Authors :
Dr. Pankaj Malik; Anmol Sharma; Anoushka Anand; Anmol Baliyan; Amisha Raj; Jasleen Singh
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/38w46h2u
Scribd :
http://tinyurl.com/45mn8sku
DOI :
https://doi.org/10.5281/zenodo.10499711
Abstract :
Protein structure prediction is a critical facet
of molecular biology, with profound implications for
understanding cellular processes and advancing drug
discovery. AlphaFold, a state-of-the-art deep learning
model, has demonstrated groundbreaking success in
predicting protein structures. However, challenges
persist, particularly in scenarios with limited data for
specific protein families. This research investigates the
augmentation of AlphaFold predictions through the
application of transfer learning techniques, leveraging
knowledge gained from one set of proteins to enhance
predictions for related protein families.
In this study, we present a comprehensive analysis
and benchmarking of the transfer learning approach
applied to AlphaFold. Our methodology involves careful
selection of source and target protein datasets, meticulous
preprocessing steps, and thoughtful modifications to the
model architecture to facilitate effective knowledge
transfer. We employ established evaluation metrics to
quantitatively assess the performance of our enhanced
AlphaFold model, comparing it against the original
model.
The results of our experiments demonstrate notable
improvements in prediction accuracy, particularly for
protein families that traditionally pose challenges for
structure prediction. We discuss the implications of
transfer learning on AlphaFold's generalizability and
applicability across diverse protein structures.
Additionally, we address observed limitations and outline
potential avenues for further refinement.
Keywords :
Protein structure prediction, protein families, alphafold predictions.
Protein structure prediction is a critical facet
of molecular biology, with profound implications for
understanding cellular processes and advancing drug
discovery. AlphaFold, a state-of-the-art deep learning
model, has demonstrated groundbreaking success in
predicting protein structures. However, challenges
persist, particularly in scenarios with limited data for
specific protein families. This research investigates the
augmentation of AlphaFold predictions through the
application of transfer learning techniques, leveraging
knowledge gained from one set of proteins to enhance
predictions for related protein families.
In this study, we present a comprehensive analysis
and benchmarking of the transfer learning approach
applied to AlphaFold. Our methodology involves careful
selection of source and target protein datasets, meticulous
preprocessing steps, and thoughtful modifications to the
model architecture to facilitate effective knowledge
transfer. We employ established evaluation metrics to
quantitatively assess the performance of our enhanced
AlphaFold model, comparing it against the original
model.
The results of our experiments demonstrate notable
improvements in prediction accuracy, particularly for
protein families that traditionally pose challenges for
structure prediction. We discuss the implications of
transfer learning on AlphaFold's generalizability and
applicability across diverse protein structures.
Additionally, we address observed limitations and outline
potential avenues for further refinement.
Keywords :
Protein structure prediction, protein families, alphafold predictions.
