Dataset Information

Comparing Conformational Ensembles Using the Kullback-Leibler Divergence Expansion.

ABSTRACT: We present a thermodynamical approach to identify changes in macromolecular structure and dynamics in response to perturbations such as mutations or ligand binding, using an expansion of the Kullback-Leibler Divergence that connects local population shifts in torsion angles to changes in the free energy landscape of the protein. While the Kullback-Leibler Divergence is a known formula from information theory, the novelty and power of our implementation lies in its formal developments, connection to thermodynamics, statistical filtering, ease of visualization of results, and extendability by adding higher-order terms. We present a formal derivation of the Kullback-Leibler Divergence expansion and then apply our method at a first-order approximation to molecular dynamics simulations of four protein systems where ligand binding or pH titration is known to cause an effect at a distant site. Our results qualitatively agree with experimental measurements of local changes in structure or dynamics, such as NMR chemical shift perturbations and hydrogen-deuterium exchange mass spectrometry. The approach produces easy-to-analyze results with low background, and as such has the potential to become a routine analysis when molecular dynamics simulations in two or more conditions are available. Our method is implemented in the MutInf code package and is available on the SimTK website at https://simtk.org/home/mutinf.

SUBMITTER: McClendon CL

PROVIDER: S-EPMC3538811 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Comparing Conformational Ensembles Using the Kullback-Leibler Divergence Expansion.

McClendon Christopher L CL Hua Lan L Barreiro Abriela A Jacobson Matthew P MP

Journal of chemical theory and computation 20120413 6

We present a thermodynamical approach to identify changes in macromolecular structure and dynamics in response to perturbations such as mutations or ligand binding, using an expansion of the Kullback-Leibler Divergence that connects local population shifts in torsion angles to changes in the free energy landscape of the protein. While the Kullback-Leibler Divergence is a known formula from information theory, the novelty and power of our implementation lies in its formal developments, connection ...[more]

PMID: 23316121

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Project description:PurposePositron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual's risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual's risk of conversion from MCI to AD.MethodsFDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual's metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell's concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics.ResultsThe KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77-4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model).ConclusionThe KLSE indicator identifies abnormal brain networks predicting an individual's risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.

Dataset Information

Comparing Conformational Ensembles Using the Kullback-Leibler Divergence Expansion.

Publications

Comparing Conformational Ensembles Using the Kullback-Leibler Divergence Expansion.

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