Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning.

The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting.

Decreased serotonin transporter activity in the mitral valve contributes to progression of degenerative mitral regurgitation.

Degenerative mitral valve (MV) regurgitation (MR) is a highly prevalent heart disease that requires surgery in severe cases. Here, we show that a decrease in the activity of the serotonin transporter (SERT) accelerates MV remodeling and progression to MR. Through studies of a population of patients with MR, we show that selective serotonin reuptake inhibitor (SSRI) use and promoter polymorphism 5-HTTLPR LL genotype were associated with MV surgery at younger age.

Optimizing Antibody Affinity and Developability Using a Framework-CDR Shuffling Approach-Application to an Anti-SARS-CoV-2 Antibody.

The computational methods used for engineering antibodies for clinical development have undergone a transformation from three-dimensional structure-guided approaches to artificial-intelligence- and machine-learning-based approaches that leverage the large sequence data space of hundreds of millions of antibodies generated by next-generation sequencing (NGS) studies. Building on the wealth of available sequence data, we implemented a computational shuffling approach to antibody components, using the complementarity-determining region (CDR) and the framework region (FWR) to optimize an antibody for improved affinity and developability. This approach uses a set of rules to suitably combine the CDRs and FWRs derived from naturally occurring antibody sequences to engineer an antibody with high affinity and specificity.

Mitigation of Blood Borne Cell Attachment to Metal Implants through CD47-Derived Peptide Immobilization.

The key complications associated with bare metal stents and drug eluting stents are in-stent restenosis and late stent thrombosis, respectively. Thus, improving the biocompatibility of metal stents remains a significant challenge. The goal of this protocol is to describe a robust technique of metal surface modification by biologically active peptides to increase biocompatibility of blood contacting medical implants, including endovascular stents.

Stability and bioactivity of pepCD47 attachment on stainless steel surfaces.

In-stent restenosis (ISR) and late stent thrombosis are the major complications associated with the use of metal stents and drug eluting stents respectively. Our lab previously investigated the use of peptide CD47 in improving biocompatibility of bare metal stents in a rat carotid stent model and our results demonstrated a significant reduction in platelet deposition and ISR. However, this study did not characterize the stability of the pepCD47 on metal surfaces post storage, sterilization and deployment.

Cell death atlas of the postnatal mouse ventral forebrain and hypothalamus: effects of age and sex.

Naturally occurring cell death is essential to the development of the mammalian nervous system. Although the importance of developmental cell death has been appreciated for decades, there is no comprehensive account of cell death across brain areas in the mouse. Moreover, several regional sex differences in cell death have been described for the ventral forebrain and hypothalamus, but it is not known how widespread the phenomenon is.