Biology subjects

Remington, J. M.

Publications and source records attributed to Remington, J. M..

5 recordsLinked to original sources

bioRxiv2026

Progressive Backmapping of Highly Coarse-Grained Protein Models

Reconstructing all-atom (AA) structures from highly coarse-grained (HCG) models remains a significant challenge in multiscale molecular dynamics (MD) simulations, particularly for mesoscale biomolecular assemblies that are beyond the reach of conventional MD methods. Building upon ProNet Backmapping, a neural-network-based thermodynamically consistent approach, we introduce a progressive backmapping framework that reconstructs AA models in a stepwise manner across neighboring resolutions, for example, from a 3-residue-per-site HCG model to a 1-residue-per-site model, then to an AA model. This progressive backmapping method achieves high accuracy across a wide range of proteins and effectively reconstructs flexible linkers in multidomain architectures. Moreover, it supports hierarchical reconstruction of complex protein assemblies, including multiple virus-like particles spanning tens of nanometers and containing hundreds of subunits. Using this framework, we demonstrate--for the first time--the ability to hierarchically backmap entire viral assemblies from HCG to full AA resolution, covering at least three different resolutions. Overall, our method provides a scalable framework for incorporating atomistic detail into mesoscale simulations of complex systems across many applications in chemistry and biology. Table of contents figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/709104v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@4af423org.highwire.dtl.DTLVardef@e2669borg.highwire.dtl.DTLVardef@1be80eforg.highwire.dtl.DTLVardef@2e679_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

A modular intramolecular triplex photo-switching motif that enables rapid and reversible control of aptamer binding activity

DNA switches that can change conformation in response to certain wavelengths of light could enable rapid and non-invasive control of chemical processes for a wide range of applications. However, most current photo-responsive DNA switches are limited either by irreversible switching or reversible switching with impractically slow kinetics. Here, we report the design of an intramolecular triplex photoswitch (TPS) design based on single-stranded DNA that undergoes rapid and reversible photoswitching between folded and unfolded states through isomerization of internal azobenzene modifications. After optimizing the performance of our photoswitch design, we used molecular dynamics (MD) simulations to reveal how individual azobenzenes contribute to the stabilization or destabilization of the triplex depending on their photoisomerization state. By coupling our TPS to an existing aptamer, we can reversibly modulate its binding affinity with less than 15 seconds of UV light exposure. We further demonstrate reproducible shifting in affinity over multiple cycles of UV and blue light irradiation without substantial photobleaching. Given that our TPS can introduce switching functionality to aptamers without manipulating the aptamer sequence itself, we believe our design methodology should offer a versatile means for integrating photo-responsive properties into DNA nanostructures.

bioengineering↗

bioRxiv2020

AMPGAN v2: Machine Learning Guided Discovery of Anti-Microbial Peptides

Antibiotic resistance is a critical public health problem. Each year ~2.8 million resistant infections lead to more than 35,000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applications of known AMPs have encountered issues in development, production, and shelf-life. To drive the development of AMP-based treatments it is necessary to create design approaches with higher precision and selectivity towards resistant targets. Previously we developed AMPGAN and obtained proof-of-concept evidence for the generative approach to design AMPs with experimental validation. Building on the success of AMPGAN, we present AMPGAN v2 a bidirectional conditional generative adversarial network (BiCGAN) based approach for rational AMP design. AMPGAN v2 uses generator-discriminator dynamics to learn data driven priors and controls generation using conditioning variables. The bidirectional component, implemented using a learned encoder to map data samples into the latent space of the generator, aids iterative manipulation of candidate peptides. These elements allow AMPGAN v2 to generate of candidates that are novel, diverse, and tailored for specific applications--making it an efficient AMP design tool.

biophysics↗

bioRxiv2020

A Generative Approach toward Precision Antimicrobial Peptide Design

As the emergence of bacterial resistance is outpacing the development of new antibiotics, we must find cost-effective and innovative approaches to discover new antibacterial therapeutics. Antimicrobial peptides (AMPs) represent one promising solution to fill this void, since they generally undergo faster development, display rapid onsets of killing, and most importantly, show lower risks of induced resistance. Despite prior success in AMP design with physics- and/or knowledge-based approaches, an efficient approach to precisely design peptides with high activity and selectivity is still lacking. Toward this goal, we have invented a novel approach which utilizes a generative model to predict AMP-like sequences, followed by molecular modeling to rank the candidates. Thus, we can identify peptides with desirable sequences, structures, and potential specific interactions with bacterial membranes. For the proof of concept, we curated a dataset that comprises 500,000 non-AMP peptide sequences and nearly 8,000 labeled AMP sequences to train the generative model. For 12 generated peptides that are cationic and likely helical, we assessed the membrane binding propensity via extensive all-atom molecular simulations. The top six peptides were promoted for synthesis, chemical characterizations, and antibacterial assays, showing various inhibition to bacterial growth. Three peptides were validated with broad-spectrum antibacterial activity. In aggregate, the combination of AMP generator and sophisticated molecular modeling affords enhanced speed and accuracy in AMP design. Our approach and results demonstrate the viability of a generative approach to develop novel AMPs and to help contain the rise of antibiotic resistant microbes.

biophysics↗

bioRxiv2020

Molecular Basis for the Adaptive Evolution of Environment Sensing by H-NS Proteins

The DNA-binding protein H-NS is a pleiotropic gene regulator in gram-negative bacteria. Through its capacity to sense temperature and other environmental factors, H-NS allows pathogens like Salmonella to adapt their gene expression, and hence toxicity and biological responses, to their presence inside or outside warm-blooded hosts. To investigate how this sensing mechanism may have evolved to fit different bacterial lifestyles, we compared H-NS orthologs from bacteria that infect humans, plants, and insects, and from bacteria that live on a deep-sea hypothermal vent. The combination of biophysical characterization, high-resolution proton-less NMR spectroscopy and molecular simulations revealed, at an atomistic level, how the same general mechanism was adapted to specific habitats and lifestyles. In particular, we demonstrate how environment-sensing characteristics arise from specifically positioned intra- or intermolecular electrostatic interactions. Our integrative approach clarified the mechanism for H-NS-mediated environmental sensing and suggests that it resulted from the exaptation of an ancestral protein feature.

biochemistry↗