The Network and Structural Bioinformatics (NeStOme) Lab

Dr. Khalique Newaz

khalique.newaz[a.t_))uni-hamburg.de

I am a Junior Group Leader at Cosy.Bio since Jan. 2025 (group webpage: NeStOme). 

From Oct. 2021 until Dec. 2024, I was a postdoc at the Center for Data Science and Computing, University of Hamburg.

Before that, I completed my Ph.D. in Computer Science at the University of Notre Dame, USA.

My research interest are:  modeling and analysis of protein 3D structures, 

development of network theoretic strategies to analyze biological data,

prediction and analysis of protein-protein interactions, and disease informatics with a specific focus on cancer.

Jeanine Liebold

jeanine.liebold[a.t_))uni-hamburg.de

Since April 2022, I am a PhD student at CoSy.Bio and at the Chair of Genome Informatics of Prof. Stefan Kurtz, both at the University of Hamburg. My research interests are mainly in machine learning and deep learning, with a focus on the application of these methods to bioinformatics problems, in particular, the prediction of protein-protein interactions in the event of alternative splicing. I hold a master’s degree in Intelligent Adaptive Systems from the University of Hamburg, where I wrote my master’s thesis on feature recognition in audio and video data with deep learning. Before that, I received my bachelor’s degree in Electrical and Information Engineering from HAW Hamburg.

Sina Pralle 

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I am currently a master’s student in Intelligent Adaptive Systems at the University of Hamburg. Before moving to Hamburg, I completed my Bachelor’s in Electrical and Computer Engineering in Bremen, where I gained some research experience in joint source-channel coding and signal classification.
In Fall 2024, I will join the CoSy.Bio team as a HiWi, where I will work on developing a protein language model that integrates secondary structure information.

Franco Salvatore

franco.salvatore[a.t_))uni-hamburg.de

I will join CoSy.Bio in October 2025 as a PhD candidate. I graduated as a Licenciated in Biological Sciences from the University of Buenos Aires (UBA), where I worked for more than four years in a Structural Bioinformatics Laboratory at the Institute of Biological Chemistry (IQUIBICEN–UBA). My thesis focused on developing a machine learning model to predict the pathogenicity of all possible missense variants in human hemoglobin (HbA). I benchmarked this model against AlphaMissense and complemented it with detailed structural bioinformatics analyses to better understand the molecular basis of disease-associated missense mutations. 

In addition to my thesis work, I contributed to a project with the Global Antibiotic Research & Development Partnership (GARDP), where I characterized the active sites of bacterial ATPase proteins, built and validated AlphaFold structural models, and supported the search and identification of potential inhibitory ligands. I also worked at the LUCAI Bio start-up, where I led and contributed to projects on enzyme design using structural modeling and deep learning tools such as AlphaFold, RoseTTAFold-All-Atom, and Boltz, as well as on the discovery and characterization of naturally occurring sweet proteins as alternatives to sugar, among several other AI-driven projects.

My main expertise lies in structural bioinformatics combined with a programming background and machine learning, aiming towards deep learning and genomics. I am particularly interested in how these computational approaches can be applied to biomedical and clinical questions.

Jan-Ole Schulze

jan-ole.schulze[at.))uni-hamburg.de

I joined CosyBio as a PhD student in November 2024. I have a background in computer science, holding a master’s degree in Informatics from UHH, and I previously worked as a software engineer.  My research interests encompass data science in biomedicine, particularly machine and deep learning techniques for patient stratification

Mohamed Abouzid

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Mohamed Abouzid is currently pursuing his Master’s degree in Bioinformatics at Justus-Liebig University Gießen. For his Master’s thesis, he is working at the CosyBio. His research focuses on StrucTFactor, a software tool that utilizes deep learning methods to predict transcription factor proteins, bridging the gap between machine learning and biological networks. He earned his Bachelor’s degree in Pharmaceutical Sciences from Suez Canal University in Egypt and he is passionate about applying data science and machine learning to solve complex biological problems, with a particular interest in biological network analysis.