Goupil-Lamy, A. | bioRxiv Science

bioRxiv2026

Molecular basis of Salla Disease: R39C Mutation Effects on the Lysosomal Transporter Sialin

Salla disease is caused by a genetic mutation in sialin, a lysosomal membrane transporter, which exports sialic acid from lysosomes. Substrate translocation occurs via a rocker-switch mechanism that alternately exposes the substrate-binding site to the lysosomal lumen and the cytosol. The pathogenic mutation R39C found in most Salla disease patients decreases the lysosomal localisation and the transport activity. In this study, we used computational and mutagenesis approaches to elucidate the molecular effects of the R39C mutation. Using three-dimensional models of human sialin in the lumen-open (LO) and cytosol-open (CO) states combined with the mutagenesis of selected residues, we identify a critical "triplet" motif comprising R39, E194, and E262, which is associated with an ionic lock formed between K197 and D350 in the LO conformation. Molecular dynamics simulations suggest that the electrostatic triplet negatively modulates the ionic lock, and are consistent with a strengthened ionic lock in R39C sialin, potentially favouring the LO state. To assess the global effects of the R39C mutation, we computed dynamic cross-correlation matrices and identified correlation patterns consistent with an allosteric coupling between the ionic lock K197/D350 and the region surrounding the sialic acid binding site in wild-type sialin, whereas in the LO state of R39C sialin, this communication preferentially bypasses this region. Therefore, the R39C mutation may impede the LO to CO conformational transition required for sialic acid transport, providing a plausible mechanistic framework for the decreased transport activity, and possibly the decreased lysosomal localisation, observed in Salla disease. HighlightsO_LIThe R39 residue participates in an interaction triplet, which negatively regulates an ionic lock stabilising the lumen-open conformation C_LIO_LIThe R39C mutation is associated with a stronger ionic lock in the simulations, and may favour the lumen-open state C_LIO_LICorrelation network analysis suggests an allosteric coupling between the ionic lock and the region surrounding the sialic acid binding site C_LIO_LIThe R39C mutation alters the inferred allosteric coupling between the ionic lock and the region surrounding the sialic acid binding site C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/719580v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1bf7144org.highwire.dtl.DTLVardef@1a53ab8org.highwire.dtl.DTLVardef@b2249forg.highwire.dtl.DTLVardef@1827244_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

AI driven approaches in Nanobody Epitope Prediction: Are We There Yet?

Nanobodies have emerged as a versatile class of biologics with promising therapeutic applications, driving the need for robust tools to predict their epitopes, a critical step for in silico affinity maturation and epitope-targeted design. While molecular docking has long been employed for epitope identification, it requires substantial expertise. With the advent of AI driven tools, epitope identification has become more accessible to a broader community increasing the risk of models misinterpretation. In this study, we critically evaluate the nanobody epitope prediction performance of two leading models: AlphaFold3 and AlphaFold2-Multimer (v.2.3.2), highlighting their strengths and limitations. Our analysis revealed that the overall success rate remains below 50% for both tools, with AlphaFold3 achieving a modest overall improvement. Interestingly, a significant improvement in AlphaFold3s performance was observed within a specific nanobody class. To address this discrepancy, we explored factors influencing epitope identification, demonstrating that accuracy heavily depends on CDR3 characteristics, such as its 3D spatial conformation and length, which drive binding interactions with the antigen. Additionally, we assessed the robustness of AlphaFold3s confidence metrics, highlighting their potential for broader applications. Finally, we evaluated different strategies aimed at improving prediction success rate. This study can be extended to assess the accuracy of emerging deep learning models adopting a similar approach to AlphaFold3.

bioinformatics↗