Remy Nguyen

Allegations that Thomas Jefferson fathered the children of the enslaved Sally Hemings emerged in the context of U.S. presidential politics in the late 1700s and early 1800s and entailed strongly contested denials and affirmations by family members, biographers, and historians ever since. Previous analysis of Y chromosome markers in contemporary members of the Jefferson and Hemings patrilines was consistent with the paternity hypothesis but rejected by those who pointed out that, absent Jefferson’s DNA and its direct comparison to the DNA of possible descendants, other Jefferson males could have fathered Sally Hemings’ children. In this study, we developed an innovative technique to recover DNA from multiple rootless hair samples in a museum collection whose provenance, mitochondrial genomes, Y chromosome markers, and identity to each other indicate that these hairs and the DNA from them are from Thomas Jefferson. Using these DNA data, we find clear evidence of Thomas Jefferson ancestry in descendants of both his wife Martha Wayles Jefferson and Sally Hemings. We measured the amount of identical-by-descent DNA shared between Thomas Jefferson and his known and putative descendants. We find that the amount of shared Jefferson DNA is more likely under the model of direct Thomas Jefferson paternity than other proposed scenarios.

Several methods exist for detecting genetic relatedness or identity by comparing DNA information. These methods generally require genotype calls, either single-nucleotide polymorphisms or short tandem repeats, at the sites used for comparison. For some DNA samples, like those obtained from bone fragments or single rootless hairs, there is often not enough DNA present to generate genotype calls that are accurate and complete enough for these comparisons. Here, we describe IBDGem, a fast and robust computational procedure for detecting genomic regions of identity-by-descent by comparing low-coverage shotgun sequence data against genotype calls from a known query individual. At less than 1× genome coverage, IBDGem reliably detects segments of relatedness and can make high-confidence identity detections with as little as 0.01× genome coverage.

Membrane-bound protein receptors are a primary biological drug target, but the computational analysis of membrane proteins has been limited. In order to improve molecular mechanics Poisson–Boltzmann surface area (MMPBSA) binding free energy calculations for membrane protein–ligand systems, we have optimized a new heterogeneous dielectric implicit membrane model, with respect to free energy simulations in explicit membrane and explicit water, and implemented it into the Amber software suite. This new model supersedes our previous uniform, single dielectric implicit membrane model by allowing the dielectric constant to vary with depth within the membrane. We calculated MMPBSA binding free energies for the human purinergic platelet receptor (P2Y₁₂R) and two of the muscarinic acetylcholine receptors (M2R and M3R) bound to various antagonist ligands using both membrane models, and we found that the heterogeneous dielectric membrane model has a stronger correlation with experimental binding affinities compared to the older model under otherwise identical conditions. This improved membrane model increases the utility of MMPBSA calculations for the rational design and improvement of future drug candidates.