RosettaDDGPrediction for high-throughput mutational scans: From stability to binding.

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

A scored human protein-protein interaction network to catalyze genomic interpretation.

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

TIMP1 overexpression mediates resistance of MCF-7 human breast cancer cells to fulvestrant and down-regulates progesterone receptor expression.

High levels of Tissue Inhibitor of Metalloproteinases-1 (TIMP1) are associated with poor prognosis, reduced response to chemotherapy, and, potentially, also poor response to endocrine therapy in breast cancer patients. Our objective was to further investigate the hypothesis that TIMP1 is associated with endocrine sensitivity. We established a panel of 11 MCF-7 subclones with a wide range of TIMP1 mRNA and protein expression levels. Cells with high expression of TIMP1 versus low TIMP1 displayed significantly reduced sensitivity to the antiestrogen fulvestrant (ICI 182,780, Faslodex®), while TIMP1 levels did not influence the sensitivity to 4-hydroxytamoxifen. An inverse correlation between expression of the progesterone receptor and TIMP1 was found, but TIMP1 levels did not correlate with estrogen receptor levels or growth-promoting effects of estrogen (estradiol, E2). Additionally, the effects of fulvestrant, 4-hydroxytamoxifen, or estrogen on estrogen receptor expression were not associated with TIMP1 levels. Gene expression analyses revealed associations between expression of TIMP1 and genes involved in metabolic pathways, epidermal growth factor receptor 1/cancer signaling pathways, and cell cycle. Gene and protein expression analyses showed no general defects in estrogen receptor signaling except from lack of progesterone receptor expression and estrogen inducibility in clones with high TIMP1. The present study suggests a relation between high expression level of TIMP1 and loss of progesterone receptor expression combined with fulvestrant resistance. Our findings in vitro may have clinical implications as the data suggest that high tumor levels of TIMP1 may be a predictive biomarker for reduced response to fulvestrant.

HExpoChem: a systems biology resource to explore human exposure to chemicals.

SUMMARY: Humans are exposed to diverse hazardous chemicals daily. Although an exposure to these chemicals is suspected to have adverse effects on human health, mechanistic insights into how they interact with the human body are still limited. Therefore, acquisition of curated data and development of computational biology approaches are needed to assess the health risks of chemical exposure. Here we present HExpoChem, a tool based on environmental chemicals and their bioactivities on human proteins with the objective of aiding the qualitative exploration of human exposure to chemicals. The chemical-protein interactions have been enriched with a quality-scored human protein-protein interaction network, a protein-protein association network and a chemical-chemical interaction network, thus allowing the study of environmental chemicals through formation of protein complexes and phenotypic outcomes enrichment. AVAILABILITY: HExpoChem is available at http://www.cbs.dtu.dk/services/HExpoChem-1.0/.

Prediction of drug efficacy for cancer treatment based on comparative analysis of chemosensitivity and gene expression data.

The NCI60 database is the largest available collection of compounds with measured anti-cancer activity. The strengths and limitations for using the NCI60 database as a source of new anti-cancer agents are explored and discussed in relation to previous studies. We selected a sub-set of 2333 compounds with reliable experimental half maximum growth inhibitions (GI(50)) values for 30 cell lines from the NCI60 data set and evaluated their growth inhibitory effect (chemosensitivity) with respect to tissue of origin. This was done by identifying natural clusters in the chemosensitivity data set and in a data set of expression profiles of 1901 genes for the corresponding tumor cell lines. Five clusters were identified based on the gene expression data using self-organizing maps (SOM), comprising leukemia, melanoma, ovarian and prostate, basal breast, and luminal breast cancer cells, respectively. The strong difference in gene expression between basal and luminal breast cancer cells was reflected clearly in the chemosensitivity data. Although most compounds in the data set were of low potency, high efficacy compounds that showed specificity with respect to tissue of origin could be found. Furthermore, eight potential topoisomerase II inhibitors were identified using a structural similarity search. Finally, a set of genes with expression profiles that were significantly correlated with anti-cancer drug activity was identified. Our study demonstrates that the combined data sets, which provide comprehensive information on drug activity and gene expression profiles of tumor cell lines studied, are useful for identifying potential new active compounds.

Dissecting spatio-temporal protein networks driving human heart development and related disorders.

Aberrant organ development is associated with a wide spectrum of disorders, from schizophrenia to congenital heart disease, but systems-level insight into the underlying processes is very limited. Using heart morphogenesis as general model for dissecting the functional architecture of organ development, we combined detailed phenotype information from deleterious mutations in 255 genes with high-confidence experimental interactome data, and coupled the results to thorough experimental validation. Hereby, we made the first systematic analysis of spatio-temporal protein networks driving many stages of a developing organ identifying several novel signaling modules. Our results show that organ development relies on surprisingly few, extensively recycled, protein modules that integrate into complex higher-order networks. This design allows the formation of a complicated organ using simple building blocks, and suggests how mutations in the same genes can lead to diverse phenotypes. We observe a striking temporal correlation between organ complexity and the number of discrete functional modules coordinating morphogenesis. Our analysis elucidates the organization and composition of spatio-temporal protein networks that drive the formation of organs, which in the future may lay the foundation of novel approaches in treatments, diagnostics, and regenerative medicine.

A human phenome-interactome network of protein complexes implicated in genetic disorders.

We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.

Regional radiation risk and vulnerability assessment by integration of mathematical modelling and GIS analysis.

The Kola Peninsula, Russian Arctic exceeds all other regions in the world in the number of nuclear reactors. The study was aimed at estimating possible radiation risks to the population in the Nordic countries in case of a severe accident in the Kola Peninsula. Two approaches were tested: (1) probabilistic analysis of modelled possible pathways of radionuclide transport and precipitation and (2) deterministic approach (case studies) for most possible or worst-case scenarios of modelled transport and deposition of radionuclides. For the general population, Finland is at most risk with respect to the Kola nuclear power plant (NPP) because of (a) relatively high population density or proximity to the radiation-risk sites and (b) rather high probability of an airflow trajectory there and precipitation. After considering the critical group, northern counties in Norway, Finland and Sweden appear to be most vulnerable. The case scenarios demonstrate that population in many counties in each country, both near and far away from a nuclear site, might be subject to high risk depending on the meteorological situation.

Environmental impact assessment of the mining and concentration activities in the Kola Peninsula, Russia by multidate remote sensing.

On the Kola Peninsula, the mining and concentration industry exerts anthropogenic impact on the environment. Tailing dumps cause airborne pollution through dusting, and waterborne pollution due to direct dumping and accidental releases. The objectives were: (1) to analyse multidate satellite images for 1964-1996 to assess the environmental pollution from the mining and concentration activity in the Kola in temporal perspective; (2) to evaluate remote sensing methods for integrated environmental impact assessment. The area of mining and industrial sites steadily expands and amounted to 94 km2 in 1996. The polluted water surface amounted to at least 150 km2 through dumping in 1978 and to 106 km2 in 1986 due to dusting. Thus, the impact from the mining and concentration activity should be reconsidered as more significant than it was officially anticipated. In the past the main mechanism of pollution was direct dumping into the lakes. Currently and in future, airborne pollution after dusting storms will dominate. The effective recultivation of the tailing dumps will be a long-term process. For effective assessment of impacts from the mining and concentration industry, remote sensing methods should be complemented by in-situ measurements, fieldwork, and mathematical modelling.