AI for infectious disease modelling and therapeutics

AI for infectious disease modelling and therapeutics is an emerging area that leverages new computational approaches and data in this area. Genomics, proteomics, biomedical literature, social media, and other resources are proving to be critical tools to help understand and solve complicated issues ranging from understanding the process of infection, diagnosis and discovery of the precise molecular details, to developing possible interventions and safety profiling of possible treatments

Order-Disorder Interface Characterization Reveals Critical Factors for Disease and Drug Targets

Signal transduction pathways are of critical importance in disease and regulation of cellular functions. Proteins that do not fold to a state of stable tertiary structure, known as intrinsically disordered proteins, are highly represented in signaling pathways and protein interaction networks. Important examples of disordered signaling proteins include p53 and BRCA1, and approximately 40% of Eukaryotic proteins are estimated to have significant disordered regions. Certain regions within these disordered proteins, however, can take on an ordered structure upon binding to a partner. The nature of the resulting protein-protein interactions has not yet been established. Here we categorize and identify interactions between binding segments of disordered proteins and their ordered partners using a Bayesian network framework, constructed on a test set of 964 proteins mined for Molecular Recognition Feature (MoRF) characteristics from the PDB. This framework, more specifically Bayesian network learning, enables us to investigate the underlying biological processes involved, including the sequential and structural determinants of these interactions. After the construction of the training set (80% of data), features were successively eliminated to determine relative significances. The Bayesian network model was validated on the test set with excellent accuracy(>90% AUC). Examining features underlying the model provides a plethora of new and potentially useful biological information. The results also lend themselves to a strategy for rational drug design whereby disordered regions can be targeted with a high degree of specificity and small molecule peptide mimetics of their binding regions can be utilized as drugs

Many-to-one binding by intrinsically disordered protein regions

Disordered binding regions (DBRs), which are embedded within intrinsically disordered proteins or regions (IDPs or IDRs), enable IDPs or IDRs to mediate multiple protein-protein interactions. DBR-protein complexes were collected from the Protein Data Bank for which two or more DBRs having different amino acid sequences bind to the same (100% sequence identical) globular protein partner, a type of interaction herein called many-to-one binding. Two distinct binding profiles were identified: independent and overlapping. For the overlapping binding profiles, the distinct DBRs interact by means of almost identical binding sites (herein called “similar”), or the binding sites contain both common and divergent interaction residues (herein called “intersecting”). Further analysis of the sequence and structural differences among these three groups indicate how IDP flexibility allows different segments to adjust to similar, intersecting, and independent binding pockets

Novel reference genes in colorectal cancer identify a distinct subset of high stage tumors and their associated histologically normal colonic tissues

© 2019 The Author(s). Background: Reference genes are often interchangeably called housekeeping genes due to 1) the essential cellular functions their proteins provide and 2) their constitutive expression across a range of normal and pathophysiological conditions. However, given the proliferative drive of malignant cells, many reference genes such as beta-actin (ACTB) and glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) which play critical roles in cell membrane organization and glycolysis, may be dysregulated in tumors versus their corresponding normal controls Methods: Because Next Generation Sequencing (NGS) technology has several advantages over hybridization-based technologies, such as independent detection and quantitation of transcription levels, greater sensitivity, and increased dynamic range, we evaluated colorectal cancers (CRC) and their histologically normal tissue counterparts by NGS to evaluate the expression of 21 classical reference genes used as normalization standards for PCR based methods. Seventy-nine paired tissue samples of CRC and their patient matched healthy colonic tissues were subjected to NGS analysis of their mRNAs. Results: We affirmed that 17 out of 21 classical reference genes had upregulated expression in tumors compared to normal colonic epithelial tissue and dramatically so in some cases. Indeed, tumors were distinguished from normal controls in both unsupervised hierarchical clustering analyses (HCA) and principal component analyses (PCA). We then identified 42 novel potential reference genes with minimal coefficients of variation (CV) across 79 CRC tumor pairs. Though largely consistently expressed across tumors and normal control tissues, a subset of high stage tumors (HSTs) as well as some normal tissue samples (HSNs) located adjacent to these HSTs demonstrated dysregulated expression, thus identifying a subset of tumors with a potentially distinct and aggressive biological profile. Conclusion: While classical CRC reference genes were found to be differentially expressed between tumors and normal controls, novel reference genes, identified via NGS, were more consistently expressed across malignant and normal colonic tissues. Nonetheless, a subset of HST had profound dysregulation of such genes as did many of the histologically normal tissues adjacent to such HSTs, indicating that the HSTs so distinguished may have unique biological properties and that their histologically normal tissues likely harbor a small population of microscopically undetected but metabolically active tumors