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Diharce, J.

Publications and source records attributed to Diharce, J..

2 recordsLinked to original sources

Impact of the N-glycosylation on full-length IgG2 and IgG4 antibodies: a comparative study using molecular dynamics simulations.

Monoclonal antibodies are among the most important biomolecules in the pharmaceutic field. They could undergo several post-translational modifications, most notably N-glycosylation, yet the effect of these glycans remains poorly understood at the atomistic level, and the few existing studies focus on the prevalent immunoglobulin G1. We compared N-glycosylation in two structurally divergent monoclonal antibodies, Mab231, a murine IgG2a, and pembrolizumab, a human IgG4, which differ in species, sequence, architecture, and in the location and composition of their glycans. Using molecular dynamics simulations, we studied both antibodies with and without their glycans. In both, the glycan interactions extend beyond the Fc to reach Fab residues, more so for the more mobile Fc glycan and differently in the two antibodies. Allosteric network calculations reveal a potential impact of the glycan that can affect the Fab framework regions, which could in turn affect antigen binding. The glycans do not drastically alter the conformational landscape, and neither the inter-domain correlated motions nor the orientation of the Fab arms relative to the Fc can be resolved as glycan effects at three replicates per system. We also find that the effect of the Fc glycans on CH2 opening depends on the geometric criterion used, which underscores the need to consider full-length structures and the diversity of IgG scaffolds in glyco-engineering.

bioinformatics↗

Assessment of variant effect predictors unveils variants difficulty as a critical performance indicator

Amino acid substitutions in protein sequences are generally harmless, but a certain number of these changes can lead to disease. Accurate prediction of the impact of genetic variants is crucial for clinicians as it accelerates the diagnosis of patients with missense variants associated with health issues. Numerous computational tools have been developed for prediction of the pathogenicity of genetic variants based on different methodologies. Nowadays, many approaches are based on Machine Learning. Assessment of the performance of these diverse computational tools is crucial to provide guidance to both future users and especially clinicians. In this study, a large-scale study of 65 tools was conducted. Variants from both clinical and functional context have been used, incorporating data from the ClinVar database and bibliographic sources. The analysis showed that AlphaMissense is often performing very well and is actually the best option among existing tools. Additionally, meta-predictors, as expected, are of high quality and perform well on average. Tools using evolution information demonstrated highest performances on functional variants. These results also highlighted some variations in the difficulty to predict some specific variants while others are always well categorized. Strikingly, the majority of variants from the ClinVar database appear to be easy to predict, while variants from other sources of data are more challenging. These results demonstrate that this variant predictability can be classified into three distinct classes: easy, moderate and hard to predict. We analyzed the parameters leading to these differences and show that classes are linked to structural and functional information.

bioinformatics↗