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1. Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms

   https://doi.org/10.1093/bioinformatics/btx230  

   Xu, Min ; Chai, Xiaoqi ; Muthakana, Hariank ; Liang, Xiaodan ; Yang, Ge ; Zeev-Ben-Mordehai, Tzviya ; Xing, Eric P. ( July 2017 , Bioinformatics)

   Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data.

   To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data.

   Source code freely available at http://www.cs.cmu.edu/∼mxu1/software.

   Supplementary data are available at Bioinformatics online.

    

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2. Improving the generalizability of protein-ligand binding predictions with AI-Bind

   https://doi.org/10.1038/s41467-023-37572-z  

   Chatterjee, Ayan ; Walters, Robin ; Shafi, Zohair ; Ahmed, Omair Shafi ; Sebek, Michael ; Gysi, Deisy ; Yu, Rose ; Eliassi-Rad, Tina ; Barabási, Albert-László ; Menichetti, Giulia ( December 2023 , Nature Communications)

   Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. AI-Bind is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery. 

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3. One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography

   https://doi.org/10.3389/fmolb.2020.613347  

   Zhou, Bo ; Yu, Haisu ; Zeng, Xiangrui ; Yang, Xiaoyan ; Zhang, Jing ; Xu, Min ( January 2021 , Frontiers in Molecular Biosciences)

   null (Ed.)

   Cryo-electron Tomography (cryo-ET) generates 3D visualization of cellular organization that allows biologists to analyze cellular structures in a near-native state with nano resolution. Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured by cryo-ET, but training individual deep learning models requires large amounts of manually labeled and segmented data from previously observed classes. To perform classification and segmentation in the wild (i.e., with limited training data and with unseen classes), novel deep learning model needs to be developed to classify and segment unseen macromolecules captured by cryo-ET. In this paper, we develop a one-shot learning framework, called cryo-ET one-shot network (COS-Net), for simultaneous classification of macromolecular structure and generation of the voxel-level 3D segmentation, using only one training sample per class. Our experimental results on 22 macromolecule classes demonstrated that our COS-Net could efficiently classify macromolecular structures with small amounts of samples and produce accurate 3D segmentation at the same time. 

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4. Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization

   https://doi.org/10.1093/bioinformatics/btab794  

   Bandyopadhyay, Hmrishav ; Deng, Zihao ; Ding, Leiting ; Liu, Sinuo ; Uddin, Mostofa Rafid ; Zeng, Xiangrui ; Behpour, Sima ; Xu, Min ; Xu, ed., Jinbo ( November 2021 , Bioinformatics)

   Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms.

   In this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with ‘warp’ modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data.

   https://github.com/xulabs/aitom.

   Supplementary data are available at Bioinformatics online.

    

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5. Hybrid supervised and reinforcement learning for the design and optimization of nanophotonic structures

   https://doi.org/10.1364/OE.512159  

   Yeung, Christopher ; Pham, Benjamin ; Zhang, Zihan ; Fountaine, Katherine_T ; Raman, Aaswath_P ( March 2024 , Optics Express)

   From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components. However, both data-driven and exploration-based machine learning strategies have limitations in their effectiveness for nanophotonic inverse design. Supervised machine learning approaches require large quantities of training data to produce high-performance models and have difficulty generalizing beyond training data given the complexity of the design space. Unsupervised and reinforcement learning-based approaches on the other hand can have very lengthy training or optimization times associated with them. Here we demonstrate a hybrid supervised learning and reinforcement learning approach to the inverse design of nanophotonic structures and show this approach can reduce training data dependence, improve the generalizability of model predictions, and significantly shorten exploratory training times. The presented strategy thus addresses several contemporary deep learning-based challenges, while opening the door for new design methodologies that leverage multiple classes of machine learning algorithms to produce more effective and practical solutions for photonic design.

    

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