Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information

Kabir, Muhammad; Arif, Muhammad; Ahmad, Saeed; Ali, Zakir; Swati, Zar Nawab Khan; Yu, Dong‐Jun

doi:10.1016/j.chemolab.2018.09.007

Cited by 58 publications

(26 citation statements)

References 56 publications

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“…Recently, many sequence-based computational methods, for example, AntiCP [23], Hajisharifi et al’s method [24], ACPP [25], iACP [26], Li and Wang’s method [27], iACP-GAEnsC [28], MLACP [29], SAP [30] and TargetACP [31], have been developed using a wide range of machine learning methods and peptide features as summarized in Table 1. In 2014, Hajisharifi et al [24] established a benchmark dataset spanning 138 experimentally confirmed ACPs (positive dataset) and 227 non-AMPs (negative dataset).…”

Section: Introductionmentioning

confidence: 99%

“…This method yielded better prediction accuracy (95.06%) than the two previous methods [23,24]. Thusfar, state-of-the-art ACP predictors includes iACP-GAEnsC [28] and TargetACP [31] in which both afforded high prediction accuracies of 96.45% and 98.78%, respectively. The iACP-GAEnsC [28] method follows the concept of evolutionary intelligent genetic algorithm-based ensemble model for improving the true classification rate.…”

Section: Introductionmentioning

confidence: 99%

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ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

et al. 2019

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Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

et al. 2019

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“…Finally, the ensemble learning approach using a genetic algorithm was applied by combining the prediction rates of the five individual classifiers and reported high accuracy of 96.45%. Furthermore, Kabir et al proposed a "TargetACP" model using the HC dataset, that extracts sequential and evolutionary descriptors from peptide sequences [18]. Besides, an oversampling method was also applied to reduce the biases of the majority class.…”

Section: Introductionmentioning

confidence: 99%

cACP-2LFS: Classification of Anticancer Peptides Using Sequential Discriminative Model of KSAAP and Two-Level Feature Selection Approach

et al. 2020

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Cancer is a leading killer disease globally, it occurs when the cellular changes cause the abnormal growth and division of the cells. Conventional treatment such as therapies and wet experimental methods are deemed unsatisfactory and worthless because of its huge cost and laborious nature. However, the recent innovation of anticancer peptides (ACPs) offers an effective way to treat cancer affected cells. Due to the rapid growth of biological sequences, truly identification of ACPs has become a difficult task for scientists. Therefore, measuring the importance of ACPs, an efficient and reliable intelligent model is highly essential to accurately identify its pattern. In this study, three distinct nature encoding schemes are employed to obtain features from peptide sequences. However, K-space amino acid pair (KSAAP) is used to extract highly correlated and effective descriptors. Apart from the sequential features, composite physiochemical properties are applied to gather local structure descriptors. Furthermore, to represent the intrinsic residue information of amino acids, autocovariance is also used. Additionally, a novel two-level feature selection (2LFS) method is utilized to select high discriminative features and to minimize the dimensionality of the proposed descriptors. At last, to examine the performance of the proposed model, several learning hypotheses are investigated to select a superior operational engine. To measure the generalization capability, two diverse benchmark datasets are used. After evaluating the empirical outcomes, KSAAP using 2LFS reported high classification results on both datasets. Whereas, the classification outcomes reveal that our proposed cACP-2LFS achieved ~11% improved performance accuracy than present models in the literature so far. It is expected that our proposed model might be useful in the area of medicine, proteomics, and research academia. The source code and all datasets are publicly available at https://github.com/shahidawkum/cACP-2LFS.

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“…At the same time Kabir et al, proposed TargetACP, where the peptides were represented using split AAC, correlation factors extracted from PSSM profiles (PsePSSM), and composite protein sequence representation (CPSR). They also used SVM, RF and KNN classifiers as their employed models [13].…”

Section: Introductionmentioning

confidence: 99%

“…ACP-DL extracts features from two one-hot vector-based peptide representation techniques (binary profile and k-mer sparse matrix) that only depict the presence of a specific amino acid or a group of amino acids along different positions of the sequences. As a result, physicochemical properties or evolutionary substitution information of the residues, which contain significant signals regarding anticancer activities of peptide sequences are not utilized in ACP-DL's feature extraction process [3,14,11,13]. As a result, although the predictive performance of ACP-DL is quite impressive, there is still room for significant improvement.…”

Section: Introductionmentioning

confidence: 99%

ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides

Ahmed

Muhammod

Adilina

et al. 2020

Preprint

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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 through simultaneous interaction with 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. 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.

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Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information

Cited by 58 publications

References 56 publications

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

cACP-2LFS: Classification of Anticancer Peptides Using Sequential Discriminative Model of KSAAP and Two-Level Feature Selection Approach

ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides

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