Biology subjects

Illig, A.-M.

Publications and source records attributed to Illig, A.-M..

3 recordsLinked to original sources

bioRxiv2024

KnowVolution of an efficient polyamidase through molecular dynamics simulations of incrementally docked oligomeric substrates

Management of synthetic polymer waste is one of the most pressing challenges for society today. Enzymatic recycling of polycondensates like polyamides (PA), however, remains limited due to a lack of efficient polyamidases. This study reports the directed evolution of the polyamidase NylCp2-TS. Key positions involved in enzyme-substrate interactions and PA 6 hydrolysis are identified through random mutagenesis and molecular dynamics (MD) simulations. The final variant, NylC-HP (NylCp2-TSF134W/D304M/R330A), exhibits a 6.9-fold increased specific activity (520 {+/-} 19 {micro}mol6-AHAeq. h-1 mgenzyme-1) and enhanced thermal stability (Tm = 90 {degrees}C, {Delta}Tm = 4.2 {degrees}C), making NylC-HP the fastest polyamidase for PA 6 and PA 6,6 hydrolysis. Despite the improved reaction rate, the degree of depolymerization remains below 1 %. To understand the molecular basis of achieved improvements and factors limiting the degree of depolymerization, intra- and intermolecular interactions of various enzyme-substrate complexes are analyzed by incremental docking of PA 6 tetramers and MD simulations. After optimizing the activity and stability of NylC-HP, the findings suggest that widening the substrate binding pocket is likely necessary to improve substrate accessibility to target more buried attack sites on the polymer surface, and thereby enhancing the degree of depolymerization.

bioengineering↗

bioRxiv2022

A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineering

Protein engineering through directed evolution and (semi-)rational approaches has been applied successfully to optimize protein properties for broad applications in molecular biology, biotechnology, and biomedicine. The potential of protein engineering is not yet fully realized due to the limited screening throughput hampering the efficient exploration of the vast protein sequence space. Data-driven strategies have emerged as a powerful tool to leverage protein engineering by providing a model of the sequence-fitness landscape that can exhaustively be explored in silico and capitalize on the high diversity potential offered by nature However, as both the quality and quantity of the inputted data determine the success of such approaches, the applicability of data-driven strategies is often limited due to sparse data. Here, we present a hybrid model that combines direct coupling analysis and machine learning techniques to enable data-driven protein engineering when only few labeled sequences are available. Our method achieves high performance in predicting a proteins fitness based on its sequence regardless of the number of sequences-fitness pairs in the training dataset. Besides reducing the computational effort compared to state-of-the-art methods, it outperforms them for sparse data situations, i.e., 50 - 250 labeled sequences available for training. In essence, the developed method is auspicious for data-driven protein engineering, especially for protein engineers who have only access to a limited amount of data for sequence-fitness landscape modeling.

synthetic biology↗

bioRxiv2020

Molecular dynamics simulations of protein aggregation: protocols for simulation setup and analysis with Markov state models and transition networks

Protein disorder and aggregation play significant roles in the pathogenesis of numerous neuro-degenerative diseases, such as Alzheimers and Parkinsons disease. The end products of the aggregation process in these diseases are {beta}-sheet rich amyloid fibrils. Though in most cases small, soluble oligomers formed during amyloid aggregation are the toxic species. A full understanding of the physicochemical forces behind the protein aggregation process is required if one aims to reveal the molecular basis of the various amyloid diseases. Among a multitude of biophysical and biochemical techniques that are employed for studying protein aggregation, molecular dynamics (MD) simulations at the atomic level provide the highest temporal and spatial resolution of this process, capturing key steps during the formation of amyloid oligomers. Here we provide a step-by-step guide for setting up, running, and analyzing MD simulations of aggregating peptides using GROMACS. For the analysis we provide the scripts that were developed in our lab, which allow to determine the oligomer size and inter-peptide contacts that drive the aggregation process. Moreover, we explain and provide the tools to derive Markov state models and transition networks from MD data of peptide aggregation.

biophysics↗