Biological processes are carried out through molecular conformational transitions, ranging from the structural changes within biomolecules to the formation of macromolecular complexes and the associations between the complexes themselves. These transitions cover a vast range of timescales and are governed by a tangled network of molecular interactions. The resulting hierarchy of interactions, in turn, becomes encoded in the experimentally measurable "mechanical fingerprints" of the biomolecules, their force-extension curves. However, how can we decode these fingerprints so that they reveal the kinetic barriers and the associated timescales of a biological process? Here, we show that this can be accomplished with a simple, model-free transformation that is general enough to be applicable to molecular interactions involving an arbitrarily large number of kinetic barriers. Specifically, the transformation converts the mechanical fingerprints of the system directly into a map of force-dependent rate constants. This map reveals the kinetics of the multitude of rate processes in the system beyond what is typically accessible to direct measurements. With the contributions from individual barriers to the interaction network now "untangled", the map is straightforward to analyze in terms of the prominent barriers and timescales. Practical implementation of the transformation is illustrated with simulated biomolecular interactions that comprise different patterns of complexity--from a cascade of activation barriers to competing dissociation pathways.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3799329 | PMC |
http://dx.doi.org/10.1073/pnas.1309101110 | DOI Listing |
J Phys Chem Lett
January 2025
Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.
Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments.
View Article and Find Full Text PDFFoods
January 2025
Center of Excellence Polymer Processing, Faculty of Engineering, Dunarea de Jos University of Galați, Domnească Street, No. 111, 800201 Galați, Romania.
Electrospinning is a versatile technique for obtaining nano/micro fibers which are able to significantly change the active properties of composite materials and bring in new dimensions to agri-food applications. Composite bio-based packaging materials obtained from whey proteins, functionalized with thyme essential oil (TEO) and reinforced by electrospun polylactic acid (PLA) fibers, represent a promising solution for developing new active food packaging using environmentally friendly materials. The aim of this study is to obtain and characterize one-side-active composite films covered with a PLA fiber mat: (i) WF/G1, WF/G2, and WF/G3 resulting from electrospinning with one needle at different electrospinning times of 90, 150, and 210 min, respectively, and (ii) WF/G4 obtained with two face-to-face needles after 210 min of electrospinning.
View Article and Find Full Text PDFCells
December 2024
Department of Herbal Pharmacology, College of Korean Medicine, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure-activity relationship (QSAR) modeling, structure-activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson-Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Oral and Maxillofacial Surgery, University Hospital Tübingen, 72076 Tübingen, Germany.
Cell functionality, driven by remarkable plasticity, is strongly influenced by mechanical forces that regulate mesenchymal stem cell (MSC) fate. This study explores the biomechanical properties of jaw periosteal cells (JPCs) and induced mesenchymal stem cells (iMSCs) under different culture conditions. We cultured both JPCs and iMSCs (n = 3) under normoxic and hypoxic environments, with and without osteogenic differentiation, and on laminin- or gelatin-coated substrates.
View Article and Find Full Text PDFInt J Pharm
January 2025
Department of Stem Cells and Regenerative Medicine, D. Y. Patil Education Society (Deemed to be University), Kolhapur 416006, India. Electronic address:
Managing wounds and accompanying consequences like exudation and microbiological infections is challenging in clinical practice. Bioactive compounds from traditional medicinal plants help heal wounds, although their bioavailability is low. This study uses sodium alginate (SA), gelatin (G), and Santalum album oil (SAL) to 3D print a polymeric hydrogel scaffold to circumvent these difficulties.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!