The subcellular distribution of small molecules: a meta-analysis

Abstract

To explore the extent to which current knowledge about the organelle-targeting features of small molecules may be applicable toward controlling the accumulation and distribution of exogenous chemical agents inside cells, molecules with known subcellular localization properties (as reported in the scientific literature) were compiled into a single data set. This data set was compared to a reference data set of approved drug molecules derived from the DrugBank database, and to a reference data set of random organic molecules derived from the PubChem database. Cheminformatic analysis revealed that molecules with reported subcellular localizations were comparably diverse. However, the calculated physicochemical properties of molecules reported to accumulate in different organelles were markedly overlapping. In relation to the reference sets of DrugBank and PubChem molecules, molecules with reported subcellular localizations were biased toward larger, more complex chemical structures possessing multiple ionizable functional groups and higher lipophilicity. Stratifying molecules based on molecular weight revealed that many physicochemical properties' trends associated with specific organelles were reversed in smaller vs larger molecules. Most likely, these reversed trends are due to the different transport mechanisms determining the subcellular localization of molecules of different sizes. Molecular weight can be dramatically altered by tagging molecules with fluorophores or by incorporating organelle targeting motifs. Generally, in order to better exploit structure-localization relationships, subcellular targeting strategies would benefit from analysis of the biodistribution effects resulting from variations in the size of the molecules.

Figure 1

Descriptor distributions of molecules with reported subcellular localization (filled gray area) and a random PubChem sample (solid line). Z-scores with an asterisk indicate a significant difference between the mean values of a descriptor in the group of compounds with reported localization and the reference PubChem dataset (p-value < 0.001). a_don: Hydrogen bond donor count. b_rotR: The fraction of rotatable bonds. glob: Globularity, a value of 1 indicates a perfect sphere while a value of 0 indicates a two- or one-dimensional object. logP_ow: Log of the octanol/water partition coefficient. weight: Molecular weight (including implicit hydrogens) in atomic mass units.

Figure 2

Descriptor distributions of molecules with reported subcellular localization (filled gray area) and random DrugBank dataset (solid line). Z-scores with an asterisk indicate a significant difference between the mean values of a descriptor in the group of compounds with reported localization and the reference DrugBank sample (p-value < 0.001). a_don: Hydrogen bond donor count. b_rotR: The fraction of rotatable bonds. glob: Globularity, with a value of 1 indicating a perfect sphere and a value of 0 indicating a two- or one-dimensional object. logP_ow: Log of the octanol/water partition coefficient. weight: Molecular weight (including implicit hydrogens) in atomic mass units.

Figure 3

Descriptor distributions of lower molecular weight (filled gray area; <500 Daltons) and higher molecular weight (solid line; > 500 Daltons) molecules with reported subcellular localization. Z-scores with an asterisk indicate a significant difference between the mean values of the descriptor in the lower and higher molecular weight groups (p-value < 0.001). a_don: Hydrogen bond donor count. b_rotR: The fraction of rotatable bonds. dipole moment: Dipole moment calculated from the partial charges of the molecule. glob: Globularity, with value of 1 indicating a perfect sphere and a value of 0 indicating a two- or one-dimensional object. logP_ow: Log of the octanol/water partition coefficient.

Figure 4

Calculated, formal charge distributions of lower molecular weight (filled gray area; <500 Daltons) and higher molecular weight (solid line; >500 Daltons) compounds with reported subcellular localization, at three different pH values.

Figure 5

Linear discriminant analysis of low (<500 Daltons) and high (>500 Daltons) molecular weight compounds with reported subcellular localizations. The axes of the plot represent linear combinations of seven molecular properties, identified using linear discriminant analysis to maximize the separation amongst the localization classes. LDA1 and LDA2 corresponded to the two, dominant linear combinations, with the “between class” variance accounting for 37% and 11% of the total variance, respectively. Additional discriminant factors (not shown) explained less than 3% of the total variance. The units on the two axes are relative and arbitrary.

Figure 6. The major subcellular localization categories are represented by diverse subsets of molecules

In the plot, the x-axis indicates the percentage similarity threshold and the y-axis indicates the percentage of population in the group that falls between the similarity thresholds. With a Tc threshold of 0.85, above 65% percent of the compounds in each localization category were similar to no more than 2.5% of the subset, indicating highly diverse compounds representing each category. The relatively high percentage (21%) of PM molecules that are similar to 2.5% to 5% other molecules in PM group indicates that PM molecules are least diverse (Key: Lyso = lysosomes; Mito = Mitochondria; Nuc = nuclei; PM = plasma membrane; EG = Endoplasmic reticulum/Golgi body; Cyto = cytosolic; Multi = multiple localizations).