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The work of molecular machines such as the ribosome is accompanied by conformational changes, often characterized by relative motions of their domains. The method we have developed seeks to quantify these motions in a general way, facilitating comparisons of results obtained by different researchers. Typically there are multiple snapshots of a structure in the form of pdb coordinates resulting from flexible fitting of low-resolution density maps, from X-ray crystallography, or from molecular dynamics simulation trajectories. Our objective is to characterize the motion of each domain as a coordinate transformation using moments of inertia tensor, a method we developed earlier. What has been missing until now are ancillary tools that make this task practical, general, and biologically informative. We have provided a comprehensive solution to this task with a set of tools implemented on the VMD platform. These tools address the need for reproducible segmentation of domains, and provide a generalized description of their motions using principal axes of inertia. Although this methodology has been specifically developed for studying ribosome motion, it is applicable to any molecular machine.
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http://dx.doi.org/10.1021/acs.jpcb.6b10732 | DOI Listing |
J Chem Theory Comput
September 2025
Dipartimento di Chimica, Università di Pavia, Via Taramelli 12, Pavia 27100, Italy.
Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models' predictions. Indeed, we previously demonstrated that combining molecular dynamics (MD)-derived descriptors with ML models allows to effectively classify kinase ligands as allosteric or orthosteric.
View Article and Find Full Text PDFPharm Res
September 2025
Axcelead Tokyo West Partners, Inc. Translational Science, Discovery DMPK, Hino-Shi, Tokyo, 191-0065, Japan.
Purpose: Accurate prediction of human clearance (CL) is essential in early drug development. Single Species Scaling (SSS) using rat pharmacokinetic (PK) data, particularly with unbound plasma fraction (f), is widely used. However, its accuracy declines for compounds with extremely low f, and no systematic method has addressed this limitation.
View Article and Find Full Text PDFNature
September 2025
Department of Translational Genomics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Small cell lung cancer (SCLC) is a highly aggressive type of lung cancer, characterized by rapid proliferation, early metastatic spread, frequent early relapse and a high mortality rate. Recent evidence has suggested that innervation has an important role in the development and progression of several types of cancer. Cancer-to-neuron synapses have been reported in gliomas, but whether peripheral tumours can form such structures is unknown.
View Article and Find Full Text PDFNucleic Acids Res
September 2025
Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland.
Spatial omics allow for the molecular characterization of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, give rise to very different data modalities. The characteristics of the two data types are well known in spatial statistics as point patterns and lattice data.
View Article and Find Full Text PDFJ Genet Genomics
September 2025
State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangd
The genetic basis of early-stage salt tolerance in alfalfa (Medicago sativa L.), a key factor limiting its productivity, remains poorly understand. To dissect this complex trait, we integrate genome-wide association study (GWAS) and transcriptomics (RNA-seq) from 176 accessions within a machine learning based genomic prediction framework.
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