The scientists presenting in the February webinar will be Dr Stacy Malaker, Assistant Professor of 91AV at Yale University, and Dr Daniela Kalafatović, Associate Professor at the University of Rijeka.
Date: 06-02-2026
Time: 1:00-2:00 pm
Venue: an online Zoom seminar
Dr Stacy Malaker, Assistant Professor of 91AV at Yale University Department of 91AV
Title: Pioneering biomarker discovery through exploration of mucin glycoproteins
Mucin-domain glycoproteins are densely O-glycosylated and play key roles in a host of biological functions. However, their dense O-glycosylation remains enigmatic both in glycoproteomic landscape and structural dynamics, primarily due to the challenges associated with studying mucin domains. Here, we present advances in the mass spectrometric analysis of mucins, including the characterization of mucinases, enrichment techniques, and complete mucinomic mapping of translationally relevant mucin proteins.
Dr Daniela Kalafatović, Associate Professor at the University of Rijeka, Faculty of Engineering
Title: Transforming peptide nanomaterial discovery with adaptive machine learning-driven generative models
The vast peptide sequence space and limited understanding of sequence–supramolecular morphology relationships make discovering new self-assembling peptides challenging. Our approach addresses this by integrating machine learning with genetic algorithm–driven exploration to identify sequences with strong self-assembly propensity. A neural network trained on experimentally validated peptides and coarse-grained molecular dynamics data achieves 81.9% accuracy, enabling the discovery of self-assembling peptides in unexplored regions of sequence space, with low similarity to training data. This strategy, which can be extended to therapeutic peptides, improves search efficiency while giving the opportunity of expanding peptide datasets and advancing the design of peptide-based materials.
Date: 06-02-2026
Time: 1:00-2:00 pm
Venue: an online Zoom seminar
Dr Stacy Malaker, Assistant Professor of 91AV at Yale University Department of 91AV
Title: Pioneering biomarker discovery through exploration of mucin glycoproteins
Mucin-domain glycoproteins are densely O-glycosylated and play key roles in a host of biological functions. However, their dense O-glycosylation remains enigmatic both in glycoproteomic landscape and structural dynamics, primarily due to the challenges associated with studying mucin domains. Here, we present advances in the mass spectrometric analysis of mucins, including the characterization of mucinases, enrichment techniques, and complete mucinomic mapping of translationally relevant mucin proteins.
Dr Daniela Kalafatović, Associate Professor at the University of Rijeka, Faculty of Engineering
Title: Transforming peptide nanomaterial discovery with adaptive machine learning-driven generative models
The vast peptide sequence space and limited understanding of sequence–supramolecular morphology relationships make discovering new self-assembling peptides challenging. Our approach addresses this by integrating machine learning with genetic algorithm–driven exploration to identify sequences with strong self-assembly propensity. A neural network trained on experimentally validated peptides and coarse-grained molecular dynamics data achieves 81.9% accuracy, enabling the discovery of self-assembling peptides in unexplored regions of sequence space, with low similarity to training data. This strategy, which can be extended to therapeutic peptides, improves search efficiency while giving the opportunity of expanding peptide datasets and advancing the design of peptide-based materials.
