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Research Title: Application of Language Models in Protein Engineering
Research Abstract:
The advancement of machine learning (ML), particularly deep learning (DL) and
natural language processing (NLP) technologies, along with increased
computing power, has further enhanced biotechnological applications,
including protein design and engineering . These developments have led to the
creation of Large Protein Language Models (LPLMs), which assist in
discovering the evolutionary, structural, and functional properties across
protein space by encoding amino-acid sequences into numeric vector
representations we leverage pretrained LPLMs to extract features for antibody
design specifically targeting ADAM17 (A Disintegrin and Metalloproteinase 17)
and MMP-9cd (Matrix Metalloproteinase-9 catalytic domain). Both ADAM17 and
MMP-9 play critical roles in pathological processes such as inflammation,
cancer metastasis, and tissue remodeling, making them promising therapeutic
targets.



Project Type: Faculty, Student
Lead Researcher(s):
- Name: Elham Buxton
  Email: esahe2@uis.edu
  Phone: 2172067327
- Name: Maryam Raeeszadeh Sarmazdeh
  Email: maryamr@unr.edu

Assistant Researcher(s):
- Name: Kalanther Meerasa, Iftikhar
  Email: ikala2@uis.edu

Department(s):
Computer Science (218)

Keywords:
- Protein Language Models (2600004330)
- Antibody Design (2600004331)
- Machine Ñî¹óåú´«Ã½ (ML) (2600003350)
- Natural Language Processing (NLP) (2600003351)

Has this research project received IRB approval?: Not required
Links:
- https://www-sciencedirect-com.uis.idm.oclc.org/science/article/pii/S2001037024003258 ()