Biology subjects

Fischer, A.-L. M.

Publications and source records attributed to Fischer, A.-L. M..

3 recordsLinked to original sources

bioRxiv2025

Chemical mechanism of allosteric and asymmetric dark reversion in a bacterial phytochrome uncovered by cryo-EM.

Phytochromes are light-sensitive proteins found in plants, fungi, and bacteria. They exist in two functional states, Pr and Pfr, distinguished by Z/E isomers of their bilin chromophore. The chromophore can photoswitch between these states, but also thermally converts in darkness. Despite the importance of the latter reaction, it remains unclear how it is controlled by the phytochrome. Here, we present single-particle cryo-EM measurements on the Pseudomonas aeruginosa bacteriophytochrome (PaBphP) carried out at multiple time points during dark reversion from Pr to Pfr. These experiments resolve the structure of a PrPfr hybrid state. Surprisingly, we find that only protomer B converts back to Pfr in the hybrid, while protomer A remains in Pr. We identify structural asymmetries in the precursor Pr state, which extend from the homodimer interface to a conserved histidine (H277). The hydrogen-bonding network around the chromophore is modulated, explaining how the phytochrome gains control over the activation energy of the isomerization reaction. These findings establish that dark reversion is governed by conformational selection between two substates, whereby one is "dark-reversion ready" and the other one blocks the reaction. Moreover, we explain how the equilibrium of the states is allosterically controlled across the dimer. Together, these findings provide a structural framework for tuning phytochrome signaling lifetimes in optogenetic applications. Significance statementThe dark reversion reaction of phytochromes is crucial to their signalling role in plants, bacteria, and fungi, but it is vastly understudied in terms of its chemical mechanism. It remains unsolved how the reaction can proceed at all, given that the activation energy is prohibitively high for the isomerization to occur in solution. Using time-resolved cryo-EM, we present a chemical and structural framework for understanding how the protein binding pocket regulates the reaction. Our results establish that conformational selection between two substates controls dark reversion, providing a rare example of strongly asymmetric reactivity across a dimeric protein. This opens the way for rational engineering of the lifetimes of the signaling states in phytochromes.

biochemistry↗

bioRxiv2024

Assessing AF2's ability to predict structural ensembles of proteins

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined, setting new benchmarks for accuracy and efficiency in the field. However, the fundamental mechanisms of biological processes at a molecular level are often connected to conformational changes of proteins. Molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein structures by manipulating the input multiple sequence alignment (MSA). These approaches operate under the assumption that the MSA also contains information about the heterogeneity of protein structures. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction focusing on the subsampling of the MSA as implemented in ColabFold and compare the obtained structures with ensembles obtained from MD simulations and NMR. As test cases, we chose four proteins namely the bovine pancreatic inhibitor protein (BPTI), thrombin and two antigen binding fragments (antibody Fv and nanobody), for which reliable experimentally validated structural information (X-ray and/or NMR) was available. Thus, we provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. In three out of the four test cases, we find structural variations fall within the predicted ensembles. Nevertheless, significant minima of the free energy surfaces remain undetected. This study highlights the possibilities and pitfalls when generating ensembles with AF2 and thus may guide the development of future tools while informing upon the results of currently available applications.

biophysics↗

bioRxiv2022

Challenges in antibody structure prediction

The tremendous advances in structural biology and the exponential increase of high-quality experimental structures available in the PDB motivated numerous studies to tackle the grand challenge of predicting protein structures. AlphaFold2 revolutionized the field of protein structure prediction, by combining artificial intelligence with evolutionary information. Antibodies are one of the most important classes of biotherapeutic proteins. Accurate structure models are a prerequisite to advance biophysical property predictions and consequently antibody design. Various specialized tools are available to predict antibody structures based on different principles and profiting from current advances in protein structure prediction based on artificial intelligence. Here, we want to emphasize the importance of reliable protein structure models and highlight the enormous advances in the field. At the same time, we want to raise the awareness that protein structure models--and in particular antibody models--may suffer from structural inaccuracies, namely incorrect cis-amid bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the significance of carefully reviewing protein structure models before investing further computing power and setting up experiments. To facilitate the assessment of model quality, we provide a tool "TopModel" to validate structure models.

bioinformatics↗