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基于机器学习的芳香化酶相关蛋白计算方法。

Computational method for aromatase-related proteins using machine learning approach.

机构信息

Data Center/Bioinformatics, MTCC, CSIR-Institute of Microbial Technology, Chandigarh, India.

Department of Biophysics, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.

出版信息

PLoS One. 2023 Mar 29;18(3):e0283567. doi: 10.1371/journal.pone.0283567. eCollection 2023.

DOI:10.1371/journal.pone.0283567
PMID:36989252
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10057777/
Abstract

Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.

摘要

人类芳香酶是一种微粒体细胞色素 P450，在类固醇生成过程中催化雄激素向雌激素的芳香化。对于乳腺癌的治疗，第三代芳香酶抑制剂（AIs）已被证明是有效的；然而，患者对目前的 AIs 产生了耐药性。因此，有必要预测与芳香酶相关的蛋白质，以开发有效的 AIs。本研究采用五重交叉验证技术，建立了一种使用机器学习方法识别与芳香酶相关的蛋白质的方法。在这项研究中，使用了不同的基于 SVM 方法的模型，包括氨基酸、二肽组成、混合和进化特征的位置特异性评分矩阵（PSSM）形式；分别具有 87.42%、84.05%、85.12%和 92.02%的最大准确性。基于一级序列，该方法对预测与芳香酶相关的蛋白质具有很高的准确性。使用已知数据集开发了预测得分图，以检查该方法的性能。基于上述方法，开发了一个从原始序列数据预测与芳香酶相关蛋白质的网络服务器，并在 https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html 上实现。我们希望开发的方法将对与芳香酶蛋白相关的研究有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/03812e96f0bc/pone.0283567.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/77123d518d89/pone.0283567.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/b4ae2350277b/pone.0283567.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/136318a2c034/pone.0283567.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/4dc989b81596/pone.0283567.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/4b89802087c6/pone.0283567.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/03812e96f0bc/pone.0283567.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/77123d518d89/pone.0283567.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/b4ae2350277b/pone.0283567.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/136318a2c034/pone.0283567.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/4dc989b81596/pone.0283567.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/4b89802087c6/pone.0283567.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b0/10057777/03812e96f0bc/pone.0283567.g006.jpg

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