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1. Latent generative landscapes as maps of functional diversity in protein sequence space

   https://doi.org/10.1038/s41467-023-37958-z  

   Ziegler, Cheyenne; Martin, Jonathan; Sinner, Claude; Morcos, Faruck (December 2023, Nature Communications)

   Abstract Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins,β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design. 

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2. Visualizing and Annotating Protein Sequences using A Deep Neural Network

   https://doi.org/10.1109/IEEECONF51394.2020.9443364  

   Zhao, Zhengqiao; Rosen, Gail (November 2020, Visualizing and Annotating Protein Sequences using A Deep Neural Network)

   null (Ed.)

   It is critical for biological studies to annotate amino acid sequences and understand how proteins function. Protein function is important to medical research in the health industry (e.g., drug discovery). With the advancement of deep learning, accurate protein annotation models have been developed for alignment free protein annotation. In this paper, we develop a deep learning model with an attention mechanism that can predict Gene Ontology labels given a protein sequence input. We believe this model can produce accurate predictions as well as maintain good interpretability. We further show how the model can be interpreted by examining and visualizing the intermediate layer output in our deep neural network. 

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3. Improving protein function prediction by learning and integrating representations of protein sequences and function labels

   https://doi.org/10.1093/bioadv/vbae120  

   Boadu, Frimpong; Cheng, Jianlin (August 2024, Bioinformatics Advances)

   Zhu, Shanfeng (Ed.)

   Abstract MotivationAs fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt. ResultsWe introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins. TransFew leverages a large pre-trained protein language model (ESM2-t48) to learn function-relevant representations of proteins from raw protein sequences and uses a biological natural language model (BioBert) and a graph convolutional neural network-based autoencoder to generate semantic representations of GO terms from their textual definition and hierarchical relationships, which are combined together to predict protein function via the cross-attention. Integrating the protein sequence and label representations not only enhances overall function prediction accuracy, but delivers a robust performance of predicting rare function terms with limited annotations by facilitating annotation transfer between GO terms. Availability and implementationhttps://github.com/BioinfoMachineLearning/TransFew. 

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4. FoldMark: Safeguarding Protein Structure Generative Models with Distributional and Evolutionary Watermarking

   https://doi.org/10.1101/2024.10.23.619960  

   Zhang, Zaixi; Jin, Ruofan; Xu, Guangxue; Wang, Xiaotong; Zitnik, Marinka; Cong, Le; Wang, Mengdi (October 2024, bioRxiv)

   Proteins are the principal architects of life, fueling advances in bioengineering, drug discovery, and synthetic biology. The integration of generative AI with computational protein science has revolutionized protein design while also posing dual-use risks, such as enabling the creation of pandemic-capable proteins that require strong biosecurity safeguards. Here, we introduce FoldMark, a first-of-its-kind watermarking strategy leveraging distributional and evolutionary principles tailored for protein generative models, balancing watermark capacity and structural quality. FoldMark achieves over 95% watermark bit accuracy at 32 bits with minimal impact on structural quality (>0.9 scTM scores) for leading models including AlphaFold3, ESMFold, RFDiffusion, and RFDiffusionAA. For user tracing, FoldMark can successfully trace up to1 millionusers. To validate FoldMark in wet lab, we applied it to structure-based design of EGFP and CRISPR-Cas13, showing wildtype-level function (98% fluorescence, 95% editing efficiency) and >90% watermark detection, demonstrating its practical utility for safeguarding AI-driven protein research. 

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5. NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity

   https://doi.org/10.1093/bioinformatics/btab098  

   Barot, Meet; Gligorijević, Vladimir; Cho, Kyunghyun; Bonneau, Richard (February 2021, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Abstract Motivation Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to protein functional annotation use sequence similarity to transfer knowledge between species. These approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular context for meaningful prediction. To supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, most of these methods are tied to a network for a single species, and many species lack biological networks. Results In this work, we integrate sequence and network information across multiple species by computing IsoRank similarity scores to create a meta-network profile of the proteins of multiple species. We use this integrated multispecies meta-network as input to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and consequently leads to significant improvements in function prediction performance compared to two network-based methods, a deep learning sequence-based method and the BLAST annotation method used in the Critial Assessment of Functional Annotation. We are able to demonstrate that our approach performs well even in cases where a species has no network information available: when an organism’s PPI network is left out we can use our multi-species method to make predictions for the left-out organism with good performance. Availability and implementation The code is freely available at https://github.com/nowittynamesleft/NetQuilt. The data, including sequences, PPI networks and GO annotations are available at https://string-db.org/. Supplementary information Supplementary data are available at Bioinformatics online. 

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