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Schmitz M, Ballestin JB, Liang J, Tomas F, Freist L, Voigt K, Di Ventura B, Öztürk MA. Int&in: A machine learning-based web server for active split site identification in inteins. Protein Sci 2024; 33:e4985. [PMID: 38717278 PMCID: PMC11078102 DOI: 10.1002/pro.4985] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/06/2024] [Accepted: 03/24/2024] [Indexed: 05/12/2024]
Abstract
Inteins are proteins that excise themselves out of host proteins and ligate the flanking polypeptides in an auto-catalytic process called protein splicing. In nature, inteins are either contiguous or split. In the case of split inteins, the two fragments must first form a complex for the splicing to occur. Contiguous inteins have previously been artificially split in two fragments because split inteins allow for distinct applications than contiguous ones. Even naturally split inteins have been split at unnatural split sites to obtain fragments with reduced affinity for one another, which are useful to create conditional inteins or to study protein-protein interactions. So far, split sites in inteins have been heuristically identified. We developed Int&in, a web server freely available for academic research (https://intein.biologie.uni-freiburg.de) that runs a machine learning model using logistic regression to predict active and inactive split sites in inteins with high accuracy. The model was trained on a dataset of 126 split sites generated using the gp41-1, Npu DnaE and CL inteins and validated using 97 split sites extracted from the literature. Despite the limited data size, the model, which uses various protein structural features, as well as sequence conservation information, achieves an accuracy of 0.79 and 0.78 for the training and testing sets, respectively. We envision Int&in will facilitate the engineering of novel split inteins for applications in synthetic and cell biology.
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Affiliation(s)
- Mirko Schmitz
- BIOSS and CIBSS Research Signalling Centers, University of FreiburgFreiburgGermany
- Institute of Biology II, University of FreiburgFreiburgGermany
- 4HF Biotec GmbHFreiburgGermany
| | - Jara Ballestin Ballestin
- BIOSS and CIBSS Research Signalling Centers, University of FreiburgFreiburgGermany
- Institute of Biology II, University of FreiburgFreiburgGermany
- Bioprocess Innovation Unit, ViraTherapeutics GmbHRumAustria
| | - Junsheng Liang
- BIOSS and CIBSS Research Signalling Centers, University of FreiburgFreiburgGermany
- Institute of Biology II, University of FreiburgFreiburgGermany
| | - Franziska Tomas
- BIOSS and CIBSS Research Signalling Centers, University of FreiburgFreiburgGermany
- Institute of Biology II, University of FreiburgFreiburgGermany
- Department of Molecular Life SciencesUniversity of ZurichZurichSwitzerland
| | - Leon Freist
- Institute of Biology III, University of FreiburgFreiburgGermany
| | - Karsten Voigt
- Institute of Biology III, University of FreiburgFreiburgGermany
| | - Barbara Di Ventura
- BIOSS and CIBSS Research Signalling Centers, University of FreiburgFreiburgGermany
- Institute of Biology II, University of FreiburgFreiburgGermany
| | - Mehmet Ali Öztürk
- BIOSS and CIBSS Research Signalling Centers, University of FreiburgFreiburgGermany
- Institute of Biology II, University of FreiburgFreiburgGermany
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Carneiro J, Magalhães RP, de la Oliva Roque VM, Simões M, Pratas D, Sousa SF. TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa. J Comput Aided Mol Des 2023; 37:265-278. [PMID: 37085636 DOI: 10.1007/s10822-023-00505-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/12/2023] [Indexed: 04/23/2023]
Abstract
Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.
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Affiliation(s)
- João Carneiro
- Interdisciplinary Centre of Marine and Environmental Research, CIIMAR, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, Porto, 4450-208, Portugal.
| | - Rita P Magalhães
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
| | - Victor M de la Oliva Roque
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
| | - Manuel Simões
- Faculty of Engineering, LEPABE Laboratory for Process Engineering, Environment, Biotechnology and Energy, University of Porto, Rua Dr. Roberto Frias, s/n, Porto, 4200-465, Portugal
- Faculty of Engineering, ALiCE-Associate Laboratory in Chemical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
| | - Diogo Pratas
- Institute of Electronics and Informatics Engineering of Aveiro, IEETA, University of Aveiro, Aveiro, Portugal
- Department of Electronics, Telecommunications and Informatics, DETI, University of Aveiro, Aveiro, Portugal
- Department of Virology, DoV, University of Helsinki, Helsinki, Finland
| | - Sérgio F Sousa
- Faculty of Medicine, Associate Laboratory i4HB-Institute for Health and Bioeconomy, University of Porto, 4200-319, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, UCIBIO-Applied Molecular Biosciences Unit, University of Porto, BioSIM, Porto, 4200-319, Portugal
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Aybey E, Gümüş Ö. SENSDeep: An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction. Interdiscip Sci 2023; 15:55-87. [PMID: 36346583 DOI: 10.1007/s12539-022-00543-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 10/15/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein-protein interaction sites (PPISs), these are costly, time-consuming, and require expertise. Therefore, many computational methods have been proposed to accelerate this type of research, but they are generally insufficient to predict PPISs accurately. There is a need for development in this field. METHODS In this study, we introduce a new PPISs prediction method. This method is a sequence-based Stacking ENSemble Deep (SENSDeep) learning method that has an ensemble learning model including the models of RNN, CNN, GRU sequence to sequence (GRUs2s), GRU sequence to sequence with an attention layer (GRUs2satt) and a multilayer perceptron. Two embedded features, secondary structure, and protein sequence information are added to the training data set in addition to twelve existing features to improve the prediction performance of the method. RESULTS SENSDeep trained on the training data set without two extra features obtains a better performance on some of the independent testing data sets than that of the other methods in the literature, especially on scoring metrics of sensitivity, F1, MCC, and AUPRC, having increments up to 63.5%, 19.3%, 18.5%, 11.4%, respectively. It is shown that the added extra features improve the performance of the method by having almost the same performance with less data as the method trained on the data set without these added features. On the other hand, different sizes of the sliding window are tried on the data sets and an optimal sliding window size for SENSDeep is found. Moreover, SENSDeep has also been compared to structure-based methods. Some of these methods have been found to perform better. Using SENSDeep obtained by training with both training data sets, PPISs prediction examples of various proteins that are not in these training data sets are also presented. Furthermore, execution times for SENSDeep and its submodels are shown. AVAILABILITY AND IMPLEMENTATION https://github.com/enginaybey/SENSDeep.
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Affiliation(s)
- Engin Aybey
- Department of Health Bioinformatics, Ege University, 35100, Bornova, Izmir, Turkey.
- Rectorate, Marmara University, 34722, Kadıköy, Istanbul, Turkey.
| | - Özgür Gümüş
- Department of Computer Engineering, Ege University, 35100, Bornova, Izmir, Turkey
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Li M, Wu Z, Wang W, Lu K, Zhang J, Zhou Y, Chen Z, Li D, Zheng S, Chen P, Wang B. Protein-Protein Interaction Sites Prediction Based on an Under-Sampling Strategy and Random Forest Algorithm. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3646-3654. [PMID: 34705656 DOI: 10.1109/tcbb.2021.3123269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The computational methods of protein-protein interaction sites prediction can effectively avoid the shortcomings of high cost and time in traditional experimental approaches. However, the serious class imbalance between interface and non-interface residues on the protein sequences limits the prediction performance of these methods. This work therefore proposed a new strategy, NearMiss-based under-sampling for unbalancing datasets and Random Forest classification (NM-RF), to predict protein interaction sites. Herein, the residues on protein sequences were represented by the PSSM-derived features, hydropathy index (HI) and relative solvent accessibility (RSA). In order to resolve the class imbalance problem, an under-sampling method based on NearMiss algorithm is adopted to remove some non-interface residues, and then the random forest algorithm is used to perform binary classification on the balanced feature datasets. Experiments show that the accuracy of NM-RF model reaches 87.6% and 84.3% on Dtestset72 and PDBtestset164 respectively, which demonstrate the effectiveness of the proposed NM-RF method in differentiating the interface or non-interface residues.
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Wang P, Zhang G, Yu ZG, Huang G. A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites. Front Genet 2021; 12:752732. [PMID: 34764983 PMCID: PMC8576272 DOI: 10.3389/fgene.2021.752732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022] Open
Abstract
Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.
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Affiliation(s)
- Pan Wang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
| | - Guiyang Zhang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China
| | - Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
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Deng A, Zhang H, Wang W, Zhang J, Fan D, Chen P, Wang B. Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm. Int J Mol Sci 2020; 21:E2274. [PMID: 32218345 PMCID: PMC7178137 DOI: 10.3390/ijms21072274] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/10/2020] [Accepted: 03/23/2020] [Indexed: 12/27/2022] Open
Abstract
The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.
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Affiliation(s)
- Aijun Deng
- Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui University of Technology), Ministry of Education, Ma'anshan 243002, China
- School of Metallurgical Engineering, Anhui University of Technology, Ma'anshan 243032, China
- Department of Engineering, University of Leicester, Leicester LE1 7RH, UK
| | - Huan Zhang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China
| | - Wenyan Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China
| | - Jun Zhang
- Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230032, China
| | - Dingdong Fan
- School of Metallurgical Engineering, Anhui University of Technology, Ma'anshan 243032, China
| | - Peng Chen
- Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230032, China
| | - Bing Wang
- Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui University of Technology), Ministry of Education, Ma'anshan 243002, China
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China
- Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230032, China
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