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Holmes, J. B.

Publications and source records attributed to Holmes, J. B..

4 recordsLinked to original sources

Imaging active site chemistry and protonation states: NMR crystallography of the tryptophan synthase α-aminoacrylate intermediate

NMR-assisted crystallography - the synergistic combination of solid-state NMR, X-ray crystallography, and first-principles computational chemistry - holds remarkable promise for mechanistic enzymology: by providing atomic-resolution characterization of stable intermediates in the enzyme active site - including hydrogen atom locations and tautomeric equilibria - it offers insight into structure, dynamics, and function. Here, we make use of this combined approach to characterize the -aminoacrylate intermediate in tryptophan synthase, a defining species for pyridoxal-5'-phosphate-dependent enzymes on the {beta}-elimination and replacement pathway. By uniquely identifying the protonation states of ionizable sites on the cofactor, substrates, and catalytic side chains, as well as the location and orientation of structural waters in the active site, a remarkably clear picture of structure and reactivity emerges. Most incredibly, this intermediate appears to be mere tenths of angstroms away from the preceding transition state in which the {beta}-hydroxyl of the serine substrate is lost. The position and orientation of the structural water immediately adjacent to the substrate {beta}-carbon suggests not only the fate of the hydroxyl group, but also the pathway back to the transition state and the identity of the active site acid-base catalytic residue. Reaction of this intermediate with benzimidazole (BZI), an isostere of the natural substrate, indole, shows BZI bound in the active site and poised for, but unable to initiate, the subsequent bond formation step. When modeled into the BZI position, indole is positioned with C3 in contact with the -aminoacrylate C{beta} and aligned for nucleophilic attack. Significance StatementThe determination of active site protonation states is critical to gaining a full mechanistic understanding of enzymatic transformations; yet hydrogen positions are challenging to extract using the standard tools of structural biology. Here we make use of a joint solid-state NMR, X-ray crystallography, and first-principles computational approach that unlocks the investigation of enzyme catalytic mechanism at this fine level of chemical detail. For tryptophan synthase, this allows us to peer along the reaction coordinates into and out of the -aminoacrylate intermediate. Through this process, we are developing a high-resolution probe for structural biology that is keenly sensitive to proton positions - rivaling that of neutron diffraction, yet able to be applied under conditions of active catalysis to microcrystalline and non-crystalline materials.

biophysics↗

Evaluating and improving heritability models using summary statistics

There is currently much debate regarding the best way to model how heritability varies across the genome. The authors of GCTA recommend the GCTA-LDMS-I Model, the authors of LD Score Regression recommend the Baseline LD Model, while we have instead recommended the LDAK Model. Here we provide a statistical framework for assessing heritability models using summary statistics from genome-wide association studies. Using data from studies of 31 complex human traits (average sample size 136,000), we show that the Baseline LD Model is the most realistic of the existing heritability models, but that it can be improved by incorporating features from the LDAK Model. Our framework also provides a method for estimating the selection-related parameter from summary statistics. We find strong evidence (P<1e-6) of negative genome-wide selection for traits including height, systolic blood pressure and college education, and that the impact of selection is stronger inside functional categories such as coding SNPs and promoter regions.

genetics↗

SPDI: Data Model for Variants and Applications at NCBI

MotivationNormalizing diverse representations of sequence variants is critical to the elucidation of the genetic basis of disease and biological function. NCBI has long wrestled with integrating data from multiple submitters to build databases such as dbSNP and ClinVar. Inconsistent representation of variants among variant callers, local databases, and tools results in discrepancies and duplications that complicate analysis. Current tools are not robust enough to manage variants in different formats and different reference sequence coordinates. ResultsThe SPDI (pronounced "speedy") data model defines variants as a sequence of 4 operations: start at the boundary before the first position in the sequence S, advance P positions, delete D positions, then insert the sequence in the string I, giving the data model its name, SPDI. The SPDI model can thus be applied to both nucleotide and protein variants, but the services discussed here are limited to nucleotide. Current services convert representations between HGVS, VCF, and SPDI and provide two forms of normalization. The first, based on the NCBI Variant Overprecision Correction Algorithm, returns a unique, normalized representation termed the "Contextual Allele" for any input. The SPDI name, with its four operations, defines exactly the reference subsequence potentially affected by the variant, even in low complexity regions such as homopolymer and dinucleotide sequence repeats. The second level of normalization depends on an alignment dataset (ADS). SPDI services perform remapping (AKA lift-over) of variants from the input reference sequence to return a list of all equivalent Contextual Alleles based on the transcript or genomic sequences that were aligned. One of these contextual alleles is selected to represent all, usually that based on the latest genomic assembly such as GRCh38 and is designated as the unique "Canonical Allele". ADS includes alignments between non-assembly RefSeq sequences (prefixed NM, NR, NG), as well inter- and intra-assembly-associated genomic sequences (NCs, NTs and NWs) and this allow for robust remapping and normalization of variants across sequences and assembly versions. Availability and implementationThe SPDI services are available for open access at: https://api.ncbi.nlm.nih.gov/variation/v0/ Contactholmesbr@ncbi.nlm.nih.gov

genomics↗

Summary statistic analyses do not correct confounding bias

LD SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability and genetic correlation using only genome wide association study (GWAS) test statistics. SumHer is a newly-introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was inadequate adjustment for confounding in the original GWAS analysis. We also show that choice of summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic when LDSC or SumHer is applied to test statistics from a meta-analysis of GWAS with different covariate adjustments.

genetics↗