A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features

Feng, Changli; Ma, Zhaogui; Dan, Yang; Li, Xin; Zhang, Jun; Li, Yanjuan

doi:10.3389/fbioe.2020.00285

Cited by 31 publications

(29 citation statements)

References 96 publications

(92 reference statements)

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“…When g = 0, the 0‐gap dipeptide of a protein can be defined as an amino acid pair without any other amino acid between them. The whole extraction process is similar to the work of Feng [17] and Tang, [35] and can be described as follows:

…”

Section: Methodsmentioning

confidence: 99%

“…They constructed a new dataset using the data extracted from the Universal Protein Resource (UniProt) database [15] and calculated the mixed features, including the amino acid composition and dipeptide composition. After the feature selection step, an optimized feature set was processed by the naïve Bayes (NB [16, 17]) method, whose predictive accuracy of 66.88% suggests that the proposed method is an effective method for antioxidant protein identification. Later, Feng et al.…”

Section: Introductionmentioning

confidence: 99%

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ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

et al. 2021

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Antioxidant proteins can terminate a chain of reactions caused by free radicals and protect cells from damage. To identify antioxidant proteins rapidly, a computational model was proposed based on the optimized recoding scheme, sequence information and machine learning methods. First, over 600 recoding schemes were collected to build a scheme set. Then, the original sequence was recoded as a reduced expression whose g‐gap dipeptides (g = 0, 1, 2) were used as the features of proteins. Furthermore, a random forest method was used to evaluate the classification ability of the obtained dipeptide features. After going through all schemes, the best predictive performance scheme was chosen as the optimized reduction scheme. Finally, for the RF method, a grid search strategy was used to select a better parameter combination to identify antioxidant proteins. In the experiment, the present method correctly recognized 90.13–99.87% of the antioxidant samples. Other experimental results also proved that the present method was efficient to identify antioxidant proteins. Finally, we also developed a web server that was freely accessible to researchers.

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…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

et al. 2021

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“…Below, we propose some example applications: First, we can now sort genomes or homologous gene sequences by their optimal growth temperature (and not just by phylogenetic origin), making it possible to explore universal strategies of adaptations to heat and cold, as opposed to idiosyncratic adaptations within each lineage of species. Such studies would have far-reaching implications in biotechnology as they can simplify the rational design of biological molecules and organisms with a desired thermal stability 33,61,68,70–75 . Second, this database simplifies studies of molecular adaptations to temperature within any given range of temperatures. This is important, because to date most studies have focused on extremophiles 49,50 , leaving mostly unexplored how mesophilic organisms adapt to relatively subtle changes in the environment (e.g.…”

Section: Applicationsmentioning

confidence: 99%

“…First, we can now sort genomes or homologous gene sequences by their optimal growth temperature (and not just by phylogenetic origin), making it possible to explore universal strategies of adaptations to heat and cold, as opposed to idiosyncratic adaptations within each lineage of species. Such studies would have far-reaching implications in biotechnology as they can simplify the rational design of biological molecules and organisms with a desired thermal stability 33,61,68,70–75 .…”

Section: Applicationsmentioning

confidence: 99%

Gosha: a database of organisms with defined optimal growth temperatures

Helena-Bueno

Brown

Konyk

et al. 2021

Preprint

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Despite the rapidly increasing number of organisms with sequenced genomes, there is no existing resource that simultaneously contains information about genome sequences and the optimal growth conditions for a given species. In the absence of such a resource, we cannot immediately sort genomic sequences by growth conditions, making it difficult to study how organisms and biological molecules adapt to distinct environments. To address this problem, we have created a database called GSHC (Genome Sequences: Hot, Cold, and everything in between). This database, available at http://melnikovlab.com/gshc, brings together information about the genomic sequences and optimal growth temperatures for 25,324 species, including ~89% of the bacterial species with known genome sequences. Using this database, it is now possible to readily compare genomic sequences from thousands of species and correlate variations in genes and genomes with optimal growth temperatures, at the scale of the entire tree of life. The database interface allows users to retrieve protein sequences sorted by optimal growth temperature for their corresponding species, providing a tool to explore how organisms, genomes, and individual proteins and nucleic acids adapt to certain temperatures. We hope that this database will contribute to medicine and biotechnology by helping to create a better understanding of molecular adaptations to heat and cold, leading to new ways to preserve biological samples, engineer useful enzymes, and develop new biological materials and organisms with the desired tolerance to heat and cold.

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“…Several computational efforts based on machine learning (ML) methods have been made in recent years to identify TPPs 20 , 21 , 24 – 33 as summarized in Table 1 . As can be seen from Table 1 , support vector machine (SVM) method is the most widely used technique for identifying TPPs 20 , 21 , 24 – 26 , 28 – 30 . For instance, Zhang and Fan 31 developed the first TPP predictor based on amino acid composition (AAC) descriptors.…”

Section: Introductionmentioning

confidence: 99%

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

Charoenkwan

Chotpatiwetchkul

Lee

et al. 2021

Sci Rep

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Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906–0.910) and 2–17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

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A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features

Cited by 31 publications

References 96 publications

ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

Gosha: a database of organisms with defined optimal growth temperatures

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

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