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Dr. Sriprasadh K, Dr. Vinay Kumar, Dr. Sujesh P Lal, Dr. Rajesh Kumar Dubey, Dr. K. Dhayalini, Dr. Reshma Jaweria
» doi: 10.31838/ecb/2023.12.si6.224


Proteins are fundamental biomolecules responsible for numerous biological processes in living organisms. Understanding the structure and function of proteins is crucial for elucidating their roles in biological systems and designing therapeutics. However, experimental determination of protein structures and functions is time-consuming and expensive. In recent years, machine learning approaches have emerged as powerful tools for predicting protein structure and function, offering significant advancements in this field. This research paper provides an overview of the machine learning techniques used for predicting protein structure and function. Homology modeling, a widely employed technique, leverages sequence similarity to known protein structures to predict the three-dimensional structure of a target protein. Machine learning algorithms have enhanced the accuracy of homology modeling by incorporating sequence-based features and structural information from templates. Moreover, deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated remarkable success in predicting protein structure from scratch, without relying on known templates. These models utilize large-scale protein sequence and structure databases to learn complex patterns and capture essential structural features, enabling accurate predictions of secondary structure, solvent accessibility, and torsion angles. In addition to structure prediction, machine learning techniques have been applied to predict protein-protein interactions (PPIs) by integrating diverse data sources, including sequence, structure, and functional annotations. Support vector machines, random forests, and deep learning models have proven effective in predicting PPIs and have provided valuable insights into the complex network of protein interactions. Furthermore, machine learning algorithms play a vital role in protein function prediction by leveraging sequence, structure, and evolutionary information. Hidden Markov models, SVMs, and deep learning models are commonly used to classify proteins into functional categories based on these features, aiding in the annotation of newly discovered proteins. Lastly, machine learning approaches have been instrumental in predicting ligand binding sites and interactions. By incorporating protein-ligand docking scores, structural information, and physicochemical properties, predictive models can identify potential binding sites and predict whether a given protein binds to a specific ligand or drug molecule. In conclusion, machine learning approaches have revolutionized the field of protein structure and function prediction. These techniques have enhanced the accuracy and efficiency of predicting protein structures, elucidating protein-protein interactions, annotating protein functions, and identifying potential ligand binding sites. As machine learning algorithms continue to evolve, they hold immense promise for accelerating protein research and facilitating the development of novel therapeutics.

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