Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein–protein interactions using machine learning methods

2016 ◽  
Vol 12 (3) ◽  
pp. 778-785 ◽  
Author(s):  
A. Srivastava ◽  
G. Mazzocco ◽  
A. Kel ◽  
L. S. Wyrwicz ◽  
D. Plewczynski
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Computational Prediction ◽  
Vital Role ◽  
Biological Processes ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
P Protein

Protein–protein interactions (PPIs) play a vital role in most biological processes.

Recent advances in predicting protein-lncRNA interactions using machine learning methods

2021 ◽  
Vol 21 ◽  
Author(s):  
Han Yu ◽  
Zi-Ang Shen ◽  
Yuan-Ke Zhou ◽  
Pu-Feng Du
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Protein Interactions ◽  
Cellular Level ◽  
Future Research ◽  
Biological Processes ◽  
Learning Methods ◽  
Protein Coding ◽  
Machine Learning Methods ◽  
Non Coding Rnas

: Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories: semi-supervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods

2021 ◽  
Vol 28 ◽  
Author(s):  
Yu-He Yang ◽  
Jia-Shu Wang ◽  
Shi-Shi Yuan ◽  
Meng-Lu Liu ◽  
Wei Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Vital Role ◽  
Atp Binding ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Protein Ligand Interactions ◽  
Protein Functions ◽  
Ligand Interactions ◽  
Binding Residues

: Protein-ligand interactions are necessary for majority protein functions. Adenosine-5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

Prediction of Compound-Protein Interactions with Machine Learning Methods

2011 ◽  
pp. 304-317
Author(s):  
Yoshihiro Yamanishi ◽  
Hisashi Kashima
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Binary Classification ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

scholarly journals Coevolution of interacting proteins through non-contacting and non-specific mutations

2021 ◽  
Author(s):  
David Ding ◽  
Anna G. Green ◽  
Boyuan Wang ◽  
Thuy-Lan Vo Lite ◽  
Eli N. Weinstein ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Design ◽  
Comprehensive Analysis ◽  
Bacterial Toxin ◽  
Interacting Proteins ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Neutral Mutations

Proteins often accumulate neutral mutations that do not affect current functions1 but can profoundly influence future mutational possibilities and functions2-4. Understanding such hidden potential has major implications for protein design and evolutionary forecasting5-7, but has been limited by a lack of systematic efforts to identify potentiating mutations8,9. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact, and promote tolerance non-specifically to, many different antitoxin mutations, despite covariation in homologs occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods10,11. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets12-14.

Predicting lncRNA-protein Interactions by Machine Learning Methods: A Review

2021 ◽  
Vol 15 (8) ◽  
pp. 831-840
Author(s):  
Zhi-Ping Liu
Keyword(s):  
Machine Learning ◽  
Transcriptional Regulation ◽  
Protein Interactions ◽  
Learning Methods ◽  
Computational Framework ◽  
Machine Learning Methods

In this work, a review of predicting lncRNA-protein interactions by bioinformatics methods is provided with a focus on machine learning. Firstly, a computational framework for predicting lncRNA-protein interactions is presented. Then, the currently available data resources for the predictions have been listed. The existing methods will be reviewed by introducing their crucial steps in the prediction framework. The key functions of lncRNA, e.g., mediator on transcriptional regulation, are often involved in interacting with proteins. The interactions with proteins provide a tunnel of leveraging the molecular cooperativity for fulfilling crucial functions. Thus, the important directions in bioinformatics have been highlighted for identifying essential lncRNA-protein interactions and deciphering the dysfunctional importance of lncRNA, especially in carcinogenesis.

Prediction of Compound-protein Interactions with Machine Learning Methods

2012 ◽  
pp. 616-630
Author(s):  
Yoshihiro Yamanishi ◽  
Hisashi Kashima
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Binary Classification ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

scholarly journals Classification of Surface Water Using Machine Learning Methods from Landsat Data in Nepal

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 43 ◽  
Author(s):  
Tri Dev Acharya ◽  
Anoj Subedi ◽  
He Huang ◽  
Dong Ha Lee
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Ground Truth ◽  
Vital Role ◽  
Google Earth ◽  
Landsat 8 ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Water Classification

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Therefore, the monitoring and estimation of surface water is an essential task. In Nepal, surface water has different characteristics such as varying temperature, turbidity, depth, and vegetation cover, for which remote sensing technology plays a vital role. Single or multiple water index methods have been applied in the classification of surface water with satisfactory results. In recent years, machine learning methods with training datasets, have been outperforming different traditional methods. In this study, we tried to use satellite images from Landsat 8 to classify surface water in Nepal. Input of Landsat bands and ground truth from high resolution images available form Google Earth is used, and their performance is evaluated based on overall accuracy. The study will be will helpful to select optimum machine learning methods for surface water classification and therefore, monitoring and management of surface water in Nepal.

scholarly journals PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

2011 ◽  
Vol 16 (2) ◽  
Author(s):  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri ◽  
Dariusz Plewczynski
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Prediction Method ◽  
Protein Sequences ◽  
Dimensional Structure ◽  
Support Vector ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Protein Functions

AbstractProtein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network

2017 ◽  
Vol 13 (7) ◽  
pp. 1336-1344 ◽  
Author(s):  
Yan-Bin Wang ◽  
Zhu-Hong You ◽  
Xiao Li ◽  
Tong-Hai Jiang ◽  
Xing Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Deep Neural Network ◽  
Protein Sequences ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
P Protein ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Protein–protein interactions (PPIs) play an important role in most of the biological processes.

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