PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Li, Yanjuan; Zhang, Zitong; Teng, Zhaogang; Liu, Xiaoyan

doi:10.1155/2020/8845133

Cited by 20 publications

(25 citation statements)

References 104 publications

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“…The Amy dataset constructed by Niu et al 20 had previously been used to train and develop the four existing state-of-the-art methods (RFAmyloid 20 , iAMY-SCM 21 , PredAmyl-MLP 22 and Mukhtar et al’s method 23 ). The Amy dataset was used as the benchmark dataset to compare the performance of the proposed method to the four existing state-of-the-art methods.…”

Section: Methodsmentioning

confidence: 99%

“…According to the findings of Charoenkwan et al 21 , iAMY-SCM performed at a comparable level to that of RFAmyloid, as evaluated via cross-validation and independent testing. Most recently, Li et al 22 and Mukhtar et al 23 used amino acid composition (AAC), tripeptide composition (TPC), physicochemical properties of amino acids (AAI), secondary structure-based alignments, and the segmented-position specific scoring matrix (PSSM) method to improve predictive performance. The computational approaches mentioned above each had their own merits and sparked interest in amyloid protein identification research.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, their predictive performance may not be robust in all cases. Secondly, PredAmyl-MLP 22 and Mukhtar et al methods 23 were developed and evaluated using cross-validation only. As a result, their AMY candidate identification performance is limited in generalizability.…”

Section: Introductionmentioning

confidence: 99%

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AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

Charoenkwan

Ahmed

Nantasenamat

et al. 2022

Sci Rep

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Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

Charoenkwan

Ahmed

Nantasenamat

et al. 2022

Sci Rep

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“…The tripeptide composition (TPC) method describes the position and order information of amino acids in a sequence [ 48 , 49 ]. Li et al [ 50 ] have confirmed that the TPC feature is beneficial for classifying amyloid proteins, and therefore we considered their utilization in the investigation of amyloidogenic regions. In this method, the occurrence frequencies of three consecutive amino acids in the sequence are used as the feature elements.…”

Section: Methodsmentioning

confidence: 99%

ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition

et al. 2021

Self Cite

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Background Amyloids are insoluble fibrillar aggregates that are highly associated with complex human diseases, such as Alzheimer’s disease, Parkinson’s disease, and type II diabetes. Recently, many studies reported that some specific regions of amino acid sequences may be responsible for the amyloidosis of proteins. It has become very important for elucidating the mechanism of amyloids that identifying the amyloidogenic regions. Accordingly, several computational methods have been put forward to discover amyloidogenic regions. The majority of these methods predicted amyloidogenic regions based on the physicochemical properties of amino acids. In fact, position, order, and correlation of amino acids may also influence the amyloidosis of proteins, which should be also considered in detecting amyloidogenic regions. Results To address this problem, we proposed a novel machine-learning approach for predicting amyloidogenic regions, called ReRF-Pred. Firstly, the pseudo amino acid composition (PseAAC) was exploited to characterize physicochemical properties and correlation of amino acids. Secondly, tripeptides composition (TPC) was employed to represent the order and position of amino acids. To improve the distinguishability of TPC, all possible tripeptides were analyzed by the binomial distribution method, and only those which have significantly different distribution between positive and negative samples remained. Finally, all samples were characterized by PseAAC and TPC of their amino acid sequence, and a random forest-based amyloidogenic regions predictor was trained on these samples. It was proved by validation experiments that the feature set consisted of PseAAC and TPC is the most distinguishable one for detecting amyloidosis. Meanwhile, random forest is superior to other concerned classifiers on almost all metrics. To validate the effectiveness of our model, ReRF-Pred is compared with a series of gold-standard methods on two datasets: Pep-251 and Reg33. The results suggested our method has the best overall performance and makes significant improvements in discovering amyloidogenic regions. Conclusions The advantages of our method are mainly attributed to that PseAAC and TPC can describe the differences between amyloids and other proteins successfully. The ReRF-Pred server can be accessed at http://106.12.83.135:8080/ReRF-Pred/.

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“…This method can extract a total of 188 feature dimensions, so it is also called 188D ( Li et al, 2020b ). The 188D top 20 extraction dimension vectors were used to calculate the frequency of the arrangement for 20 kinds of natural amino acids (A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y) ( Zheng et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D

Guo²,

Liao

et al. 2021

Front. Genet.

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Phages have seriously affected the biochemical systems of the world, and not only are phages related to our health, but medical treatments for many cancers and skin infections are related to phages; therefore, this paper sought to identify phage proteins. In this paper, a Pseudo-188D model was established. The digital features of the phage were extracted by PseudoKNC, an appropriate vector was selected by the AdaBoost tool, and features were extracted by 188D. Then, the extracted digital features were combined together, and finally, the viral proteins of the phage were predicted by a stochastic gradient descent algorithm. Our model effect reached 93.4853%. To verify the stability of our model, we randomly selected 80% of the downloaded data to train the model and used the remaining 20% of the data to verify the robustness of our model.

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PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Cited by 20 publications

References 104 publications

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition

Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D

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