Optimal Transport for Free Energy Estimation.
Ontology highlight
ABSTRACT:
SUBMITTER: Decherchi S
PROVIDER: S-EPMC9940200 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
Publications
Optimal Transport for Free Energy Estimation.
The journal of physical chemistry letters 20230207 6
Optimal transport theory is a growing field of mathematics, which has recently found many applications. Here we take advantage of optimal transport for computational free energy estimation. We show analytically, and then via simulation, that this approach is effective in terms of optimizing the barriers of an alchemical transformation. ...[more]
Similar Datasets
Project description:Inferring gene regulatory networks from gene expression data is an important and challenging problem in the biology community. We propose OTVelo, a methodology that takes time-stamped single-cell gene expression data as input and predicts gene regulation across two time points. It is known that the rate of change of gene expression, which we will refer to as gene velocity, provides crucial information that enhances such inference; however, this information is not always available due to the limitations in sequencing depth. Our algorithm overcomes this limitation by estimating gene velocities using optimal transport. We then infer gene regulation using time-lagged correlation and Granger causality via regularized linear regression. Instead of providing an aggregated network across all time points, our method uncovers the underlying dynamical mechanism across time points. We validate our algorithm on 13 simulated datasets with both synthetic and curated networks and demonstrate its efficacy on 4 experimental data sets.
| S-EPMC11429941 | biostudies-literature
Project description:BackgroundEstimation of DNA duplex hybridization free energy is widely used for predicting cross-hybridizations in DNA computing and microarray experiments. A number of software programs based on different methods and parametrizations are available for the theoretical estimation of duplex free energies. However, significant differences in free energy values are sometimes observed among estimations obtained with various methods, thus being difficult to decide what value is the accurate one.ResultsWe present in this study a quantitative comparison of the similarities and differences among four published DNA/DNA duplex free energy calculation methods and an extended Nearest-Neighbour Model for perfect matches based on triplet interactions. The comparison was performed on a benchmark data set with 695 pairs of short oligos that we collected and manually curated from 29 publications. Sequence lengths range from 4 to 30 nucleotides and span a large GC-content percentage range. For perfect matches, we propose an extension of the Nearest-Neighbour Model that matches or exceeds the performance of the existing ones, both in terms of correlations and root mean squared errors. The proposed model was trained on experimental data with temperature, sodium and sequence concentration characteristics that span a wide range of values, thus conferring the model a higher power of generalization when used for free energy estimations of DNA duplexes under non-standard experimental conditions.ConclusionsBased on our preliminary results, we conclude that no statistically significant differences exist among free energy approximations obtained with 4 publicly available and widely used programs, when benchmarked against a collection of 695 pairs of short oligos collected and curated by the authors of this work based on 29 publications. The extended Nearest-Neighbour Model based on triplet interactions presented in this work is capable of performing accurate estimations of free energies for perfect match duplexes under both standard and non-standard experimental conditions and may serve as a baseline for further developments in this area of research.
| S-EPMC2837027 | biostudies-literature
Project description:Cell-free DNA (cfDNA) is a rich source of biomarkers for various pathophysiological conditions. Preanalytical variables, such as the library preparation protocol or sequencing platform, are major confounders of cfDNA analysis. We present DAGIP, a novel data correction method that builds on optimal transport theory and deep learning, which explicitly corrects for the effect of such preanalytical variables and can infer technical biases. Our method improves cancer detection and copy number alteration analysis by alleviating the sources of variation that are not of biological origin. It also enhances fragmentomic analysis of cfDNA. DAGIP allows the integration of cohorts from different studies.
| S-EPMC11887355 | biostudies-literature
Project description:We study the sensor and relay nodes' power scheduling problem for the remote state estimation in a Wireless Sensor Network (WSN) with relay nodes over a finite period of time given limited communication energy. We also explain why the optimal infinite time and energy case does not exist. Previous work applied a predefined threshold for the error covariance gap of two contiguous nodes in the WSN to adjust the trade-off between energy consumption and estimation accuracy. However, instead of adjusting the trade-off, we employ an algorithm to find the optimal sensor and relay nodes' scheduling strategy that achieves the smallest estimation error within the given energy limit under our model assumptions. Our core idea is to unify the sensor-to-relay-node way of error covariance update with the relay-node-to-relay-node way by converting the former way of the update into the latter, which enables us to compare the average error covariances of different scheduling sequences with analytical methods and thus finding the strategy with the minimal estimation error. Examples are utilized to demonstrate the feasibility of converting. Meanwhile, we prove the optimality of our scheduling algorithm. Finally, we use MATLAB to run our algorithm and compute the average estimation error covariance of the optimal strategy. By comparing the average error covariance of our strategy with other strategies, we find that the performance of our strategy is better than the others in the simulation.
| S-EPMC7070361 | biostudies-literature
Project description:In a reaction, determination of an optimal path with a high reaction rate (or a low free energy barrier) is important for the study of the reaction mechanism. This is a complicated problem that involves lots of degrees of freedom. For simple models, one can build an initial path in the collective variable space by the interpolation method first and then update the whole path constantly in the optimization. However, such interpolation method could be risky in the high dimensional space for large molecules. On the path, steric clashes between neighboring atoms could cause extremely high energy barriers and thus fail the optimization. Moreover, performing simulations for all the snapshots on the path is also time-consuming. In this paper, we build and optimize the path by a growing method on the free energy surface. The method grows a path from the reactant and extends its length in the collective variable space step by step. The growing direction is determined by both the free energy gradient at the end of the path and the direction vector pointing at the product. With fewer snapshots on the path, this strategy can let the path avoid the high energy states in the growing process and save the precious simulation time at each iteration step. Applications show that the presented method is efficient enough to produce optimal paths on either the two-dimensional or the twelve-dimensional free energy surfaces of different small molecules.
| S-EPMC5436793 | biostudies-literature
Project description:Accuracy of binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a multidimensional optimization pipeline is used to find optimal atomic radii, specifically for binding calculations in the implicit solvent. To reduce overfitting, the optimization target includes separate, weighted contributions from both binding and hydration free energies. The resulting five-parameter radii set, OPT_BIND5D, is evaluated against experiment for binding free energies of 20 host-guest (H-G) systems, unrelated to the types of structures used in the training. The resulting accuracy for this H-G test set (root mean square error of 2.03 kcal/mol, mean signed error of -0.13 kcal/mol, mean absolute error of 1.68 kcal/mol, and Pearson's correlation of r = 0.79 with the experimental values) is on par with what can be expected from the fixed charge explicit solvent models. Best agreement with the experiment is achieved when the implicit salt concentration is set equal or close to the experimental conditions.
| S-EPMC11684022 | biostudies-literature
Project description:The collective behavior of lipids with diverse chemical and physical features determines a membrane's thermodynamic properties. Yet, the influence of lipid physicochemical properties on lipid dynamics, in particular interbilayer transport, remains underexplored. Here, we systematically investigate how the activation free energy of passive lipid transport depends on lipid chemistry and membrane phase. Through all-atom molecular dynamics simulations of 11 chemically distinct glycerophospholipids, we determine how lipid acyl chain length, unsaturation, and headgroup influence the free energy barriers for two elementary steps of lipid transport: lipid desorption, which is rate limiting, and lipid insertion into a membrane. Consistent with previous experimental measurements, we find that lipids with longer, saturated acyl chains have increased activation free energies compared to lipids with shorter, unsaturated chains. Lipids with different headgroups exhibit a range of activation free energies; however, no clear trend based solely on chemical structure can be identified, mirroring difficulties in the interpretation of previous experimental results. Compared to liquid-crystalline phase membranes, gel phase membranes exhibit substantially increased free energy barriers. Overall, we find that the activation free energy depends on a lipid's local hydrophobic environment in a membrane and that the free energy barrier for lipid insertion depends on a membrane's interfacial hydrophobicity. Both of these properties can be altered through changes in lipid acyl chain length, lipid headgroup, and membrane phase. Thus, the rate of lipid transport can be tuned through subtle changes in local membrane composition and order, suggesting an unappreciated role for nanoscale membrane domains in regulating cellular lipid dynamics.
| S-EPMC8456290 | biostudies-literature
Project description:Understanding how an amino acid sequence folds into a functional, three-dimensional structure has proved to be a formidable challenge in biological research, especially for transmembrane proteins with multiple alpha helical domains. Mechanistic folding studies on helical membrane proteins have been limited to unusually stable, single domain proteins such as bacteriorhodopsin. Here, we extend such work to flexible, multidomain proteins and one of the most widespread membrane transporter families, the major facilitator superfamily, thus showing that more complex membrane proteins can be successfully refolded to recover native substrate binding. We determine the unfolding free energy of the two-domain, Escherichia coli galactose transporter, GalP; a bacterial homologue of human glucose transporters. GalP is reversibly unfolded by urea. Urea causes loss of substrate binding and a significant reduction in alpha helical content. Full recovery of helical structure and substrate binding occurs in dodecylmaltoside micelles, and the unfolding free energy can be determined. A linear dependence of this free energy on urea concentration allows the free energy of unfolding in the absence of urea to be determined as +2.5 kcal·mol(-1). Urea has often been found to be a poor denaturant for transmembrane helical structures. We attribute the denaturation of GalP helices by urea to the dynamic nature of the transporter structure allowing denaturant access via the substrate binding pocket, as well as to helical structure that extends beyond the membrane. This study gives insight into the final, critical folding step involving recovery of ligand binding for a multidomain membrane transporter.
| S-EPMC2972933 | biostudies-literature
Project description:DNA is the target of chemical compounds (drugs, pollutants, photosensitizers, etc.), which bind through non-covalent interactions. Depending on their structure and their chemical properties, DNA binders can associate to the minor or to the major groove of double-stranded DNA. They can also intercalate between two adjacent base pairs, or even replace one or two base pairs within the DNA double helix. The subsequent biological effects are strongly dependent on the architecture of the binding motif. Discriminating between the different binding patterns is of paramount importance to predict and rationalize the effect of a given compound on DNA. The structural characterization of DNA complexes remains, however, cumbersome at the experimental level. In this contribution, we employed all-atom molecular dynamics simulations to determine the standard binding free energy of DNA with netropsin, a well-characterized antiviral and antimicrobial drug, which associates to the minor groove of double-stranded DNA. To overcome the sampling limitations of classical molecular dynamics simulations, which cannot capture the large change in configurational entropy that accompanies binding, we resort to a series of potentials of mean force calculations involving a set of geometrical restraints acting on collective variables.
| S-EPMC6017086 | biostudies-literature
Project description:Protein photosensors are versatile tools for studying ligand-regulated allostery and signaling. Fundamental to these processes is the amount of energy that can be provided by a photosensor to control downstream signaling events. Such regulation is exemplified by the phototropins--plant serine/threonine kinases that are activated by blue light via conserved LOV (light, oxygen and voltage) domains. The core photosensor of oat phototropin 1 is a LOV domain that interacts in a light-dependent fashion with an adjacent alpha-helix (J alpha) to control kinase activity. We used solution NMR measurements to quantify the free energy of the LOV domain-J alpha-helix binding equilibrium in the dark and lit states. These data indicate that light shifts this equilibrium by approximately 3.8 kcal mol(-1), thus quantifying the energy available through LOV-J alpha for light-driven allosteric regulation. This study provides insight into the energetics of light sensing by phototropins and benchmark values for engineering photoswitchable systems based on the LOV-J alpha interaction.
| S-EPMC2597337 | biostudies-literature