Full-length sequences of HLA-B*56:01:01:01, B*56:03 and B*56:04, confirmed by cloning and sequencing in Chinese donors.
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Unraveling the distinctive gut microbiome of khulans (Equus hemionus hemionus) in comparison to their drinking water and closely related equids.
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Department of Wildlife Diseases, Leibniz Institute for Zoo and Wildlife Research, Alfred- Kowalke Str. 17, 10315, Berlin, Germany.
The microbial composition of host-associated microbiomes is influenced by co-evolutionary interactions, host genetics, domestication, and the environment. This study investigates the contribution of environmental microbiota from freshwater bodies to the gastrointestinal microbiomes of wild khulans (Equus hemionus hemionus, n = 21) and compares them with those of captive khulans (n = 12) and other equids-Przewalski's horse (n = 82) and domestic horse (n = 26). Using PacBio technology and the LotuS pipeline for 16S rRNA gene sequencing, we analyze microbial diversity and conduct differential abundance, alpha, and beta diversity analyses.
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Rapid advancements in long-read sequencing have facilitated species-level microbial profiling through full-length 16S rRNA sequencing (~ 1500 bp), and more notably, by the newer 16S-ITS-23S ribosomal RNA operon (RRN) sequencing (~ 4500 bp). RRN sequencing is emerging as a superior method for species resolution, exceeding the capabilities of short-read and full-length 16S rRNA sequencing. However, being in its early stages of development, RRN sequencing has several underexplored or understudied elements, highlighting the need for a critical and thorough examination of its methodologies.
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Chemical shift assignments of large membrane proteins by solid-state NMR experiments are challenging. Recent advancements in sensitivity-enhanced pulse sequences, have made it feasible to acquire H-detected 4D spectra of these challenging protein samples within reasonable timeframes. However, obtaining unambiguous assignments remains difficult without access to side-chain chemical shifts.
View Article and Find Full Text PDFAFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.
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Department of Biostatistics and Bioinformatics, Institute of Health Sciences, Acibadem University, Atasehir, Istanbul, Turkey.
Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences and relatively limited number of structures. Leveraging the highly accurate protein structures predicted by AlphaFold2 (AF2), we introduce AFFIPred, an ensemble machine learning classifier that combines sequence and AF2-based structural characteristics to predict missense variant pathogenicity. Based on the assessments on unseen datasets, AFFIPred reached a comparable level of performance with the state-of-the-art predictors such as AlphaMissense.
View Article and Find Full Text PDFLEA_4 motifs function alone and in conjunction with synergistic cosolutes to protect a labile enzyme during desiccation.
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Department of Molecular Biology, University of Wyoming, Laramie, Wyoming, USA.
Organisms from all kingdoms of life depend on Late Embryogenesis Abundant (LEA) proteins to survive desiccation. LEA proteins are divided into broad families distinguished by the presence of family-specific motif sequences. The LEA_4 family, characterized by 11-residue motifs, plays a crucial role in the desiccation tolerance of numerous species.
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