Biosensors and Bioanalysis Laboratory (LABB), Department of Biological Chemistry and IQUIBICEN-CONICET, Exact and Natural Sciences Faculty (FCEN), University of Buenos Aires (UBA), Buenos Aires, Argentina.
Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India.
PLoS One. 2021 Mar 8;16(3):e0248159. doi: 10.1371/journal.pone.0248159. eCollection 2021.
A novel assay technique that involves quantification of lysozyme (Lys) through machine learning is put forward here. This article reports the tendency of the well- documented Ellington group anti-Lys aptamer, to produce aggregates when exposed to Lys. This property of apta-aggregation has been exploited here to develop an assay that quantifies the Lys using texture and area parameters from a photograph of the elliptical aggregate mass through machine learning. Two assay sets were made for the experimental procedure: one with high Lys concentration between 25-100 mM and another with low concentration between 1-20 mM. The high concentration set had a sample volume of 10 μl while the low concentration set had a higher sample volume of 100 μl, in order to obtain the statistical texture values reliably from the aggregate mass. The platform exhibited an experimental limit of detection of 1 mM and a response time of less than 10 seconds. Further, two potential operating modes for the aptamer were hypothesized for this aggregation property and the more accurate mode among the two was ascertained through bioinformatics studies.
本文提出了一种通过机器学习定量检测溶菌酶（Lys）的新型分析技术。本文报道了埃林顿集团经过充分验证的抗 Lys 适体在暴露于 Lys 时产生聚集物的趋势。在这里，我们利用适体聚集的特性，开发了一种通过机器学习从椭圆聚集质量的照片中提取纹理和面积参数来定量检测 Lys 的分析方法。实验过程中设置了两套检测体系：一套是高浓度 Lys（25-100mM），另一套是低浓度 Lys（1-20mM）。高浓度体系的样本体积为 10μl，而低浓度体系的样本体积为 100μl，以便从聚集质量中可靠地获得统计纹理值。该平台的实验检测限为 1mM，响应时间小于 10 秒。此外，基于这种聚集特性，我们假设了适体的两种潜在工作模式，并通过生物信息学研究确定了两种模式中更准确的一种。
