SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information
Sortase enzymes are cysteine transpeptidases that embellish the surface of Gram-positive bacteria with various proteins thereby allowing these microorganisms to interact with their neighboring environment. It is known that several of their substrates can cause pathological implications, so researche...
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Format: | Article |
Language: | English |
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Elsevier
2022-01-01
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Series: | Computational and Structural Biotechnology Journal |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037021005237 |
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author | Adeel Malik Sathiyamoorthy Subramaniyam Chang-Bae Kim Balachandran Manavalan |
author_facet | Adeel Malik Sathiyamoorthy Subramaniyam Chang-Bae Kim Balachandran Manavalan |
author_sort | Adeel Malik |
collection | DOAJ |
description | Sortase enzymes are cysteine transpeptidases that embellish the surface of Gram-positive bacteria with various proteins thereby allowing these microorganisms to interact with their neighboring environment. It is known that several of their substrates can cause pathological implications, so researchers have focused on the development of sortase inhibitors. Currently, six different classes of sortases (A-F) are recognized. However, with the extensive application of bacterial genome sequencing projects, the number of potential sortases in the public databases has exploded, presenting considerable challenges in annotating these sequences. It is very laborious and time-consuming to characterize these sortase classes experimentally. Therefore, this study developed the first machine-learning-based two-layer predictor called SortPred, where the first layer predicts the sortase from the given sequence and the second layer predicts their class from the predicted sortase. To develop SortPred, we constructed an original benchmarking dataset and investigated 31 feature descriptors, primarily on five feature encoding algorithms. Afterward, each of these descriptors were trained using a random forest classifier and their robustness was evaluated with an independent dataset. Finally, we selected the final model independently for both layers depending on the performance consistency between cross-validation and independent evaluation. SortPred is expected to be an effective tool for identifying bacterial sortases, which in turn may aid in designing sortase inhibitors and exploring their functions. The SortPred webserver and a standalone version are freely accessible at: https://procarb.org/sortpred. |
first_indexed | 2024-04-11T05:20:24Z |
format | Article |
id | doaj.art-69c1e666e3864fc6840c910707438835 |
institution | Directory Open Access Journal |
issn | 2001-0370 |
language | English |
last_indexed | 2024-04-11T05:20:24Z |
publishDate | 2022-01-01 |
publisher | Elsevier |
record_format | Article |
series | Computational and Structural Biotechnology Journal |
spelling | doaj.art-69c1e666e3864fc6840c9107074388352022-12-24T04:50:57ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-0120165174SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived informationAdeel Malik0Sathiyamoorthy Subramaniyam1Chang-Bae Kim2Balachandran Manavalan3Institute of Intelligence Informatics Technology, Sangmyung University, Seoul 03016, Republic of KoreaResearch and Development Center, Insilicogen Inc., Yongin-si 16954, Gyeonggi-do, Republic of KoreaDepartment of Biotechnology, Sangmyung University, Seoul 03016, Republic of Korea; Corresponding authors.Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea; Corresponding authors.Sortase enzymes are cysteine transpeptidases that embellish the surface of Gram-positive bacteria with various proteins thereby allowing these microorganisms to interact with their neighboring environment. It is known that several of their substrates can cause pathological implications, so researchers have focused on the development of sortase inhibitors. Currently, six different classes of sortases (A-F) are recognized. However, with the extensive application of bacterial genome sequencing projects, the number of potential sortases in the public databases has exploded, presenting considerable challenges in annotating these sequences. It is very laborious and time-consuming to characterize these sortase classes experimentally. Therefore, this study developed the first machine-learning-based two-layer predictor called SortPred, where the first layer predicts the sortase from the given sequence and the second layer predicts their class from the predicted sortase. To develop SortPred, we constructed an original benchmarking dataset and investigated 31 feature descriptors, primarily on five feature encoding algorithms. Afterward, each of these descriptors were trained using a random forest classifier and their robustness was evaluated with an independent dataset. Finally, we selected the final model independently for both layers depending on the performance consistency between cross-validation and independent evaluation. SortPred is expected to be an effective tool for identifying bacterial sortases, which in turn may aid in designing sortase inhibitors and exploring their functions. The SortPred webserver and a standalone version are freely accessible at: https://procarb.org/sortpred.http://www.sciencedirect.com/science/article/pii/S2001037021005237SortaseMachine learningRandom forestCysteine transpeptidaseHybrid featuresBioinformatics |
spellingShingle | Adeel Malik Sathiyamoorthy Subramaniyam Chang-Bae Kim Balachandran Manavalan SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information Computational and Structural Biotechnology Journal Sortase Machine learning Random forest Cysteine transpeptidase Hybrid features Bioinformatics |
title | SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information |
title_full | SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information |
title_fullStr | SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information |
title_full_unstemmed | SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information |
title_short | SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information |
title_sort | sortpred the first machine learning based predictor to identify bacterial sortases and their classes using sequence derived information |
topic | Sortase Machine learning Random forest Cysteine transpeptidase Hybrid features Bioinformatics |
url | http://www.sciencedirect.com/science/article/pii/S2001037021005237 |
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