Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map

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Abstract

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition. In this study, the features and structures of proteins were used to obtain their representations through graph convolutional networks. A protein prediction model based on graph convolutional network and contact map was proposed. The method had some advantages by testing various indexes of PDB14189 and PDB2272 on the benchmark dataset.

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud … (2018)

We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms.

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Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Sheng-Hsin Wang, Siqi Sun, Zhen Li … (2017)

Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. Availability http://raptorx.uchicago.edu/ContactMap/

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Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

Ahmet Sureyya Rifaioglu, Heval Ataş, Maria Jesús Granda Martín … (2018)

Abstract The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as ‘virtual screening’ (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

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Author and article information

Contributors

Yijie Ding:

ORCID: https://orcid.org/0000-0003-2911-7643

Journal

Journal ID (nlm-ta): Biomed Res Int

Journal ID (iso-abbrev): Biomed Res Int

Journal ID (publisher-id): BMRI

Title: BioMed Research International

Publisher: Hindawi

ISSN (Print): 2314-6133

ISSN (Electronic): 2314-6141

Publication date Collection: 2022

Publication date (Electronic): 17 January 2022

Volume: 2022

Electronic Location Identifier: 9044793

Affiliations

¹School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China

²Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China

³Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, China

Author notes

Academic Editor: Khac-Minh Thai

Author information

Yijie Ding https://orcid.org/0000-0003-2911-7643

Qiming Fu https://orcid.org/0000-0001-9757-4323

Article

DOI: 10.1155/2022/9044793

PMC ID: 8786515

PubMed ID: 35083336

SO-VID: 6b55c549-fd5e-419b-b3c9-8f385369a360

License:

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 13 April 2021

Date accepted : 24 December 2021

Funding

Funded by: Soochow University

Award ID: KJS2166

Funded by: National Research Project

Award ID: 2020YFC2006602

Funded by: National Natural Science Foundation of China

Award ID: 61902271

Award ID: 61876217

Award ID: 62176175

Award ID: 61772357

Award ID: 62073231

Award ID: 61902272

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Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map

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Most cited references 29

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

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