scholarly journals Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Feng-Min Li ◽  
Xiao-Wei Gao
Keyword(s):  
Amino Acid ◽  
Protein Function ◽  
Subcellular Location ◽  
Support Vector ◽  
Bacterial Protein ◽  
Dipeptide Composition ◽  
Gram Positive ◽  
Jackknife Test ◽  
Gram Positive Bacteria ◽  
Protein Subcellular Location

There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones. Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location. And identification of Gram-positive bacterial protein subcellular location is important for developing effective drugs. In this paper, a new Gram-positive bacterial protein subcellular location dataset was established. The amino acid composition, the gene ontology annotation information, the hydropathy dipeptide composition information, the amino acid dipeptide composition information, and the autocovariance average chemical shift information were selected as characteristic parameters, then these parameters were combined. The locations of Gram-positive bacterial proteins were predicted by the Support Vector Machine (SVM) algorithm, and the overall accuracy (OA) reached 86.1% under the Jackknife test. The overall accuracy (OA) in our predictive model was higher than those in existing methods. This improved method may be helpful for protein function prediction.

Identifying protein subcellular location with embedding features learned from networks

2020 ◽  
Vol 17 ◽  
Author(s):  
Hongwei Liu ◽  
Bin Hu ◽  
Lei Chen ◽  
Lin Lu
Keyword(s):  
Computational Methods ◽  
Protein Function ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Subcellular Location ◽  
Support Vector ◽  
Network Embedding ◽  
Validation Method ◽  
Protein Subcellular Location ◽  
The Cross

Background: Identification of protein subcellular location is an important problem because the subcellular location is highly related to protein function. It is fundamental to determine the locations with biology experiments. However, these experiments are of high costs and time-consuming. The alternative way to address such problem is to design effective computational methods. Objective: To date, several computational methods have been proposed in this regard. However, these methods mainly adopted the features derived from proteins themselves. On the other hand, with the development of network technique, several embedding algorithms have been proposed, which can encode nodes in the network into feature vectors. Such algorithms connected the network and traditional classification algorithms. Thus, they provided a new way to construct models for the prediction of protein subcellular location. Method: In this study, we analyzed features produced by three network embedding algorithms (DeepWalk, Node2vec and Mashup) that were applied on one or multiple protein networks. Obtained features were learned by one machine learning algorithm (support vector machine or random forest) to construct the model. The cross-validation method was adopted to evaluate all constructed models. Results: After evaluating models with the cross-validation method, embedding features yielded by Mashup on multiple networks were quite informative for predicting protein subcellular location. The model based on these features were superior to some classic models. Conclusion: Embedding features yielded by a proper and powerful network embedding algorithm were effective for building the model for prediction of protein subcellular location, providing new pipelines to build more efficient models.

Integrating Second-order Moving Average and Over-sampling Algorithm to Predict Apoptosis Protein Subcellular Localization

2020 ◽  
Vol 15 (6) ◽  
pp. 517-527
Author(s):  
Yunyun Liang ◽  
Shengli Zhang
Keyword(s):  
Subcellular Localization ◽  
Moving Average ◽  
Subcellular Location ◽  
Second Order ◽  
Test Method ◽  
Support Vector ◽  
Protein Subcellular Localization ◽  
Protein Subcellular Location ◽  
Apoptosis Protein ◽  
Leibler Divergence

Background: Apoptosis proteins have a key role in the development and the homeostasis of the organism, and are very important to understand the mechanism of cell proliferation and death. The function of apoptosis protein is closely related to its subcellular location. Objective: Prediction of apoptosis protein subcellular localization is a meaningful task. Methods: In this study, we predict the apoptosis protein subcellular location by using the PSSMbased second-order moving average descriptor, nonnegative matrix factorization based on Kullback-Leibler divergence and over-sampling algorithms. This model is named by SOMAPKLNMF- OS and constructed on the ZD98, ZW225 and CL317 benchmark datasets. Then, the support vector machine is adopted as the classifier, and the bias-free jackknife test method is used to evaluate the accuracy. Results: Our prediction system achieves the favorable and promising performance of the overall accuracy on the three datasets and also outperforms the other listed models. Conclusion: The results show that our model offers a high throughput tool for the identification of apoptosis protein subcellular localization.

scholarly journals Antibacterial Action of Structurally Diverse Cationic Peptides on Gram-Positive Bacteria

2000 ◽  
Vol 44 (8) ◽  
pp. 2086-2092 ◽  
Author(s):  
Carol L. Friedrich ◽  
Dianne Moyles ◽  
Terry J. Beveridge ◽  
Robert E. W. Hancock
Keyword(s):  
Amino Acid ◽  
Mechanism Of Action ◽  
Cytoplasmic Membrane ◽  
Membrane Depolarization ◽  
Membrane Permeabilization ◽  
Cationic Peptides ◽  
Nuclear Condensation ◽  
Gram Positive ◽  
Gram Positive Bacteria ◽  
Helical Peptide

ABSTRACT Antimicrobial cationic peptides are ubiquitous in nature and are thought to be a component of the first line of defense against infectious agents. It is widely believed that the killing mechanism of these peptides on bacteria involves an interaction with the cytoplasmic membrane. Cationic peptides from different structural classes were used in experiments withStaphylococcus aureus and other medically important gram-positive bacteria to gain insight into the mechanism of action. The membrane potential-sensitive fluorophore dipropylthiacarbocyanine was used to assess the interactions of selected antimicrobial peptides with the cytoplasmic membrane of S. aureus. Study of the kinetics of killing and membrane depolarization showed that, at early time points, membrane depolarization was incomplete, even when 90% or more of the bacteria had been killed. CP26, a 26-amino-acid α-helical peptide with a high MIC against S. aureus, still had the ability to permeabilize the membrane. Cytoplasmic-membrane permeabilization was a widespread ability and an action that may be necessary for reaching an intracellular target but in itself did not appear to be the killing mechanism. Transmission electron microscopy of S. aureus andStaphylococcus epidermidis treated with CP29 (a 26-amino-acid α-helical peptide), CP11CN (a 13-amino-acid, proline- and tryptophan-rich peptide), and Bac2A-NH2 (a linearized version of the 12-amino-acid loop peptide bactenecin) showed variability in effects on bacterial structure. Mesosome-like structures were seen to develop in S. aureus, whereas cell wall effects and mesosomes were seen with S. epidermidis. Nuclear condensation and abherrent septation were occasionally seen in S. epidermidis. Our experiments indicated that these peptides vary in their mechanisms of action and that the mechanism of action likely does not solely involve cytoplasmic-membrane permeabilization.

The Influence of Dipeptide Composition on Protein Folding Rates

2011 ◽  
Vol 378-379 ◽  
pp. 157-160
Author(s):  
Jian Xiu Guo ◽  
Ni Ni Rao
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequences ◽  
Dipeptide Composition ◽  
Coupling Effects ◽  
Jackknife Test ◽  
Folding Rates ◽  
Important Challenge ◽  
The Relationship

Understanding the relationship between amino acid sequences and folding rates of proteins is an important challenge in computational and molecular biology. All existing algorithms for predicting protein folding rates have never taken into account the sequence coupling effects. In this work, a novel algorithm was developed for predicting the protein folding rates from amino acid sequences. The prediction was achieved on the basis of dipeptide composition, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. Based on a non-redundant dataset of 99 proteins, the proposed method was found to provide an excellent agreement between the predicted and experimental folding rates of proteins when evaluated with the jackknife test. The correlation coefficient was 87.7% and the standard error was 2.04, which indicated the important contribution from sequence coupling effects to the determination of protein folding rates.

scholarly journals Prediction of protein function using a deep convolutional neural network ensemble

2017 ◽  
Author(s):  
Evangelia I Zacharaki
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Convolutional Neural Network ◽  
Protein Function ◽  
Protein Structures ◽  
Function Prediction ◽  
Deep Convolutional Neural Network ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Feature Maps

Background. The availability of large databases containing high resolution three-dimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function prediction. Methods. In this work, novel shape features are extracted representing protein structure in the form of local (per amino acid) distribution of angles and amino acid distances, respectively. Each of the multi-channel feature maps is introduced into a deep convolutional neural network (CNN) for function prediction and the outputs are fused through Support Vector Machines (SVM) or a correlation-based k-nearest neighbor classifier. Two different architectures are investigated employing either one CNN per multi-channel feature set, or one CNN per image channel. Results. Cross validation experiments on enzymes (n = 44,661) from the PDB database achieved 90.1% correct classification demonstrating the effectiveness of the proposed method for automatic function annotation of protein structures. Discussion. The automatic prediction of protein function can provide quick annotations on extensive datasets opening the path for relevant applications, such as pharmacological target identification.

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