Real-Time Structure Search and Structure Classification for AlphaFold Protein Models

AlphaFold2 showed a substantial improvement in the accuracy of protein structure prediction. Following the release of the software, whole-proteome protein structure predictions by AlphaFold2 for 21 organisms were made publicly available. Here, we developed the infrastructure, 3D-AF-Surfer, to enable real-time structure-based search for the AlphaFold2 models by combining molecular surface representation with 3D Zernike descriptors and deep neural networks.

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Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.25834 ◽

2019 ◽

Vol 87 (12) ◽

pp. 1141-1148 ◽

Cited By ~ 53

Author(s):

Andrew W. Senior ◽

Richard Evans ◽

John Jumper ◽

James Kirkpatrick ◽

Laurent Sifre ◽

...

Keyword(s):

Neural Networks ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Deep Neural Networks ◽

Critical Assessment

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TopModel: Template-Based Protein Structure Prediction at Low Sequence Identity Using Top-Down Consensus and Deep Neural Networks

Journal of Chemical Theory and Computation ◽

10.1021/acs.jctc.9b00825 ◽

2020 ◽

Vol 16 (3) ◽

pp. 1953-1967 ◽

Cited By ~ 6

Author(s):

Daniel Mulnaes ◽

Nicola Porta ◽

Rebecca Clemens ◽

Irina Apanasenko ◽

Jens Reiners ◽

...

Keyword(s):

Neural Networks ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Deep Neural Networks ◽

Top Down ◽

Sequence Identity

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Improved fragment sampling for ab initio protein structure prediction using deep neural networks

Nature Machine Intelligence ◽

10.1038/s42256-019-0075-7 ◽

2019 ◽

Vol 1 (8) ◽

pp. 347-355 ◽

Cited By ~ 4

Author(s):

Tong Wang ◽

Yanhua Qiao ◽

Wenze Ding ◽

Wenzhi Mao ◽

Yaoqi Zhou ◽

...

Keyword(s):

Neural Networks ◽

Protein Structure ◽

Ab Initio ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Deep Neural Networks

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Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s089006042100010x ◽

2021 ◽

Vol 35 (2) ◽

pp. 251-264

Author(s):

Dimitrios Boursinos ◽

Xenofon Koutsoukos

Keyword(s):

Neural Networks ◽

Distance Learning ◽

Real Time ◽

Speaker Recognition ◽

Deep Neural Networks ◽

Cyber Physical Systems ◽

Error Rates ◽

Traffic Sign ◽

Conformal Prediction ◽

Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

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