nextTALK Vortragsreihe

Am 18. November 2024 trägt Andrey Klymchenko (French National Centre for Scientific Research, Straßburg) zum Thema „Fluorescent Organic Nanomaterials for Bioimaging and Diagnostics“ vor. Die Veranstaltung beginnt um 16 Uhr im Seminarraum des HIPS (Geb. E8 1, Campus Saarbrücken).

Am 11. November 2024 wird Paul Wilmes (Universität Luxemburg) über "Systems ecology of the human expobiome" sprechen. Der Vortrag beginnt um 11:11 Uhr im Helmholtz-Institut für Pharmazeutische Forschung Saarland (HIPS), Seminarraum 0.27 (Campus Saarbrücken, Gebäude E8 1, 66123 Saarbrücken).

Abstract

The human microbiome, through its emergent properties, contributes essential functions to its host. Recent large-scale metagenomic studies have provided insights into its functional potential but have mostly focused on taxa-centric views. However, the functional repertoire which is actually contributed to human physiology remains largely unexplored. For example, the human microbiome produces a complex biomolecular cocktail in the form of small molecules, nucleic acids, and (poly-)peptides, recently defined as the expobiome. This cocktail has many bioactive properties but these have so far eluded systematic study. This overall gap in knowledge is limiting our understanding of the role of the human microbiome in governing human physiology and how changes to the microbiome impact chronic diseases including metabolic and neurological conditions through the triggering and exacerbation of disease pathways. Furthermore, without mechanistic understanding of the microbiome’s molecular complex, we are unable to rationally design microbiome-targeted therapies. In this context, the microbiome also represents a treasure trove for leads for the development of future diagnostic and therapeutic applications for chronic diseases. I will describe the current state of understanding of the functional microbiome in contrast to taxonomic views with a specific focus on microbiome-derived molecules in immune system stimulation and regulation. Ranging from systematic integrated multi-omic analyses of the microbiome-borne molecular complex to mechanistic studies in novel experimental systems, a clear roadmap towards translating the functional ecology of the gut microbiome into novel diagnostic applications and drugs will be drawn.

Am 27. Juni 2024 hält Dr. David B. Konrad (LMU) einen Vortrag zu "Unlocking the Potential of Late-Stage Functionalization for Medicinal Chemistry by Integrating High-Throughput Experimentation with Geometric Deep Learning". Los geht es um 15 Uhr im HIPS (Seminarraum 0.27, Geb. E8 1, Campus Saarbrücken).

Abstract

Structural novelty and complexity render the synthesis of chemical target structures challenging when aiming to access structure-activity relationships (SARs) in medicinal chemistry. Late-stage functionalization (LSF), in this regard, provides the opportunity to directly and economically modify both early building blocks or advanced molecules which circumvents the necessity for establishing alternate synthetic routes to install new substitution patterns. Synthetic methods for selective activation and modification of C–H bonds on organic scaffolds are available for both directed and non-directed, as well as chemo- and site-selective transformations. In addition to providing control over the modification sites, the abundance of LSF procedures enables accessing a variety of different modifications. To date, however, only few applications of LSF in drug discovery have been published due to the difficulty in predicting the reaction outcome if multiple functional groups and various types of C–H bonds with different bond strength, electronic properties and steric environments are present. As a consequence, running a successful LSF campaign using traditional workflows often requires time-consuming resource-intensive experimentation, which might not feasible during medicinal chemistry explorations. To approach this challenge, we have built a high throughput experimentation (HTE) platform that provides semi-automated miniaturized low-volume screenings to accelerate the performance of multiple transformations in parallel. In a proof-of-concept study, we have investigated the regioselective introduction of boronic esters into advanced drug-like molecules, which serve as synthetic handles for installing a variety of different functional groups. As starting materials for our HTE approach, we have chosen 23 structurally diverse drug molecules, 12 drug-like fragments and 5 frequently occurring literature substrates, which culminated in the performance of 956 reactions. Together with the group of Gisbert Schneider, we have used the generated reaction data along with a literature dataset to train graph neural networks. Our computational model, which considered the influence of steric and quantum mechanical information, correctly predicted the reactivity of 81% of novel substrates, while reaction yields for diverse reaction conditions were predicted with a mean absolute error margin of 4–5%. The regioselectivity of the major products was accurately captured in up to 90% of the cases studied.

References:
[1] D. F. Nippa, et al., CHIMIA 2022, 76, 258.
[2] D. F. Nippa, et al., Nat. Chem. 2024, 16, 239–248.
[2] D. F. Nippa, et al., Commun. Chem. 2023, 6, 256.
[2] D. F. Nippa, et al., ChemRxiv 2023, doi:10.26434/chemrxiv-2023-nfq7h-v2.

Am 6. Juni 2024 wird Prof. Dr. Stefan Laufer (Universität Tübingen) einen Gastvortrag mit dem Titel "Academic Drug Discovery: Fiction - Facts - Fantasy?" halten. Der Vortrag beginnt um 19 Uhr im HIPS (Geb. E8 1, Seminarraum EG).

Translationale Wirkstoffforschung im akademischen Umfeld galt lange Zeit als unmöglich, ist inzwischen aber an einigen Standorten, auch in Deutschland, gelebte Praxis. Das "Tübingen Center for Academic Drug Discovery" ist eine Plattform in der Exzellenzstrategie der Eberhard-Karls-Uiversität Tübingen und basiert auf der chemisch/pharmakologischen Validierung von genetisch identifizierten neunen Drug Targets.Am Beispiel eines siRNA-basierten Screens an Leberzellen wurden zwei neue Targets identifiziert, die rein akademisch bzw. im anderen Fall im Rahmen einer VC-basierten Ausgründung bis zum Proof of Concept am Menschen gebracht werden konnten. Der Vortrag stellt diese beiden Case Studies exemplarisch vor.

Am 5. Juni 2024 ist ab 15 Uhr Elodie Laine (Laboratory of Computational and Quantitative Biology, IBPS, Sorbonne University) zu Gast und spricht zum Thema "Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction". Veranstaltungsraum ist der Seminarraum 0.01 in Gebäude E2 1.

Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. I will introduce VespaG, a blazingly fast single amino acid variant effect predictor, leveraging embeddings of protein Language Models as input to a minimal deep learning model. To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. Assessed against the ProteinGym Substitution Benchmark (217 multiplex assays of variant effect with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 +/- 0.01, matching state-of-the-art methods such as GEMME, TranceptEVE, PoET, AlphaMissense, and VESPA. VespaG reached its top-level performance several orders of magnitude faster, predicting all mutational landscapes of the human proteome in 30 minutes on a consumer laptop (12-core CPU, 16 GB RAM).

Am 5. Juni 2024 spricht Alexey Gurevich, Helmholtz Institut für Pharmazeutische Forschung Saarland (HIPS), über "Hunting new antibiotics with computer science". Der Vortrag findet um 12:15 Uhr im CISPA, Gebäude E1 5, Raum 002, Campus Saarbrücken statt.

WHO describes antimicrobial resistance (AMR) as one of the top global public health and development threats, while the World Bank estimates its additional healthcare burden as US$ 1 trillion by 2050. One strategy to combat AMR is significantly speeding up and reducing the cost of discovering new antibiotics, particularly those existing in nature but evading all currentparticularly attempts to find them. Surprisingly, computer scientists play an increasingly important role in this endeavor. Modern biotechnology allows us to amass vast amounts of data on natural antibiotics and their tiny producers. However, specialized software and algorithms are the key to unlocking this wealth of information and turning it into medically important discoveries.

In my talk, I will demonstrate how computer science methods, from classical graph and string algorithms to deep learning, help transform antibiotic discovery into a high-throughput technology and realize the promise of already collected and rapidly growing biological datasets. The particular focus will be on analyzing sequencing (strings over the DNA alphabet of {A, C, G, T} letters) and mass spectrometry data (two-dimensional arrays of floating point values). In both cases, we deal with noisy experimental data, often rely on heuristics to make the processing time reasonable, and finally, get biologically relevant findings suitable for verification by our wet-lab collaborators.

Presenter: Alexey Gurevich

Am 23. Mai 2024 wird Alexander Tkatchenko (Universität Luxemburg) über "AI (R)Evolution in (Quantum) Chemistry and Physics" sprechen. Der Gastvortrag findet um 16:30 Uhr in Gebäude E2.1, Raum 0.01 (Campus Saarbrücken) statt.

Abstract

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search and generation, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding (quantum) dynamics of molecules and materials? This is an especially relevant question given that quantum mechanical predictions are now within experimental uncertainties for complex molecular systems (https://www.nature.com/articles/s41586-023-06587-3).

Aiming towards a unified machine learning (ML) model of molecular interactions in chemical space, I will discuss the potential and challenges for using ML techniques in chemistry and physics. ML methods can not only accurately estimate molecular properties of large datasets, but they can also lead to new insights into chemical similarity, aromaticity, reactivity, and molecular dynamics. For example, the combination of reliable molecular data with ML methods has enabled a fully quantitative simulation of protein dynamics in water (https://www.science.org/doi/full/10.1126/sciadv.adn4397). While the potential of machine learning for revealing insights into molecules and materials is high, I will conclude my talk by discussing the many remaining avenues for development.

Am 15. Mai 2024 wird Prof. Dr. Matthias Rarey (Computational Molecular Design, Universität Hamburg) im Rahmen der nextTALK Reihe zum Thema "Algorithms that Matter: Examples from Cheminformatics and Structure-Based Drug Design" sprechen.

Der Vortrag findet von 9 bis 10 Uhr im Gebäude E2 1, Raum 0.01 (Campus Saarbrücken) statt.

Am 8. Mai 2024 wird Rayan Chikhi (Institut Pasteur) einen Gastvortrag zu "Sequence bioinformatics on really big data: petabase-scale sequence alignment catalyses viral discovery" halten. Der Vortrag beginnt um 15 Uhr in Gebäude E2.1, Raum 0.01 (Campus Saarbrücken).

Abstract: Petabytes of valuable sequencing data reside in public repositories, doubling in size every two years. They contain a wealth of genetic information about viruses that would help us monitor spillovers and anticipate future pandemics. We have developed a bioinformatics cloud infrastructure, named Serratus, to perform petabase-scale sequence alignment. With it we analyzed all available RNA-seq samples (5.7 million samples, 10 petabytes) and discovered ten times more RNA viruses than previously known, including a new family of coronaviruses (Edgar et al, Nature, 2022). In this talk, I will present the computational infrastructure and the biological analyses. More recently, we have performed SRA-scale genome assembly, and I will briefly talk about the current state of this project (Logan/IndexThePlanet).

Am 8. April 2024 wird Kenneth Atz, KI-Wissenschaftler bei Roche, einen Vortrag zum Thema "Medicinal Chemistry in the Era of Geometric Deep Learning: From Generative Design to Synthesis Prediction" halten, der den Auftakt zu unserer nextTALK-Vortragsreihe bilden wird. Der Vortrag findet persönlich am HIPS (Gebäude E8.1, Seminarraum, Erdgeschoss) um 15 Uhr statt.