A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877777 | PMC |
http://dx.doi.org/10.3390/ijms19030916 | DOI Listing |
Funct Integr Genomics
January 2025
Intelligent OMICS Limited, Nottingham, United Kingdom.
Gene‒gene interactions play pivotal roles in disease pathogenesis and are fundamental in the development of targeted therapeutics, particularly through the elucidation of oncogenic gene drivers in cancer. The systematic analysis of pathways and gene interactions is critical in the drug discovery process for various cancer subtypes. SPAG5, known for its role in spindle formation during cell division, has been identified as an oncogene in several cancers, although its specific impact on AML remains underexplored.
View Article and Find Full Text PDFSci Rep
January 2025
Scientific Affairs Department, Al-Mustaqbal University, Babylon, 51001, Iraq.
This study investigates the application of various neural network-based models for predicting temperature distribution in freeze drying process of biopharmaceuticals. For heat-sensitive biopharmaceutical products, freeze drying is preferred to prevent degradation of pharmaceutical compounds. The modeling framework is based on CFD (Computational Fluid Dynamics) and machine learning (ML).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science, College of Computer and Information Sciences, Majmaah University, 11952, Al-Majmaah, Saudi Arabia.
The rapid expansion of IoT networks, combined with the flexibility of Software-Defined Networking (SDN), has significantly increased the complexity of traffic management, requiring accurate classification to ensure optimal quality of service (QoS). Existing traffic classification techniques often rely on manual feature selection, limiting adaptability and efficiency in dynamic environments. This paper presents a novel traffic classification framework for SDN-based IoT networks, introducing a Two-Level Fused Network integrated with a self-adaptive Manta Ray Foraging Optimization (SMRFO) algorithm.
View Article and Find Full Text PDFMol Autism
January 2025
Human Anatomy Department, Nanjing Medical University, No.101 Longmian Avenue, Jiangning District, Nanjing, 211166, Jiangsu, People's Republic of China.
Autism spectrum disorder (ASD) is characterized by difficulties in social interaction, communication challenges, and repetitive behaviors. Despite extensive research, the molecular mechanisms underlying these neurodevelopmental abnormalities remain elusive. We integrated microscale brain gene expression data with macroscale MRI data from 1829 participants, including individuals with ASD and typically developing controls, from the autism brain imaging data exchange I and II.
View Article and Find Full Text PDFBrain Topogr
January 2025
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for classifying EEG signals related to visual objects, employing a combination of an LSTM network and nonlinear interval type-2 fuzzy regression (NIT2FR). Here, ResNet is utilized for feature extraction from images, the LSTM network for feature extraction from EEG signals, and NIT2FR for mapping image features to EEG signal features.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!