Carpenter Eric J, Seth Shaurya, Yue Noel, Greiner Russell, Derda Ratmir
Department of Chemistry, University of Alberta Edmonton Alberta Canada
Department of Computing Science, University of Alberta Edmonton Alberta Canada.
Chem Sci. 2022 May 16;13(22):6669-6686. doi: 10.1039/d1sc05681f. eCollection 2022 Jun 7.
Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet's output of continuous values provides more detailed results than the standard binary classification models. After incorporating a simple threshold to transform such continuous outputs the resulting GlyNet classifier outperforms those standard classifiers. GlyNet is the first multi-output regression model for predicting protein-glycan interactions and serves as an important benchmark, facilitating development of quantitative computational glycobiology.
诊断、治疗、疫苗、输血和器官移植等领域的进展都建立在对聚糖-蛋白质相互作用的基本理解之上。为了辅助这一研究，我们开发了GlyNet模型，该模型能够准确预测哺乳动物聚糖与352种聚糖结合蛋白之间的相互作用（相对结合强度），其中许多蛋白在多种浓度下均可预测。对于每个聚糖输入，我们的模型会产生1257个输出，每个输出代表输入聚糖与特定蛋白质样本之间的相对相互作用强度。GlyNet使用在功能糖组学联盟聚糖阵列上对599种聚糖测量的相对荧光单位（RFU）来学习这些连续值，并将这些值外推到来自其他未测试聚糖的RFU。GlyNet的连续值输出比标准二元分类模型提供了更详细的结果。在纳入一个简单的阈值以转换此类连续输出后，所得的GlyNet分类器优于那些标准分类器。GlyNet是第一个用于预测蛋白质-聚糖相互作用的多输出回归模型，是一个重要的基准，有助于定量计算糖生物学的发展。
