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Article ; Online: Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.

Lu, Alex X / Moses, Alan M

Methods in molecular biology (Clifton, N.J.)

2024 Volume 2800, Page(s) 217–229

Abstract: High-throughput microscopy has enabled screening of cell phenotypes at unprecedented scale. Systematic identification of cell phenotype changes (such as cell morphology and protein localization changes) is a major analysis goal. Because cell phenotypes ... ...

Abstract	High-throughput microscopy has enabled screening of cell phenotypes at unprecedented scale. Systematic identification of cell phenotype changes (such as cell morphology and protein localization changes) is a major analysis goal. Because cell phenotypes are high-dimensional, unbiased approaches to detect and visualize the changes in phenotypes are still needed. Here, we suggest that changes in cellular phenotype can be visualized in reduced dimensionality representations of the image feature space. We describe a freely available analysis pipeline to visualize changes in protein localization in feature spaces obtained from deep learning. As an example, we use the pipeline to identify changes in subcellular localization after the yeast GFP collection was treated with hydroxyurea.
MeSH term(s)	Phenotype ; Image Processing, Computer-Assisted/methods ; High-Throughput Screening Assays/methods ; Microscopy/methods ; Saccharomyces cerevisiae/metabolism ; Saccharomyces cerevisiae/genetics ; Deep Learning ; Green Fluorescent Proteins/metabolism ; Green Fluorescent Proteins/genetics ; Hydroxyurea/pharmacology
Chemical Substances	Green Fluorescent Proteins (147336-22-9) ; Hydroxyurea (X6Q56QN5QC)
Language	English
Publishing date	2024-05-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3834-7_15
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Convolutions are competitive with transformers for protein sequence pretraining.

Yang, Kevin K / Fusi, Nicolo / Lu, Alex X

Cell systems

2024 Volume 15, Issue 3, Page(s) 286–294.e2

Abstract: Pretrained protein sequence language models have been shown to improve the performance of many prediction tasks and are now routinely integrated into bioinformatics tools. However, these models largely rely on the transformer architecture, which scales ... ...

Abstract	Pretrained protein sequence language models have been shown to improve the performance of many prediction tasks and are now routinely integrated into bioinformatics tools. However, these models largely rely on the transformer architecture, which scales quadratically with sequence length in both run-time and memory. Therefore, state-of-the-art models have limitations on sequence length. To address this limitation, we investigated whether convolutional neural network (CNN) architectures, which scale linearly with sequence length, could be as effective as transformers in protein language models. With masked language model pretraining, CNNs are competitive with, and occasionally superior to, transformers across downstream applications while maintaining strong performance on sequences longer than those allowed in the current state-of-the-art transformer models. Our work suggests that computational efficiency can be improved without sacrificing performance, simply by using a CNN architecture instead of a transformer, and emphasizes the importance of disentangling pretraining task and model architecture. A record of this paper's transparent peer review process is included in the supplemental information.
MeSH term(s)	Amino Acid Sequence ; Computational Biology ; Neural Networks, Computer ; Peer Review
Language	English
Publishing date	2024-02-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2854138-8
ISSN	2405-4720 ; 2405-4712
ISSN (online)	2405-4720
ISSN	2405-4712
DOI	10.1016/j.cels.2024.01.008
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Article ; Online: Protein structure generation via folding diffusion.

Wu, Kevin E / Yang, Kevin K / van den Berg, Rianne / Alamdari, Sarah / Zou, James Y / Lu, Alex X / Amini, Ava P

Nature communications

2024 Volume 15, Issue 1, Page(s) 1059

Abstract: The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly ... ...

Abstract	The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a diffusion-based generative model that generates protein backbone structures via a procedure inspired by the natural folding process. We describe a protein backbone structure as a sequence of angles capturing the relative orientation of the constituent backbone atoms, and generate structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this mirror how proteins natively twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for more complex equivariant networks. We train a denoising diffusion probabilistic model with a simple transformer backbone and demonstrate that our resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins. As a useful resource, we release an open-source codebase and trained models for protein structure diffusion.
MeSH term(s)	Protein Folding ; Proteins/metabolism ; Neural Networks, Computer ; Protein Conformation
Chemical Substances	Proteins
Language	English
Publishing date	2024-02-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-45051-2
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Book ; Online: Random Embeddings and Linear Regression can Predict Protein Function

Lu, Tianyu / Lu, Alex X. / Moses, Alan M.

2021

Abstract: Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein function prediction. However, the absence of random baselines makes it difficult to ... ...

Abstract	Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein function prediction. However, the absence of random baselines makes it difficult to conclude whether pretraining has learned useful information for protein function prediction. Here we show that one-hot encoding and random embeddings, both of which do not require any pretraining, are strong baselines for protein function prediction across 14 diverse sequence-to-function tasks.
Keywords	Quantitative Biology - Biomolecules ; Computer Science - Machine Learning
Publishing date	2021-04-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Extracting and Integrating Protein Localization Changes from Multiple Image Screens of Yeast Cells.

Lu, Alex X / Handfield, Louis-Francois / Moses, Alan M

Bio-protocol

2018 Volume 8, Issue 18, Page(s) e3022

Abstract: The evaluation of protein localization changes in cells under diverse chemical and genetic perturbations is now possible due to the increasing quantity of screens that systematically image thousands of proteins in an organism. Integrating information ... ...

Abstract	The evaluation of protein localization changes in cells under diverse chemical and genetic perturbations is now possible due to the increasing quantity of screens that systematically image thousands of proteins in an organism. Integrating information from different screens provides valuable contextual information about the protein function. For example, proteins that change localization in response to many different stressful environmental perturbations may have different roles than those that only change in response to a few. We developed, to our knowledge, the first protocol that permits the quantitative comparison and clustering of protein localization changes across multiple screens. Our analysis allows for the exploratory analysis of proteins according to their pattern of localization changes across many different perturbations, potentially discovering new roles by association.
Language	English
Publishing date	2018-09-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2833269-6
ISSN	2331-8325 ; 2331-8325
ISSN (online)	2331-8325
ISSN	2331-8325
DOI	10.21769/BioProtoc.3022
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Article ; Online: Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.

Lu, Alex X / Lu, Amy X / Pritišanac, Iva / Zarin, Taraneh / Forman-Kay, Julie D / Moses, Alan M

PLoS computational biology

2022 Volume 18, Issue 6, Page(s) e1010238

Abstract: A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as ... ...

Abstract	A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call "reverse homology", exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homolog from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.
MeSH term(s)	Amino Acid Sequence ; Evolution, Molecular ; Intrinsically Disordered Proteins/chemistry ; Protein Conformation ; Proteome/metabolism
Chemical Substances	Intrinsically Disordered Proteins ; Proteome
Language	English
Publishing date	2022-06-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010238
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Article ; Online: YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.

Lu, Alex X / Zarin, Taraneh / Hsu, Ian S / Moses, Alan M

Bioinformatics (Oxford, England)

2019 Volume 35, Issue 21, Page(s) 4525–4527

Abstract: Summary: We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step ... ...

Abstract	Summary: We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. Availability and implementation: YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Cell Count ; Microscopy ; Saccharomyces cerevisiae ; Software
Language	English
Publishing date	2019-06-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz402
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Article ; Online: Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.

Lu, Alex X / Kraus, Oren Z / Cooper, Sam / Moses, Alan M

PLoS computational biology

2019 Volume 15, Issue 9, Page(s) e1007348

Abstract: Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically ... ...

Abstract	Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.
MeSH term(s)	Cells, Cultured ; Computational Biology ; Humans ; Image Processing, Computer-Assisted/methods ; Microscopy/methods ; Neural Networks, Computer ; Single-Cell Analysis/methods ; Unsupervised Machine Learning ; Yeasts/cytology
Language	English
Publishing date	2019-09-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1007348
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Book ; Online: Evolution Is All You Need

Lu, Amy X. / Lu, Alex X. / Moses, Alan

Phylogenetic Augmentation for Contrastive Learning

2020

Abstract: Self-supervised representation learning of biological sequence embeddings alleviates computational resource constraints on downstream tasks while circumventing expensive experimental label acquisition. However, existing methods mostly borrow directly ... ...

Abstract	Self-supervised representation learning of biological sequence embeddings alleviates computational resource constraints on downstream tasks while circumventing expensive experimental label acquisition. However, existing methods mostly borrow directly from large language models designed for NLP, rather than with bioinformatics philosophies in mind. Recently, contrastive mutual information maximization methods have achieved state-of-the-art representations for ImageNet. In this perspective piece, we discuss how viewing evolution as natural sequence augmentation and maximizing information across phylogenetic "noisy channels" is a biologically and theoretically desirable objective for pretraining encoders. We first provide a review of current contrastive learning literature, then provide an illustrative example where we show that contrastive learning using evolutionary augmentation can be used as a representation learning objective which maximizes the mutual information between biological sequences and their conserved function, and finally outline rationale for this approach. Comment: Machine Learning in Computational Biology (MLCB) 2020
Keywords	Quantitative Biology - Biomolecules ; Computer Science - Machine Learning ; Computer Science - Neural and Evolutionary Computing
Subject code	006
Publishing date	2020-12-24
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Protein structure generation via folding diffusion

Wu, Kevin E. / Yang, Kevin K. / Berg, Rianne van den / Zou, James Y. / Lu, Alex X. / Amini, Ava P.

2022

Abstract	The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a new diffusion-based generative model that designs protein backbone structures via a procedure that mirrors the native folding process. We describe protein backbone structure as a series of consecutive angles capturing the relative orientation of the constituent amino acid residues, and generate new structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this mirror how proteins biologically twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for complex equivariant networks. We train a denoising diffusion probabilistic model with a simple transformer backbone and demonstrate that our resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins. As a useful resource, we release the first open-source codebase and trained models for protein structure diffusion.
Keywords	Quantitative Biology - Biomolecules ; Computer Science - Artificial Intelligence ; I.2.0 ; J.3
Subject code	612
Publishing date	2022-09-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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