Biology subjects

Oberdorfer, G.

Publications and source records attributed to Oberdorfer, G..

5 recordsLinked to original sources

bioRxiv2024

Computational design of a thermostable de novo biocatalyst for whole cell biotransformations

Over the last decades, transformative catalytic strategies have emerged, with biocatalysis cur-rently exerting a substantial influence on the pharmaceutical and fine chemical sectors. Fast pro-gress in the design of efficient enzymatic processes, however, suffers from the lack of readily available, stable, and customizable protein scaffolds that can be adapted to different catalytic functions. Here, we detail the design and experimental characterization of computationally de-signed de novo proteins with a non-natural fold for biocatalytic applications. The initial design and several variants form a helical barrel structure comprised of six antiparallel straight helices con-nected by five loops, creating an open central channel with two accessible cavities. To demon-strate the versatility of this scaffold, we designed variants with catalytic sites positioned at differ-ent locations along the central channel. All designs show high thermal stability and excellent agreement between experimental and calculated scattering profiles from small-angle X-ray scat-tering, while a crystal structure of a surface-redesigned variant confirms the close match be-tween the designed and experimental structures. Importantly, repositioning and engineering the catalytic sites enables substantial modulation of catalytic activity, with the best variant showing an approximately 11-fold increase in catalytic efficiency compared with the original design. Finally, the designs can be used for whole-cell biotransformations and tolerate up to 20% organic sol-vent. These results establish a stable de novo protein scaffold with tunable functional sites, offer-ing a versatile platform for biocatalysis, biosensing, and biosynthetic systems.

biochemistry↗

bioRxiv2024

Computational design of highly active de novo enzymes

Enzymes are broadly used as biocatalysts in industry and medicine due to their coverage of vast areas of chemical space, their exquisite selectivity and efficiency as well as the mild reaction conditions at which they operate. Custom designed enzymes can produce tailor-made biocatalysts with potential applications extending beyond natural reactions. However, current design methods require testing of high numbers of designs and mostly produce de novo enzymes with low catalytic activities. As a result, they require costly experimental optimization and high-throughput screening to be industrially viable. Here we present rotamer inverted fragment finder-diffusion (Riff-Diff), a hybrid machine learning and atomistic modelling strategy for scaffolding catalytic arrays in de novo proteins. We highlight the general applicability of Riff-Diff by designing enzymes for two mechanistically distinct chemical transformations, the retro-aldol reaction and the Morita-Baylis-Hillman reaction. We show that in both cases it is possible to generate catalysts exhibiting activities rivalling those optimized by in-vitro evolution, along with exquisite stereoselectivity. High resolution structures of six of the designs revealed an angstrom level of active site design precision. The design strategy can, in principle, be applied to any catalytically competent amino acid constellation. These findings enable the practical applicability of de novo protein catalysts in synthesis and shed light on fundamental principles of protein design and enzyme catalysis.

biochemistry↗

bioRxiv2024

Engineering of Transmembrane Alkane Monooxygenases to Improve a Key Reaction Step in the Synthesis of Polymer Precursor Tulipalin A

The -methylene-{gamma}-butyrolactone tulipalin A, naturally found in tulips can polymerize via addition at the vinyl group or via ring-opening polymerization, making it a highly promising monomer for biobased polymers. As tulipalin A biosynthesis in plants remains elusive, we propose a pathway for its synthesis starting from the metabolic intermediate isoprenol. For this, terminal hydroxylation of the -methylene substrate isoprenyl acetate is a decisive step. While a panel of fungal unspecific peroxygenases showed a preference for the undesired epoxidation of the exo-olefin group, bacterial alkane monooxygenases were specific for terminal hydroxylation. A combination of protein engineering based on de novo structure prediction of the membrane enzymes with cell engineering allowed to increase the specific activity by 6-fold to 1.83 U gcdw -1, unlocking this reaction for the fermentative production of tulipalin A from renewable resources.

bioengineering↗

bioRxiv2023

ESM-Scan - a tool to guide amino acid substitutions

Protein structure prediction and (re)design have gone through a revolution in the last three years. The tremendous progress in these fields has been almost exclusively driven by readily available machine-learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are also hard to capture with computational models. Here, we describe the implementation and use of ESM-Scan, which uses the ESM zero-shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in-silico deep mutational scanning experiments. We benchmark ESM-Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally evaluating the tools performance on a challenging test case of a blue-light-activated diguanylate cyclase from Methylotenera species (MsLadC). We used ESM-Scan to predict conservative sequence changes in a highly conserved region of this enzyme responsible for allosteric product inhibition. Our experimental results show that the ESM-zero shot model emerges as a robust method for inferring the impact of amino acid substitutions, especially when evolutionary and functional insights are intertwined. ESM-Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp

biochemistry↗

bioRxiv2023

Flattening the curve - How to get better results with small deep-mutational-scanning datasets

Proteins are utilized in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches employ some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pre-training, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.

bioinformatics↗