Biology subjects

Wu, K. E.

Publications and source records attributed to Wu, K. E..

4 recordsLinked to original sources

bioRxiv2024

ProteinCLIP: enhancing protein language models with natural language

Language models have enabled a new era of biological sequence modeling. However, extracting meaningful sequence-level embeddings from these models remains challenging. In this work, we introduce ProteinCLIP, which applies contrastive learning between a proteins amino acid sequence and curated text describing its function. ProteinCLIP thus learns to take a pre-trained protein language models sequence embedding and refines it produce a function-centric embedding. We show that this embedding space yields sequence representations that enable state-of-the-art performance across a variety of important yet challenging tasks in the study of proteins - from predicting protein protein interactions to accurately detecting homologous proteins despite low sequence similarity. More broadly, ProteinCLIP demonstrates the effectiveness of multi-modal learning in biological contexts, and how such strategies can help isolate key signals from large models and further improve their utility.

bioinformatics↗

bioRxiv2021

TCR-BERT: learning the grammar of T-cell receptors for flexible antigen-binding analyses

The T-cell receptor (TCR) allows T-cells to recognize and respond to antigens presented by infected and diseased cells. However, due to TCRs staggering diversity and the complex binding dynamics underlying TCR antigen recognition, it is challenging to predict which antigens a given TCR may bind to. Here, we present TCR-BERT, a deep learning model that applies self-supervised transfer learning to this problem. TCR-BERT leverages unlabeled TCR sequences to learn a general, versatile representation of TCR sequences, enabling numerous downstream applications. We demonstrate that TCR-BERT can be used to build state-of-the-art TCR-antigen binding predictors with improved generalizability compared to prior methods. TCR-BERT simultaneously facilitates clustering sequences likely to share antigen specificities. It also facilitates computational approaches to challenging, unsolved problems such as designing novel TCR sequences with engineered binding affinities. Importantly, TCR-BERT enables all these advances by focusing on residues with known biological significance. TCR-BERT can be a useful tool for T-cell scientists, enabling greater understanding and more diverse applications, and provides a conceptual framework for leveraging unlabeled data to improve machine learning on biological sequences.

bioinformatics↗

bioRxiv2020

BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution

Simultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility - for example generating paired measurements of scRNA-seq and scATAC-seq - wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA-seq directly from a cells scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

bioinformatics↗

bioRxiv2020

RNA-GPS Predicts SARS-CoV-2 RNA Localization to Host Mitochondria and Nucleolus

The SARS-CoV-2 coronavirus is driving a global pandemic, but its biological mechanisms are less well understood. SARS-CoV-2 is an RNA virus whose multiple genomic and sub-genomic RNA (sgRNA) transcripts hijack the host cells machinery, located across distinct cytotopic locations. Subcellular localization of its viral RNA could play important roles in viral replication and host antiviral immune response. Here we perform computational modeling of SARS-CoV-2 viral RNA localization across eight subcellular neighborhoods. We compare hundreds of SARS-CoV-2 genomes to the human transcriptome and other coronaviruses and perform systematic sub-sequence analyses to identify the responsible signals. Using state-of-the-art machine learning models, we predict that the SARS-CoV-2 RNA genome and all sgRNAs are enriched in the host mitochondrial matrix and nucleolus. The 5 and 3 viral untranslated regions possess the strongest and most distinct localization signals. We discuss the mitochondrial localization signal in relation to the formation of double-membrane vesicles, a critical stage in the coronavirus life cycle. Our computational analysis serves as a hypothesis generation tool to suggest models for SARS-CoV-2 biology and inform experimental efforts to combat the virus.

bioinformatics↗