SEMal: Accurate protein malonylation site predictor using structural and evolutionary information

Dipta, Shubhashis Roy; Taherzadeh, Ghazaleh; Ahmad, Md. Wakil; Arafat, Md. Easin; Shatabda, Swakkhar; Dehzangi, Abdollah

doi:10.1016/j.compbiomed.2020.104022

Cited by 15 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also compared ACP-MHCNN with some of the widely used classical Machine Learning classifiers in similar studies such as Support Vector Machine (SVM), Random Forest RF, Extra Tree (ET), eXtreme Gradient Boosting (XGB), k-Nearest Neighbours (KNN), Decision Tree (DT), Naive Bayes (NB), and Adaptive Boosting (AB) 54 – 56 . To do this, we convert BPF, Physicochemical Properties, and Evolutionary Information to vector from matrix and use to train these classifiers.…”

Section: Resultsmentioning

confidence: 99%

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides

Ahmed

Muhammod

Khan

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN. ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/.

show abstract

Section: Resultsmentioning

confidence: 99%

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides

Ahmed

Muhammod

Khan

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has one irregular secondary structure type, the coil region, along with two regular secondary structure states, the -helix (H), and -strand (E) (C). Structural information has been applied to several types of PTM prediction, including succinylation [ 36 ], ubiquitination [ 37 ], and malonylation [ 38 ]. However, as far as we are aware, it has not yet been used for Kglu's prediction.…”

Section: Methodsmentioning

confidence: 99%

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier

Liu,

Zhu,

Dai

et al. 2023

BMC Genomics

View full text Add to dashboard Cite

Background Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention. Results In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively. Conclusion GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite.

show abstract

“…It has one irregular secondary structure type, the coil region, along with two regular secondary structure states, the -helix (H), and -strand (E) (C). Structural information has been applied to several types of PTM prediction, including succinylation [25] , ubiquitination [26] , and malonylation [27] . However, as far as we are aware, it has not yet been used for Kglu's prediction.…”

Section: Structure-based Featurementioning

confidence: 99%

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier

Liu

Zhu

Dai

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Lysine glutarylation(Kglu) is one of the most important Post-translational modifications(PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiment, computational-based Kglu site prediction research are gaining more and more attention.Results In this study, we proposed a new model named GBDT_KgluSite to identify Kglu and non-Kglu sequences based on the gradient-boosting decision tree (GBDT). Here, sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. The imbalance between positive and negative samples was addressed using the NearMiss-3 approach, and extraneous data was eliminated using the Elastic Net. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively.Conclusion GBDT_KgluSite is an effectively computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in Mus musculus protein. The code and dataset of GBDT_KgluSite is available at https://github.com/flyinsky6/GBDT_KgluSite.

show abstract

SEMal: Accurate protein malonylation site predictor using structural and evolutionary information

Cited by 15 publications

References 43 publications

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier

Contact Info

Product

Resources

About