Database resources for proteomics-based analysis of cancer

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

In silico verification and parallel reaction monitoring prevalidation of potential prostate cancer biomarkers

PURPOSE

Targeted proteomics of potential biomarkers is often challenging. Hence, we developed an intermediate workflow to streamline potential urinary biomarkers of prostate cancer (PCa).

MATERIALS & METHODS

Using previously discovered potential PCa biomarkers; we selected proteotypic peptides for targeted validation. Preliminary in silico immunohistochemical and single reaction monitoring (SRM) verification was performed. Successful PTPs were then prevalidated using parallel reaction monitoring (PRM) and reconfirmed in 15 publicly available databases.

RESULTS

Stringency-based targetable potential biomarkers were shortlisted following in silico screening. PRM reveals top 12 potential biomarkers including the top ranking seven in silico verification-based biomarkers. Database reconfirmation showed differential expression between PCa and benign/normal prostatic urine samples.

CONCLUSION

The pragmatic penultimate screening step, described herein, would immensely improve targeted proteomics validation of potential disease biomarkers.

Inferring the Effects of Honokiol on the Notch Signaling Pathway in SW480 Colon Cancer Cells

In a tumor cell, the development of acquired therapeutic resistance and the ability to survive in extracellular environments that differ from the primary site are the result of molecular adaptations in potentially highly plastic molecular networks. The accurate prediction of intracellular networks in a tumor remains a difficult problem in cancer informatics. In order to make truly rational patient-driven therapeutic decisions, it will be critical to develop methodologies that can accurately infer the molecular circuitry in the cells of a specific tumor. Despite enormous heterogeneity, cellular networks elicit deterministic digital-like responses. We discuss the use and limitations of methodologies that model molecular networks in cancer cells as a digital circuit. We also develop a network model of Notch signaling in colon cancer using a novel reverse engineering logic-based method and published western blot data to elucidate the interactions likely present in the circuits of the SW480 colon cancer cell line. Within this framework, we make predictions related to the role that honokiol may be playing as an anti-cancer drug.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Systematic parameter estimation in data-rich environments for cell signalling dynamics

Motivation: Computational models of biological signalling networks, based on ordinary differential equations (ODEs), have generated many insights into cellular dynamics, but the model-building process typically requires estimating rate parameters based on experimentally observed concentrations. New proteomic methods can measure concentrations for all molecular species in a pathway; this creates a new opportunity to decompose the optimization of rate parameters.

Results: In contrast with conventional parameter estimation methods that minimize the disagreement between simulated and observed concentrations, the SPEDRE method fits spline curves through observed concentration points, estimates derivatives and then matches the derivatives to the production and consumption of each species. This reformulation of the problem permits an extreme decomposition of the high-dimensional optimization into a product of low-dimensional factors, each factor enforcing the equality of one ODE at one time slice. Coarsely discretized solutions to the factors can be computed systematically. Then the discrete solutions are combined using loopy belief propagation, and refined using local optimization. SPEDRE has unique asymptotic behaviour with runtime polynomial in the number of molecules and timepoints, but exponential in the degree of the biochemical network. SPEDRE performance is comparatively evaluated on a novel model of Akt activation dynamics including redox-mediated inactivation of PTEN (phosphatase and tensin homologue).

Availability and implementation: Web service, software and supplementary information are available at www.LtkLab.org/SPEDRE

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact: LisaTK@nus.edu.sg

Opportunities and challenges for nutritional proteomics in cancer prevention

Knowledge gaps persist about the efficacy of cancer prevention strategies based on dietary food components. Adaptations to nutrient supply are executed through tuning of multiple protein networks that include transcription factors, histones, modifying enzymes, translation factors, membrane and nuclear receptors, and secreted proteins. However, the simultaneous quantitative and qualitative measurement of all proteins that regulate cancer processes is not practical using traditional protein methodologies. Proteomics offers an attractive opportunity to fill this knowledge gap and unravel the effects of dietary components on protein networks that impinge on cancer. The articles presented in this supplement are from talks proffered in the "Nutrition Proteomics and Cancer Prevention" session at the American Institute for Cancer Research Annual Research Conference on Food, Nutrition, Physical Activity and Cancer held in Washington, DC on October 21 and 22, 2010. Recent advances in MS technologies suggest that studies in nutrition and cancer prevention may benefit from the adoption of proteomic tools to elucidate the impact on biological processes that govern the transition from normal to malignant phenotype; to identify protein changes that determine both positive and negative responses to food components; to assess how protein networks mediate dose-, time-, and tissue-dependent responses to food components; and, finally, for predicting responders and nonresponders. However, both the limited accessibility to proteomic technologies and research funding appear to be hampering the routine adoption of proteomic tools in nutrition and cancer prevention research.