Secreted and membrane‐bound members of the immunoglobulin superfamily (IgSF) encompass a large, diverse array of proteins that play central roles in immune response and neural development, and are implicated in diseases ranging from cancer to rheumatoid arthritis. Despite the potential biomedical benefits of understanding IgSF:IgSF cognate receptor‐ligand interactions, relatively little about them is known at a molecular level, and experimentally probing all possible receptor‐ligand pairs is prohibitively costly. The Protein Ligand Interface Design (ProtLID) algorithm is a computational pharmacophore‐based approach to identify cognate receptor‐ligand pairs that was recently validated in a pilot study on a small set of IgSF complexes. Although ProtLID has shown a success rate of 61% at identifying at least one cognate ligand for a given receptor, it currently lacks any form of confidence measure that can prioritize individual receptor‐ligand predictions to pursue experimentally. In this study, we expanded the application of ProtLID to cover all IgSF complexes with available structural data. In addition, we introduced an approach to estimate the confidence of predictions made by ProtLID based on a statistical analysis of how the ProtLID‐constructed pharmacophore matches the structures of candidate ligands. The confidence score combines the physicochemical compatibility, spatial consistency, and mathematical skewness of the distribution of matches throughout a set of candidate ligands. Our results suggest that a subset of cases meeting stringent confidence criteria will always have at least one successful receptor‐ligand prediction.

中文翻译：

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估计基于药效团检测免疫球蛋白超家族中同源受体-配体对的准确性

免疫球蛋白超家族 (IgSF) 的分泌型和膜结合型成员包含大量多样的蛋白质，这些蛋白质在免疫反应和神经发育中发挥核心作用，并涉及从癌症到类风湿性关节炎等疾病。尽管了解 IgSF:IgSF 同源受体-配体相互作用具有潜在的生物医学益处，但在分子水平上对它们的了解相对较少，并且通过实验探测所有可能的受体-配体对的成本高得令人望而却步。蛋白质配体界面设计 (ProtLID) 算法是一种基于计算药效团的方法，用于识别同源受体-配体对，最近在对一小部分 IgSF 复合物的初步研究中得到验证。尽管 ProtLID 在识别给定受体的至少一个同源配体方面显示出 61% 的成功率，它目前缺乏任何形式的置信度度量，可以优先考虑个体受体-配体预测以进行实验。在这项研究中，我们扩展了 ProtLID 的应用，以涵盖所有具有可用结构数据的 IgSF 复合物。此外，我们引入了一种基于 ProtLID 构建的药效团如何匹配候选配体结构的统计分析来估计 ProtLID 预测的置信度的方法。置信度分数结合了一组候选配体中匹配分布的物理化学相容性、空间一致性和数学偏度。我们的结果表明，满足严格置信标准的病例子集将始终具有至少一个成功的受体-配体预测。