AntiCP 2.0: An updated model for predicting anticancer peptides

Agrawal, Piyush; Bhagat, Dhruv; Mahalwal, Manish; Sharma, Neelam; Raghava, Gajendra P. S.

doi:10.1101/2020.03.23.003780

Cited by 13 publications

(21 citation statements)

References 44 publications

(14 reference statements)

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“…In order to make a fair comparison with existing methods, the most recent and high-quality benchmark datasets (i.e. main and alternative datasets) collected from the work of AntiCP_2.0 25 were used in the development and validation of the iACP-FSCM model proposed herein. Both datasets can be downloaded from https ://webs.iiitd .edu.in/ragha va/antic p2/downl oad.php.…”

Section: Methodsmentioning

confidence: 99%

“…To avoid overestimation in the prediction model, the main and alternative dataset were randomly divided as the training (named MAIN-TR and ALTER-TR) and independent sets (named MAIN-TS and ALTER-TS) using the 80:20 ratio. Further details regarding the construction of the main and alternative datasets is provided in the original work of AntiCP_2.0 25 .…”

Section: Methodsmentioning

confidence: 99%

“…The prediction results of existing methods (i.e. AntiCP 8 , iACP 10 , ACPred 19 , PEPred-Suite 20 , ACPred-FL 15 , ACPred-Fuse 18 and AntiCP_2.0 25 ) recorded in Table 5 come directly from the work 25 . By observing the results listed in Table 5, it is clearly that the performance of iACP-FSCM is superior to that of existing methods with the highest Ac (0.825), Sp (0.903) and MCC (0.646).…”

Section: Aps Dps N5ps C5ps N5c5ps N10ps C10ps N10c10ps N15psmentioning

confidence: 99%

“…AntiCP 8 , Hajisharifi et al 's method 9 , ACPP 24 , iACP 10 , Li and Wang's method 11 , iACP-GAEnsC 12 , TargetACP 14 and ACPred 19 ) and the ensemble approach (i.e. MLACP 13 , ACPred 19 , PTPD 21 , ACP-DL 22 , PEPred-Suite 20 , ACPred-FL 15 , ACPred-Fuse 18 , PPTPP 23 and AntiCP_2.0 25 ) were widely used to develop ACP predictors. As summarized in a recent review 2 , we could see that TargetACP has been developed by integrating the split amino acid composition and pseudo positionspecific scoring matrix descriptors 14 , which was shown to outperform SVM-based predictors [8][9][10][11][12]19,24 .…”

mentioning

confidence: 99%

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Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method

Charoenkwan

Chiangjong

Lee

et al. 2021

Sci Rep

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As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning have been proposed for ACP identification. Although existing methods have afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble approaches that consequently leads to low interpretability and thus poses a challenge for biologists and biochemists. Therefore, it is desirable to develop a simple, interpretable and efficient predictor for accurate ACP identification as well as providing the means for the rational design of new anticancer peptides with promising potential for clinical application. Herein, we propose a novel flexible scoring card method (FSCM) making use of propensity scores of local and global sequential information for the development of a sequence-based ACP predictor (named iACP-FSCM) for improving the prediction accuracy and model interpretability. To the best of our knowledge, iACP-FSCM represents the first sequence-based ACP predictor for rationalizing an in-depth understanding into the molecular basis for the enhancement of anticancer activities of peptides via the use of FSCM-derived propensity scores. The independent testing results showed that the iACP-FSCM provided accuracies of 0.825 and 0.910 as evaluated on the main and alternative datasets, respectively. Results from comparative benchmarking demonstrated that iACP-FSCM could outperform seven other existing ACP predictors with marked improvements of 7% and 17% for accuracy and MCC, respectively, on the main dataset. Furthermore, the iACP-FSCM (0.910) achieved very comparable results to that of the state-of-the-art ensemble model AntiCP2.0 (0.920) as evaluated on the alternative dataset. Comparative results demonstrated that iACP-FSCM was the most suitable choice for ACP identification and characterization considering its simplicity, interpretability and generalizability. It is highly anticipated that the iACP-FSCM may be a robust tool for the rapid screening and identification of promising ACPs for clinical use.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Aps Dps N5ps C5ps N5c5ps N10ps C10ps N10c10ps N15psmentioning

confidence: 99%

mentioning

confidence: 99%

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Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method

Charoenkwan

Chiangjong

Lee

et al. 2021

Sci Rep

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“…For example, the AVPpred [ 43 ] made the first attempt to predict the antivirus peptides with amino acid composition (AAC), physicochemical features and support vector machine. The AntiCP [ 2 ] is developed for predicting novel peptides with anticancer functions. The iAMPpred [ 35 ] utilizes multiple compositional and physiochemical peptide descriptors and support vector machines for predicting the functional activities of AMP, including antibacteria, antivirus and antifungus.…”

Section: Introductionmentioning

confidence: 99%

Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies

Pang

Wang

Jhong

et al. 2021

Briefings in Bioinformatics

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As the current worldwide outbreaks of the SARS-CoV-2, it is urgently needed to develop effective therapeutic agents for inhibiting the pathogens or treating the related diseases. Antimicrobial peptides (AMP) with functional activity against coronavirus could be a considerable solution, yet there is no research for identifying anti-coronavirus (anti-CoV) peptides with the computational approach. In this study, we first investigated the physiochemical and compositional properties of the collected anti-CoV peptides by comparing against three other negative sets: antivirus peptides without anti-CoV function (antivirus), regular AMP without antivirus functions (non-AVP) and peptides without antimicrobial functions (non-AMP). Then, we established classifiers for identifying anti-CoV peptides between different negative sets based on random forest. Imbalanced learning strategies were adopted due to the severe class-imbalance within the datasets. The geometric mean of the sensitivity and specificity (GMean) under the identification from antivirus, non-AVP and non-AMP reaches 83.07%, 85.51% and 98.82%, respectively. Then, to pursue identifying anti-CoV peptides from broad-spectrum peptides, we designed a double-stages classifier based on the collected datasets. In the first stage, the classifier characterizes AMPs from regular peptides. It achieves an area under the receiver operating curve (AUCROC) value of 97.31%. The second stage is to identify the anti-CoV peptides between the combined negatives of other AMPs. Here, the GMean of evaluation on the independent test set is 79.42%. The proposed approach is considered as an applicable scheme for assisting the development of novel anti-CoV peptides. The datasets and source codes used in this study are available at https://github.com/poncey/PreAntiCoV.

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