Biology subjects

Weinstein, J. J.

Publications and source records attributed to Weinstein, J. J..

3 recordsLinked to original sources

bioRxiv2022

One-shot design elevates functional expression levels of a voltage-gated potassium channel

Membrane proteins play critical physiological roles as receptors, channels, pumps, and transporters. Despite their importance, however, low expression levels often hamper the experimental characterization of membrane proteins. We present an automated and web-accessible design algorithm called mPROSS (https://mPROSS.weizmann.ac.il), which uses phylogenetic analysis and an atomistic potential, including an empirical lipophilicity scale, to improve native-state energy. As a stringent test, we apply mPROSS to the Kv1.2-Kv2.1 paddle chimera voltage-gated potassium channel. Four designs, encoding 9-26 mutations relative to the parental channel, were functional and maintained potassium-selective permeation and voltage dependence in Xenopus oocytes with up to 14-fold increase in whole-cell current densities. Additionally, single-channel recordings reveal no significant change in the channel-opening probability nor in unitary conductance, indicating that functional expression levels increase without impacting the activity profile of individual channels. Our results suggest that the expression levels of other dynamical channels and receptors may be enhanced through one-shot design calculations. Significance statementHeterologous expression levels of membrane proteins are often low, limiting research and applications. We combine homologous-sequence analysis with Rosetta atomistic calculations to enable one-shot design of dozens of mutations that improve native-state energy. Applied to a voltage-gated potassium channel, designs exhibited up to 14-fold improved functional expression levels in oocytes with almost no change in the single-channel activity profile. This design approach may accelerate research of many challenging membrane proteins, including receptors, channels, and transporters.

biochemistry↗

bioRxiv2021

Stable and functionally diverse versatile peroxidases by computational design directly from sequence

White-rot fungi secrete a repertoire of high-redox potential oxidoreductases to efficiently decompose lignin. Of these enzymes, versatile peroxidases (VPs) are the most promiscuous biocatalysts. VPs are attractive enzymes for research and industrial use, but their recombinant production is extremely challenging. To date, only a single VP has been structurally characterized and optimized for recombinant functional expression, stability and activity. Computational enzyme optimization methods can be applied to many enzymes in parallel, but they require accurate structures. Here, we demonstrate that model structures computed by deep-learning based ab initio structure prediction methods are reliable starting points for one-shot PROSS stability-design calculations. Four designed VPs encoding as many as 43 mutations relative to the wild type enzymes are functionally expressed in yeast whereas their wild type parents are not. Three of these designs exhibit substantial and useful diversity in reactivity profile and tolerance to environmental conditions. The reliability of the new generation of structure predictors and design methods increases the scale and scope of computational enzyme optimization, enabling efficient discovery and exploitation of the functional diversity in natural enzyme families.

biochemistry↗

bioRxiv2020

Multi-Task Learning based 3-Dimensional Striatal Segmentation of MRI - a Multi-modal Objective Assessment

The human striatum is a collection of subcortical nuclei that serves as a critical node in an information processing network. Recent studies have established a clear topographical and functional organization of projections to and from striatal regions that involves detailed and complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor-intensive and time-consuming, yet automated methods have also been a challenging computational problem. Recently, deep learning has emerged as a powerful tool for various tasks, including semantic segmentation. Multi-Task Learning (MTL) is a machine learning technique that allows latent representations between related tasks to be shared during model prediction of multiple tasks with better accuracy. We utilized MTL to segment subregions of the striatum consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST). Dice similarity coefficients (DSC) demonstrate excellent spatial agreement between manual and MTL-generated segmentations ([≥] 0.72 across all striatal subregions). Further quantitative task-based analysis was also conducted. Binding potential values, BPND, of [11C]raclopride PET, and ROI time-series and whole-brain connectivity using fMRI images were compared between results generated from manual segmentations and MTL-generated segmentations. BPND values from MTL-generated segmentations were shown to correlate well with manual segmentations with R2 [≥] 0.91 in all caudate and putamen subregions, and R2=0.69 in VST. Mean Pearson correlation coefficients of the fMRI data between MTL-generated and manual segmentations were also high in time-series ([≥]0.86) and whole-brain connectivity ([≥]0.89) across all subregions. We conclude that the MTL approach is a fast, robust and reliable method for 3D striatal subregion segmentation with results comparable to manually segmented ROIs.

bioengineering↗