Manfredi Matteo, Vazzana Gabriele, Babbi Giulia, Bertolini Elisa, Savojardo Castrense, Martelli Pier Luigi, Casadio Rita
Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Biocomputing Group and the Alma Climate Institute, University of Bologna, Bologna, Italy.
Methods Mol Biol. 2025;2941:189-199. doi: 10.1007/978-1-0716-4623-6_12.
The pathogenicity of human variants is an important annotation feature that may help in understanding, at a molecular level, the propensity for a human being to develop a certain disease or pathology. Recently, protein sequence embedding associated with machine and/or deep learning has been proven useful in improving results in this area. Different aspects of pathogenic variants can help in understanding the molecular mechanisms of the disease at a molecular level. These include solvent accessibility in the folded gene, the effect on the protein stability, and eventually the perturbation on interaction networks important for biological processes. Here, we describe how, once a variant is predicted "pathogenic", other important structural and functional properties can be derived computationally at the same website ( https://bioinformaticsweeties.biocomp.unibo.it/ ), including the protein structure, if not available. All the properties can help to understand variant effects within the complex context of the cell environment.
人类变异的致病性是一项重要的注释特征，它可能有助于在分子水平上理解人类患某种疾病或病理状况的倾向。最近，已证明与机器学习和/或深度学习相关的蛋白质序列嵌入有助于改善该领域的研究结果。致病性变异的不同方面有助于在分子水平上理解疾病的分子机制。这些方面包括折叠基因中的溶剂可及性、对蛋白质稳定性的影响，以及最终对生物过程重要的相互作用网络的扰动。在此，我们描述了一旦一个变异被预测为“致病性的”，如何在同一网站（https://bioinformaticsweeties.biocomp.unibo.it/ ）上通过计算得出其他重要的结构和功能特性，包括蛋白质结构（如果没有现成的）。所有这些特性都有助于在细胞环境的复杂背景下理解变异的影响。
