ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs

Al‐Saggaf, Ubaid M.; Usman, Muhammad; Naseem, Imran; Moinuddin, Muhammad; Jiman, Ahmad A.; Alsaggaf, Mohammed U; Alshoubaki, Hitham K.; Khan, Shujaat

doi:10.3389/fbioe.2021.752658

Cited by 9 publications

(4 citation statements)

References 66 publications

(81 reference statements)

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“…VirusImmu adopted Z-descriptor and E-descriptor to describe the physicochemical properties of the residues, but there are also other descriptors such as CKSAAP [30][31][32][33] which may improve the performance. The future aim should be implemented with higher accuracy without throwing away proteins or restricting protein length.…”

Section: Discussionmentioning

confidence: 99%

VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction

Li,

Zhao,

Tai

et al. 2023

Preprint

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BackgroundThe viruses threats provoke concerns regarding their sustained epidemic transmission, making the development of vaccines particularly important. In the prolonged and costly process of vaccine development, the most important initial step is to identify protective immunogens. Machine learning (ML) approaches are productive in analyzing big data such as microbial proteomes, and can remarkably reduce the cost of experimental work in developing novel vaccine candidates.ResultsWe intensively evaluated the immunogenicity prediction power of eight commonly-used ML methods by random sampling cross validation on a large dataset consisting of known viral immunogens and non-immunogens we manually curated from the public domain. XGBoost, kNN and RF showed the strongest predictive power. We then proposed a novel soft-voting based ensemble approach (VirusImmu), which demonstrated a powerful and stable capability for viral immunogenicity prediction across the test set and external test set irrespective of protein sequence length. VirusImmu was successfully applied to facilitate identifying linear B cell epitopes against African Swine Fever Virus as confirmed by indirect ELISA in vitro.ConclusionsVirusImmu exhibited tremendous potentials in predicting immunogenicity of viral protein segments. It is freely accessible athttps://github.com/zhangjbig/VirusImmu.

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Section: Discussionmentioning

confidence: 99%

VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction

Li,

Zhao,

Tai

et al. 2023

Preprint

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“…The CKSAAP coding scheme consists of k-spaced residue pairs (separated by k amino acids) in a peptide segment which has been used to predict PTM [ 32 , 33 ], and extracellular matrix proteins [ 34 ]. For instance, there are 484 pairings of 22 amino acid combinations (i.e., AA, AC, …, XX).…”

Section: Methodsmentioning

confidence: 99%

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier

Liu,

Zhu,

Dai

et al. 2023

BMC Genomics

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Background Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention. Results In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively. Conclusion GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite.

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“…The CKSAAP coding scheme consists of k-spaced residue pairs (separated by k amino acids) in a peptide segment which has been used to predict PTM [21,22] , and extracellular matrix proteins [23] . For instance, there are 484 pairings of 22 amino acids combinations (i.e., AA, AC, ..., XX).…”

Section: Cksaapmentioning

confidence: 99%

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier

Liu

Zhu

Dai

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Lysine glutarylation(Kglu) is one of the most important Post-translational modifications(PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiment, computational-based Kglu site prediction research are gaining more and more attention.Results In this study, we proposed a new model named GBDT_KgluSite to identify Kglu and non-Kglu sequences based on the gradient-boosting decision tree (GBDT). Here, sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. The imbalance between positive and negative samples was addressed using the NearMiss-3 approach, and extraneous data was eliminated using the Elastic Net. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively.Conclusion GBDT_KgluSite is an effectively computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in Mus musculus protein. The code and dataset of GBDT_KgluSite is available at https://github.com/flyinsky6/GBDT_KgluSite.

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ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs

Cited by 9 publications

References 66 publications

VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction

VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier

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