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The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.
We present here a neural network-based method for detection of signal peptides (abbreviation used: SP) in proteins. The method is trained on sequences of known signal peptides extracted from the Swiss-Prot protein database and is able to work separately on prokaryotic and eukaryotic proteins. A query protein is dissected into overlapping short sequence fragments, and then each fragment is analyzed with respect to the probability of it being a signal peptide and containing a cleavage site. While the accuracy of the method is comparable to that of other existing prediction tools, it provides a significantly higher speed and portability. The accuracy of cleavage site prediction reaches 73% on heterogeneous source data that contains both prokaryotic and eukaryotic sequences while the accuracy of discrimination between signal peptides and non-signal peptides is above 93% for any source dataset. As a consequence, the method can be easily applied to genome-wide datasets. The software can be downloaded freely from http://rpsp.bioinfo. pl/RPSP.tar.gz.
An active form of p38 protein kinase, belonging to the mitogen-activated protein kinases subfamily, has been designed based on crystallographically known structures of two other kinases, an active form of protein kinase A (PKA) and an inactive form of extracellular signal-regulated kinase 2 (ERK2). The modelling procedure is described. Its general scheme can also be applied to other kinases. The structure of the active forms of p38 and PKA is very similar in the region which binds the substrate. The ATP-binding mode is very similar in the active forms of all the three studied kinases. Models of the active forms allow for further studies on transphosphorylation processes at the molecular level, and modelling of inhibitors competitive with ATP and/or substrates.
An active form of an insulin receptor tyrosine kinase (IRK) catalytic core was modelled based on its experimentally known inactive form and the active form of a serine/threonine kinase, protein kinase A (PKA). This theoretical model was compared with the crystallographic structure of the active form of IRK reported later. The structures are very similar, which shows that all the most important features and interactions have been taken into account in the modelling procedure. The elaborated procedure can be applied to other tyrosine kinases. This would allow designing of a wide class of tyrosine kinase inhibitors, very important potential anti-cancer and/or anti-viral drugs.
Alzheimer’s disease is the most common form of dementia characterized by a progressive deterioration of cognitive functions and by overproduction of toxic form of β-amyloid (Aβ) and intracellular accumulation of the microtubule-associated protein tau into neurofibrillary tangles (NFTs). In the past few years, our research team has investigated the genetic variability of PSEN1, PSEN2 and APP genes in AD patients, especially familial early-onset AD (fEOAD). We have identified mutations in PSEN1 and PSEN2, including novel ones located in exons coding for the large cytosolic loop of presenilin 1. To test functional nature of the aforementioned mutations we have performed analysis of the whole transcriptome using RNA sequencing method and total RNA isolated from primary fibroblasts cultures derived from fEOAD patients. Using RNA-Seq data we have performed differential gene expression (DGE) analysis, which was estimated by three independent bioinformatic tools (i.e. Cuffdiff, EgdeR and Deseq2). Further DGE enrichment analysis revealed a number of signaling pathways significantly altered in the samples from fEOAD patients, which varied depending on identified mutations in PSEN1 or PSEN2 genes. Next to cell cycle, pro-apoptotic, TNF, adherent junction, p53, or Wnt signaling pathways, we have found several changes in the pathways that have not been previously linked to AD. Among the aforementioned pathways we have focused on HIF-1 signaling, Hippo signaling as well as DNA mismatch repair, base excision repair, and transcriptional misregulation mechanisms. Interestingly, two novel PSEN1 mutations changing the amino acid sequence of the large cytoplasmic loop have been linked to TNF and HIF-1 signaling pathways, suggesting induction of proinflammatory response and opening future directions of the research on fEOAD pathomechanism. Supported by NCN G1119-2013/09/D/NZ3/01348.
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