This collection of articles highlights the rapid integration of computational and machine learning approaches in the study of proteins, immunology, and pathogen-host interactions. Several works focus on advanced deep learning and AI methods, such as DeepPredict and DCMA, which provide state-of-the-art predictions for protein secondary structure, solvent accessibility, and backbone dihedral angles, while also reducing computational requirements. The application of these tools extends to practical biomedical problems, including antigen design for vaccine development, where machine learning guides the engineering and validation of broadly protective antigens, and protein-ligand or protein-protein binding site prediction, which is critical for drug discovery. Other studies emphasize the importance of high-throughput techniques and computational frameworks—such as PhIP-Seq for comprehensive antibody profiling, and large-scale molecular docking to unravel host–virus interactions, exemplified in the ongoing investigation of SARS-CoV-2 cytokine interactions. The collection also explores structural and functional diversity within pathogenic proteins (e.g., ClpV ATPases in Enterobacter cloacae) and the identification of antiviral peptides targeting emerging threats like Nipah virus. Collectively, these studies underscore the transformative role of modern computational strategies in understanding protein structure and function, accelerating therapeutic and vaccine development, and equipping biomedical research to meet contemporary clinical challenges.
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The field of protein function and engineering revolves around the deep understanding of evolutionary data, shedding light on the intricate relationship between protein sequences, their functions, and how this connection has evolved over time. This understanding serves as the cornerstone for advancements in protein engineering.
A central objective within this domain is the prediction of protein function based on sequence and/or structural data. This prediction can be achieved through a variety of computational methods, such as machine learning, deep learning, and molecular dynamics simulations. These approaches allow researchers to decipher the evolutionary data of proteins and apply this knowledge to design novel proteins with specific, tailored functions, finding application in vital areas like drug development and biotechnology.
This Research Topic is an invitation to researchers to share their valuable insights and contributions to the understanding of evolutionary data for protein function and engineering. We eagerly await manuscripts that delve into this critical facet of the protein sciences.
This collection of articles highlights the rapid integration of computational and machine learning approaches in the study of proteins, immunology, and pathogen-host interactions. Several works focus on advanced deep learning and AI methods, such as DeepPredict and DCMA, which provide state-of-the-art predictions for protein secondary structure, solvent accessibility, and backbone dihedral angles, while also reducing computational requirements. The application of these tools extends to practical biomedical problems, including antigen design for vaccine development, where machine learning guides the engineering and validation of broadly protective antigens, and protein-ligand or protein-protein binding site prediction, which is critical for drug discovery. Other studies emphasize the importance of high-throughput techniques and computational frameworks—such as PhIP-Seq for comprehensive antibody profiling, and large-scale molecular docking to unravel host–virus interactions, exemplified in the ongoing investigation of SARS-CoV-2 cytokine interactions. The collection also explores structural and functional diversity within pathogenic proteins (e.g., ClpV ATPases in Enterobacter cloacae) and the identification of antiviral peptides targeting emerging threats like Nipah virus. Collectively, these studies underscore the transformative role of modern computational strategies in understanding protein structure and function, accelerating therapeutic and vaccine development, and equipping biomedical research to meet contemporary clinical challenges.
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The field of protein function and engineering revolves around the deep understanding of evolutionary data, shedding light on the intricate relationship between protein sequences, their functions, and how this connection has evolved over time. This understanding serves as the cornerstone for advancements in protein engineering.
A central objective within this domain is the prediction of protein function based on sequence and/or structural data. This prediction can be achieved through a variety of computational methods, such as machine learning, deep learning, and molecular dynamics simulations. These approaches allow researchers to decipher the evolutionary data of proteins and apply this knowledge to design novel proteins with specific, tailored functions, finding application in vital areas like drug development and biotechnology.
This Research Topic is an invitation to researchers to share their valuable insights and contributions to the understanding of evolutionary data for protein function and engineering. We eagerly await manuscripts that delve into this critical facet of the protein sciences.