Accuracy of genomic prediction under scenario S1 for fat percentage by including actual genotypes from the two SNPs with largest effects into the low density SNP panel.

<p>Bulls in the training were genotyped on the 50 k panel, and bulls in the validation set were genotyped on low density panels.</p

Impact of Genotype Imputation on the Performance of GBLUP and Bayesian Methods for Genomic Prediction

<div><p>The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k). Five low density single nucleotide polymorphism (SNP) panels, containing 6,177, 2,480, 1,536, 768 and 384 SNPs, were simulated from the 50 k panel. A fraction of 0%, 33% and 66% of the animals were randomly selected from the training sets to have low density genotypes which were then imputed into 50 k genotypes. A GBLUP and a Bayesian method were used to predict direct genomic values (DGV) for validation animals using imputed or their actual 50 k genotypes. Traits studied included milk yield, fat percentage, protein percentage and somatic cell score (SCS). Results showed that performance of both GBLUP and Bayesian methods was influenced by imputation errors. For traits affected by a few large QTL, the Bayesian method resulted in greater reductions of accuracy due to imputation errors than GBLUP. Including SNPs with largest effects in the low density panel substantially improved the accuracy of genomic prediction for the Bayesian method. Including genotypes imputed from the 6 k panel achieved almost the same accuracy of genomic prediction as that of using the 50 k panel even when 66% of the training population was genotyped on the 6 k panel. These results justified the application of the 6 k panel for genomic prediction. Imputations from lower density panels were more prone to errors and resulted in lower accuracy of genomic prediction. But for animals that have close relationship to the reference set, genotype imputation may still achieve a relatively high accuracy.</p></div

Imputation accuracy under scenario S1 for two SNPs with largest effects on fat percentage.

<p>Bulls in the training were genotyped on the 50 k panel, and bulls in the validation set were genotyped on low density panels.</p>1<p>Locations of SNPs are shown as from the bovine genome assembly Btau4.2.</p

Accuracy of genomic prediction using imputed 50: Accuracies from using Bayesian model are parenthesized and accuracies for GBLUP are presented outside the parenthesis.

1<p>0%, 33%, and 66% of the training set in scenario S1, S2, and S3, respectively, and all bulls in the validation set were genotyped on the low density panel.</p

Accuracy of genotype imputation under different scenarios.

1<p>The reference population sizes used for imputation were n = 7,077, n = 4,741 and n = 2,406 for scenario S1, S2 and S3, respectively; 0%, 33%, and 66% of the training set in scenario S1, S2, and S3, respectively, and all bulls in the validation set were genotyped on the low density panel.</p

Additional file 3: of Genome-wide association and genomic prediction of breeding values for fatty acid composition in subcutaneous adipose and longissimus lumborum muscle of beef cattle

List of significant single nucleotide polymorphisms (SNP) for 81 fatty acid composition traits in longissimus lumborum muscle (LL). Results include trait name, SNP name, chromosome, position on the UMD3.1 genome assembly, alleles, allele substitution effect, percentage of total genetic variance explained, and posterior probability of inclusion of SNP. (CSV 27 kb

Structure-Guided Chemical Optimization of Bicyclic Peptide (<i>Bicycle</i>) Inhibitors of Angiotensin-Converting Enzyme 2

Angiotensin-converting enzyme 2 (ACE2) is a metalloprotease that cleaves angiotensin II, a peptide substrate involved in the regulation of hypertension. Here, we identified a series of constrained bicyclic peptides, Bicycle, inhibitors of human ACE2 by panning highly diverse bacteriophage display libraries. These were used to generate X-ray crystal structures which were used to inform the design of additional Bicycles with increased affinity and inhibition of ACE2 enzymatic activity. This novel structural class of ACE2 inhibitors is among the most potent ACE2 inhibitors yet described in vitro, representing a valuable tool to further probe ACE2 function and for potential therapeutic utility

Structure-Guided Chemical Optimization of Bicyclic Peptide (<i>Bicycle</i>) Inhibitors of Angiotensin-Converting Enzyme 2

Angiotensin-converting enzyme 2 (ACE2) is a metalloprotease that cleaves angiotensin II, a peptide substrate involved in the regulation of hypertension. Here, we identified a series of constrained bicyclic peptides, Bicycle, inhibitors of human ACE2 by panning highly diverse bacteriophage display libraries. These were used to generate X-ray crystal structures which were used to inform the design of additional Bicycles with increased affinity and inhibition of ACE2 enzymatic activity. This novel structural class of ACE2 inhibitors is among the most potent ACE2 inhibitors yet described in vitro, representing a valuable tool to further probe ACE2 function and for potential therapeutic utility

Discovery of BT8009: A Nectin‑4 Targeting Bicycle Toxin Conjugate for the Treatment of Cancer

Bicycle toxin conjugates (BTCs) are a promising new class of molecules for targeted delivery of toxin payloads into tumors. Herein we describe the discovery of BT8009, a Nectin-4 targeting BTC currently under clinical evaluation. Nectin-4 is overexpressed in multiple tumor types and is a clinically validated target for selective delivery of cytotoxic payloads. A Nectin-4 targeting bicyclic peptide was identified by phage display, which showed highly selective binding for Nectin-4 but suffered from low plasma stability and poor physicochemical properties. Multiparameter chemical optimization involving introduction of non-natural amino acids resulted in a lead Bicycle that demonstrated high affinity for Nectin-4, good stability in biological matrices, and a much-improved physicochemical profile. The optimized Bicycle was conjugated to the cytotoxin Monomethyl auristatin E via a cleavable linker to give the targeted drug conjugate BT8009, which demonstrates potent anticancer activity in in vivo rodent models