to validate the models. While demonstrated here as effective for the prediction of meso-scale morphologies and macro-scale properties under quasi-equilibrium processing conditions, the protocol has far ranging implications for Virtual Design.

Graphs and networks in chemical and biological informatics: past, present and future.

Chemical and biological network analysis has recently garnered intense interest from the perspective of drug design and discovery. While graph theoretic concepts have a long history in chemistry - predating quantum mechanics - and graphical measures of chemical structures date back to the 1970s, it is only recently with the advent of public repositories of information and availability of high-throughput assays and computational resources that network analysis of large-scale chemical networks, such as protein-protein interaction networks, has become possible. Drug design and discovery are undergoing a paradigm shift, from the notion of 'one target, one drug' to a much more nuanced view that relies on multiple sources of information: genomic, proteomic, metabolomic and so on. This holistic view of drug design is an incredibly daunting undertaking still very much in its infancy. Here, we focus on current developments in graph- and network-centric approaches in chemical and biological informatics, with particular reference to applications in the fields of SAR modeling and drug design. Key insights from the past suggest a path forward via visualization and fusion of multiple sources of chemical network data.

Predictive cheminformatics in drug discovery: statistical modeling for analysis of micro-array and gene expression data.

The vast amounts of chemical and biological data available through robotic high-throughput assays and micro-array technologies require computational techniques for visualization, analysis, and predictive -modeling. Predictive cheminformatics and bioinformatics employ statistical methods to mine this data for hidden correlations and to retrieve molecules or genes with desirable biological activity from large databases, for the purpose of drug development. While many statistical methods are commonly employed and widely accessible, their proper use involves due consideration to data representation and preprocessing, model validation and domain of applicability estimation, similarity assessment, the nature of the structure-activity landscape, and model interpretation. This chapter seeks to review these considerations in light of the current state of the art in statistical modeling and to summarize the best practices in predictive cheminformatics.

Annexin-V promotes anti-tumor immunity and inhibits neuroblastoma growth in vivo.

The goal of the current study is to determine the effects of blocking phosphatidylserine (PS) on the growth of neuroblastoma in mice. PS, an anionic phospholipid restricted to the cytoplasmic surface of plasma membranes in most cells, is externalized to the surface of apoptotic cells. PS has been shown to induce immune tolerance to self-antigens. PS can also be found on the surface of live cells and in particular tumor cells. Annexin-V (AnV) is a protein that specifically binds and blocks PS. To determine the effects of blocking PS with AnV on tumor growth and immunogenicity, mice were inoculated with AGN2a, a poorly immunogenic murine neuroblastoma that expresses high level of PS on the cell surface. Survival and anti-tumor T cell response were determined. AGN2a were engineered to secrete AnV. Secreted protein effectively blocked tumor PS. 40 % of mice inoculated with AnV-expressing AGN2a cells survived free of tumor, whereas none of the mice inoculated with control cells survived (p = 0.0062). The benefits of AnV were lost when mice were depleted of T cells. The findings suggest that AnV could protect mice from tumor challenge through an immune mediated mechanism. Mice were then immunized with irradiated AnV-secreting or control cells, and challenged with wild-type AGN2a cells. AnV-secreting cell vaccine protected 80 % of mice from AGN2a challenge, while control cell vaccine prevented tumor growth in only 30 % of animals (p = 0.012). ELISPOT analysis demonstrated that AnV-secreting cell vaccine induced a greater frequency of interferon-gamma producing splenic T cells. T cells isolated from mice immunized with AnV-secreting but not control vaccine lysed AGN2a. In summary, AnV blocked PS, enhanced T cell mediated tumor immunity, and inhibited tumor growth.

Exploiting domain knowledge for improved quantitative high-throughput screening curve fitting.

Least-squares fitting of the Hill equation to quantitative high-throughput screening (qHTS) assays results in frequent unsatisfactory fits. We learn and exploit prior knowledge to improve the Hill fitting in a nonlinear regression method called domain knowledge fitter (DK-fitter). This paper formulates and solves DK-fitter for 44 public qHTS data sets. This new Hill parameter estimation technique is validated using three unbiased approaches, including a novel method that involves generating simulated samples. This paper fosters the extraction of higher quality information from screens for improved potency evaluation.

Exploration of the topology of chemical spaces with network measures.

Discontinuous changes in molecular structure (resulting from continuous transformations of molecular coordinates) lead to changes in chemical properties and biological activities that chemists attempt to describe through structure-activity or structure-property relationships (QSAR/QSPR). Such relationships are commonly envisioned in a continuous high-dimensional space of numerical descriptors, referred to as chemistry space. The choice of descriptors defining coordinates within chemistry space and the choice of similarity metrics thus influence the partitioning of this space into regions corresponding to local structural similarity. These are the regions (known as domains of applicability) most likely to be successfully modeled by a structure-activity relationship. In this work the network topology and scaling relationships of chemistry spaces are first investigated independent of a specific biological activity. Chemistry spaces studied include the ZINC data set, a qHTS PubChem bioassay, as well as the space of protein binding sites from the PDB. The characteristics of these networks are compared and contrasted with those of the bioassay SALI subnetwork, which maps discontinuities or cliffs in the structure-activity landscape. Mapping the locations of activity cliffs and comparing the global characteristics of SALI subnetworks with those of the underlying chemistry space networks generated using different representations, can guide the choice of a better representation. A higher local density of SALI edges with a particular representation indicates a more challenging structure-activity relationship using that fingerprint in that region of chemistry space.

Outcomes of plastic closure in gastroschisis.

BACKGROUND: Gastroschisis is a congenital abdominal wall defect in which the intestines develop outside the abdomen and are exposed to amniotic fluid. When the defect is small, lymphatic, venous, and intestinal obstruction may occur and contribute to the formation of intestinal edema, atresia, ischemia, and a thick inflammatory peel. Treatment requires early coverage of abdominal contents either by primary closure or by the placement of temporary Silastic silo followed by abdominal wall closure. Currently, both traditional suture closure and the sutureless plastic closure are being employed to repair the gastroschisis defect. The goal of the current study is to evaluate plastic closure. We predict no difference will be found in clinical outcomes between plastic closure and traditional suture closure. METHODS: A retrospective review of 80 patients treated between 2000 and 2009 was performed. Plastic closure was used in 52 (65%) and traditional suture closure in 28 (35%) babies. The surgical procedure was determined by surgeon preference. Of the 31(39%) babies who required silos, 15 (19%) were treated with plastic closure and 16 (20%) underwent traditional closure. We collected the following demographic data and clinical progression data. Using SAS 9.2 (SAS Institute Inc, Cary, NC), we conducted linear regression, logistic regression, and time to event models to compare the following outcomes: days on ventilator, days to start enteral feeds, days to reach goal enteral feeds, days on total parenteral nutrition, hospital charges, duration of stay, mortality, and complications. RESULTS: The mean duration of follow-up was 11.4 months. Patients spent an average of 6 days on the ventilator. There were 2 mortalities. A multivariate analysis demonstrated that no differences were found between the 2 closures with most of the outcomes; however, when compared with traditional suture closure, those babies treated with plastic closure spent 4 days fewer days on the ventilator (P < .01). Those babies who underwent suture closure were more likely to have an infection or sepsis (odds ratio, 5.15; P < .001). When the entire cohort was considered, no significant difference was found between plastic and suture closure in time to start feeds, time to reach goal feeds, time on parenteral nutrition, hospital charges, duration of stay, or complications. Ventral hernias were noted in 46 (58%) patients, 32 (62%) after plastic closure and 14 (50%) after suture closure (P = .32). Hernia repair was required in 16 (20%) patients, 11 (21%) after plastic closure, and 5 (18%) after traditional repair (P = .32). In the silo cohort, children treated with plastic closure required 7.5(P < .01) fewer days to start enteral feeds than those treated with suture closure. CONCLUSION: Plastic closure of abdominal wall defects in gastroschisis is effective both as a primary procedure and after silo placement. A multivariate analysis shows plastic closure to be associated with fewer days of mechanical ventilation and less likelihood of developing infection or sepsis.

Modeling Choices for Virtual Screening Hit Identification.

Making suitable modeling choices is crucial for successful in silico drug design, and one of the most important of these is the proper extraction and curation of data from qHTS screens, and the use of optimized statistical learning methods to obtain valid models. More specifically, we aim to learn the top-1 % most potent compounds against a variety of targets in a procedure we call virtual screening hit identification (VISHID). To do so, we exploit quantitative high-throughput screens (qHTS) obtained from PubChem, descriptors derived from molecular structures, and support vector machines (SVM) for model generation. Our results illustrate how an appreciation of subtle issues underlying qHTS data extraction and the resulting SVM models created using these data can enhance the effectiveness of solutions and, in doing so, accelerate drug discovery.

PESDserv: a server for high-throughput comparison of protein binding site surfaces.

SUMMARY: Structure-based approaches complement ligand-based approaches for lead-discovery and cross-reactivity prediction. We present to the scientific community a web server for comparing the surface of a ligand bound site of a protein against a ligand bound site surface database of 106 796 sites. The web server implements the property encoded shape distributions (PESD) algorithm for surface comparison. A typical virtual screen takes 5 min to complete. The output provides a ranked list of sites (by site similarity), hyperlinked to the corresponding entries in the PDB and PDBeChem databases. AVAILABILITY: The server is freely accessible at http://reccr.chem.rpi.edu/Software/pesdserv/

Binding affinity prediction with property-encoded shape distribution signatures.

We report the use of the molecular signatures known as "property-encoded shape distributions" (PESD) together with standard support vector machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore, a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (DeltaH/-TDeltaS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach, and further improvement in accuracy would require accounting for these components rigorously.

Bioinformatics and cheminformatics: where do the twain meet?

Bridging the domains of cheminformatics and bioinformatics in the post-genomic era requires the convergence of goals, tools, techniques and annotations. This article reviews recent research at the interface of the domains that shows evidence of this convergence. While graph theoretical representations have long been used to develop simple topological descriptions of molecules, graph theory-based network concepts are also widely employed in systems biology. Shape and conformation are important for understanding intermolecular interactions, and several structure-based cheminformatic descriptors have been developed and applied to drug-like molecules and biomolecules. Data fusion methods and shared ontologies can also help integrate data from multiple sources in order to generate a holistic picture of the shared molecular informatics domain.