Functionality of intrinsic disorder in tumor necrosis factor-α and its receptors.

Tumor necrosis factor-α (TNF-α) is a pleiotropic inflammatory cytokine that exerts potent cytotoxic effects on solid tumor cells, while not affecting their normal counterparts. It is also known that TNF-α exerts many of its biological functions via interaction with specific receptors. To understand the potential roles of intrinsic disorder in the functioning of this important cytokine, we explored the peculiarities of intrinsic disorder distribution in human TNF-α and its homologs from various species, ranging from zebrafish to chimpanzee. We also studied the peculiarities of intrinsic disorder distribution in human TNF-α receptors, TNFR1 and TNFR2. Analysis revealed that cytoplasmic domains of TNF-α and its receptors are expected to be highly disordered. Furthermore, although the sequence identities of analyzed TNF-α homologs range from 99.57% (between human and chimpanzee proteins) to 22.33% (between frog and fish proteins), their intrinsic disorder profiles are characterized by a remarkable similarity. These observations indicate that the peculiarities of distribution of the intrinsic disorder propensity within the amino acid sequences are evolutionary conserved, and therefore could be of functional importance for this family of proteins. We also show that disordered and flexible regions of human TNF-α and its TNFR1 and TNFR2 receptors are crucial for some of their biological activities.

How disordered is my protein and what is its disorder for? A guide through the "dark side" of the protein universe.

In the last 2 decades it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins lack a stable 3D structure, are ubiquitous and fulfill essential biological functions. Their conformational heterogeneity is encoded in their amino acid sequences, thereby allowing intrinsically disordered proteins or regions to be recognized based on properties of these sequences. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to structural determination with X-ray crystallization. This article discusses a comprehensive selection of databases and methods currently employed to disseminate experimental and putative annotations of disorder, predict disorder and identify regions involved in induced folding. It also provides a set of detailed instructions that should be followed to perform computational analysis of disorder.

Responses of proteins to different ionic environment are linearly interrelated.

Protein partitioning in aqueous two-phase systems (ATPS) is widely used as a convenient, inexpensive, and readily scaled-up separation technique. Protein partition behavior in ATPS is known to be readily manipulated by ionic composition. However, the available data on the effects of salts and buffer concentrations on protein partitioning are very limited. To fill this gap, partitioning of 15 proteins was examined in dextran-poly(ethylene glycol) ATPSs with different salt additives (Na2SO4, NaClO4, NaSCN, CsCl) in 0.11 M sodium phosphate buffer, pH 7.4. This analysis reveals that there is a linear relationship between the logarithms of the protein partition coefficients determined in the presence of different salts. This relationship suggests that the protein response to ionic environment is determined by the protein structure and type and concentrations of the ions present. Analysis of the differences between protein structures (described in terms of proteins responses to different salts) and that of cytochrome c chosen as a reference showed that the peculiarities of the protein surface structure and B-factor used as a measure of the protein flexibility are the determining parameters. Our results provide better insight into the use of different salts in manipulating protein partitioning in aqueous two-phase systems. These data also demonstrate that the protein responses to different ionic environments are interrelated and are determined by the structural peculiarities of protein surface. It is suggested that changes in ionic microenvironment of proteins may regulate protein transport and behavior in biological systems.

Casting a Wider Net: Data Driven Discovery of Proxies for Target Diagnoses.

BACKGROUND: The Hospital Readmissions Reduction Program (HRRP) introduced in October 2012 as part of the Affordable Care Act (ACA), ties hospital reimbursement rates to adjusted 30-day readmissions and mortality performance for a small set of target diagnoses. There is growing concern and emerging evidence that use of a small set of target diagnoses to establish reimbursement rates can lead to unstable results that are susceptible to manipulation (gaming) by hospitals. METHODS: We propose a novel approach to identifying co-occurring diagnoses and procedures that can themselves serve as a proxy indicator of the target diagnosis. The proposed approach constructs a Markov Blanket that allows a high level of performance, in terms of predictive accuracy and scalability, along with interpretability of obtained results. In order to scale to a large number of co-occuring diagnoses (features) and hospital discharge records (samples), our approach begins with Google's PageRank algorithm and exploits the stability of obtained results to rank the contribution of each diagnosis/procedure in terms of presence in a Markov Blanket for outcome prediction. RESULTS: Presence of target diagnoses acute myocardial infarction (AMI), congestive heart failure (CHF), pneumonia (PN), and Sepsis in hospital discharge records for Medicare and Medicaid patients in California and New York state hospitals (2009-2011), were predicted using models trained on a subset of California state hospitals (2003-2008). Using repeated holdout evaluation, we used ~30,000,000 hospital discharge records and analyzed the stability of the proposed approach. Model performance was measured using the Area Under the ROC Curve (AUC) metric, and importance and contribution of single features to the final result. The results varied from AUC=0.68 (with SE<1e-4) for PN on cross validation datasets to AUC=0.94, with (SE<1e-7) for Sepsis on California hospitals (2009 - 2011), while the stability of features was consistently better with more training data for each target diagnosis. Prediction accuracy for considered target diagnoses approaches or exceeds accuracy estimates for discharge record data. CONCLUSIONS: This paper presents a novel approach to identifying a small subset of relevant diagnoses and procedures that approximate the Markov Blanket for target diagnoses. Accuracy and interpretability of results demonstrate the potential of our approach.

On the intrinsic disorder status of the major players in programmed cell death pathways.

Earlier computational and bioinformatics analysis of several large protein datasets across 28 species showed that proteins involved in regulation and execution of programmed cell death (PCD) possess substantial amounts of intrinsic disorder. Based on the comprehensive analysis of these datasets by a wide array of modern bioinformatics tools it was concluded that disordered regions of PCD-related proteins are involved in a multitude of biological functions and interactions with various partners, possess numerous posttranslational modification sites, and have specific evolutionary patterns (Peng et al. 2013). This study extends our previous work by providing information on the intrinsic disorder status of some of the major players of the three major PCD pathways: apoptosis, autophagy, and necroptosis. We also present a detailed description of the disorder status and interactomes of selected proteins that are involved in the p53-mediated apoptotic signaling pathways.