2025 Barry L. Karger Medal in Analytical Chemistry Celebration

March 10, 12:00 – 6:00 p.m.
EXP 8th Floor, Northeastern University

Join us!

The 2025 Barry L. Karger Medal in Analytical Chemistry Celebration will be held Monday, March 10 from 12:00 to 6:00 p.m. at the EXP Building at Northeastern University in Boston, MA.  You’ll hear about the latest advancements in technologies for biopharmaceutical characterization and proteomics and systems biology and from our guest of honor, the 2025 Karger Medal recipient Dr. Bernhard Küster –  renowned expert in proteomics and precision medicine.

Register today

2025 Barry L. Karger Medal in Analytical Chemistry Recipient

Bernhard Küster, PhD

Professor of Proteomics and Bioanalytics at Technical University of Munich and Director of the Bavarian Biomolecular Mass Spectrometry Center

Dr. Bernhard Küster is being recognized for pioneering proteome-wide technologies that have transformed our ability to study drug mechanisms of action and for advancing bioanalytical methods that impact drug discovery and pharmacology.

Program
Moderated by Olga Vitek, PhD, Director of Barnett Institute for Chemical and Biological Analysis and Raymond Bradford Bradstreet Professor, Khoury College of Computer Sciences

12:00-12:50 p.m.

Networking & refreshments

12:50-1:00 p.m.

Hazel Sive, PhD
Dean, College of Science, Northeastern University

Welcome remarks

1:00-1:30 p.m.

Nikolai Slavov, PhD
Professor, College of Engineering, and Faculty Fellow, Barnett Institute for Chemical and Biological Analysis, Northeastern University

From Protein Variation to Biological Functions
Biological functions are reflected in the natural variation of proteome configurations across individual cells. Single-cell proteomics methods may decode this variation and empower inference of biological mechanisms with minimal assumptions. This promise is beginning to be realized by sensitive and scalable mass spectrometry methods. I will discuss approaches that have allowed us to measure and interpret protein covariation in different biological systems, including primary macrophages and melanoma cells expressing markers for drug-resistance priming. The focus of the talk will be on conceptual innovations and data interpretation leading towards molecular mechanisms.

1:30-2:00 p.m.

Benjamin Gyori, PhD
Associate Professor, Khoury College of Computer Sciences and the College of Engineering, and Faculty Fellow, Barnett Institute for Chemical and Biological Analysis, Northeastern University

Large Scale AI-Assisted Knowledge Integration for Protein Biology
Discovery in biomedicine requires interpreting experiments in the context of prior knowledge and existing data on the function of proteins and other biomolecules. However, this process is fundamentally limited by the fact that the large body of existing data and published knowledge is fragmented such that it cannot be readily used in an actionable form by scientists. Through examples from our recent work, we introduce the key components of a technical framework that could overcome these issues. First artificial intelligence-based data annotation and machine reading, coupled to semantic technologies leveraging bio-ontologies enable large-scale data integration across literature and structured databases. From this unified knowledge base, predictive and explanatory models of protein function and cellular behavior can be assembled that allow interpreting novel experimental observations and proposing hypotheses to be validated experimentally. These technologies enable a cycle between experimentation and data interpretation in which human scientists can leverage machine-assisted data integration and interpretation to accelerate discovery.

2:00-2:30 p.m.

Wengong Jin, PhD
Assistant Professor, Khoury College of Computer Sciences and Faculty Fellow, Barnett Institute for Chemical and Biological Analysis Northeastern University

Accelerating Drug Discovery Via Geometric and Generative AI
AI for drug discovery is an emerging field that aims to computationally design new proteins or molecules with desired properties. Traditional experimental approaches to drug discovery are time-consuming and labor-intensive, due to the large combinatorial search space of molecule and protein structures. In this talk, I will describe how to accelerate drug discovery via novel generative and geometric deep learning methods. First, I will introduce junction tree variational autoencoder (JT-VAE), a generative model for molecular graphs. Inspired by probabilistic graphical models, JT-VAE leverages the low tree-width of molecular graphs and represents a molecule as a junction tree of chemical motifs. Second, I will present Neural Euler’s Rotation Equation (NERE), an equivariant rotation prediction network inspired by rigid-body dynamics. Based on NERE, they developed an unsupervised binding energy prediction method that estimates the likelihood of a protein complex via SE(3) denoising score matching. Lastly, I will demonstrate how these algorithmic innovations make real-world impacts on drug discovery. Through collaboration with biologists in wet labs, they successfully designed new antibiotics to fight against antimicrobial resistance and new antibodies with potential for cancer immunotherapy.

2:30-3:15 p.m.

Poster sessions and refreshments

3:15-4:30 p.m.

Bernhard Küster, PhD
Professor of Proteomics and Bioanalytics at Technical University of Munich and Director of the Bavarian Biomolecular Mass Spectrometry Center

Understanding What Therapeutic Drugs Really Do
Almost all drugs act on proteins, are proteins, make or degrade proteins and it has been known since the days of Paracelsus that drugs exert their effects in a dose-dependent fashion. The molecular processes leading to a drug-induced change in cellular phenotype can be roughly divided into: i) target binding, ii) pathway engagement, and iii) cellular reprogramming to arrive at a new viable state or cell death, together forming the mechanism of action (MoA) of a drug. Today, quantitative mass spectrometry is the most comprehensive approach for the proteome-wide characterization of drugs on all three levels because of its unique ability to assay thousands of proteins and their post-translational modifications in complex cellular backgrounds in parallel.

In this presentation, I will introduce the proteome-wide decryptT, decryptM and decryptE technologies that measure target deconvolution, pathway engagement and cellular reprogramming in a fully dose-dependent fashion respectively. Based on the analysis of >3,000 drugs including small molecules and antibodies, examples for drug characterization at all three levels will be discussed, particularly focusing on unexpected or even surprising findings. These include drug repurposing opportunities for kinase inhibitors, the long elusive MoA or Rituximab or the loss of T-cell receptor components in T-cells in response to HDAC inhibitors.

We have developed CurveCurator to put proteome-wide dose-response measurements on a solid statistical foundation and deposited the millions of dose-response curves and derived cellular EC50 values obtained into proteomicsdb.org for FAIR data sharing and mining by the scientific community. Examples for how the data may be used will be highlighted by ascribing new functions to proteins and phosphorylation sites based on their consistent responses to drugs that target known proteins, signaling pathways or cellular machines. We expect that the various implementations of the general “decrypt” approach will become a standard in drug discovery and pharmacology.

4:30 p.m.-6:00 p.m.

Networking reception (open to all)

Featured Speakers

Professor Bernhard Küster is a renowned expert in proteomics and precision medicine, focusing on drug mechanisms, cancer biology, and personalized treatment strategies. After studying chemistry at the University of Cologne and earning a doctorate in biochemistry at the University of Oxford, he held research roles in Heidelberg and Odense before becoming Vice President of Cellzome (now GSK).

Since 2007, he has been a professor of proteomics and bioanalytics at the Technical University of Munich (TUM) and directs the Bavarian Biomolecular Mass Spectrometry Center. He is also the founder of biotech companies OmicScouts and MSAID, driving innovation in life sciences.

Nikolai Slavov

Nikolai Slavov is a Professor and researcher specializing in single-cell proteomics. He earned his Ph.D. from Princeton University, where he studied the coordination of cellular growth with gene expression and metabolism. Dr. Slavov’s lab has developed groundbreaking methods for single-cell proteomics, linking protein covariation to functional phenotypes such as macrophage polarization, drug resistance, and stem cell differentiation. He is also the founding director of Parallel Squared Technology Institute (PTI).

Benjamin M. Gyori is an Associate Professor at Northeastern University’s Khoury College of Computer Sciences and the Department of Bioengineering. His research focuses on computational modeling and AI to improve our understanding of human biology and therapeutics. Formerly a principal investigator at Harvard Medical School, Gyori led DARPA-funded research on AI-driven studies of biological systems. He is an advocate for open science and open source scientific software development to advance biomedical innovation.

Wengong Jin is an Assistant Professor at Northeastern University’s Khoury College of Computer Sciences and a Visiting Research Scientist at the Broad Institute. His research focuses on geometric and generative AI models for drug discovery and biology. Dr. Jin’s work has been published in leading journals such as Nature and Science and recognized with awards including the BroadIgnite Award and the MIT EECS Outstanding Thesis Award.