1,721,069 research outputs found
Exploring the role of cyclodextrins as a cholesterol scavenger: a molecular dynamics investigation of conformational changes and thermodynamics
Full text linkThis study presents a comprehensive analysis of the cholesterol binding mechanism and conformational changes in cyclodextrin (CD) carriers, namely βCD, 2HPβCD, and MβCD. The results revealed that the binding of cholesterol to CDs was spontaneous and thermodynamically favorable, with van der Waals interactions playing a dominant role, while Coulombic interactions have a negligible contribution. The solubility of cholesterol/βCD and cholesterol/MβCD complexes was lower compared to cholesterol/2HPβCD complex due to stronger vdW and Coulombic repulsion between water and CDs. Hydrogen bonding was found to have a minor role in the binding process. The investigation of mechanisms and kinetics of binding demonstrated that cholesterol permeates into the CD cavities completely. Replicas consideration indicated that while the binding to 2HPβCD occurred perpendicularly and solely through positioning cholesterol's oxygen toward the primary hydroxyl rim (PHR), the mechanism of cholesterol binding to βCD and MβCD could take place with the orientation of oxygen towards both rims. Functionalization resulted in decreased cavity polarity, increased constriction tendency, and altered solubility and configuration of the carrier. Upon cholesterol binding, the CDs expanded, increasing the cavity volume in cholesterol-containing systems. The effects of cholesterol on the relative shape anisotropy (κ 2) and asphericity parameter (b) in cyclodextrins were investigated. βCD exhibited a spherical structure regardless of cholesterol presence, while 2HPβCD and MβCD displayed more pronounced non-sphericity in the absence of cholesterol. Loading cholesterol transformed 2HPβCD and MβCD into more spherical shapes, with increased probabilities of higher κ 2. MβCD showed a higher maximum peak of κ 2 compared to 2HPβCD after cholesterol loading, while 2HPβCD maintained a significant maximum peak at 0.2 for b
OSCP subunit of mitochondrial ATP synthase: role in regulation of enzyme function and of its transition to a pore.
No full textThe permeability transition pore (PTP) is a latent, high-conductance channel of the inner mitochondrial membrane. When
activated, it plays a key role in cell death and therefore in several diseases. The investigation of the PTP took an unexpected turn
after the discovery that cyclophilin D (the target of the PTP inhibitory effect of cyclosporin A) binds to FOF1 (F)-ATP synthase, thus
inhibiting its catalytic activity by about 30%. This observation was followed by the demonstration that binding occurs at a
particular subunit of the enzyme, the oligomycin sensitivity conferral protein (OSCP), and that F-ATP synthase can form
Ca2+-activated, high-conductance channels with features matching those of the PTP, suggesting that the latter originates from a
conformational change in F-ATP synthase. This review is specifically focused on the OSCP subunit of F-ATP synthase, whose
unique features make it a potential pharmacological target both for modulation of F-ATP synthase and its transition to a pore
Entropy of Two-Molecule Correlated Translational-Rotational Motions Using the kth Nearest Neighbor Method
No full textThe entropy associated with rotations, translations, and their coupled motions provides an important contribution to the free energy of many physicochemical processes such as association and solvation. The kth nearest neighbor method, which offers a convenient way to estimate the entropy in high-dimensional spaces, has been previously applied for translational-rotational entropy estimation. Here, we explore the possibility of extending the kth nearest neighbor method to the computation of the entropy of correlated translation-rotations of two molecules, i.e., in the product space of two translation-rotations, both referred to the same independent reference system, which is relevant for all cases in which the correlated translational-rotational motion of more than two molecules is involved. Numerical tests show that, albeit the relatively high dimensionality (12) of the space, the kth nearest neighbor approach provides an accurate estimate for the entropy of two correlated translational-rotational motions, even when computed from a limited number of samples
A novel de novo NIPA1 missense mutation associated to hereditary spastic paraplegia
No full textSPG6 accounts for 1% of autosomal dominant Hereditary Spastic Paraplegia (HSP) and is caused by pathogenic variants in NIPA1, which encodes a magnesium transporter located in plasma membrane and early endosomes, implicated in neuronal development and maintenance. Here we report a 39-year-old woman affected by progressive gait disturbance associated to absence seizures episodes within childhood. Clinical exome sequencing identified a likely pathogenic de novo heterozygous variant in NIPA1 (NM_144599.5 c.249 C > G; p.Asn83Lys). Molecular modelling was performed to evaluate putative functional consequence of the NIPA1 protein. Indeed, the Asn83Lys modification is predicted to induce a significant perturbation of the protein structure, altering signal transduction or small-molecule transport by modulating the length of the second transmembrane domain. This is the first study reporting a SPG6-affected patient harbouring the NIPA1 p.Asn83Lys mutation
The kth nearest neighbor method for estimation of entropy changes from molecular ensembles
Full text linkAll processes involving molecular systems entail a balance between associated enthalpic and entropic changes. Molecular dynamics simulations of the end-points of a process provide in a straightforward way the enthalpy as an ensemble average. Obtaining absolute entropies is still an open problem and most commonly pathway methods are used to obtain free energy changes and thereafter entropy changes. The kth nearest neighbor (kNN) method has been first proposed as a general method for entropy estimation in the mathematical community 20 years ago. Later, it has been applied to compute conformational, positional–orientational, and hydration entropies of molecules. Programs to compute entropies from molecular ensembles, for example, from molecular dynamics (MD) trajectories, based on the kNN method, are currently available. The kNN method has distinct advantages over traditional methods, namely that it is possible to address high-dimensional spaces, impossible to treat without loss of resolution or drastic approximations with, for example, histogram-based methods. Application of the method requires understanding the features of: the kth nearest neighbor method for entropy estimation; the variables relevant to biomolecular and in general molecular processes; the metrics associated with such variables; the practical implementation of the method, including requirements and limitations intrinsic to the method; and the applications for conformational, position/orientation and solvation entropy. Coupling the method with general approximations for the multivariable entropy based on mutual information, it is possible to address high dimensional problems like those involving the conformation of proteins, nucleic acids, binding of molecules and hydration. This article is categorized under: Molecular and Statistical Mechanics > Free Energy Methods Theoretical and Physical Chemistry > Statistical Mechanics Structure and Mechanism > Computational Biochemistry and Biophysics
Homology model building of the thyroid transcription factor 1 homeodomain
No full textA possible structure for the homeodomain of rat thyroid transcription factor 1 is proposed on the basis of the homology with other homeodomains whose structures have been solved by X-ray crystallography. A structure very similar to the reference ones is feasible and may account for the observed DNA-binding specificity. Structural features of the model, which are likely to be shared by other homeodomains, are discussed and may help researchers in the field to analyse their own experimental data
Generalized Born radii computation using linear models and neural networks
No full textMOTIVATION: Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. RESULTS: GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of 'perfect' GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with 'perfect' GB radii of 0.97 and ca. 20% smaller root mean square error. AVAILABILITY AND IMPLEMENTATION: We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
Molecular dynamics simulations of β2-microglobulin interaction with hydrophobic surfaces
No full textHydrophobic surfaces are known to adsorb and unfold proteins, a process that has been studied only for a few proteins. Here we address the interaction of β2-microglobulin, a paradigmatic protein for the study of amyloidogenesis, with hydrophobic surfaces. A system with 27 copies of the protein surrounded by a model cubic hydrophobic box is studied by implicit solvent molecular dynamics simulations. Most proteins adsorb on the walls of the box without major distortions in local geometry, whereas free molecules maintain proper structures and fluctuations as observed in explicit solvent molecular dynamics simulations. The major conclusions from the simulations are as follows: (i) the adopted implicit solvent model is adequate to describe protein dynamics and thermodynamics; (ii) adsorption occurs readily and is irreversible on the simulated timescale; (iii) the regions most involved in molecular encounters and stable interactions with the walls are the same as those that are important in protein-protein and protein-nanoparticle interactions; (iv) unfolding following adsorption occurs at regions found to be flexible by both experiments and simulations; (v) thermodynamic analysis suggests a very large contribution from van der Waals interactions, whereas unfavorable electrostatic interactions are not found to contribute much to adsorption energy. Surfaces with different degrees of hydrophobicity may occur in vivo. Our simulations show that adsorption is a fast and irreversible process which is accompanied by partial unfolding. The results and the thermodynamic analysis presented here are consistent with and rationalize previous experimental work
Statistical accuracy of molecular dynamics-based methods for sampling conformational ensembles of disordered proteins
Full text linkThe characterization of the statistical ensemble of conformations of intrinsically disordered regions (IDRs) is a great challenge both from experimental and computational points of view. In this respect, a number of protocols have been developed using molecular dynamics (MD) simulations to sample the huge conformational space of the molecule. In this work, we consider one of the best methods available, replica exchange solute tempering (REST), as a reference to compare the results obtained using this method with the results obtained using other methods, in terms of experimentally measurable quantities. Along with the methods assessed, we propose here a novel protocol called probabilistic MD chain growth (PMD-CG), which combines the flexible-meccano and hierarchical chain growth methods with the statistical data obtained from tripeptide MD trajectories as the starting point. The system chosen for testing is a 20-residue region from the C-terminal domain of the p53 tumor suppressor protein (p53-CTD). Our results show that PMD-CG provides an ensemble of conformations extremely quickly, after suitable computation of the conformational pool for all peptide triplets of the IDR sequence. The measurable quantities computed on the ensemble of conformations agree well with those based on the REST conformational ensemble
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