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Deep Learning-Based Advances in Protein Structure Prediction

Subash C Pakhrin, Bikash Shrestha, Badri Adhikari, Dukka B KC
2021 International Journal of Molecular Sciences  
Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment  ...  of protein Structure Prediction (CASP14).  ...  Deep Learning-Based Advances in Overall Protein Structure Prediction Pipelines In this section, we highlight the recent advances in overall protein structure prediction pipeline using Deep Learning.  ... 
doi:10.3390/ijms22115553 pmid:34074028 fatcat:jzlwbnvdkzf5ta3h55ki7ul5ia

Machine learning in protein structure prediction

Mohammed AlQuraishi
2021 Current Opinion in Chemical Biology  
; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks.  ...  Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases.  ...  Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Zídek A, Nelson AWR, Bridgland A, et al.: Improved protein structure prediction using potentials from deep learning.  ... 
doi:10.1016/j.cbpa.2021.04.005 pmid:34015749 fatcat:v6jgfwns25dajfoiayjqexbe5a

DeepQA: Improving the estimation of single protein model quality with deep belief networks [article]

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng
2016 arXiv   pre-print
DeepQA is a useful tool for protein single model quality assessment and protein structure prediction.  ...  The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and  ...  The computational structure prediction methods have the potential to fill the gap, since it is much faster and cheaper than experimental techniques, and also can be used for proteins whose structures are  ... 
arXiv:1607.04379v1 fatcat:rnbkcvpfijeczg7jeylxp6dbsi

Deep learning in proteomics

Bo Wen, Wenfeng Zeng, Yuxing Liao, Zhiao Shi, Sara R Savage, Wen Jiang, Bing Zhang
2020 Proteomics  
complex-peptide binding affinity prediction, and protein structure prediction.  ...  Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich research scientific domains.  ...  In silico protein structure prediction has the potential to fill the gap and we will focus on the application of deep learning in the prediction of protein secondary and tertiary structures here.  ... 
doi:10.1002/pmic.201900335 pmid:32939979 fatcat:gq6yffww6reu7eqrky7u6l2454

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

Matt Spencer, Jesse Eickholt, Jianlin Cheng
2015 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins.  ...  This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q 3 accuracy of 80.7% and a Sov accuracy of 74.2%.  ...  Acknowledgment This work was supported in part by a grant from the National Institutes of Health (grant no. R01GM093123) to JC. Biography  ... 
doi:10.1109/tcbb.2014.2343960 pmid:25750595 pmcid:PMC4348072 fatcat:22gx442ok5e7ze5io3zsq5zc6e

Improved protein structure prediction by deep learning irrespective of co-evolution information [article]

Jinbo Xu, Matthew Mcpartlon, Jin Li
2020 bioRxiv   pre-print
This marks a significant improvement over the top co-evolution-based, non-deep learning methods at CASP13, and other non-coevolution-based deep learning models, such as the popular recurrent geometric  ...  These results suggest that ResNet does not simply denoise co-evolution signals, but instead is able to learn important sequence-structure relationship from experimental structures.  ...  J.L. studied the deep learning algorithms and generated the RGN results.  ... 
doi:10.1101/2020.10.12.336859 fatcat:sf3o5zhyqrdnjdwmgwpxymbnxa

Improved protein model quality assessment by integrating sequential and pairwise features using deep learning [article]

Xiaoyang Jing, Jinbo Xu
2020 bioRxiv   pre-print
The 2D ResNet module extracts useful information from pairwise features such as model-derived distance maps, co-evolution information and predicted distance potential.  ...  Motivation: Accurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection, but also useful for model refinement.  ...  structures and solvent accessibilities; or 3) predicted distance potentials.  ... 
doi:10.1101/2020.09.30.321661 fatcat:sts37425bbaebep6iiu5drjrju

Deep Homology-Based Protein Contact-Map Prediction [article]

Omer Ronen, Or Zuk
2020 bioRxiv   pre-print
deep learning methods.  ...  Homology modelling is a popular and successful approach, where the structure of a protein is determined using information from known template structures of similar proteins, and has been shown to improve  ...  Introduction Computational prediction of a protein three-dimensional structure from its sequence has seen massive progress lately due to the introduction of new deep learning models (e.g.  ... 
doi:10.1101/2020.10.04.325274 fatcat:efkxacexhrb4bgzrjr3dk55i6q

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation

Wei Zheng, Qiqige Wuyun, Xiaogen Zhou, Yang Li, Peter L Freddolino, Yang Zhang
2022 Nucleic Acids Research  
Deep learning techniques have significantly advanced the field of protein structure prediction.  ...  LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep  ...  ACKNOWLEDGEMENTS This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation (ACI-1548562).  ... 
doi:10.1093/nar/gkac248 pmid:35420129 fatcat:cqopstbcsvaa3mtbazbffja6n4

DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

Wessam Elhefnawy, Min Li, Jianxin Wang, Yaohang Li
2020 BMC Bioinformatics  
The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition.  ...  DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and  ...  The fundamental idea in Deep-Frag-k is to predict the potential structural fragments that a target protein sequence will form [8] during folding, represented as a fragment vector, which contains highly  ... 
doi:10.1186/s12859-020-3504-z pmid:33203392 fatcat:vvcoa7djkvhnjnyfvlotgdlnzi

DeepQA: improving the estimation of single protein model quality with deep belief networks

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng
2016 BMC Bioinformatics  
Conclusion: DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction.  ...  Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction.  ...  The computational structure prediction methods have the potential to fill the gap, since it is much faster and cheaper than experimental techniques, and also can be used for proteins whose structures are  ... 
doi:10.1186/s12859-016-1405-y pmid:27919220 pmcid:PMC5139030 fatcat:d63b3akvurfyhgfjcvz6locqxi

Deep learning methods in protein structure prediction

Quan Le, Mirko Torrisi, Gianluca Pollastri
2020 Computational and Structural Biotechnology Journal  
Since the '60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels  ...  Protein Structure Prediction is a central topic in Structural Bioinformatics.  ...  In the remainder of this section we summarise the main Deep Learning modules which are used in previous research in Protein Structure Prediction.  ... 
doi:10.1016/j.csbj.2019.12.011 pmid:32612753 pmcid:PMC7305407 fatcat:pgnqeqjggrhlxoza3ejylhdc2e

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Kartikay Prasad, Vijay Kumar
2021 Current Research in Pharmacology and Drug Discovery  
Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further  ...  This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic.  ...  They have used unsupervised deep learning architecture based on a convolutional variational autoencoder to systematically compare S protein ensembles from MD simulations.  ... 
doi:10.1016/j.crphar.2021.100042 pmid:34870150 pmcid:PMC8317454 fatcat:ayz42cti5nagvd2cejy4menxli

Geometric Potentials from Deep Learning Improve Prediction of CDR H3 Loop Structures [article]

Jeffrey A. Ruffolo, Carlos Guerra, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray
2020 bioRxiv   pre-print
This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence.  ...  In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure.  ...  Application of deep learning techniques has yielded significant advances in the prediction of protein structure in recent years.  ... 
doi:10.1101/2020.02.09.940254 fatcat:ohbk4fpalnf77bljmytbpfxutq

Simulations meet machine learning in structural biology

Adrià Pérez, Gerard Martínez-Rosell, Gianni De Fabritiis
2018 Current Opinion in Structural Biology  
We envisage that machine learning (ML) will be able to solve both the accuracy and time-to-prediction problem by learning predictive models using expensive simulation data.  ...  The synergies between classical, quantum simulations and ML methods, such as artificial neural networks, have the potential to drastically reshape the way we make predictions in computational structural  ...  This augmented dataset build at high computational and time cost is then used for learning fast predictive models, e.g. K DEEP .  ... 
doi:10.1016/j.sbi.2018.02.004 pmid:29477048 fatcat:x3aszn6chvhgvbkuwph52jijvy
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