novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model

To increase the number of value-added chemicals that can be produced by metabolic engineering and synthetic biology, constructing metabolic space with novel reactions/pathways is crucial. However, with the large number of reactions that existed in the metabolic space and complicated metabolisms within hosts, identifying novel pathways linking two molecules or heterologous pathways when engineering a host to produce a target molecule is an arduous task. Hence, we built a user-friendly web server, novoPathFinder, which has several features: (i) enumerate novel pathways between two specified molecules without considering hosts; (ii) construct heterologous pathways with known or putative reactions for producing target molecule within Escherichia coli or yeast without giving precursor; (iii) estimate novel pathways with considering several categories, including enzyme promiscuity, Synthetic Complex Score (SCScore) and LD50 of intermediates, overall stoichiometric conversions, pathway length, theoretical yields and thermodynamic feasibility. According to the results, novoPathFinder is more capable to recover experimentally validated pathways when comparing other rule-based web server tools. Besides, more efficient pathways with novel reactions could also be retrieved for further experimental exploration. novoPathFinder is available at http://design.rxnfinder.org/novopathfinder/

Osteogenesis and Antibacterial Activity of Graphene Oxide and Dexamethasone Coatings on Porous Polyetheretherketone via Polydopamine-Assisted Chemistry

Endowing implants with antibacterial ability and osteogenic ability plays important roles in preventing post-operative bacterial contamination and facilitating integration between implants and osseous tissue, consequently reducing implant failure rates. In this study, we develop a facile and versatile strategy with dopamine as an auxiliary for construction of dexamethasone (Dex)/liposome porous coatings. In detail, the surfaces of sulfonated polyetheretherketone (SP) plates are coated with polydopamine firstly and then modified with graphene oxide (GO) and dexamethasone (Dex)-loaded liposome, which is verified by contact angle, X-ray photoelectron spectroscopy (XPS), attenuated total reflection infrared (ATR), and Raman spectra. The results of our study suggest that the GO and Dex are successfully coated on the samples’ surfaces. In vitro cell attachment, growth, differentiation, and apatite deposition tests all illustrate that the substrate coated with GO and Dex can significantly accelerate the proliferation and osteogenic differentiation of MC3T3 cells compared with the pristine sulfonated polyetheretherketone (PEEK). Additionally, it exhibits acceptable antibacterial activity against E. coli and S. aureus in vitro. Altogether, our results demonstrate that the modified GO- and Dex-loaded substrates are endowed with impressive biocompatibility and certain antibacterial qualities, making it possible for future application as a perspective implant material

PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways

The current network pharmacology study encountered a bottleneck with a lot of public data scattered in different databases. There is a lack of an open-access and consolidated platform that integrates this information for systemic research. To address this issue, we have developed PhID, an integrated pharmacology database which integrates >400 000 pharmacology elements (drug, target, disease, gene, side-effect, and pathway) and >200 000 element interactions in branches of public databases. PhID has three major applications: (1) assisting scientists searching through the overwhelming amount of pharmacology element interaction data by names, public IDs, molecule structures, or molecular substructures; (2) helping visualizing pharmacology elements and their interactions with a web-based network graph; and (3) providing prediction of drug–target interactions through two modules: PreDPI-ki and FIM, by which users can predict drug–target interactions of PhID entities or some drug–target pairs of their own interest. To get a systems-level understanding of drug action and disease complexity, PhID as a network pharmacology tool was established from the perspective of data layer, visualization layer, and prediction model layer to present information untapped by current databases

AddictedChem: A Data-Driven Integrated Platform for New Psychoactive Substance Identification

The mechanisms underlying drug addiction remain nebulous. Furthermore, new psychoactive substances (NPS) are being developed to circumvent legal control; hence, rapid NPS identification is urgently needed. Here, we present the construction of the comprehensive database of controlled substances, AddictedChem. This database integrates the following information on controlled substances from the US Drug Enforcement Administration: physical and chemical characteristics; classified literature by Medical Subject Headings terms and target binding data; absorption, distribution, metabolism, excretion, and toxicity; and related genes, pathways, and bioassays. We created 29 predictive models for NPS identification using five machine learning algorithms and seven molecular descriptors. The best performing models achieved a balanced accuracy (BA) of 0.940 with an area under the curve (AUC) of 0.986 for the test set and a BA of 0.919 and an AUC of 0.968 for the external validation set, which were subsequently used to identify potential NPS with a consensus strategy. Concurrently, a chemical space that included the properties of vectorised addictive compounds was constructed and integrated with AddictedChem, illustrating the principle of diversely existing NPS from a macro perspective. Based on these potential applications, AddictedChem could be considered a highly promising tool for NPS identification and evaluation

ChemHub: a knowledgebase of functional chemicals for synthetic biology studies

The field of synthetic biology lacks a comprehensive knowledgebase for selecting synthetic target molecules according to their functions, economic applications and known biosynthetic pathways. We implemented ChemHub, a knowledgebase containing >90 000 chemicals and their functions, along with related biosynthesis information for these chemicals that was manually extracted from >600 000 published studies by more than 100 people over the past 10 years

EcoSynther: A Customized Platform To Explore the Biosynthetic Potential in <i>E. coli</i>

Developing computational tools for a chassis-centered biosynthetic pathway design is very important for a productive heterologous biosynthesis system by considering enormous foreign biosynthetic reactions. For many cases, a pathway to produce a target molecule consists of both native and heterologous reactions when utilizing a microbial organism as the host organism. Due to tens of thousands of biosynthetic reactions existing in nature, it is not trivial to identify which could be served as heterologous ones to produce the target molecule in a specific organism. In the present work, we integrate more than 10,000 <i>E. coli</i> non-native reactions and utilize a probability-based algorithm to search pathways. Moreover, we built a user-friendly Web server named EcoSynther. It is able to explore the precursors and heterologous reactions needed to produce a target molecule in <i>Escherichia coli K12 MG1655</i> and then applies flux balance analysis to calculate theoretical yields of each candidate pathway. Compared with other chassis-centered biosynthetic pathway design tools, EcoSynther has two unique features: (1) allow for automatic search without knowing a precursor in <i>E. coli</i> and (2) evaluate the candidate pathways under constraints from <i>E. coli</i> physiological states and growth conditions. EcoSynther is available at http://www.rxnfinder.org/ecosynther/