Discovery of High-Affinity Protein Binding Ligands – Backwards

BACKGROUND: There is a pressing need for high-affinity protein binding ligands for all proteins in the human and other proteomes. Numerous groups are working to develop protein binding ligands but most approaches develop ligands using the same strategy in which a large library of structured ligands is screened against a protein target to identify a high-affinity ligand for the target. While this methodology generates high-affinity ligands for the target, it is generally an iterative process that can be difficult to adapt for the generation of ligands for large numbers of proteins. METHODOLOGY/PRINCIPAL FINDINGS: We have developed a class of peptide-based protein ligands, called synbodies, which allow this process to be run backwards--i.e. make a synbody and then screen it against a library of proteins to discover the target. By screening a synbody against an array of 8,000 human proteins, we can identify which protein in the library binds the synbody with high affinity. We used this method to develop a high-affinity synbody that specifically binds AKT1 with a K(d)<5 nM. It was found that the peptides that compose the synbody bind AKT1 with low micromolar affinity, implying that the affinity and specificity is a product of the bivalent interaction of the synbody with AKT1. We developed a synbody for another protein, ABL1 using the same method. CONCLUSIONS/SIGNIFICANCE: This method delivered a high-affinity ligand for a target protein in a single discovery step. This is in contrast to other techniques that require subsequent rounds of mutational improvement to yield nanomolar ligands. As this technique is easily scalable, we believe that it could be possible to develop ligands to all the proteins in any proteome using this approach

Estimating Uncertainty in Deep Learning for Reporting Confidence : An Application on Cell Type Prediction in Testes Based on Proteomics

Multi-label classification in deep learning is a practical yet challenging task, because class overlaps in the feature space means that each instance is associated with multiple class labels. This requires a prediction of more than one class category for each input instance. To the best of our knowledge, this is the first deep learning study which quantifies uncertainty and model interpretability in multi-label classification; as well as applying it to the problem of recognising proteins expressed in cell types in testes based on immunohistochemically stained images. Multi-label classification is achieved by thresholding the class probabilities, with the optimal thresholds adaptively determined by a grid search scheme based on Matthews correlation coefficients. We adopt MC-Dropweights to approximate Bayesian Inference in multi-label classification to evaluate the usefulness of estimating uncertainty with predictive score to avoid overconfident, incorrect predictions in decision making. Our experimental results show that the MC-Dropweights visibly improve the performance to estimate uncertainty compared to state of the art approaches

De novo NAD+ synthesis enhances mitochondrial function and improves health

Nicotinamide adenine dinucleotide (NAD+) is a co-substrate for several enzymes, including the sirtuin family of NAD+-dependent protein deacylases. Beneficial effects of increased NAD+ levels and sirtuin activation on mitochondrial homeostasis, organismal metabolism and lifespan have been established across species. Here we show that α-amino-β-carboxymuconate-ε-semialdehyde decarboxylase (ACMSD), the enzyme that limits spontaneous cyclization of α-amino-β-carboxymuconate-ε-semialdehyde in the de novo NAD+ synthesis pathway, controls cellular NAD+ levels via an evolutionarily conserved mechanism in Caenorhabditis elegans and mouse. Genetic and pharmacological inhibition of ACMSD boosts de novo NAD+ synthesis and sirtuin 1 activity, ultimately enhancing mitochondrial function. We also characterize two potent and selective inhibitors of ACMSD. Because expression of ACMSD is largely restricted to kidney and liver, these inhibitors may have therapeutic potential for protection of these tissues from injury. In summary, we identify ACMSD as a key modulator of cellular NAD+ levels, sirtuin activity and mitochondrial homeostasis in kidney and liver