• Wissenschaftliche Publikationen

Veröffentlichungen der HSWT

Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).

8 Ergebnisse

  • Prof. Dr. Jörg Ewald, Herbert Formayer, Josef Gadermaier, Tobias Huber, Klaus Katzensteiner, David Kessler, Michael Kessler, Ralf Klosterhuber, Roland Köck, F. Lehner, Manfred Lexer, Dr. Alois Simon, Gerfried Winkler, Markus Wilhelmy, Prof. Dr. Harald Vacik, Klaus Klebinder, Michael Englisch

    • Berechtigungen:  Peer Reviewed

    Supporting the management of protection forests in a changing climate (2024) 15th Congress INTERPRAEVENT 2024, June 10th - 13th .

  • Ashima Khanna, Prof. Dr. Florian Haselbeck, Prof. Dr. Dominik Grimm

    Predicting Protein Thermostability through Deep Learning Leveraging 3D Structural Information (2024) Biological Materials Science - A workshop on biogenic, bioinspired, biomimetic and biohybrid materials for innovative optical, photonics and optoelectronics applications 2024 .

    In protein engineering, improving thermostability is essential for many industrial and pharmaceutical applications. However, the experimental process of identifying stabilizing mutations is time-consuming due to the enormous search space. With the increasing availability of protein structural and thermostability data, computational approaches using deep learning to identify thermostable candidates are gaining popularity. In this work, we present and benchmark a novel graph neural network, ProtGCN, that incorporates geometric and energetic details of proteins to predict changes in Gibbs free energy (ΔG), a key indicator of thermostability, upon single point mutations. Unlike conventional methods that rely on sequence or structural features, our model uses protein graphs with rich node features, carefully preprocessed from a comprehensive dataset of approximately 4149 mutated sequences across 117 protein families. In addition, ProtGCN is enhanced by incorporating embeddings from the Evolutionary Scale Modeling (ESM) protein language model into the protein graphs. This integration allows ProtGCN (ESM) to outperform comparison models, achieving competitive performance with XGBoost and a protein language model-based multi-layer perceptron on all evaluation metrics, and outperforming all models on further analyses. A strength of ProtGCN (ESM) is its ability to correctly identify and predict stabilizing and destabilizing mutations with extreme effects, which are typically underrepresented in thermostability datasets. These results suggest a promising direction for future computational protein engineering research.
  • Prof. Dr. Florian Haselbeck, Maura John, Yuqi Zhang, Jonathan Pirnay, Juan Pablo Fuenzalida-Werner, Ruben Costa, Prof. Dr. Dominik Grimm

    Superior Protein Thermophilicity Prediction With Protein Language Model Embeddings (2024) Biological Materials Science - A workshop on biogenic, bioinspired, biomimetic and biohybrid materials for innovative optical, photonics and optoelectronics applications 2024 .

    Protein thermostability is an essential property for many biotechnological fields, such as enzyme engineering and protein-hybrid optoelectronics. In this context, machine learning-based in silico predictions have the potential to reduce costs and development time by identifying the most promising candidates for subsequent experiments. The development of such prediction models is enabled by ever-growing protein databases and information on protein stability at different temperatures. In this study, we leverage protein language model embeddings for thermophilicity prediction with ProLaTherm, a Protein Language model-based Thermophilicity predictor. We assess ProLaTherm against several feature-, sequence-, and literature-based comparison partners on a new benchmark dataset derived from a significant update of published data. ProLaTherm outperforms all comparison partners both in a nested cross-validation setup and on protein sequences from species not seen during training with respect to multiple evaluation metrics. In terms of Matthew's correlation coefficient, ProLaTherm surpasses the second-best competitor by 18.1% in the nested cross-validation setup. Using proteins from species that do not overlap with species from the training data, ProLaTherm outperforms all competitors by at least 9.7%. On this data, it misclassified only one non-thermophilic protein as thermophilic. Furthermore, it correctly identified 97.4% of all thermophilic proteins in our test set with an optimal growth temperature above 70°C.

Betreuung der Publikationsseiten

Zentrum für Forschung und Wissenstransfer - Lageplan in Weihenstephan an der HSWT


Hochschule Weihenstephan-Triesdorf
Zentrum für Forschung und Wissenstransfer
Gebäude H21
Am Staudengarten 9
85354 Freising

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