Scientific Context of the Project
Demineralization, the loss of tooth minerals, is the root cause of many dental diseases. With a better understanding of the biology of dental tissues and tooth development, the regenerative strategies to treat lost or damaged dental tissues have become very attractive. However, the clinical adaptation of regenerative therapeutics has remained limited. The obstacle chiefly stems from incomplete knowledge of the functional proteins or, if known, simply the inadequate availability of these complex proteins. Deriving short peptides that mimic the functionalities of their protein counterparts represent a practical solution to accelerate the adaptation of regenerative therapeutics in dental health care. Proposed herein is developing an ML-based predictive peptide design platform called "Biomineralization Intelligence, BioMInt" using the peptide (big) data sets acquired via deep-directed evolution and custom high-throughput (HTP) assays. The accelerated design of (re)mineralizing peptides through this platform will enable the development of a molecular toolkit called for targeted regeneration and repair of dental tissues. Aligned with the current vision of tele-dentistry that aims to adapt methodologies to generate big data and tools to enable fast-track discoveries with healthcare delivery, this study marks the onset of implementing ML and HTP screening methods in dental research. Successful completion of this project will result in a line of preventive oral care products with the long-term vision of complete tooth regeneration.
Innovative Aspects of the Project
There have been considerable attempts, both commercially and scientifically, to develop therapies that boost remineralization, but none have had clinical success so far. This project will lay the groundwork for biomimetic dental tissue repair with a long-term goal of developing a universal hard tissue regeneration therapy using algorithms (BioMInt) that enable accelerated discovery of tissue-specific (enamel, cementum, etc.) therapeutics to eventually establish a molecular toolkit for complete tissue regeneration. Once developed successfully, the biomimetic remineralization therapy is anticipated to be a game changer in dentistry and preventive daily oral healthcare. No other works have aimed to address hard tissue regeneration in the fields of ML and HTP experimentation. This universal design algorithm will significantly impact peptide/protein therapeutics research. In addition, the interdisciplinary methodology developed herein will address big gaps in the scientific literature, including (1) Development of novel high-throughput therapeutic design protocols and (2) Design of ML-based predictive peptide design platform, BioMInt, trained with the dataset acquired from HTP experimentation.
Research Environment and Infrastructure
IZTECH has been distinguished as "one of the Top 5 Research Universities" in Turkey. Faculty of Engineering hosts more than 100 research labs in diverse fields, including biomimetics, advanced manufacturing (Dept. of Bioengineering), deep learning, artificial intelligence, optimization, and search (Dept. of Computer Engineering), as well as high-throughput (HTP) experimentation facilities providing an excellent base and vibrant research environment for the project. Yucesoy Research Group has required facilities for the experimental selection and characterization of the mineralizing peptides, computational resources for constructing predictive design platforms, and HTP validation tools (https://yucesoylab.com/).
Institute of Biomedical Engineering (BOUN) has dedicated facilities for the production and in-depth characterization of the biomaterials, including Fourier Infra-red Spectroscopy (FTIR) (Perkin Elmer), X-Ray Diffraction Spectroscopy (XRD) (Rikagu), X-ray Photoelectron Spectroscopy (XPS) (Thermo Scientific), and Scanning Electron Microscopy (SEM) (Phillips-FEI XL-30).
In addition to secondment institutions listed below, Yucesoy Research Group has a strong academic and industry network, including leading dental manufacturing companies from the US and Europe, which will provide the R&D and manufacturing facilities access and necessary expert training in product design, validation, and benchmarking.
Preferred Academic Background
Materials Science and Engineering, Bioengineering, Data Science/Computer Engineering
Required GRE Score
GRE Quantitative 157.00
Asst. Prof. Deniz T. Yucesoy (IZTECH)
Asst. Prof. Işıl Öz (IZTECH)
Assoc. Prof. Duygu Ege (BOUN)
İzmir Institute of Technology, Graduate School, Urla/İzmir
İzmir Institute of Technology, Graduate School
PhD in Bioengineering
Barcelona Supercomputing Center, Barcelona, Spain or Polytechnic University of Catalonia, Barcelona, Spain
SIEMENS Healthineers (TR) and LetGen Biotech (TR) and Istanbul Health Industry Cluster (ISEK)