Biology subjects

Lerer, A.

Publications and source records attributed to Lerer, A..

4 recordsLinked to original sources

bioRxiv2023

Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells

Advances in cryo-electron tomography (cryo-ET) have produced new opportunities to visualize the structures of dynamic macromolecular machinery in native cellular environments. Here, we describe a machine learning approach that can reconstruct the structural landscape and dynamics of biomolecular complexes present in cryo-ET subtomograms. This method, cryoDRGN-ET, learns a deep generative model of 3D density maps directly from subtomogram tilt series images and can capture states diverse in both composition and conformation. We use this approach to reconstruct the in situ translation dynamics of prokaryotic ribosomes, and we reveal the distribution of functional states during translation elongation populated by S. cerevisiae ribosomes inside cells.

biophysics↗

bioRxiv2022

Learning inverse folding from millions of predicted structures

We consider the problem of predicting a protein sequence from its backbone atom coordinates. Machine learning approaches to this problem to date have been limited by the number of available experimentally determined protein structures. We augment training data by nearly three orders of magnitude by predicting structures for 12M protein sequences using AlphaFold2. Trained with this additional data, a sequence-to-sequence transformer with invariant geometric input processing layers achieves 51% native sequence recovery on structurally held-out backbones with 72% recovery for buried residues, an overall improvement of almost 10 percentage points over existing methods. The model generalizes to a variety of more complex tasks including design of protein complexes, partially masked structures, binding interfaces, and multiple states.

systems biology↗

bioRxiv2021

SIMBA: SIngle-cell eMBedding Along with features

Recent advances in single-cell omics technologies enable the individual and joint profiling of cellular measurements. Currently, most single-cell analysis pipelines are cluster-centric and cannot explicitly model the interactions between different feature types. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a unified way and thus simplifies the development of new analyses and integration of other single-cell modalities. SIMBA is implemented as an efficient, comprehensive, and extensible Python library (https://simba-bio.readthedocs.io) for the analysis of single-cell omics data using graph embedding.

bioinformatics↗

bioRxiv2020

Predictive coding as a unifying principle for explaining a broad range of brightness phenomena

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a predictive coding mechanism, which reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other (response equalization). Response equalization is implemented with a dynamic filtering process, which (dynamically) adapts to each input image. Dynamic filtering is applied to the responses of complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast. Author summaryWe hardly notice that what we see is often different from the physical world "outside" of the brain. This means that the visual experience that the brain actively constructs may be different from the actual physical properties of objects in the world. In this work, we propose a hypothesis about how the visual system of the brain may construct a representation for achromatic images. Since this process is not unambiguous, sometimes we notice "errors" in our perception, which cause visual illusions. The challenge for theorists, therefore, is to propose computational principles that recreate a large number of visual illusions and to explain why they occur. Notably, our proposed mechanism explains a broader set of visual illusions than any previously published proposal. We achieved this by trying to suppress predictable information. For example, if an image contained repetitive structures, then these structures are predictable and would be suppressed. In this way, non-predictable structures stand out. Predictive coding mechanisms act as early as in the retina (which enhances luminance changes but suppresses uniform regions of luminance), and our computational model holds that this principle also acts at the next stage in the visual system, where representations of perceived luminance (brightness) are created.

neuroscience↗