Targeted, steered, and biased molecular dynamics (MD) are widely used methods for studying transition processes of biomolecules. They share the common feature of adding external perturbations along a conformational progress variable to guide the transition in a predefined direction in conformational space, yet differ in how these perturbations are applied. In the present paper, we report a comparison of these three methods on generating transition paths for two different processes: the unfolding of the B domain of protein A and a conformational transition of the catalytic domain of a Src kinase Lyn. Transition pathways were calculated with different simulation parameters including the choice of progress variable and the simulation length or biasing force constant. A comparison of the generated paths based on structural similarity finds that the three perturbation MD methods generate similar transition paths for a given progress variable in most cases. On the other hand, the path depends more strongly on the choice of progress variable used to move the system between the initial and final states. Potentials of mean force (PMF) were calculated starting from unfolding trajectories to estimate the relative probabilities of the paths. A lower PMF was found for the lowest biasing force constant with BMD.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2731424 | PMC |
http://dx.doi.org/10.1021/ct9000153 | DOI Listing |
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
Int J Surg
January 2025
School of Medicine, South China University of Technology, Guangzhou, China.
Background: The asymptomatic onset and extremely high mortality rate of aortic aneurysm (AA) highlight the urgency of early detection and timely intervention. The alteration of retinal vascular features (RVFs) can reflect the systemic vascular properties, and be widely used as the biomarker for cardiovascular disease risk prediction. Therefore, we aimed to investigate associations of RVFs with AA and its progression.
View Article and Find Full Text PDFBiotechniques
January 2025
Department of Cardiothoracic Surgery, Stanford University, Stanford, California, USA.
World J Gastroenterol
January 2025
Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou 225000, Jiangsu Province, China.
Background: The relationship between patient nutritional, immune, and inflammatory status is linked to tumor progression and prognosis. However, there are limited studies on the prognosis of esophageal squamous cell carcinoma (ESCC) after surgery based on the comprehensive indicators of these factors.
Aim: To develop and validate a novel nomogram based on a nutritional immune-inflammatory status (NIIS) score for predicting postoperative outcomes in ESCC.
Heliyon
January 2025
School of Architecture, Tianjin University, 300072, Tianjin, China.
Air pollution has become a major challenge to global urban sustainable development, necessitating urgent solutions. Meteorological variables are key determinants of air quality; however, research on their impact across different urban gradients remains limited, and their mechanisms are largely unexplored. This study investigates the dynamic effects of meteorological variables on air quality under varying levels of urbanization using Kaohsiung City, Taiwan, as a case study.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!