Dataset Information

IBPred: A sequence-based predictor for identifying ion binding protein in phage.

ABSTRACT: Ion binding proteins (IBPs) can selectively and non-covalently interact with ions. IBPs in phages also play an important role in biological processes. Therefore, accurate identification of IBPs is necessary for understanding their biological functions and molecular mechanisms that involve binding to ions. Since molecular biology experimental methods are still labor-intensive and cost-ineffective in identifying IBPs, it is helpful to develop computational methods to identify IBPs quickly and efficiently. In this work, a random forest (RF)-based model was constructed to quickly identify IBPs. Based on the protein sequence information and residues' physicochemical properties, the dipeptide composition combined with the physicochemical correlation between two residues were proposed for the extraction of features. A feature selection technique called analysis of variance (ANOVA) was used to exclude redundant information. By comparing with other classified methods, we demonstrated that our method could identify IBPs accurately. Based on the model, a Python package named IBPred was built with the source code which can be accessed at https://github.com/ShishiYuan/IBPred.

SUBMITTER: Yuan SS

PROVIDER: S-EPMC9474292 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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IBPred: A sequence-based predictor for identifying ion binding protein in phage.

Yuan Shi-Shi SS Gao Dong D Xie Xue-Qin XQ Ma Cai-Yi CY Su Wei W Zhang Zhao-Yue ZY Zheng Yan Y Ding Hui H

Computational and structural biotechnology journal 20220828

Ion binding proteins (IBPs) can selectively and non-covalently interact with ions. IBPs in phages also play an important role in biological processes. Therefore, accurate identification of IBPs is necessary for understanding their biological functions and molecular mechanisms that involve binding to ions. Since molecular biology experimental methods are still labor-intensive and cost-ineffective in identifying IBPs, it is helpful to develop computational methods to identify IBPs quickly and effi ...[more]

PMID: 36147670

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Project description:Motivationmetal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability.Resultswe developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred's utility in analyzing microbiome metal requirements.Availability and implementationmebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:BACKGROUND: Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research. RESULTS: In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode. CONCLUSION: Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.

Dataset Information

IBPred: A sequence-based predictor for identifying ion binding protein in phage.

Publications

IBPred: A sequence-based predictor for identifying ion binding protein in phage.

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