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Open AccessProtein remote homology detection and structural alignment using deep learning
Exploiting sequence–structure–function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep lea...
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Article
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. ...
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Article
Open AccessPMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization
Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorizat...
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Open AccessStructure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference
Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interact...
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Article
Open AccessMulti-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut–brain axis (GBA) has been implicated in...
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Article
Open AccessCRISPR arrays as high-resolution markers to track microbial transmission during influenza infection
Disruption of the microbial community in the respiratory tract due to infections, like influenza, could impact transmission of bacterial pathogens. Using samples from a household study, we determined whether m...
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Article
Open AccessSequence-structure-function relationships in the microbial protein universe
For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain ...
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Article
Open AccessExposure to the Russian Internet Research Agency foreign influence campaign on Twitter in the 2016 US election and its relationship to attitudes and voting behavior
There is widespread concern that foreign actors are using social media to interfere in elections worldwide. Yet data have been unavailable to investigate links between exposure to foreign influence campaigns a...
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Specificities of Modeling of Membrane Proteins Using Multi-Template Homology Modeling
Structures of membrane proteins are challenging to determine experimentally and currently represent only about 2% of the structures in the Protein Data Bank. Because of this disparity, methods for modeling mem...
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Article
Open AccessEnsuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional ...
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Article
Genetic and epigenetic coordination of cortical interneuron development
One of the hallmarks of the cerebral cortex is the extreme diversity of interneurons1–3. The two largest subtypes of cortical interneurons, parvalbumin- and somatostatin-positive cells, are morphologically and fu...
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Article
Open AccessStructure-based protein function prediction using graph convolutional networks
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph...
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Article
Open AccessMasked graph modeling for molecule generation
De novo, in-silico design of molecules is a challenging problem with applications in drug discovery and material design. We introduce a masked graph model, which learns a distribution over graphs by capturing ...
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Article
Reply to: Examining microbe–metabolite correlations by linear methods
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Open AccessAuthor Correction: OutPredict: multiple datasets can improve prediction of expression and inference of causality
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Macromolecular modeling and design in Rosetta: recent methods and frameworks
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions...
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Article
Open AccessOutPredict: multiple datasets can improve prediction of expression and inference of causality
The ability to accurately predict the causal relationships from transcription factors to genes would greatly enhance our understanding of transcriptional dynamics. This could lead to applications in which one ...
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Article
Open AccessCharacterization of antibiotic resistance and host-microbiome interactions in the human upper respiratory tract during influenza infection
The abundance and diversity of antibiotic resistance genes (ARGs) in the human respiratory microbiome remain poorly characterized. In the context of influenza virus infection, interactions between the virus, t...
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Article
Open AccessCharacterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling
ATAC-seq has become a leading technology for probing the chromatin landscape of single and aggregated cells. Distilling functional regions from ATAC-seq presents diverse analysis challenges. Methods commonly u...
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Article
Learning representations of microbe–metabolite interactions
Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks ( ...
