Knowledge-guided inference of domain–domain interactions from incomplete protein–protein interaction networks

Motivation: Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain–domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs

Predicting domain-domain interactions using a parsimony approach

We propose a novel approach to predict domain-domain interactions from a protein-protein interaction network. In our method we apply a parsimony-driven explanation of the network, where the domain interactions are inferred using linear programming optimization, and false positives in the protein network are handled by a probabilistic construction. This method outperforms previous approaches by a considerable margin. The results indicate that the parsimony principle provides a correct approach for detecting domain-domain contacts

Differences in evolutionary pressure acting within highly conserved ortholog groups

<p>Abstract</p> <p>Background</p> <p>In highly conserved widely distributed ortholog groups, the main evolutionary force is assumed to be purifying selection that enforces sequence conservation, with most divergence occurring by accumulation of neutral substitutions. Using a set of ortholog groups from prokaryotes, with a single representative in each studied organism, we asked the question if this evolutionary pressure is acting similarly on different subgroups of orthologs defined as major lineages (e.g. Proteobacteria or Firmicutes).</p> <p>Results</p> <p>Using correlations in entropy measures as a proxy for evolutionary pressure, we observed two distinct behaviors within our ortholog collection. The first subset of ortholog groups, called here informational, consisted mostly of proteins associated with information processing (i.e. translation, transcription, DNA replication) and the second, the non-informational ortholog groups, mostly comprised of proteins involved in metabolic pathways. The evolutionary pressure acting on non-informational proteins is more uniform relative to their informational counterparts. The non-informational proteins show higher level of correlation between entropy profiles and more uniformity across subgroups.</p> <p>Conclusion</p> <p>The low correlation of entropy profiles in the informational ortholog groups suggest that the evolutionary pressure acting on the informational ortholog groups is not uniform across different clades considered this study. This might suggest "fine-tuning" of informational proteins in each lineage leading to lineage-specific differences in selection. This, in turn, could make these proteins less exchangeable between lineages. In contrast, the uniformity of the selective pressure acting on the non-informational groups might allow the exchange of the genetic material via lateral gene transfer.</p

Knowledge attitude and practice of materiovigilance among healthcare professionals in tertiary care hospitals

Background: Materiovigilance (Mv) refers to the systematic monitoring and evaluation of medical devices in order to assess their performance and safety during all stages of their life. The purpose of Mv is to identify and avoid any potential hazards or issues linked with medical equipment. In brief, Mv plays an important role in ensuring the performance and safety of medical equipment. It consists of the systematic observation, the collection, and analysis of data on occurrences and adverse events related to medical equipment. The study was conducted to assess knowledge, attitude, and practice regarding Mv among healthcare professionals in tertiary care hospitals. Methods: Self-prepared and validated questionnaires were distributed among healthcare Professionals in India through online forms from December 2022 to July 2023, a cross-sectional questionnaire-based survey was used, with convenience sampling utilized. Responses from 220 subjects were analyzed. Results: The primary objective of this study was to assess the demographic details of the Healthcare Professionals as well as the distribution of knowledge, Attitude, Practice on Mv, in a list of 220 responses, 40% of them know about the Mv. Remaining 60% of them they don’t know about the Mv. Total 220 responses, 76.8% of the Healthcare professionals suggested to thought about the Mv, remaining 23.1% were disagreed. 75 out of 220 healthcare professionals acknowledged to seeing the reporting form and experiencing AE in their patients, while the remaining 145 denied it. Conclusions: This study determined the knowledge, attitude and practice of Mv among healthcare professionals in tertiary care hospitals. According to the current study, the majority of healthcare professionals were knowledgeable and supportive of Mv, however they are not as effective in practice

Security Enabled Acoustic Based Assessment System

Security Enabled Acoustic Based Assessment System is a desktop application that establishes a secure evaluation scheme for the benefit of both the institutional organizations and the students. This site is an attempt to remove the existing flaws in the current assessment systems and it would help not only visually challenged people but also physically impaired candidates to attend the test by automatic question dictation feature and the automatic answer recognition feature. Institutes enter on the site the questions they want in the exam. The questions are completely randomized and thus security is maintained. Fernet Key is used for the encryption of the questions that will appear on screen. Face detection is used for the user authentication and then the live monitoring is done until the candidate completes the exam and thus anti-cheating/anti-fraud mechanisms is ensured. Students can take exams and acquire their results as soon as they complete the exams. Online Tests save more time for evaluation. It also saves paper which is an issue in the past few years. The answers entered by the students are then evaluated and their score is calculated and saved. This score then can be accessed by the institutes to determine the passed-out students or to evaluate their performance

DOMINE: a comprehensive collection of known and predicted domain-domain interactions

DOMINE is a comprehensive collection of known and predicted domain–domain interactions (DDIs) compiled from 15 different sources. The updated DOMINE includes 2285 new domain–domain interactions (DDIs) inferred from experimentally characterized high-resolution three-dimensional structures, and about 3500 novel predictions by five computational approaches published over the last 3 years. These additions bring the total number of unique DDIs in the updated version to 26 219 among 5140 unique Pfam domains, a 23% increase compared to 20 513 unique DDIs among 4346 unique domains in the previous version. The updated version now contains 6634 known DDIs, and features a new classification scheme to assign confidence levels to predicted DDIs. DOMINE will serve as a valuable resource to those studying protein and domain interactions. Most importantly, DOMINE will not only serve as an excellent reference to bench scientists testing for new interactions but also to bioinformaticans seeking to predict novel protein–protein interactions based on the DDIs. The contents of the DOMINE are available at http://domine.utdallas.edu

DOMINE: a database of protein domain interactions

DOMINE is a database of known and predicted protein domain interactions compiled from a variety of sources. The database contains domain–domain interactions observed in PDB entries, and those that were predicted by eight different computational approaches. DOMINE contains a total of 20 513 unique domain–domain interactions among 4036 Pfam domains, out of which 4349 are inferred from PDB entries and 17 781 were predicted by at least one computational approach. This database will serve as a valuable resource to those working in the field of protein and domain interactions. DOMINE may not only serve as a reference to experimentalists who test for new protein and domain interactions, but also offers a consolidated dataset for analysis by bioinformaticians who seek to test ideas regarding the underlying factors that control the topological structure of interaction networks. DOMINE is freely available at http://domine.utdallas.edu

Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture

Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome-scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top-layer TFs are relatively abundant, long-lived, and noisy compared with the core- and bottom-layer TFs. Although variability in expression of top-layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population

INO80 Facilitates Pluripotency Gene Activation in Embryonic Stem Cell Self-Renewal, Reprogramming, and Blastocyst Development

The master transcription factors play integral roles in the pluripotency transcription circuitry of embryonic stem cells (ESCs). How they selectively activate expression of the pluripotency network while simultaneously repressing genes involved in differentiation is not fully understood. Here we define a requirement for the INO80 complex, a SWI/SNF family chromatin remodeler, in ESC self-renewal, somatic cell reprogramming, and blastocyst development. We show that Ino80, the chromatin remodeling ATPase, co-occupies pluripotency gene promoters with the master transcription factors, and its occupancy is dependent on Oct4 and Wdr5. At the pluripotency genes, Ino80 maintains open chromatin architecture and licenses recruitment of Mediator and RNA Polymerase II for gene activation. Our data reveal an essential role for INO80 in the expression of the pluripotency network, and illustrate the coordination among chromatin remodeler, transcription factor, and histone modifying enzyme in the regulation of the pluripotent state

Acute depletion of Tet1-dependent 5-hydroxymethylcytosine levels impairs LIF/Stat3 signaling and results in loss of embryonic stem cell identity

The TET family of FE(II) and 2-oxoglutarate-dependent enzymes (Tet1/2/3) promote DNA demethylation by converting 5-methylcytosine to 5-hydroxymethylcytosine (5hmC), which they further oxidize into 5-formylcytosine and 5-carboxylcytosine. Tet1 is robustly expressed in mouse embryonic stem cells (mESCs) and has been implicated in mESC maintenance. Here we demonstrate that, unlike genetic deletion, RNAi-mediated depletion of Tet1 in mESCs led to a significant reduction in 5hmC and loss of mESC identity. The differentiation phenotype due to Tet1 depletion positively correlated with the extent of 5hmC loss. Meta-analyses of genomic data sets suggested interaction between Tet1 and leukemia inhibitory factor (LIF) signaling. LIF signaling is known to promote self-renewal and pluripotency in mESCs partly by opposing MAPK/ERK-mediated differentiation. Withdrawal of LIF leads to differentiation of mESCs. We discovered that Tet1 depletion impaired LIF-dependent Stat3-mediated gene activation by affecting Stat3's ability to bind to its target sites on chromatin. Nanog overexpression or inhibition of MAPK/ERK signaling, both known to maintain mESCs in the absence of LIF, rescued Tet1 depletion, further supporting the dependence of LIF/Stat3 signaling on Tet1. These data support the conclusion that analysis of mESCs in the hours/days immediately following efficient Tet1 depletion reveals Tet1's normal physiological role in maintaining the pluripotent state that may be subject to homeostatic compensation in genetic models