New method of identification of dynamical domains in proteins - Hierarchical Clustering of the Correlation Patterns (HCCP) is proposed. HCCP allows to identify the domains using single three-dimensional structure of the studied proteins and does not require any adjustable parameters that can influence the results. The method is based on hierarchical clustering performed on the matrices of correlation patterns, which are obtained by the transformation of ordinary pairwise correlation matrices. This approach allows to extract additional information from the correlation matrices, which increases reliability of domain identification. It is shown that HCCP is insensitive to small variations of the pairwise correlation matrices. Particularly it produces identical results if the data obtained for the same protein crystallized with different spatial positions of domains are used for analysis. HCCP can utilize correlation matrices obtained by any method such as normal mode or essential dynamics analysis, Gaussian network or anisotropic network models, etc. These features make HCCP an attractive method for domain identification in proteins.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.bpc.2005.07.004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Genetic Correlations Among Dental, Mandibular, and Postcranial Dimensions in Rhesus Macaques (Macaca mulatta).
Am J Biol Anthropol
January 2025
Department of Medical Anatomical Sciences, College of Osteopathic Medicine of the Pacific-Northwest, Western University of Health Sciences, Lebanon, Oregon, USA.
Objectives: Tooth dimensions typically scale with mandibular and postcranial size in primates, although the exact pattern of scaling varies. This study assesses whether correlations by tissue type, anatomical region, or function (mastication or intrasexual competition) are present and could therefore act as evolutionary constraints on tooth-jaw-body size relationships by estimating genetic and phenotypic correlations between dental, mandibular, and postcranial dimensions in rhesus macaques (Macaca mulatta).
Materials And Methods: The teeth, mandibles, and postcrania of 362 adults from the Cayo Santiago skeletal collection were measured.
Quantifying Monomer-Dimer Distribution of Nanoparticles from Uncorrelated Optical Images Using Deep Learning.
ACS Omega
January 2025
Nanotechnology, IoT and Applied Machine Learning Research Group, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh.
Nanoparticles embedded in polymer matrices play a critical role in enhancing the properties and functionalities of composite materials. Detecting and quantifying nanoparticles from optical images (fixed samples-in vitro imaging) is crucial for understanding their distribution, aggregation, and interactions, which can lead to advancements in nanotechnology, materials science, and biomedical research. In this article, we propose an ensembled deep learning approach for automatic nanoparticle detection and oligomerization quantification in a polymer matrix for optical images.
View Article and Find Full Text PDFReal-time wastewater quality monitoring by fluorescence sensors: Validation for COD and CEC monitoring and implication for carbon footprint reduction.
Sci Total Environ
January 2025
Department of Civil Engineering and Architecture, University of Catania, Viale A. Doria 6, Catania, Italy. Electronic address:
This study investigated the applicability of a protein-like fluorescence sensor for wastewater quality monitoring. Several wastewater matrices, including raw, primary, secondary and tertiary effluents from three different wastewater treatment plants were used. Furthermore, the sensor was tested for the monitoring of quaternary effluent in a pilot scale plant installed downstream of a water reuse facility.
View Article and Find Full Text PDFExploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing.
Sci Rep
January 2025
Fischell Department of Bioengineering, University of Maryland, College Park, USA.
The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing.
View Article and Find Full Text PDFInhibition of chondroitin sulphate-degrading enzyme Chondroitinase ABC by dextran sulphate.
Glycoconj J
January 2025
School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia.
Chondroitin sulphate (CS) is a sulphated glycosaminoglycan (GAG) polysaccharide found on proteoglycans (CSPGs) in extracellular and pericellular matrices. Chondroitinase ABC (CSase ABC) derived from Proteus vulgaris is an enzyme that has gained attention for the capacity to cleave chondroitin sulphate (CS) glycosaminoglycans (GAG) from various proteoglycans such as Aggrecan, Neurocan, Decorin etc. The substrate specificity of CSase ABC is well-known for targeting various structural motifs of CS chains and has gained popularity in the field of neuro-regeneration by selective degradation of CS GAG chains.
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!