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• W1885320193 abstract "Proteins have a crucial role in all cellular processes. The function of a protein is intrinsically linked to its structure, so solving protein tertiary structures is the key to understanding the biological functions of proteins. Resolving protein three-dimensional (3D) structures is complicated and time-consuming, so despite recent efforts to determine representative structures for each protein family, the number of known protein structures is dwarfed by the number of known protein sequences. Computational methods for the prediction of protein tertiary structures directly from their amino acid sequences have been developed to bridge this gap. These structure prediction algorithms are based on the observation that there are a limited number of protein folds and that most protein sequences will fold into one of these limited globular structures. The field of structure prediction is now quite mature and many stable methods exist for generating alignments between sequences and structures and for building 3D models. Key Concepts: Structure can be predicted from sequence because protein folds are relatively stable. A wide variety of methods exist for the prediction of 3D structure from protein sequence. The easiest targets are those for which it is possible to detect an evolutionary related template structure that aligns with more than 30% sequence and few gaps. In these cases, structure prediction is trivial and the emphasis should be placed on all atom refinement measures. Where template structures are more remotely related, maximum effort should be put into obtaining an alignment between template and target sequences. Structural domains, disorder, secondary structure and important functional residues need to be considered when building a model of a target protein." @default.
• W1885320193 created "2016-06-24" @default.
• W1885320193 creator A5088945131 @default.
• W1885320193 date "2013-10-18" @default.
• W1885320193 modified "2023-10-16" @default.
• W1885320193 title "Protein Tertiary Structures: Prediction from Amino Acid Sequences" @default.
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• W1885320193 doi "https://doi.org/10.1002/9780470015902.a0003040.pub2" @default.
• W1885320193 hasPublicationYear "2013" @default.
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