Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and antibody docking methods are still unreliable.

Benchmarking and improving the performance of variant-calling pipelines with RecallME.

Motivation: The steady increment of Whole Genome/Exome sequencing and the development of novel Next Generation Sequencing-based gene panels requires continuous testing and validation of variant calling (VC) pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize VC parameters remains unmet.

Evaluation of Selenomethionine Entrapped in Nanoparticles for Oral Supplementation Using In Vitro, Ex Vivo and In Vivo Models.

Selenium methionine (SeMet) is an essential micronutrient required for normal body function and is associated with additional health benefits. However, oral administration of SeMet can be challenging due to its purported narrow therapeutic index, low oral bioavailability, and high susceptibility to oxidation. To address these issues, SeMet was entrapped in zein-coated nanoparticles made from chitosan using an ionic gelation formulation.

Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathogenicity likelihood score (PLS).