Biology subjects

Lubock, N. B.

Publications and source records attributed to Lubock, N. B..

5 recordsLinked to original sources

bioRxiv2024

High resolution deep mutational scanning of the melanocortin-4 receptor enables target characterization for drug discovery

Deep Mutational Scanning (DMS) is an emerging method to systematically test the functional consequences of thousands of sequence changes to a protein target in a single experiment. Because of its utility in interpreting both human variant effects and protein structure-function relationships, it holds substantial promise to improve drug discovery and clinical development. However, applications in this domain require improved experimental and analytical methods. To address this need, we report novel DMS methods to precisely and quantitatively interrogate disease-relevant mechanisms, protein-ligand interactions, and assess predicted response to drug treatment. Using these methods, we performed a DMS of the melanocortin-4 receptor (MC4R), a G protein-coupled receptor (GPCR) implicated in obesity and an active target of drug development efforts. We assessed the effects of >6,600 single amino acid substitutions on MC4Rs function across 18 distinct experimental conditions, resulting in >20 million unique measurements. From this, we identified variants that have unique effects on MC4R-mediated Gs- and Gq-signaling pathways, which could be used to design drugs that selectively bias MC4Rs activity. We also identified pathogenic variants that are likely amenable to a corrector therapy. Finally, we functionally characterized structural relationships that distinguish the binding of peptide versus small molecule ligands, which could guide compound optimization. Collectively, these results demonstrate that DMS is a powerful method to empower drug discovery and development.

synthetic biology↗

bioRxiv2020

Genome-wide Functional Characterization of Escherichia coli Promoters and Regulatory Elements Responsible for their Function.

Despite decades of intense genetic, biochemical, and evolutionary characterizations of bacterial promoters, we lack the ability to identify or predict transcriptional activities of promoters using primary sequence. Even in simple, well-characterized organisms such as E. coli there is little agreement on the number, location, and strength of promoters. We use a genomically-encoded massively parallel reporter assay to perform the first full characterization of autonomous promoter activity across the E. coli genome. We measure promoter activity of >300,000 sequences spanning the entire genome and map 2,228 promoters active in rich media. Surprisingly, 944 of these promoters were found within intragenic sequences and are associated with conciliatory sequence adaptations by both the protein-coding regions and overlapping RNAP binding sites. Furthermore, we perform a scanning mutagenesis of 2,057 promoters to uncover sequence elements regulating promoter activity, revealing 3,317 novel regulatory elements. Finally, we show that despite these large datasets and modern machine learning algorithms, predicting endogenous promoter activity from primary sequence is still challenging.

microbiology↗

bioRxiv2019

Structural and Functional Characterization of G Protein-Coupled Receptors with Deep Mutational Scanning

In humans, the 813 G protein-coupled receptors (GPCRs) are responsible for transducing diverse chemical stimuli to alter cell state, and are the largest class of drug targets. Their myriad structural conformations and various modes of signaling make it challenging to understand their structure and function. Here we developed a platform to characterize large libraries of GPCR variants in human cell lines with a barcoded transcriptional reporter of G-protein signal transduction. We tested 7,800 of 7,828 possible single amino acid substitutions to the beta-2 adrenergic receptor ({beta}2AR) at four concentrations of the agonist isoproterenol. We identified residues specifically important for {beta}2AR signaling, mutations in the human population that are potentially loss of function, and residues that modulate basal activity. Using unsupervised learning, we resolve residues critical for signaling, including all major structural motifs and molecular interfaces. We also find a previously uncharacterized structural latch spanning the first two extracellular loops that is highly conserved across Class A GPCRs and is conformationally rigid in both the inactive and active states of the receptor. More broadly, by linking deep mutational scanning with engineered transcriptional reporters, we establish a generalizable method for exploring pharmacogenomics, structure and function across broad classes of drug receptors.

molecular biology↗

bioRxiv2018

A Scalable, Multiplexed Assay for Decoding Receptor-Ligand Interactions

Chemicals such as drugs, hormones, and odorants can have many potential interactions with endogenous targets, and uncovering these relationships is critical for understanding and modulating function. Mammalian olfactory receptors (ORs), a large family of G protein-coupled receptors, mediate olfaction through activation by small molecules. Each OR can respond to many odorants, and vice versa, making exploring this space one interaction at a time difficult. We developed a high-throughput receptor screening platform in human cell lines to screen libraries of chemicals against a multiplexed library of receptors using next-generation sequencing of barcoded genetic reporters. We screened three concentrations of 181 odorants, where in each well we record the activity of 39 ORs simultaneously, and identified 79 novel associations, including ligands for 15 orphan receptors. This platform allows the cost-effective mapping of large chemical libraries to receptor repertoires at scale.

biochemistry↗

bioRxiv2017

A systematic comparison of error correction enzymes by next-generation sequencing

Gene synthesis, the process of assembling gene-length fragments from shorter groups of oligonucleotides (oligos), is becoming an increasingly important tool in molecular and synthetic biology. The length, quality, and cost of gene synthesis is limited by errors produced during oligo synthesis and subsequent assembly. Enzymatic error correction methods are cost-effective means to ameliorate errors in gene synthesis. Previous analyses of these methods relied on cloning and Sanger sequencing to evaluate their efficiencies, limiting quantitative assessment and throughput. Here we develop a method to quantify errors in synthetic DNA by next-generation sequencing. We analyzed errors in a model gene assembly and systematically compared six different error correction enzymes across 11 conditions. We find that ErrASE and T7 Endonuclease I are the most effective at decreasing average error rates (up to 5.8-fold relative to the input), whereas MutS is the best for increasing the number of perfect assemblies (up to 25.2-fold). We are able to quantify differential specificities such as ErrASE preferentially corrects C/G [->] G/C transversions whereas T7 Endonuclease I preferentially corrects A/T [->] T/A transversions. More generally, this experimental and computational pipeline is a fast, scalable, and extensible way to analyze errors in gene assemblies, to profile error correction methods, and to benchmark DNA synthesis methods.

synthetic biology↗