Structure-based drug design identifies novel LPA3 antagonists

Compound 5 ([5-(3-nitrophenoxy)-1,3-dioxo-1,3-dihydro-2-isoindol-2-yl]acetic acid) was identified as a weak selective LPA₃ antagonist (IC₅₀ = 4504 nM) in a virtual screening effort to optimize a dual LPA_{2 and 3} antagonist. Structure-based drug design techniques were used to prioritize similarity search matches of compound 5. This strategy rapidly identified 10 novel antagonists. The two most efficacious compounds identified inhibit activation of the LPA₃ receptor by 200 nM LPA with IC₅₀ values of 752 nM and 2992 nM. These compounds additionally define changes to our previously reported pharmacophore that will improve its ability to identify more potent and selective LPA₃ receptor antagonists. The results of the combined computational and experimental screening are reported.     

Interaction between Intrinsically Disordered Proteins Frequently Occurs in a Human Protein-Protein Interaction Network

Intrinsic protein disorder is a widespread phenomenon characterised by a lack of stable three-dimensional structures and is considered to play an important role in protein-protein interactions (PPIs). This study examined the genome-wide preference of disorder in PPIs by using exhaustive disorder prediction in human PPIs. We categorised the PPIs into three types (interaction between disordered proteins, interaction between structured proteins, and interaction between a disordered protein and a structured protein) with regard to the flexibility of molecular recognition and compared these three interaction types in an existing human PPI network with those in a randomised network. Although the structured regions were expected to become the identifiers for binding recognition, this comparative analysis revealed unexpected results. The occurrence of interactions between disordered proteins was significantly frequent, and that between a disordered protein and a structured protein was significantly infrequent. We found that this propensity was much stronger in interactions between nonhub proteins. We also analysed the interaction types from a functional standpoint by using GO, which revealed that the interaction between disordered proteins frequently occurred in cellular processes, regulation, and metabolic processes. The number of interactions, especially in metabolic processes between disordered proteins, was 1.8 times as large as that in the randomised network. Another analysis conducted by using KEGG pathways provided results where several signaling pathways and disease-related pathways included many interactions between disordered proteins. All of these analyses suggest that human PPIs preferably occur between disordered proteins and that the flexibility of the interacting protein pairs may play an important role in human PPI networks.     

Ab initio protein modelling reveals novel human MIT domains

Database searches can fail to detect all truly homologous sequences, particularly when dealing with short, highly sequence diverse protein families. Here, using microtubule interacting and transport (MIT) domains as an example, we have applied an approach of profile-profile matching followed by ab initio structure modelling to the detection of true homologues in the borderline significant zone of database searches. Novel MIT domains were confidently identified in USP54, containing an apparently inactive ubiquitin carboxyl-terminal hydrolase domain, a katanin-like ATPase KATNAL1, and an uncharacterized protein containing a VPS9 domain. As a proof of principle, we have confirmed the novel MIT annotation for USP54 by in vitro profiling of binding to CHMP proteins.

Structured summary

USP8 binds:CHMPs 1A 1B 2A 2B 4CUSP54 binds:CHMPs 1B 2A 2B 4C 6     

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large‐scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2‐DE/MS‐based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2‐DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2‐DE experiments with MS‐data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our “PROTEOMER” database is its high cross‐referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross‐referencing can transform information into biological knowledge.     

A database of computational models of a half-center oscillator for analyzing how neuronal parameters influence network activity

A half-center oscillator (HCO) is a common circuit building block of central pattern generator networks that produce rhythmic motor patterns in animals. Here we constructed an efficient relational database table with the resulting characteristics of the Hill et al.’s (J Comput Neurosci 10:281–302, 2001) HCO simple conductance-based model. The model consists of two reciprocally inhibitory neurons and replicates the electrical activity of the oscillator interneurons of the leech heartbeat central pattern generator under a variety of experimental conditions. Our long-range goal is to understand how this basic circuit building block produces functional activity under a variety of parameter regimes and how different parameter regimes influence stability and modulatability. By using the latest developments in computer technology, we simulated and stored large amounts of data (on the order of terabytes). We systematically explored the parameter space of the HCO and corresponding isolated neuron models using a brute-force approach. We varied a set of selected parameters (maximal conductance of intrinsic and synaptic currents) in all combinations, resulting in about 10 million simulations. We classified these HCO and isolated neuron model simulations by their activity characteristics into identifiable groups and quantified their prevalence. By querying the database, we compared the activity characteristics of the identified groups of our simulated HCO models with those of our simulated isolated neuron models and found that regularly bursting neurons compose only a small minority of functional HCO models; the vast majority was composed of spiking neurons.     

The RNA expression signature of the HepG2 cell line as determined by the integrated analysis of miRNA and mRNA expression profiles

Understanding miRNAs' regulatory networks and target genes could facilitate the development of therapies for human diseases such as cancer. Although much useful gene expression profiling data for tumor cell lines is available, microarray data for miRNAs and mRNAs in the human HepG2 cell line have only been compared with that of other cell lines separately. The relationship between miRNAs and mRNAs in integrated expression profiles for HepG2 cells is still unknown. To explore the miRNA–mRNA correlations in hepatocellular carcinoma (HCC) cells, we performed miRNA and mRNA expression profiling in HepG2 cells and normal liver HL-7702 cells at the genome scale using next-generation sequencing technology. We identified 193 miRNAs that are differentially expressed in these two cell lines. Of these, 89 miRNAs were down-regulated in HepG2 cells compared with HL-7702 cells, while 104 miRNAs were up-regulated. We also observed 3035 mRNAs that are significantly dys-regulated in HepG2 cells. We then performed an integrated analysis of the expression data for differentially expressed miRNAs and mRNAs and found several miRNA–mRNA pairs that are significantly correlated in HepG2 cells. Further analysis suggested that these differentially expressed genes were enriched in four tumorigenesis-related signaling pathways, namely, ErbB, JAK–STAT, mTOR, and WNT, which until now had not been fully reported. Our results could be helpful in understanding the mechanisms of HCC occurrence and development.     

Challenges in assessing pathogenicity based on frequency of variants in mismatch repair genes: an extreme case of a MSH2 variant and a meta-analysis

The clinical interpretation of variants in mismatch repair (MMR) genes associated with Lynch syndrome can be confusing when the functional nature of the variant is not clearly defined. We report an extreme case where a polymorphism in the MSH2 gene which had a low minor allele frequency, was misclassified as a mutation based on low evidential methods in the database and previous publications. We expanded this experience to perform a systematic meta-analysis in order to investigate other variants that have potentially been misclassified. Our results suggested that the interpretation of pathogenicity should be more cautious and emphasized the need for solid validation through multiple analyses including functional analysis for variants in MMR genes.     

Recent advances in biocatalyst discovery,development and applications

Enzymes catalyze a wide range of biotransformations and have a great potential as environmentally friendly alternatives to classical chemical catalysts in various industrial applications. Recently, advanced techniques and strategies in enzyme discovery and engineering have led to the significant expansion of the quantity and functional diversity of biocatalysts, which has further allowed broader uses of biocatalysts in new processes, especially those traditionally enabled only by chemical catalysts. Here we highlight some of these recent advances with the focus on new approaches in biocatalyst discovery and development, and discuss new applications of selected biocatalysts including transaminases, cytochrome P450s, and Baeyer–Villiger monooxygenases.