Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

Pande, A. K.; Patiyal, Sumeet; Lathwal, Anjali; Arora, Chakit; Kaur, Dilraj; Dhall, Anjali; Mishra, Gaurav; Kaur, Harpreet; Sharma, Neelam; Jain, Shreyansh; Usmani, Salman Sadullah; Agrawal, Piyush; Kumar, Rajesh; Kumar, Vinod; Raghava, Gajendra P. S.

doi:10.1089/cmb.2022.0241

Cited by 35 publications

(31 citation statements)

References 105 publications

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“…PSSM-400 is a 20 × 20 dimension vector for a protein sequence which comprises the measure of occurrences of 20 amino acids in the sequence. We have created a PSSM matrix for each sequence which was first normalized within the range of 0 to 1 and converted into a PSSM composition of size 20 × 20 vector using Pfeature software [31].…”

Section: Methodsmentioning

confidence: 99%

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A random forest model for predicting exosomal proteins using evolutionary information and motifs

Arora

Patiyal

Sharma

et al. 2023

Preprint

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Identification of secretory proteins in body fluids is one of the key challenges in the development of non-invasive diagnostics. It has been shown in the part that a significant number of proteins are secreted by cells via exosomes called exosomal proteins. In this study, an attempt has been made to build a model that can predict exosomal proteins with high precision. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method BLAST was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning based models have been developed using compositional features of proteins and achieved highest AUROC of 0.70. The performance of the ML-based models improved significantly to AUROC of 0.73 when evolutionary information in the form of PSSM profiles was used for building models. Our analysis indicates that exosomal proteins have wide range of motifs. In addition, it was observed that exosomal proteins contain different types of sequence-based motifs, which can be used for predicting exosomal proteins. Finally, a hybrid method has been developed that combines a motif-based approach and an ML-based model for predicting exosomal proteins, achieving a maximum AUROC 0.85 and MCC of 0.56 on an independent dataset. The hybrid model in this study performs better than the presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred has been created for the scientific community to provide service, code, and data. (https://webs.iiitd.edu.in/raghava/exopropred/).

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Section: Methodsmentioning

confidence: 99%

“…A number of feature encoding techniques have been used in previous studies [27][28][29][30]. We used a standalone tool called Pfeature to compute numerous features for the proteins, including evolutionary information-based features and composition-based features [31].…”

Section: Feature Generationmentioning

confidence: 99%

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Arora

Patiyal

Sharma

et al. 2023

Preprint

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“…Furthermore, in order to predict protein residue-level annotation, protein function and chemically modified peptides’ function, the tool Pfeature has been developed ( ; accessed on 22 February 2023) ( Table 1 ). It is divided into six categories: “composition” to compute the majority of the compositional features; “binary profiles” to compute the composition and position of each type of residues; “evolutionary information” to compute information about protein evolution using a position-specific scoring matrix based on PSI-BLAST; “structural features” to compute structural characteristics and descriptors from the tertiary structure of a protein; “patterns” to compute pattern-based descriptors; and “model building” to develop classification and regression models [ 104 ].…”

Section: Overview Of Tools For In Silico Prediction Of Bioactive Pept...mentioning

confidence: 99%

In Silico Methodologies to Improve Antioxidants’ Characterization from Marine Organisms

et al. 2023

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Marine organisms have been reported to be valuable sources of bioactive molecules that have found applications in different industrial fields. From organism sampling to the identification and bioactivity characterization of a specific compound, different steps are necessary, which are time- and cost-consuming. Thanks to the advent of the -omic era, numerous genome, metagenome, transcriptome, metatranscriptome, proteome and microbiome data have been reported and deposited in public databases. These advancements have been fundamental for the development of in silico strategies for basic and applied research. In silico studies represent a convenient and efficient approach to the bioactivity prediction of known and newly identified marine molecules, reducing the time and costs of “wet-lab” experiments. This review focuses on in silico approaches applied to bioactive molecule discoveries from marine organisms. When available, validation studies reporting a bioactivity assay to confirm the presence of an antioxidant molecule or enzyme are reported, as well. Overall, this review suggests that in silico approaches can offer a valuable alternative to most expensive approaches and proposes them as a little explored field in which to invest.

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“…Further, we compared the average composition of each residue in IFN-γ inducing and non-inducing dataset for human as well as mouse host. We have implemented the composition-based module of Pfeature [43] to compute the amino acid composition of each peptide.…”

Section: Composition Analysismentioning

confidence: 99%

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

Dhall

Patiyal

Raghava

2023

Preprint

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A host-specific technique has been developed for annotating interferon-gamma (IFN-γ) inducing peptides, it is an updated version of IFNepitope. In this study, dataset used for developing prediction method contain experimentally validated 25492 and 7983 IFN-γ inducing peptides in human and mouse host, respectively. In initial phase, machine learning techniques have been exploited to develop classification model using wide range of peptide features. In most of the case, models based on extra tree perform better than other machine learning techniques. In case of peptide features, compositional feature particularly dipeptide composition performs better than one-hot encoding or binary profile. Our best machine learning based models achieved AUROC 0.89 and 0.83 for human and mouse host, respectively. In order to improve machine learning based models or alignment free models, we explore potential of similarity-based technique BLAST. Finally, a hybrid model has been developed that combine best machine learning based model with BLAST and achieved AUROC 0.90 and 0.85 for human and mouse host, respectively. All models have been evaluated on an independent/validation dataset not used for training or testing these models. Newly developed method performs better than existing method on independent dataset. The major objective of this study is to predict, design and scan IFN-γ inducing peptides, thus server/software have been developed (https://webs.iiitd.edu.in/raghava/ifnepitope2/).

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Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

Cited by 35 publications

References 105 publications

A random forest model for predicting exosomal proteins using evolutionary information and motifs

A random forest model for predicting exosomal proteins using evolutionary information and motifs

In Silico Methodologies to Improve Antioxidants’ Characterization from Marine Organisms

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

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