Deep_CNN_LSTM_GO: Protein function prediction from amino-acid sequences

2021 ◽  
Vol 95 ◽  
pp. 107584
Author(s):  
Mohamed E.M. Elhaj-Abdou ◽  
Hassan El-Dib ◽  
Amr El-Helw ◽  
Mohamed El-Habrouk
Keyword(s):  
Amino Acid ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Amino Acid Sequences

Sars-Cov-2 Spike protein function prediction using a convolutional neural network ensemble

2021 ◽  
pp. 7831-7845
Author(s):  
Raghad Monther Eid, Eman K. Elsayed, Fatma T. Ghanam
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Small Error ◽  
Amino Acid Sequences ◽  
Spike Protein ◽  
Neural Network Ensemble ◽  
Classification Problems ◽  
Past Experiences

Introduction: SARS-CoV-2 has become a worldwide pandemic that affects all aspects of life; therefore, numerous organizations and open exploration foundations focus their efforts on research for viable therapeutics. Given past experiences and involvement in SARS, the essential focus has been the Spike protein, considered as the perfect objective for COVID-19 immunotherapies. Most of the vaccines being developed target the spike proteins because this protein covers the virus and helps it invade human cells. Methods: Applications of deep neural network is a quickly expanding field now reaching many areas including proteomics. Results: To be precise, convolutional neural networks have been used for identifying the functional role of amino acid sequences, because of its ability to give nearly accurate results for multi-label classification problems. Here we present a modified convolutional deep learning model that can  identify if a given amino acid sequence is a spike protein or not based on the length of the sequence and the function of the protein, that will be done  with a short execution time and a relatively small error rate. Conclusion: CNN is an efficient tool at supervised multilabel classification problems

scholarly journals ProteInfer: deep networks for protein functional inference

2021 ◽  
Author(s):  
Theo Sanderson ◽  
Maxwell L Bileschi ◽  
David Belanger ◽  
Lucy Colwell
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Query Sequence ◽  
Functional Space ◽  
Amino Acid Sequences ◽  
Deep Convolutional Neural Networks ◽  
Software Interfaces ◽  
Downstream Analysis

Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we instead employ deep convolutional neural networks to directly predict a variety of protein functions -- EC numbers and GO terms -- directly from an unaligned amino acid sequence. This approach provides precise predictions which complement alignment-based methods, and the computational efficiency of a single neural network permits novel and lightweight software interfaces, which we demonstrate with an in-browser graphical interface for protein function prediction in which all computation is performed on the user's personal computer with no data uploaded to remote servers. Moreover, these models place full-length amino acid sequences into a generalised functional space, facilitating downstream analysis and interpretation. To read the interactive version of this paper, visit https://google-research.github.io/proteinfer/

scholarly journals Using Deep Learning to Annotate the Protein Universe

2019 ◽  
Author(s):  
Maxwell L. Bileschi ◽  
David Belanger ◽  
Drew Bryant ◽  
Theo Sanderson ◽  
Brandon Carter ◽  
...  
Keyword(s):  
Deep Learning ◽  
Amino Acid ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Structural Disorder ◽  
Amino Acid Sequences ◽  
Computationally Efficient ◽  
Learning Sequence ◽  
Convolutional Networks ◽  
The Relationship

AbstractUnderstanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms. In this paper, we explore an alternative methodology based on deep learning that learns the relationship between unaligned amino acid sequences and their functional annotations across all 17929 families of the Pfam database. Using the Pfam seed sequences we establish rigorous benchmark assessments that use both random and clustered data splits to control for potentially confounding sequence similarities between train and test sequences. Using Pfam full, we report convolutional networks that are significantly more accurate and computationally efficient than BLASTp, while learning sequence features such as structural disorder and transmembrane helices. Our model co-locates sequences from unseen families in embedding space, allowing sequences from novel families to be accurately annotated. These results suggest deep learning models will be a core component of future protein function prediction tools.

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

2017 ◽  
Vol 3 ◽  
pp. e124 ◽  
Author(s):  
Evangelia I. Zacharaki
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Convolutional Neural Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Deep Convolutional Neural Network ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Homologous Proteins

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 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 single-functional enzymes (n = 44,661) from the PDB database achieved 90.1% correct classification, demonstrating an improvement over previous results on the same dataset when sequence similarity was not considered. 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. The proposed method shows promise for structure-based protein function prediction, but sufficient data may not yet be available to properly assess the method’s performance on non-homologous proteins and thus reduce the confounding factor of evolutionary relationships.

scholarly journals Protein function prediction with gene ontology: from traditional to deep learning models

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12019
Author(s):  
Thi Thuy Duong Vu ◽  
Jaehee Jung
Keyword(s):  
Gene Ontology ◽  
Deep Learning ◽  
Protein Function ◽  
High Throughput Sequencing ◽  
Protein Function Prediction ◽  
Rapid Development ◽  
Function Prediction ◽  
Amino Acid Sequences ◽  
Go Annotation ◽  
Go Terms

Protein function prediction is a crucial part of genome annotation. Prediction methods have recently witnessed rapid development, owing to the emergence of high-throughput sequencing technologies. Among the available databases for identifying protein function terms, Gene Ontology (GO) is an important resource that describes the functional properties of proteins. Researchers are employing various approaches to efficiently predict the GO terms. Meanwhile, deep learning, a fast-evolving discipline in data-driven approach, exhibits impressive potential with respect to assigning GO terms to amino acid sequences. Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach. Further, we selected some suitable predictors from among the reviewed tools and conducted a mini comparison of their performance using a worldwide challenge dataset. Finally, we discussed the remaining major challenges in the field, and emphasized the future directions for protein function prediction with GO.

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