ABSTRACT
Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.
Subject(s)
Algorithms , Computational Biology , Neural Networks, Computer , Protein Structure, Secondary , Proteins , Proteins/chemistry , Proteins/metabolism , Proteins/genetics , Computational Biology/methods , Databases, Protein , Gene Ontology , Sequence Analysis, Protein/methods , SoftwareABSTRACT
The structure, energetics and radical scavenging potency of theaflavin (TF), a natural polyphenolic antioxidant found in oxidised tea, have been characterised by a series of density functional theory (DFT) determinations. Exploratory conformational searches yielded 153 distinct neutral structures. Results showed TF's structural preferences to be regulated by its unique fused double ring benzotropolone moiety, and its degree of planarity, with structural diversity, principally arising from variations of its nine -OH groups. The distinct 3D conformational 'poses' are shown to be stabilised by a complex network of intra-system interactions, damping overall structural floppiness. This rigidification, together with stability, is shown to be coupled with radical scavenging potency in the TF system. Radical scavenging via hydrogen atom abstraction (HAB) in H2O solution was determined to be spontaneous with very low reaction barriers (ΔGrel â¼ 4 kJ mol-1).
