Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In this chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
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http://dx.doi.org/10.1007/978-1-4939-0366-5_1 | DOI Listing |
Virol J
January 2025
Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China.
Infection with Influenza A virus (IAV) induces severe inflammatory responses and lung injury, contributing significantly to mortality and morbidity rates. Alterations in the microbial composition of the lungs and intestinal tract resulting from infection could influence disease progression and treatment outcomes. Xiyanping (XYP) injection has demonstrated efficacy in clinical treatment across various viral infections.
View Article and Find Full Text PDFJ Neuroinflammation
January 2025
Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA.
Background: The retinal degenerative diseases retinitis pigmentosa (RP) and atrophic age- related macular degeneration (AMD) are characterized by vision loss from photoreceptor (PR) degeneration. Unfortunately, current treatments for these diseases are limited at best. Genetic and other preclinical evidence suggest a relationship between retinal degeneration and inflammation.
View Article and Find Full Text PDFBMC Neurosci
January 2025
Department of Operative Dentistry and Periodontology, University Hospital Erlangen, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany.
Background: Parkinson's disease (PD) is a neurodegenerative disorder characterized by protein aggregates mostly consisting of misfolded alpha-synuclein (αSyn). Progressive degeneration of midbrain dopaminergic neurons (mDANs) and nigrostriatal projections results in severe motor symptoms. While the preferential loss of mDANs has not been fully understood yet, the cell type-specific vulnerability has been linked to a unique intracellular milieu, influenced by dopamine metabolism, high demand for mitochondrial activity, and increased level of oxidative stress (OS).
View Article and Find Full Text PDFBMC Genomics
January 2025
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue 1095, Wuhan, Hubei, 430030, China.
Background: Left-right (LR) asymmetry disorders present a complex etiology, with genetic factors emerging as a primary contributor. This study aims to explore the genetic underpinnings of chromosomal variants and individual genes in fetuses afflicted with prenatal LR asymmetry disorder.
Methods: Through a retrospective analysis conducted between 2020 and 2023 at Tongji Hospital, Huazhong University of Science and Technology, genetic outcomes of LR asymmetric disorder were scrutinized utilizing copy number variation sequencing (CNV-seq) and whole exome sequencing (WES) methodologies.
NPJ Digit Med
January 2025
State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This study proposes DMFF-DTA, a dual-modality neural network model integrates sequence and graph structure information from drugs and proteins for drug-target affinity prediction. The model introduces a binding site-focused graph construction approach to extract binding information, enabling more balanced and efficient modeling of drug-target interactions.
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