Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “protein engineering”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An autoinhibited state of 53BP1 revealed by small molecule antagonists and protein engineering

The recruitment of 53BP1 to chromatin, mediated by its recognition of histone H4 dimethylated at lysine 20 (H4K20me2), is important for DNA double-strand break repair. Using a series of small molecule antagonists, we demonstrate a conformational equilibrium between an open and a pre-existing lowly populated closed state of 53BP1 in which the H4K20me2 binding surface is buried at the interface between two interacting 53BP1 molecules. In cells, these antagonists inhibit the chromatin recruitment of wild type 53BP1, but do not affect 53BP1 variants unable to access the closed conformation despite preservation of the H4K20me2 binding site. Thus, this inhibition operates by shifting the conformational equilibrium toward the closed state. Our work therefore identifies an auto-associated form of 53BP1—autoinhibited for chromatin binding—that can be stabilized by small molecule ligands encapsulated between two 53BP1 protomers. Such ligands are valuable research tools to study the function of 53BP1 and have the potential to facilitate the development of new drugs for cancer therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Engineered microcompartment protein and related methods and systems of engineering bacterial systems for non-native protein expression and purification

Engineered microcompartment proteins, and related engineered microcompartment, vectors, cells compositions, methods and systems configured to provide within a cell one or more proteins non-native to the cell are described, wherein the one or more proteins non-native to the cell are contained in at least one engineered microcompartment within the cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A generalized platform for artificial intelligence-powered autonomous enzyme engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-fold improvement in substrate preference and 16-fold improvement in ethyltransferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Directed Evolution of CRISPR/Cas Systems for Precise Gene Editing

CRISPR technology is a universal tool for genome engineering that has revolutionized biotechnology. Recently identified unique CRISPR/Cas systems, as well as re-engineered Cas proteins, have rapidly expanded the functions and applications of CRISPR/Cas systems. The structures of Cas proteins are complex, containing multiple functional domains. These protein domains are evolutionarily conserved polypeptide units that generally show independent structural or functional properties. Here, we propose using protein domains as a new way to classify protein engineering strategies for these proteins and discuss common ways to engineer key domains to modify the functions of CRISPR/Cas systems.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2002

Ordered nanoparticle arrays formed on engineered chaperonin protein templates

Traditional methods for fabricating nanoscale arrays are usually based on lithographic techniques. Alternative new approaches rely on the use of nanoscale templates made of synthetic or biological materials. Some proteins, for example, have been used to form ordered two-dimensional arrays. Here, we fabricated nanoscale ordered arrays of metal and semiconductor quantum dots by binding preformed nanoparticles onto crystalline protein templates made from genetically engineered hollow double-ring structures called chaperonins. Using structural information as a guide, a thermostable recombinant chaperonin subunit was modified to assemble into chaperonins with either 3 nm or 9 nm apical pores surrounded by chemically reactive thiols. These engineered chaperonins were crystallized into two-dimensional templates up to 20 microm in diameter. The periodic solvent-exposed thiols within these crystalline templates were used to size-selectively bind and organize either gold (1.4, 5 or 10nm) or CdSe-ZnS semiconductor (4.5 nm) quantum dots into arrays. The order within the arrays was defined by the lattice of the underlying protein crystal. By combining the self-assembling properties of chaperonins with mutations guided by structural modelling, we demonstrate that quantum dots can be manipulated using modified chaperonins and organized into arrays for use in next-generation electronic and photonic devices.

Chaperonins/chemistry/ultrastructure↗
DOE OSTI2024

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning

Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization. The pipeline facilitates data-driven protein engineering by consolidating sequence-function data to inform directed evolution and provide the requisite data for machine learning-guided protein engineering (MLPE). LevSeq enables quality control of mutagenesis libraries prior to screening, which reduces time and resource costs. Simulation studies demonstrate LevSeq’s ability to accurately detect variants under various experimental conditions. Lastly, we show LevSeq’s utility in engineering protoglobins for new-to-nature chemistry. Widespread adoption of LevSeq and sharing of the data will enhance our understanding of protein sequence-function landscapes and empower data-driven directed evolution.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2007

Effects of Bone Morphogenic Proteins on Engineered Cartilage

A report describes experiments on the effects of bone morphogenic proteins (BMPs) on engineered cartilage grown in vitro. In the experiments, bovine calf articular chondrocytes were seeded onto biodegradable polyglycolic acid scaffolds and cultured in, variously, a control medium or a medium supplemented with BMP-2, BMP-12, or BMP-13 in various concentrations. Under all conditions investigated, cell-polymer constructs cultivated for 4 weeks macroscopically and histologically resembled native cartilage. At a concentration of 100 ng/mL, BMP-2, BMP-12, or BMP-13 caused (1) total masses of the constructs to exceed those of the controls by 121, 80, or 62 percent, respectively; (2) weight percentages of glycosaminoglycans in the constructs to increase by 27, 18, or 15, respectively; and (3) total collagen contents of the constructs to decrease to 63, 89, or 83 percent of the control values, respectively. BMP-2, but not BMP-12 or BMP-13, promoted chondrocyte hypertrophy. These observations were interpreted as suggesting that the three BMPs increase the growth rates and modulate the compositions of engineered cartilage. It was also concluded that in vitro engineered cartilage is a suitable system for studying effects of BMPs on chondrogenesis in a well-defined environment.

Gooch, Keith, J.↗
DOE OSTI2024

Self-driving laboratories to autonomously navigate the protein fitness landscape

Abstract Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence–function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent’s understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.

Rapp, Jacob T. (ORCID:0000000226835204)↗
DOE OSTI2023

Biochemical Process Modeling and Simulation (BPMS)

The Biochemical Process Modeling and Simulation project aims to reduce the cost and time of research by applying theory, modeling, and simulation to the most relevant bottlenecks in the biochemical process. We use molecular modeling, quantum mechanics, metabolic modeling, fluid dynamics, and reaction-diffusion methods in close collaboration with pretreatment, hydrolysis, upgrading, and TEA. The project's outcomes are increased yields and efficiency of the biochemical process, added value to products, and reduced price of fuels by specifically targeting catalytic efficiency, reactor design, enzyme efficiency, and microbial design. We work closely with experimental projects to identify problems and iterate with experiments to find and refine solutions. By working with experimentalists, we decide on problems that can be solved with simulation that could otherwise not be solved or would take too long with experiment alone to reach BETO's targets. Over the years, we have produced solutions that have resulted in determining the most likely fatty-acid derivative for passive transport out of bacteria that upgrade biomass, and we have also designed enzyme mutations for enhanced lignin upgrading. Metabolic models have been developed to tune the activity of 2,3 butanediol production for the 2030 target. A computational method to deliver understanding of how complex omics data can be interpreted in the metabolic pathways of organisms used in the Agile Biofoundry. We have found methods to overcome specific barriers and continue to develop those methods. Our reactor studies have guided the design of both the microbes and reactors for aerobic and micro-aerobic production at all scales and have been instrumental in improving the accuracy of techno-economic analysis models. This project is essential in the process of selecting the final processes for 2030 SAF production targets. More specifically, recently, we have: 1) Predicted the strength of the basic structural interactions in commodity plastics to provide guidance for plastics upcycling strategies. 2) Developed computational tool to improve the characterization of lignin-derived compounds 3) Developed new methodologies to enable Machine Learning-based Directed Evolution for protein engineering. 4) Developed Machine Learning methods to predict protein promiscuity and mutations to further improve microbial and enzymatic driven processes and demonstrated the utility of ML approaches to engineering proteins from sparse experimental datasets. 5) Developed new methods to enable high-fidelity simulation of aerobic fermentation at industrial scale and resolving mismatch of time scales through subcycling/operator splitting 7) Identified the difficulty in preventing local high-oxygen conditions in industrial bubble columns, which leads to less-desirable acetoin production, suggesting future research directions in alternative reactor configurations (e.g loop reactors, shallow-channel reactors).

BIOMASS FUELS↗
DOE OSTI2023

Plug & play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

luoyunan/ECNet: First release

ECNet (evolutionary context-integrated neural network) is a deep learning model that guides protein engineering by predicting protein fitness from the sequence. It integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe.

Luo, Yunan↗
DOE OSTI2023

In vitro continuous protein evolution empowered by machine learning and automation

Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, here we propose a closed-loop in vitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.

59 BASIC BIOLOGICAL SCIENCES↗