Abhishek Arun Cukkemane

Dr. Abhishek Arun Cukkemane

Research Topics

Protein Crystallography, Solid-state NMR spectroscopy, Biophysics, Biochemistry, NMR-metabolomics

Address

IBI-7
Structural Biochemistry

Room No 2019

Forschungszentrum Jülich

D-52425 Juelich
Germany

Institute of Biological Information Processing (IBI)

Structural Biochemistry (IBI-7)

Building 05.8v / Room 2019

You can find us here

Welcome to the research group of “The Unmelodramatics”

Hey There,

I am Abhishek and my group’s research activities revolve around comprehending the molecular basis of the psychiatric disorder schizophrenia. With a foundation in basic sciences and analytical methodologies, we specialize in leveraging biochemical and biophysical techniques to drive innovation and excellence in the field using clinical applications, and life sciences.

On one hand, using structural biology and biophysics, we are studying the the influence of protein risk-fators using a variety of structural biology tools such as X-ray crystallography, solid-state NMR spectroscopy and electron microscopy to comprehend the 3D architecture of the schizophrenia protein risk factor called Disrupted-in-schizophrenia-1 (DISC1); at the same time we are combining bio-physical tools such as iso-thermal titration calorimetry and dynamic light scattering to study the thermodynamics and kinetics of DISC1 aggregation; and surface-plasmon resonance and bio-layer interferometry to study the interaction of DISC1 with other relevant risk factors namely the NUDEL proteins and Lis1.

On the other hand, our research focuses on the biochemical profiling of biological fluids from people suffering from psychiatric disorders such as schizophrenia and circadian sleep disorders to gain a deeper understanding of the metabolites and pathways that play a crucial role in identifying individuals affected by this condition. To achieve this, we are conducting quantitative NMR (qNMR) metabolomics studies at the BioNMR facility on campus. By integrating these findings with big data analytics, we aim to uncover unique metabolic signatures associated with schizophrenia. In the later stages of our research, we plan to leverage machine learning (ML) techniques to enhance the accuracy and reliability of biomarker predictions, thereby improving early diagnosis and personalized treatment strategies for clinical settings.

Vita
Publications
Last Modified: 17.03.2025