Protein post-translational modifications (PTMs) introduce new functionalities and play a critical role in the regulation of protein functions. Characterizing these modifications, especially PTM sites, is essential for unraveling complex biological systems. However, traditional experimental approaches, such as mass spectrometry, are time-consuming and expensive.
View Article and Find Full Text PDFMotivation: Recent advancements in natural language processing have highlighted the effectiveness of global contextualized representations from protein language models (pLMs) in numerous downstream tasks. Nonetheless, strategies to encode the site-of-interest leveraging pLMs for per-residue prediction tasks, such as crotonylation (Kcr) prediction, remain largely uncharted.
Results: Herein, we adopt a range of approaches for utilizing pLMs by experimenting with different input sequence types (full-length protein sequence versus window sequence), assessing the implications of utilizing per-residue embedding of the site-of-interest as well as embeddings of window residues centered around it.
-linked β--acetylglucosamine (-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. -GlcNAc modification (i.e.
View Article and Find Full Text PDFPhosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harnessed the knowledge distilled by pretrained protein language models. Herein, we present a novel deep learning-based approach called LMPhosSite for the general phosphorylation site prediction that integrates embeddings from the local window sequence and the contextualized embedding obtained using global (overall) protein sequence from a pretrained protein language model to improve the prediction performance.
View Article and Find Full Text PDFOlder adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and societal costs. However, there is a lack of automatic fall detection systems for older adults. This paper reports (1) a wireless, flexible, skin-wearable electronic device for both accurate motion sensing and user comfort, and (2) a deep learning-based classification algorithm for reliable fall detection of older adults.
View Article and Find Full Text PDFProtein N-linked glycosylation is an important post-translational mechanism in Homo sapiens, playing essential roles in many vital biological processes. It occurs at the N-X-[S/T] sequon in amino acid sequences, where X can be any amino acid except proline. However, not all N-X-[S/T] sequons are glycosylated; thus, the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation.
View Article and Find Full Text PDFWith the technological advancement in recent years and the widespread use of magnetism in every sector of the current technology, a search for a low-cost magnetic material has been more important than ever. The discovery of magnetism in alternate materials such as metal chalcogenides with abundant atomic constituents would be a milestone in such a scenario. However, considering the multitude of possible chalcogenide configurations, predictive computational modeling or experimental synthesis is an open challenge.
View Article and Find Full Text PDFBackground: Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
November 2022
Background: Colorectal cancer (CRC) is a heterogeneous disease with different responses to targeted therapies due to various factors, and the treatment effect differs significantly between individuals. Personalize medical treatment (PMT) is a method that takes individual patient characteristics into consideration, making it the most effective way to deal with this issue. Patient similarity and clustering analysis is an important aspect of PMT.
View Article and Find Full Text PDFProtein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites.
View Article and Find Full Text PDFPosttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology.
View Article and Find Full Text PDFBackground: The COVID-19 pandemic is a significant public health crisis that is hitting hard on people's health, well-being, and freedom of movement, and affecting the global economy. Scientists worldwide are competing to develop therapeutics and vaccines; currently, three drugs and two vaccine candidates have been given emergency authorization use. However, there are still questions of efficacy with regard to specific subgroups of patients and the vaccine's scalability to the general public.
View Article and Find Full Text PDFObjective: The aim of this study is to compute similarities between patient records in an electronic health record (EHR). This is an important problem because the availability of effective methods for the computation of patient similarity would allow for assistance with and automation of tasks such as patients stratification, medical prognosis and cohort selection, and for unlocking the potential of medical analytics methods for healthcare intelligence. However, health data in EHRs presents many challenges that make the automatic computation of patient similarity difficult; these include: temporal aspects, multivariate, heterogeneous and irregular data, and data sparsity.
View Article and Find Full Text PDFIn the present world, International Consensus Frameworks, commonly called global frameworks or global agendas, guide international development policies and practices. They guide the development of all countries and influence the development initiatives by their respective governments. Recent global frameworks, adopted mostly post-2015, include both a group of over-arching frameworks (eg, the Sendai Framework for Disaster Risk Reduction [SFDRR]) and a group of frameworks addressing specific issues (eg, the Dhaka Declaration on Disability and Disaster Risk Management).
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