Computational Prediction of Linear Interacting Peptides.

Methods Mol Biol

Michael Smith Laboratories, the University of British Columbia, Vancouver, BC, Canada.

Published: November 2024

Intrinsically disordered protein regions, IDRs, are observed in many eukaryotic proteins. They play critical roles in essentially all cellular processes because segments of these regions, known as linear interacting peptides (LIPs), are heavily involved in regulatory protein interactions across proteomes. This chapter presents an integrated summary of the results from the last two Critical Assessments of protein Intrinsic Disorder predictions, known as CAID events, on the computational prediction of LIP segments. Because the CAID community questioned the quality of the test dataset used by the first CAID event, we reannotated this dataset using more accurate annotations from the latest DisProt database release. Then, we compared the results of the first CAID with the updated data and the results of the second CAID event. Our comparison highlights the importance of data annotation on the evaluation outcome and provides recommendations for users of LIP predictors.

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-0716-4196-5_14DOI Listing

Publication Analysis

Top Keywords

computational prediction
8
linear interacting
8
interacting peptides
8
caid event
8
caid
5
prediction linear
4
peptides intrinsically
4
intrinsically disordered
4
disordered protein
4
protein regions
4

Similar Publications

deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities.

J Chem Inf Model

January 2025

School of Information and Artificial Intelligence, Anhui Provincial Engineering Research Center for Beidou Precision Agriculture Information, Key Laboratory of Agricultural Sensors for Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei, Anhui 230036, China.

Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years.

View Article and Find Full Text PDF

GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery.

Mol Divers

January 2025

Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases Ministry of Education, Jiangxi Province Key Laboratory of Biomaterials and Biofabrication for Tissue Engineering, Gannan Medical University, Ganzhou, 341000, Jiangxi, China.

Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise.

View Article and Find Full Text PDF

Introduction: A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis.

Materials And Methods: Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals.

View Article and Find Full Text PDF

Purpose: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients.

Methods: A total of 323 patients from The Cancer Genome Atlas (TCGA) were used as the training and internal validation set, with image features extracted using a visual encoder called UNI. We investigated the ability to predict LNM status while assessing the prognostic value of aiN score.

View Article and Find Full Text PDF

Prediction of body weight (BW) using biometric measurements is an important tool especially for animal welfare and automatic phenotyping tools that needs mathematical models. In this study, it was aimed to predict the BW using body length (BL), chest girth (CG) and width of the waist (WW) for rabbits of the maternal form of Hyla NG. The standard rabbit-raising practices were applied for the animals.

View Article and Find Full Text PDF

Want 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!