Malfunctions in transcriptional regulation are associated with a number of critical human diseases. As a result, there is considerable interest in designing artificial transcription activators (ATAs) that specifically control genes linked to human diseases. Like native transcriptional activator proteins, an ATA must minimally contain a DNA-binding domain (DBD) and a transactivation domain (TAD) and, although there are several reliable methods for designing artificial DBDs, designing artificial TADs has proven difficult. In this manuscript, we present a structure-based strategy for designing short peptides containing natural amino acids that function as artificial TADs. Using a segment of the TAD of p53 as the scaffolding, modifications are introduced to increase the helical propensity of the peptides. The most active artificial TAD, termed E-Cap-(LL), is a 13-mer peptide that contains four key residues from p53, an N-capping motif and a dileucine hydrophobic bridge. In vitro analysis demonstrates that E-Cap-(LL) interacts with several known p53 target proteins, while in vivo studies in a yeast model system show that it is a 20-fold more potent transcriptional activator than the native p53-13 peptide. These results demonstrate that structure-based design represents a promising approach for developing artificial TADs that can be combined with artificial DBDs to create potent and specific ATAs.
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http://dx.doi.org/10.1021/ja208999e | DOI Listing |
Bioinformatics
January 2025
Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
Motivation: Understanding the associations between traits and microbial composition is a fundamental objective in microbiome research. Recently, researchers have turned to machine learning (ML) models to achieve this goal with promising results. However, the effectiveness of advanced ML models is often limited by the unique characteristics of microbiome data, which are typically high-dimensional, compositional, and imbalanced.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Department of Conservative Dentistry, College of Dentistry, Kyung Hee University, 26-6, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02453, Republic of Korea.
Background: This study aims to compare design, phase transformation behavior, and torsional resistance of the ProGlider (PG) and ProTaper ultimate slider (PUS) and to compare the performance of two files in the glide-path preparation of a double-curved artificial canal.
Methods: Scanning electron microscopy, micro-computed tomography, and differential scanning calorimetry were used to characterize the samples. A torsional resistance test was performed to obtain ultimate strength and distortion angle.
BMC Oral Health
January 2025
Department of Endodontics, Faculty of Dentistry, Marmara University, Başıbüyük, Başıbüyük Yolu Marmara Üniversitesi Başıbüyük Sağlık Yerleşkesi 9/3, Başıbüyük - Maltepe, PO Box: 34854, İstanbul, Turkey.
Introduction: The integration of artificial intelligence (AI) technologies in healthcare is revolutionizing the workflows of healthcare professionals, enabling faster and more accurate patient treatment. This study aims to evaluate the accuracy of responses provided by different AI chatbots to questions that dentists might ask regarding regenerative endodontic treatment (RET), a procedure that shows promising biological healing potential.
Methods: A total of 23 questions related to RET procedures were developed based on the American Association of Endodontists (AAE) 2022 guidelines.
Sci Rep
January 2025
College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, 010010, China.
In the face of forest fire emergencies, fast and efficient dispatching of rescue vehicles is an important means of mitigating the damage caused by forest fires, and is an effective method of avoiding secondary damage caused by forest fires, minimizing the damage caused by forest fires to the ecosystem, and mitigating the losses caused by economic development. this paper takes the actual problem as the starting point, constructs a reasonable mathematical model of the problem, for the special characteristics of the emergency rescue vehicle scheduling problem of forest fires, taking into account the actual road conditions in the northern pristine forest area, through the analysis of the cost of paths between the forest area and the highway, to obtain the least obstructed rescue paths, to narrow the gap between the theoretical model and the problem of the actual. Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm's solution efficiency and accuracy, through the northern pristine forest area of Daxing'anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program.
View Article and Find Full Text PDFArch Phys Med Rehabil
January 2025
H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA; Brain Injury Research Center, TIRR Memorial Hermann, Houston, TX, USA.
Objective: To test the efficacy of a randomized control trial low-touch mobile health intervention designed to promote care partner self-awareness and self-care.
Design: This randomized controlled trial (RCT) included a baseline assessment of self-report surveys of health-related quality of life (HRQOL), care partner-specific outcomes, and the functional/mental status of the person with TBI, as well as a 6-month home monitoring period that included three daily questions about HRQOL, monthly assessments of 12 HRQOL domains, and the use of a Fitbit® to continuously monitor physical activity and sleep. HRQOL surveys were repeated at 3- and 6-months post-home monitoring.
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