Biology subjects

Doerr, M.

Publications and source records attributed to Doerr, M..

5 recordsLinked to original sources

bioRxiv2026

The genome of the coral model sea anemone Exaiptasia diaphana (Aiptasia) strain F003

We present a genome assembly of Aiptasia strain F003, a broadly used laboratory strain of the sea anemone and coral model organism Exaiptasia diaphana (Cnidaria; Anthozoa; Hexacorallia; Actiniaria; Aiptasiidae; Exaiptasia). The genome assembly spans 237.34 Mb across 12,480 contigs with a contig N50 of 76.47 kb (12,423 scaffolds with a scaffold N50 of 77.93 kb), including a single-contig mitochondrial genome with a length of 19.79 kb. The assembly is highly complete with a BUSCO completeness of 96.50% based on the metazoa dataset, including 94.80% single-copy, 1.70% duplicated, 1.70% fragmented, and 1.80% missing BUSCO genes. Genome annotation identified 29,589 protein-coding genes (including 2 pseudogenes) and a repeat content of 32.89%. The genome of the female Aiptasia strain F003 enhances the utility of a key cnidarian model organism by enabling comparisons among Aiptasia strains in studies of symbiosis, microbiomes, and thermal stress. It thereby strengthens the value of Aiptasia as a model for investigating the mechanisms underlying coral holobiont function, response, and resilience to environmental change.

genomics↗

bioRxiv2025

SoluProtMut: Siamese Deep Learning for Predicting Solubility Effects of Protein Mutations with Experimental Validation

Protein solubility is an attractive engineering target because it is a critical property influencing the scalability of protein production and the success of therapeutic proteins in biomedical applications. However, predicting solubility changes upon mutation in silico is challenging due to data heterogeneity and protein bias. Here, we explore how different sources of solubility data can be used for machine learning and present SoluProtMut, a Siamese deep geometric neural network trained to predict the impact of mutations on protein solubility. Our final model was trained exclusively on deep mutational scanning data. We compare our model with five established solubility prediction methods. The model achieves state-of-the-art performance on an independent dataset of various proteins, especially in predicting the effects of multipoint mutations (informedness of 26.5 %). Our findings also reaffirm that the scarcity of solubility data continues to hamper progress in this field. To address this limitation, we experimentally quantified solubility changes for hundreds of single-point and multipoint mutants of haloalkane dehalogenase. Complemented with recent deep-mutational-scanning data on myoglobin, we employed both these data for external validation. Although the generalization to unseen proteins remains limited, our findings demonstrate the potential of integrating high-throughput assays with deep learning to improve the accuracy and scope of solubility prediction. Highlights- We present a novel anti-symmetric Siamese architecture for mutational prediction on structures built on graph-based convolutional neural networks ensuring SE(3) invariance. - We demonstrate that a model trained only on single-point mutants of a single protein derived from high-throughput experiments generalizes to multipoint mutants of unseen proteins, achieving the state-of-the-art binary prediction informedness of 26.5 %. - We address the key limitation in the domain by extending the available data with 277 single-point and multipoint mutants of haloalkane dehalogenase labelled in-house and a selection of recently published 1037 single-point mutants of myoglobin. - We systematically study how different data subsets affect the performance of the trained models, revealing that including yeast-derived high-throughput data in training hampers generalization to low-throughput assays but recovers the performance on the yeast-derived myoglobin dataset.

bioinformatics↗

bioRxiv2024

From Bulk to Binding: Decoding the Entry of PET into Hydrolase Binding Pockets

Plastic-degrading enzymes hold promise for biocatalytic recycling of poly(ethylene terephthalate) (PET), a key synthetic polymer. Despite their potential, the current activity of PET hydrolases is not sufficient for industrial use. To unlock their full potential, a deep mechanistic understanding followed by protein engineering is required. Using cuttingedge molecular dynamics simulations and free energy analysis methods, we uncover the entire pathway from the initial binding of two PET hydrolases - the thermophilic leaf-branch compost cutinase (LCC) and polyester hydrolase 1 (PES-H1) - to an amorphous PET material to a PET chain entering the active site and adopting a hydrolyzable geometry. Our results reveal the initial PET binding and elucidate its non-specific nature driven by electrostatic and hydrophobic forces. Upon PET entry into the active site, we uncover that this process can occur via one of three key pathways and detect barriers to it arising from both PET-PET and PET-enzyme interactions, with specific residues identified by in silico and in vitro mutagenesis. These insights not only advance our understanding of PET degradation mechanisms and pave the way for targeted enzyme enhancement strategies, but also offer an innovative approach applicable to enzyme studies across disciplines.

bioengineering↗

bioRxiv2023

Short-term heat stress assays provide a standardized screening tool to assess impacts of microbiome manipulation on Aiptasia thermal stress tolerance

The ongoing loss of corals and their reef ecosystems hastens the need to develop approaches that mitigate the impacts of climate change. Given the strong reliance of corals on their associated prokaryotic and microalgal symbionts, microbiome-targeted interventions in the form of probiotics or microbiome transplants are emerging as potential solutions. Although inoculation with beneficial microorganisms was shown to improve coral bleaching recovery, the mechanistic underpinnings and extent to which microbiomes can be manipulated are largely unknown. Research progress in this regard is often hindered by coral holobiont complexity and a lack of standardized diagnostics to assess physiological and phenotypic changes following microbial manipulation. Here we address these shortcomings by establishing short-term acute thermal stress assays using the CBASS (Coral Bleaching Automated Stress System) as a standardized and reproducible experimental platform to assess the impact of microbiome manipulation on stress tolerance phenotypes of the coral model Aiptasia. We show that thermal tolerance phenotypes following acute heat stress assays are highly reproducible, host species-specific, and can exert legacy effects with consequences for long-term thermal resilience. We further demonstrate the ability to resolve phenotypic differences in thermal tolerance following microbiome manipulation through pathogen incubation. By employing acute heat stress assays in conjunction with a tractable model organism, we present a standardized experimental platform that allows functional screening for microbes that affect thermal stress tolerance. Such effort may accelerate the discovery of microbes and microbial mechanisms mediating thermal tolerance and our ability to harness them to increase stress resilience.

microbiology↗

bioRxiv2018

The Healthy Pregnancy Research Program: Transforming Pregnancy Research Through a ResearchKit App

Although maternal morbidity and mortality in the U.S. is among the worst of developed countries, pregnant women have been under-represented in research studies, resulting in deficiencies in evidence-based guidance for treatment. There are over two billion smartphone users worldwide, enabling researchers to easily and cheaply conduct extremely large-scale research studies through smartphone apps, especially among pregnant women in whom app use is exceptionally high, predominantly as an information conduit. We developed the first pregnancy research app that is embedded within an existing, popular pregnancy app for self-management and education of expectant mothers. Through the large-scale and simplified collection of survey and sensor generated data via the app, we aim to improve our understanding of factors that promote a healthy pregnancy for both the mother and developing fetus. From the launch of this cohort study on March 16, 2017 through December 17, 2017, we have enrolled 2,058 pregnant women from all 50 states. Our study population is diverse geographically and demographically, and fairly representative of U.S. population averages. We have collected 14,045 individual surveys and 11,669 days of sleep, activity, blood pressure and heart rate measurements during this time. On average, women stayed engaged in the study for 59 days and 45 percent who reached their due date filled out the final outcome survey. During the first nine months, we demonstrated the potential for a smartphone-based research platform to capture an ever-expanding array of longitudinal, objective and subjective participant-generated data from a continuously growing and diverse population of pregnant women.\n\nFundingSupported in part by the National Institutes of Health (NIH)/National Center for Advancing Translational Sciences grant UL1TR001114 and a grant from the Qualcomm Foundation.

epidemiology↗