Research

A major focus of our work is the computational investigation of proteins, biomolecular complexes, and their interactions with small molecules, peptides, glycans, and other biological partners. By studying structural stability, conformational dynamics, binding interactions, and molecular recognition, we aim to understand how molecular changes influence biological function.

Our research also incorporates structure-based drug discovery and computational design, including the identification and characterization of potential bioactive compounds and the investigation of molecular mechanisms underlying disease-related processes.

We are particularly interested in developing interdisciplinary and data-driven computational approaches that can complement experimental studies and contribute to emerging areas of molecular biotechnology. Through collaborations with researchers from different disciplines, we aim to bridge computational modeling with experimental and structural biology and translate molecular-level insights into meaningful biological and biomedical applications.

Investigation of enzyme and protein dynamics and design of small molecule modulators

We investigate protein dynamics and 3D structures using classical and constant-pH molecular dynamics (MD) simulations. These studies provide atomistic insights into modulator binding sites and protein structural changes, supporting the rational design of novel drug candidates. 

Structural Analysis of N-Glycosylation in Influenza A (H3N2) Hemagglutinin Protein and Impacts on Immune Evasion and Receptor Binding

We investigate the structural effects of N-glycosylation in swine-origin A/H3N2 viruses, focusing on key antigenic residues and representative HA structures. Structural analyses suggest that N-glycosylation may alter local flexibility, solvent accessibility, and intramolecular communication around antigenic regions, potentially contributing to changes in antibody recognition, immune escape, and receptor binding. These structural changes may also influence receptor-binding properties, highlighting N-glycosylation as a potential contributor to antigenic evolution and adaptation of swine-origin IAVs.

Small molecule design targeting proteins in retinal and ocular diseases

We focus on structure-based design of small molecules that modulate disease-associated proteins involved in genetic visual cycle disorders, such as Leber Congential Amaurosis (LCA) and Stargardt Disease. By combining molecular modeling and molecular dynamics simulations, we aim to identify small molecules that could restore normal protein function. and ultimately slow down or prevent progressive vision loss.

Design of inhibitory peptides mimicking protein-protein interaction interfaces

We focus on the structure-based design of stapled peptidic modulators targeting critical protein-protein interactions involved in NLRP3 inflammasome-driven inflammatory and metabolic disorders. We aim to design cell-permeable peptidomimetics that specifically target the cytosolic NLRP3-LRR interface to disrupt the NLRP3–NEK7 interaction and prevent downstream inflammatory cascades by computational modeling and chemical stapling strategies. 

Machine learning-accelerated virtual screening of large compound libraries for the discovery of therapeutic candidates 

We develop machine learning models that rapidly prioritize promising compounds from ultra-large chemical libraries, reducing millions of molecules to a small set of high-confidence hits. The top candidates are then refined with docking and molecular dynamics simulations to identify novel therapeutic leads. 

Computational design and optimization of therapeutic antibodies

We design therapeutic antibodies binding and inhibiting autoantibodies producing during pathogenesis of Multiple Sclerosis (MS). We aim to develop safer, more specific, higher-affinity, and more effective therapeutic biomolecules for MS by integrating immunoinformatics, computational biochemistry, AI-based antibody design, and molecular dynamics simulations. Our work focuses on modeling neuronal autoantigens and autoantibodies, predicting B-cell epitopes, and designing anti-idiotypic antibodies to target and inhibit pathogenic autoantibodies while reducing the cost and need for extensive experimental approaches. 

Computational screening of phytochemicals targeting cell death signaling pathways

We computationally screen phytochemicals, known for their anti-inflammatory, anti-cancer, and antioxidant properties, against key proteins in cell death signaling pathways that link inflammation and cancer. We identify natural compounds with the potential to bind to and modulate these proteins as candidates for novel therapeutic strategies using molecular docking and molecular dynamics simulations. Additionally, we evaluate the drug-like properties of these compounds to assess their potential as drug candidates. 

Utilization of Super Computing in Computational Biochemistry

Assoc. Prof. Mehmet Özbil introduces the use of high-performance computing in computational biochemistry, with a practical overview of molecular dynamics simulations using GROMACS and HPC resources.

The lecture was presented as part of the ULAKBİM TRUBA EUROCC Turkey Seminar Series.