Biology subjects

Bera, A. K.

Publications and source records attributed to Bera, A. K..

45 records · Page 3Linked to original sources

bioRxiv2022

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. Retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the novel nanoparticles we describe may be broadly useful in biotechnological applications.

biochemistry↗

bioRxiv2022

Hallucinating protein assemblies

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 [A]), as are 3 cryoEM structures of giant rings with up to 1550 residues, C33 symmetry, and 10 nanometer in diameter; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be created using deep learning, and pave the way for the design of increasingly complex nanomachines and biomaterials. One-sentence summaryDeep network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.

biophysics↗

bioRxiv2021

Reconfigurable asymmetric protein assemblies through implicit negative design

Asymmetric multi-protein complexes that undergo subunit exchange play central roles in biology, but present a challenge for protein design. The individual components must contain interfaces enabling reversible addition to and dissociation from the complex, but be stable and well behaved in isolation. Here we employ a set of implicit negative design principles to generate beta sheet mediated heterodimers which enable the generation of a wide variety of structurally well defined asymmetric assemblies. Crystal structures of the heterodimers are very close to the design models, and unlike previously designed orthogonal heterodimer sets, the subunits are stable, folded and monomeric in isolation and rapidly assemble upon mixing. Rigid fusion of individual heterodimer halves to repeat proteins yields central assembly hubs that can bind two or three different proteins across different interfaces. We use these connectors to assemble linearly arranged hetero-oligomers with up to 6 unique components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub, and demonstrate such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems.

synthetic biology↗

bioRxiv2021

Large-scale design and refinement of stable proteins using sequence-only models

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

biophysics↗

bioRxiv2020

De novo design of transmembrane beta-barrels

The ability of naturally occurring transmembrane {beta}-barrel proteins (TMBs) to spontaneously insert into lipid bilayers and form stable transmembrane pores is a remarkable feat of protein evolution and has been exploited in biotechnology for applications ranging from single molecule DNA and protein sequencing to biomimetic filtration membranes. Because it has not been possible to design TMBs from first principles, these efforts have relied on re-engineering of naturally occurring TMBs that generally have a biological function very different from that desired. Here we leverage the power of de novo computational design coupled with a "hypothesis, design and test" approach to determine principles underlying TMB structure and folding, and find that, unlike almost all other classes of protein, locally destabilizing sequences in both the {beta}-turns and {beta}-strands facilitate TMB expression and global folding by modulating the kinetics of folding and the competition between soluble misfolding and proper folding into the lipid bilayer. We use these principles to design new eight stranded TMBs with sequences unrelated to any known TMB and show that they insert and fold into detergent micelles and synthetic lipid membranes. The designed proteins fold more rapidly and reversibly in lipid membranes than the TMB domain of the model native protein OmpA, and high resolution NMR and X-ray crystal structures of one of the designs are very close to the computational model. The ability to design TMBs from first principles opens the door to custom design of TMBs for biotechnology and demonstrates the value of de novo design to investigate basic protein folding problems that are otherwise hidden by evolutionary history. One sentence summarySuccess in de novo design of transmembrane {beta}-barrels reveals geometric and sequence constraints on the fold and paves the way to design of custom pores for sequencing and other single-molecule analytical applications.

synthetic biology↗

bioRxiv2020

Hierarchical design of multi-scale protein complexes by combinatorial assembly of oligomeric helical bundle and repeat protein building blocks

A goal of de novo protein design is to develop a systematic and robust approach to generating complex nanomaterials from stable building blocks. Due to their structural regularity and simplicity, a wide range of monomeric repeat proteins and oligomeric helical bundle structures have been designed and characterized. Here we describe a stepwise hierarchical approach to building up multi-component symmetric protein assemblies using these structures. We first connect designed helical repeat proteins (DHRs) to designed helical bundle proteins (HBs) to generate a large library of heterodimeric and homooligomeric building blocks; the latter have cyclic symmetries ranging from C2 to C6. All of the building blocks have repeat proteins with accessible termini, which we take advantage of in a second round of architecture guided rigid helical fusion (WORMS) to generate larger symmetric assemblies including C3 and C5 cyclic and D2 dihedral rings, a tetrahedral cage, and a 120 subunit icosahedral cage. Characterization of the structures by small angle x-ray scattering, x-ray crystallography, and cryo-electron microscopy demonstrates that the hierarchical design approach can accurately and robustly generate a wide range of macromolecular assemblies; with a diameter of 43nm, the icosahedral nanocage is the largest structurally validated designed cage to date. The computational methods and building block sets described here provide a very general route to new de novo designed symmetric protein nanomaterials.

biochemistry↗

bioRxiv2020

A generative algorithm for de novo design of proteins with diverse pocket structures

To create new enzymes and biosensors from scratch, precise control over the structure of small molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed a generative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The generative algorithm was tested and refined through feedback from two rounds of large scale experimental testing, involving in total, the assembly of synthetic genes encoding 7896 generated designs and assessment of their stability on the yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

biochemistry↗

bioRxiv2019

A revised mechanism of action of hyperaldosteronism-linked mutations in cytosolic domains of GIRK4 (KCNJ5).

G-protein gated, inwardly rectifying potassium channels (GIRK) mediate inhibitory transmission in brain, heart, and adrenal cortex. GIRK4 (KCNJ5) subunits are abundant in the heart and adrenal cortex. Multiple mutations of KCNJ5 cause primary aldosteronism (PA). According to a leading concept, mutations in the pore region of GIRK4 cause loss of K+ selectivity; the ensuing Na+ influx depolarizes zona glomerulosa cells and activates voltage gated Ca2+ channels, inducing hypersecretion of aldosterone. The concept of selectivity loss has been extended to mutations in cytosolic domains of GIRK4 channels, remote from the pore region. We expressed GIRK4R52H, GIRK4E246K, and GIRK4G247R mutants in Xenopus oocytes and human adrenocortical carcinoma cell line (HAC15). Whole-cell currents of heterotetrameric GIRK1/4R52H and GIRK1/4E246K (but not GIRK1/4G247R) channels were greatly reduced compared to GIRK1/4WT. Nevertheless, all heterotetrameric mutants retained full K+ selectivity and inward rectification. When expressed as homotetramers, only GIRK4WT, but none of the mutants, produced whole-cell currents. Confocal imaging, single channel and Forster Resonance Energy Transfer (FRET) analyses showed: 1) reduction of membrane abundance of all mutated channels, especially as homotetramers, 2) impaired interaction with G{beta}{gamma} subunits, and 3) reduced open probability of GIRK1/4R52H. VU0529331, a GIRK4 opener, activated homotetrameric GIRK4G247R channels, but not GIRK4R52H and GIRK4E246K. Our results suggest impaired gating (GIRK4R52H) and expression in plasma membrane (all mutants). We suggest that, contrary to the previously proposed mechanism, R52H and E246K mutants are loss-of-function rather than gain-of-function/selectivity-loss mutants. Hence, GIRK4 openers may be a potential course of treatment for patients with cytosolic N- and C-terminal mutations. Significance StatementMutations in KCNJ5 gene, which encodes for the GIRK4 subunit of G-protein inwardly rectifying K+ channels, are the main cause of primary aldosteronism, a major contributor to secondary hypertension. We report that three mutations in the cytosolic domain of GIRK4 cause loss-of-function, contrary to the prevailing concept that these mutations cause loss of selectivity and subsequent depolarization, i.e. essentially gain-of-function. Our findings correct the existing misconception regarding the biophysical mechanism that impairs the channel function, and may provide indications for future personalized treatment of the disease.

physiology↗

bioRxiv2019

G protein-coupled potassium channels implicated in mouse and cellular models of GNB1 Encephalopathy

De novo mutations in GNB1, encoding the G{beta}1 subunit of G proteins, cause a neurodevelopmental disorder with global developmental delay and epilepsy. Mice carrying a pathogenic mutation, K78R, recapitulate aspects of the disorder, including developmental delay and frequent spike-wave discharges (SWD). Cultured mutant cortical neurons display aberrant bursting activity on multi-electrode arrays. Strikingly, the antiepileptic drug ethosuximide (ETX) restores normal neuronal network behavior in vitro and suppresses SWD in vivo. In contrast, while valproic acid suppresses SWD, it does not restore normal network behavior, suggesting that ETX has mechanistic specificity for the effects of aberrant G{beta}1 signaling. Consistent with this, we show that K78R is a gain-of-function of G protein-coupled potassium channel (GIRK) activation that is potently inhibited by ETX. This work suggests that altered G{beta}1 signaling causes disease in part through effects on GIRK channels, illustrates the utility of cultured neuronal networks in pharmacological screening, and establishes effective pre-clinical models for GNB1 Encephalopathy.

neuroscience↗