10/2/2023 0 Comments Pytorch sequentialThe researchers also propose a geometric pretraining approach based on contrastive learning to train the protein structure encoder. The use of sparse edge message passing is a novel approach that improves the protein structure encoder. This encoder conducts relational message passing on protein residue graphs, incorporating spatial information and various structural or sequential edges. To address these challenges, researchers have developed a protein encoder called the GeomEtry-Aware Relational Graph Neural Network (GearNet). However, these models do not explicitly capture interactions between edges, which are essential for simulating protein structure. To address this, structure-based protein encoders have been proposed. While recent advances in deep learning-based protein structure prediction have made it possible to predict structures with confidence, these techniques do not fully utilize the known information about protein structure and its relationship to protein function. Self-supervised learning techniques have been utilized to pretrain protein encoders on millions of sequences. To overcome this, researchers have used unlabeled protein sequence data to develop representations of existing proteins. The number of published protein structures is much lower than the number of datasets in other machine-learning fields due to the difficulty of experimental structure identification. These representations are useful for tasks such as protein design, structure classification, model quality assessment, and function prediction. Many data-driven approaches focus on learning representations of protein structures, as protein functions are often determined by their folding. Therefore, accurate and efficient in silico protein function annotation methods are needed to bridge the current sequence-function gap. However, functional annotation of these sequences is expensive and time-consuming. With the advancement of low-cost sequencing technology, a significant number of novel protein sequences have been discovered. They consist of amino acid chains that fold into specific shapes. Proteins play a crucial role in various applications, including materials and treatments.
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