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Computational method for calculating fluorescence intensities within three-dimensional structures in cells.

ABSTRACT: The use of fluorescence microscopy is central to cell biology in general, and essential to many fields (e.g., membrane traffic) that rely upon it to identify cellular locations of molecules under study and the extent to which they co-localize with others. Rigorous localization or co-localization data require quantitative image analyses that can vary widely between fields and laboratories. While most published data use two-dimensional images, there is an increasing appreciation for the advantages of collecting three-dimensional data sets. These include the ability to evaluate the entire cell and avoidance of focal plane bias. This is particularly important when imaging and quantifying changes in organelles with irregular borders and which vary in appearance between cells in a population, e.g., the Golgi. We describe a method developed for quantifying changes in signal intensity of one protein within any three-dimensional structure, defined by the presence of a different marker. We use as examples of this method the quantification of adaptor recruitment to transmembrane protein cargos at the Golgi though it can be directly applied to any site in the cell. Together, these advantages facilitate rigorous statistical testing of differences between conditions, despite variations in organelle structure, and we believe that this method of quantification of fluorescence data can be productively applied to a wide array of experimental questions.

SUBMITTER: Caster AH

PROVIDER: S-EPMC3607619 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Computational method for calculating fluorescence intensities within three-dimensional structures in cells.

Caster Amanda H AH Kahn Richard A RA

Cellular logistics 20121001 4

The use of fluorescence microscopy is central to cell biology in general, and essential to many fields (e.g., membrane traffic) that rely upon it to identify cellular locations of molecules under study and the extent to which they co-localize with others. Rigorous localization or co-localization data require quantitative image analyses that can vary widely between fields and laboratories. While most published data use two-dimensional images, there is an increasing appreciation for the advantages ...[more]

PMID: 23538475

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Project description:Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (??G) could be calculated by linear scaling of the scores. The error of calculated ??G was within ? ± 1.5 kcal.mol(-1) of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.

Dataset Information

Computational method for calculating fluorescence intensities within three-dimensional structures in cells.

Publications

Computational method for calculating fluorescence intensities within three-dimensional structures in cells.

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