A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

Charoenkwan, Phasit; Chotpatiwetchkul, Warot; Lee, Vannajan Sanghiran; Nantasenamat, Chanin; Shoombuatong, Watshara

doi:10.1038/s41598-021-03293-w

Cited by 38 publications

(52 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This might be due to the fact that LogitBoost were trained using larger samples. For the last independent dataset, it was constructed by Charoenkwan et al (2021[ 11 ]). This dataset was utilized to evaluate the performance of ThermoPred and SCMTPP.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, SCMTPP was applied to determine informative physicochemical properties (PCPs). Charoenkwan et al (2021[ 11 ]) reported that FUKS010101 (R = 0.616), FUKS010101 (R = 0.523) and FUKS010109 (R = 0.307) were considered as the top three informative PCP used for analyzing TPPs and non-TPPs. Charoenkwan et al's analysis showed that the content of hydrophobic amino acids in TPPs was not different from non-TPPs.…”

Section: Resultsmentioning

confidence: 99%

“…Unlike black-box methods, white-box models are able to determine which features provide essential contribution to the prediction performance, such as DT (Wu et al, 2009[ 58 ]), MLR (Wang and Li, 2014[ 57 ]), PLS (Zhang and Fang, 2006[ 61 ]) and SCM (Charoenkwan et al, 2022[ 10 ]). Amongst several white-box methods, SCM has been indicated to achieve comparable performance to those of black-box methods, such as NN and SVM (Charoenkwan et al, 2013[ 14 ], 2021[ 8 ][ 11 ], 2022[ 10 ]). Huang et al first introduced the original SCM method (Huang et al, 2012[ 31 ]), while Charoenkwan et al developed an improved version by integrating both global and local sequence information (Charoenkwan et al, 2021[ 8 ]).…”

Section: State-of-the-art Computational Approaches For Tpp Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

Charoenkwan,

Schaduangrat,

Moni

et al. 2022

EXCLI Journal; 21:Doc554; ISSN 1611-2156

Self Cite

View full text Add to dashboard Cite

Thermophilic proteins (TPPs) are critical for basic research and in the food industry due to their ability to maintain a thermodynamically stable fold at extremely high temperatures. Thus, the expeditious identification of novel TPPs through computational models from protein sequences is very desirable. Over the last few decades, a number of computational methods, especially machine learning (ML)-based methods, for in silico prediction of TPPs have been developed. Therefore, it is desirable to revisit these methods and summarize their advantages and disadvantages in order to further develop new computational approaches to achieve more accurate and improved prediction of TPPs. With this goal in mind, we comprehensively investigate a large collection of fourteen state-of-the-art TPP predictors in terms of their dataset size, feature encoding schemes, feature selection strategies, ML algorithms, evaluation strategies and web server/software usability. To the best of our knowledge, this article represents the first comprehensive review on the development of ML-based methods for in silico prediction of TPPs. Among these TPP predictors, they can be classified into two groups according to the interpretability of ML algorithms employed (i.e., computational black-box methods and computational white-box methods). In order to perform the comparative analysis, we conducted a comparative study on several currently available TPP predictors based on two benchmark datasets. Finally, we provide future perspectives for the design and development of new computational models for TPP prediction. We hope that this comprehensive review will facilitate researchers in selecting an appropriate TPP predictor that is the most suitable one to deal with their purposes and provide useful perspectives for the development of more effective and accurate TPP predictors.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: State-of-the-art Computational Approaches For Tpp Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

Charoenkwan,

Schaduangrat,

Moni

et al. 2022

EXCLI Journal; 21:Doc554; ISSN 1611-2156

Self Cite

View full text Add to dashboard Cite

show abstract

“…After performing parameter optimization, and λ values of 0.5 and 10, respectively, were used. The parameter optimization in the current study is the same as employed in our previous studies 31 – 34 .…”

Section: Methodsmentioning

confidence: 99%

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

Charoenkwan

Ahmed

Nantasenamat

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.

show abstract

“…More detailed information for the SCM classifier construction is provided in our previous studies. 13 − 15 , 17 , 20 …”

Section: Methodsmentioning

confidence: 99%

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides

et al. 2022

Self Cite

View full text Add to dashboard Cite

Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobial peptides have captured attention as novel drug candidates due to their rapid and broad-spectrum antimicrobial activity, anti-MRSA peptides have emerged as potential therapeutics for the treatment of bacterial infections. Although experimental approaches can precisely identify anti-MRSA peptides, they are usually cost-ineffective and labor-intensive. Therefore, computational approaches that are able to identify and characterize anti-MRSA peptides by using sequence information are highly desirable. In this study, we present the first computational approach (termed SCMRSA) for identifying and characterizing anti-MRSA peptides by using sequence information without the use of 3D structural information. In SCMRSA, we employed an interpretable scoring card method (SCM) coupled with the estimated propensity scores of 400 dipeptides. Comparative experiments indicated that SCMRSA was more effective and could outperform several machine learning-based classifiers with an accuracy of 0.960 and Matthews correlation coefficient of 0.848 on the independent test data set. In addition, we employed the SCMRSA-derived propensity scores to provide a more in-depth explanation regarding the functional mechanisms of anti-MRSA peptides. Finally, in order to serve community-wide use of the proposed SCMRSA, we established a user-friendly webserver which can be accessed online at http://pmlabstack.pythonanywhere.com/SCMRSA. SCMRSA is anticipated to be an open-source and useful tool for screening and identifying novel anti-MRSA peptides for follow-up experimental studies.

show abstract

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

Cited by 38 publications

References 66 publications

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides

Contact Info

Product

Resources

About