Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies.

Target evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on the dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. Our derived Fi-score can be used to classify protein regions or individual structural subsets of interest and the described scoring system could be integrated into other discovery pipelines, such as protein classification databases, or applied to investigate new targets. We further demonstrated how our method can be integrated into machine learning Gaussian mixture models to predict different structural elements. Fi-score, in combination with other biophysical analytical methods depending on the research goals, could help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites. The described methodology could greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets. HIGHLIGHTSDescription and derivation of a first-of-its-kind in silico protein fingerprinting method using B-factors and dihedral angles.Derived Fi-score allows to characterise the whole protein or selected regions of interest.Demonstration how machine learning using Gaussian mixture models on Fi-scores captures and allows to predict functional protein topology elements.Fi-score is a novel method to help evaluate therapeutic targets and engineer effective biologics.Communicated by Ramaswamy H. Sarma.

In silico drug discovery for a complex immunotherapeutic target - human c-Rel protein.

Target evaluation and rational drug design rely on identifying and characterising small-molecule binding sites on therapeutically relevant target proteins. Immunotherapeutics development is especially challenging because of complex disease etiology and heterogenous nature of targets. c-Rel protein, a promising target in many human inflammatory and cancer pathologies, was selected as a case study for an effective in silico screening platform development since this transcription factor currently has no successful therapeutic inhibitors or modulators. This study introduces a novel in silico screening approach to probe binding sites using structural validation sets, molecular modelling and describes a method of a computer-aided drug design when a crystal structure is not available for the target of interest. In addition, we showed that binding sites can be analysed with the machine learning as well as molecular simulation approaches to help assess and systematically analyse how drug candidates can exert their mode of action. Finally, this cutting-edge approach was subjected to a high through-put virtual screen of selected 34 M drug-like compounds filtered from a library of 659 M compounds by identifying the most promising structures and proposing potential action mechanisms for the future development of highly selective human c-Rel inhibitors and/or modulators.

Development of a human primary gut-on-a-chip to model inflammatory processes.

Inflammatory bowel disease (IBD) is a complex multi-factorial disease for which physiologically relevant in vitro models are lacking. Existing models are often a compromise between biological relevance and scalability. Here, we integrated intestinal epithelial cells (IEC) derived from human intestinal organoids with monocyte-derived macrophages, in a gut-on-a-chip platform to model the human intestine and key aspects of IBD. The microfluidic culture of IEC lead to an increased polarization and differentiation state that closely resembled the expression profile of human colon in vivo. Activation of the model resulted in the polarized secretion of CXCL10, IL-8 and CCL-20 by IEC and could efficiently be prevented by TPCA-1 exposure. Importantly, upregulated gene expression by the inflammatory trigger correlated with dysregulated pathways in IBD patients. Finally, integration of activated macrophages offers a first-step towards a multi-factorial amenable IBD platform that could be scaled up to assess compound efficacy at early stages of drug development or in personalized medicine.

Development of a Gut-On-A-Chip Model for High Throughput Disease Modeling and Drug Discovery.

A common bottleneck in any drug development process is finding sufficiently accurate models that capture key aspects of disease development and progression. Conventional drug screening models often rely on simple 2D culture systems that fail to recapitulate the complexity of the organ situation. In this study, we show the application of a robust high throughput 3D gut-on-a-chip model for investigating hallmarks of inflammatory bowel disease (IBD). Using the OrganoPlate platform, we subjected enterocyte-like cells to an immune-relevant inflammatory trigger in order to recapitulate key events of IBD and to further investigate the suitability of this model for compound discovery and target validation activities. The induction of inflammatory conditions caused a loss of barrier function of the intestinal epithelium and its activation by increased cytokine production, two events observed in IBD physiopathology. More importantly, anti-inflammatory compound exposure prevented the loss of barrier function and the increased cytokine release. Furthermore, knockdown of key inflammatory regulators RELA and MYD88 through on-chip adenoviral shRNA transduction alleviated IBD phenotype by decreasing cytokine production. In summary, we demonstrate the routine use of a gut-on-a-chip platform for disease-specific aspects modeling. The approach can be used for larger scale disease modeling, target validation and drug discovery purposes.

SOCS1 in cancer: An oncogene and a tumor suppressor.

The Suppressor Of Cytokine Signaling 1 (SOCS1) has been extensively investigated in immune cells where it works as a potent inhibitor of inflammation by negative feedback regulation of the cytokine-activated JAK-STAT signaling pathways. SOCS1 is also recognized as a tumor suppressor in numerous cancers and its critical functional relevance in non-immune cells, including epithelial cells, has just begun to emerge. Most notably, conflicting results from clinical and experimental studies suggest that SOCS1 may function as either a tumor suppressor or a tumor promoter, in a cell context-dependent manner. Here, we present an overview of the mechanisms underlying SOCS1 function as a tumor suppressor and discuss the emerging evidences of SOCS1 activity as an oncogene.

Tumour-promoting role of SOCS1 in colorectal cancer cells.

The SOCS1 (Suppressor Of Cytokine Signalling 1) protein is considered a tumour suppressor. Notably, the SOCS1 gene is frequently silenced in cancer by hypermethylation of its promoter. Besides blocking inflammation, SOCS1 tumour suppressor activity involves Met receptor inhibition and enhancement of p53 tumour suppressor activity. However, the role of SOCS1 in colorectal cancer (CRC) remains understudied and controversial. Here, we investigated SOCS1 relevance for CRC by querying gene expression datasets of human CRC specimens from The Cancer Genome Atlas (TCGA), and by SOCS1 gain/loss-of-function analyses in murine and human colon carcinoma cells. Our results show that SOCS1 mRNA levels in tumours were more often elevated than reduced with respect to matched adjacent normal tissue of CRC specimens (n = 41). The analysis of TCGA dataset of 431 CRC patients revealed no correlation between SOCS1 expression and overall survival. Overexpression of SOCS1 in CRC cells triggered cell growth enhancement, anchorage-independent growth and resistance to death stimuli, whereas knockdown of SOCS1 reduced these oncogenic features. Moreover, SOCS1 overexpression in mouse CT26 cells increased tumourigenesis in vivo. Biochemical analyses showed that SOCS1 pro-oncogenic activity correlated with the down-modulation of STAT1 expression. Collectively, these results suggest that SOCS1 may work as an oncogene in CRC.