Pred-BVP-Unb: Fast prediction of bacteriophage Virion proteins using un-biased multi-perspective properties with recursive feature elimination

Arif, Muhammad; Ali, Farman; Ahmad, Saeed; Kabir, Muhammad; Ali, Zakir; Hayat, Maqsood

doi:10.1016/j.ygeno.2019.09.006

Cited by 49 publications

(46 citation statements)

References 79 publications

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“…PhagePred [13] Multinomial NB g-gap DPC feature tree -c Tan et al's method [14] SVM GDC -Pred-BVP-Unb [16] SVM CT, SAAC, bi-PSSM -PVPred-SCM (This study) SCM DPC a ANN: artificial neural network, NB: Naïve Bayes, SCM: scoring card method, SVM: support vector machine. b AAC: amino acid composition, ATC: atomic composition, bi-PSSM: bi-profile position specific scoring matrix, CTD: chain-transition-distribution, CT: composition and translation, DPC: dipeptide composition, g-gap DPC: g-gap dipeptide composition, PCP: physicochemical properties, PIP: protein isoelectric points, SAAC: split amino acid composition.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers have exploited various types of machine learning (ML) algorithms using sequence features to directly predict PVPs including Seguritan et al's method [8], Feng et al's method [9], PVPred [10], Zhang et al's method [11], PVP-SVM [12], PhagePred [13], Tan et al's method [14], Ru et al's method [15], and Pred-BVP-Unb [16], as summarized in Table 1. In 2012, Seguritan et al [8] proposed the first predictor to identify viral structural proteins using an artificial neural network cooperating with a feature combination of amino acid composition (AAC) and protein isoelectric points.…”

Section: Introductionmentioning

confidence: 99%

“…Until now, the state-of-the-art PVP predictors include PVP-SVM [12] and Pred-BVP-Unb [16] of which both provide high prediction accuracies of 79.80% and 83.06%, respectively, as evaluated by the independent validation test. PVP-SVM was developed by utilizing many types of sequence features, i.e., AAC, DPC, atomic composition, chain-transition-distribution and physicochemical properties, as input feature and SVM as the prediction engine.…”

Section: Introductionmentioning

confidence: 99%

“…This study also gave a high number of PVPs and non-PVPs on the independent dataset (30 PVPs and 64 non-PVPs) by gathering protein sequences from the works of Ding et al [10] and the Universal Protein Resource (UniProt). More recently, Arif et al proposed a novel method called Pred-BVP-Unb [16] for discriminating PVPs from non-PVPs. Pred-BVP-Unb was developed by using the SVM model in conjunction with three types of protein features, i.e., composition and translation, split amino acid composition, and bi-profile position specific scoring matrix.…”

Section: Introductionmentioning

confidence: 99%

“…Although, Pred-BVP-Unb and other existing methods have their own merit and yielded encouraging performances with reasonably high prediction accuracies, the overall utility of the existing methods is limited in terms of few points as follows. Firstly, the underlying mechanisms of the investigated bioactivity for PVP-SVM [12] and Pred-BVP-Unb [16] affords limited interpretability for experimental scientists. Owing to the complex architecture of computational models and low interpretable features used in the study, it is not easy to identify and assess which features are beneficial for the biological activities of PVPs.…”

Section: Introductionmentioning

confidence: 99%

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PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Charoenkwan

Kanthawong

Schaduangrat

et al. 2020

Cells

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Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Charoenkwan

Kanthawong

Schaduangrat

et al. 2020

Cells

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Identifying blood‐brain barrier peptides by using amino acids physicochemical properties and features fusion method

Zou

2021

Peptide Science

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Blood-brain barrier peptides (BBPs) play a promising role in current drug study of central nervous system diseases. Hence, it is an urgent need to rapidly and accurately discriminating BBPs from non-BBPs. Experimental approaches are the first choice, however, these methods are expensive and take a lot of time. Thus, more and more researchers focused their attention on computational models. In current work, we developed a support vector machine (SVM) based model to identify BBPs. First, amino acids physicochemical properties were employed to represent peptide sequences, and Pearson's correlation coefficient (PCC) and maximal information coefficient (MIC) were applied to extract useful information. Then, similarity network fusion algorithm was utilized to integrate these two different kinds of features. Next, Fisher algorithm was used to pick out the discriminative features. Finally, these selected features were input into SVM for distinguishing BBPs from non-BBPs. The proposed model achieved 100.00% and 89.47% classification accuracies on training and independent datasets, respectively. Additionally, we found that pK2 (NH3) property of amino acid plays a key role in discriminating BBPs from non-BBPs. The results showed that our proposed method is effective, and achieved a significantly improvement in identifying BBPs, as compared with the state-of-the-art approach. The Matlab codes and datasets are freely available at https://figshare.com/articles/ online_resource/iBBPs_zip/14723766.blood-brain barrier peptides, MIC, PCC, similarity network fusion algorithm, support vector machine | INTRODUCTIONCurrently, central nervous system (CNS) diseases, such as Alzheimer's disease, [1,2] Parkinson's disease, [1,2] and brain tumor [3] are a serious threat to people's health. With the increasing of incidence of CNS diseases, more and more resource was devoted to study and treat CNS diseases.It is reported that CNS diseases account for about 25% of the burden of all diseases in high-income countries and Europe. [4] At present, the blood-brain barrier (BBB) is one of the key points in treatment of CNS diseases, which prevents drugs to arrive at their targets. [3,5] Fortunately, recent study found that some peptides have the ability to cross the BBB, these peptides were called as blood-brain barrier peptides (BBPs). [6] BBPs play an important role in CNS study and often show hormesis, [7] which provide a new idea to diagnose and treat brain diseases. Compared with conventional drugs, BBPs with the following advantages that are low immunogenicity and toxicity. [7,8] Hence, BBPs may be an ideal choice for the treatment of CNS diseases.Up to now, many efforts have been made in investigating BBPs, however, it is still a challenging task to identify BBPs. Experimental approaches are the first choice in BBPs identification, but these methods are expensive and take a lot of time, so researchers turned their attention to computational approaches. In fact, various computational models

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TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree

Arif

Ahmad

Ali

et al. 2020

J Comput Aided Mol Des

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Pred-BVP-Unb: Fast prediction of bacteriophage Virion proteins using un-biased multi-perspective properties with recursive feature elimination

Cited by 49 publications

References 79 publications

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Identifying blood‐brain barrier peptides by using amino acids physicochemical properties and features fusion method

TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree

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