Diversity of α-helical host defense peptides (αHDPs) contributes to immunity against a broad spectrum of pathogens via multiple functions. Thus, resolving common structure-function relationships among αHDPs is inherently difficult, even for artificial-intelligence-based methods that seek multifactorial trends rather than foundational principles. Here, bioinformatic and pattern recognition methods were applied to identify a unifying signature of eukaryotic αHDPs derived from amino acid sequence, biochemical, and three-dimensional properties of known αHDPs. The signature formula contains a helical domain of 12 residues with a mean hydrophobic moment of 0.50 and favoring aliphatic over aromatic hydrophobes in 18-aa windows of peptides or proteins matching its semantic definition. The holistic α-core signature subsumes existing physicochemical properties of αHDPs, and converged strongly with predictions of an independent machine-learning-based classifier recognizing sequences inducing negative Gaussian curvature in target membranes. Queries using the α-core formula identified 93% of all annotated αHDPs in proteomic databases and retrieved all major αHDP families. Synthesis and antimicrobial assays confirmed efficacies of predicted sequences having no previously known antimicrobial activity. The unifying α-core signature establishes a foundational framework for discovering and understanding αHDPs encompassing diverse structural and mechanistic variations, and affords possibilities for deterministic design of antiinfectives.
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http://dx.doi.org/10.1073/pnas.1819250116 | DOI Listing |
Mol Biol Evol
January 2025
School of Biological Sciences, Monash University, Clayton, Victoria 3800, Australia.
When introduced to multiple distinct ranges, invasive species provide a compelling natural experiment for understanding the repeatability of adaptation. Ambrosia artemisiifolia is an invasive, noxious weed, and chief cause of hay fever. Leveraging over 400 whole-genome sequences spanning the native-range in North America and 2 invasions in Europe and Australia, we inferred demographically distinct invasion histories on each continent.
View Article and Find Full Text PDFCancer Med
January 2025
Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, China.
Background: Distinctive heterogeneity characterizes diffuse large B-cell lymphoma (DLBCL), one of the most frequent types of non-Hodgkin's lymphoma. Mitochondria have been demonstrated to be closely involved in tumorigenesis and progression, particularly in DLBCL.
Objective: The purposes of this study were to identify the prognostic mitochondria-related genes (MRGs) in DLBCL, and to develop a risk model based on MRGs and machine learning algorithms.
Diabetes Metab Res Rev
January 2025
Division of Research, Kaiser Permanente Northern California, Pleasanton, California, USA.
Aims: Gestational diabetes mellitus (GDM) poses a significant risk for developing type 2 diabetes mellitus (T2D) and exhibits heterogeneity. However, understanding the link between different types of post-GDM individuals without diabetes and their progression to T2D is crucial to advance personalised medicine approaches.
Materials And Methods: We employed a discovery-based unsupervised machine learning clustering method to generate clustering models for analysing metabolomics, clinical, and biochemical datasets.
Clin Transl Med
January 2025
Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Thyroid cancer is one of the most common endocrine tumors worldwide, especially among women and the metastatic mechanism of papillary thyroid carcinoma remains poorly understood.
Methods: Thyroid cancer tissue samples were obtained for single-cell RNA-sequencing and spatial transcriptomics, aiming to intratumoral and antimetastatic heterogeneity of advanced PTC. The functions of APOE in PTC cell proliferation and invasion were confirmed through in vivo and in vitro assays.
Anal Chem
January 2025
Department of Chemistry, and Minhang Hospital, Fudan University, Shanghai 200000, China.
Intact glycopeptide characterization by mass spectrometry has proven to be a versatile tool for site-specific glycoproteomics analysis and biomarker screening. Here, we present a method using a new model of a Q-TOF instrument equipped with a Zeno trap for intact glycopeptide identification and demonstrate its ability to analyze large-cohort glycoproteomes. From 124 clinical serum samples of breast cancer, noncancerous diseases, and nondisease controls, a total of 6901 unique site-specific glycans on 807 glycosites of proteins were detected.
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