The demand for antimicrobial peptides (AMPs) is rising because of the increased occurrence of pathogens that are tolerant or resistant to conventional antibiotics. Since naturally occurring AMPs could serve as templates for the development of new anti-infectious agents to which pathogens are not resistant, a resource that contains relevant information on AMP is of great interest. To that extent, we developed the Dragon Antimicrobial Peptide Database (DAMPD, http://apps.sanbi.ac.za/dampd) that contains 1232 manually curated AMPs. DAMPD is an update and a replacement of the ANTIMIC database. In DAMPD an integrated interface allows in a simple fashion querying based on taxonomy, species, AMP family, citation, keywords and a combination of search terms and fields (Advanced Search). A number of tools such as Blast, ClustalW, HMMER, Hydrocalculator, SignalP, AMP predictor, as well as a number of other resources that provide additional information about the results are also provided and integrated into DAMPD to augment biological analysis of AMPs.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244992 | PMC |
| http://dx.doi.org/10.1093/nar/gkr1063 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Newly Isolated LAMA1607 for Enhanced Biological Phosphorus Removal: A Genomic and Functional Characterization.
Front Biosci (Elite Ed)
December 2024
Polytechnic School, University of Vale do Itajaí (Univali), Itajaí, SC 88302-202, Brazil.
Background: Enhanced biological phosphorus removal (EBPR) systems utilize phosphorus-accumulating organisms (PAOs) to remove phosphorus from wastewater since excessive phosphorus in water bodies can lead to eutrophication. This study aimed to characterize a newly isolated PAO strain for its potential application in EBPR systems and to screen for additional biotechnological potential. Here, sequencing allowed for genomic analysis, identifying the genes and molecules involved, and exploring other potentials.
View Article and Find Full Text PDFLarge Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis.
J Med Internet Res
December 2024
Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored.
Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide.
High specificity of an AI-powered framework in cross-checking male professional football anterior cruciate ligament tear reports in public databases.
Knee Surg Sports Traumatol Arthrosc
December 2024
Department of Orthopaedic Surgery, Centre Hospitalier de Luxembourg - Clinique d'Eich, Luxembourg, Luxembourg.
Purpose: While public databases like Transfermarkt provide valuable data for assessing the impact of anterior cruciate ligament (ACL) injuries in professional footballers, they require robust verification methods due to accuracy concerns. We hypothesised that an artificial intelligence (AI)-powered framework could cross-check ACL tear-related information from large publicly available data sets with high specificity.
Methods: The AI-powered framework uses Google Programmable Search Engine to search a curated, multilingual list of websites and OpenAI's GPT to translate search queries, appraise search results and analyse injury-related information in search result items (SRIs).
Impact of annotation imperfections and auto-curation for deep learning-based organ-at-risk segmentation.
Phys Imaging Radiat Oncol
October 2024
Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiation Oncology, De Boelelaan 1117, Amsterdam, the Netherlands.
Background And Purpose: Segmentation imperfections (noise) in radiotherapy organ-at-risk segmentation naturally arise from specialist experience and image quality. Using clinical contours can result in sub-optimal convolutional neural network (CNN) training and performance, but manual curation is costly. We address the impact of simulated and clinical segmentation noise on CNN parotid gland (PG) segmentation performance and provide proof-of-concept for an easily implemented auto-curation countermeasure.
View Article and Find Full Text PDFComprehensive evaluation of AlphaMissense predictions by evidence quantification for variants of uncertain significance.
Front Genet
December 2024
Genomenon, Ann Arbor, MI, United States.
Accurate variant classification is critical for genetic diagnosis. Variants without clear classification, known as "variants of uncertain significance" (VUS), pose a significant diagnostic challenge. This study examines AlphaMissense performance in variant classification, specifically for VUS.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!