Prediction of Protein Secondary Structure

Inbunden, Engelska, 2024

Del i serien Methods in Molecular Biology

2 066 kr

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Beskrivning

This second edition volume expands on the previous edition with updates on the latest methods, resources, and studies concerning analysis and prediction of various structural and functional aspects of proteins and ncRNAs. The chapters in this book cover topics such as secondary structure characterization and prediction; the use and impact of AI (including AlphaFold, large language models, and deep neural networks) in the protein structure prediction field; methods and resources for the prediction of posttranslational modifications, residue-residue contacts, subcellular localization, intrinsic disorder, protein-ligand interactions, and protein aggregation; analysis of cryo-EM data; and analysis of noncoding RNAs in the context of human diseases. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions and surveys of the respective topics, list the necessary materials and methods, cover step-by-step instructions on how to use predictive tools and interpret their results, and provide tips on troubleshooting and avoiding known pitfalls.Cutting-edge and thorough, Prediction of Protein Secondary Structure, Second Edition is a valuable resource for anyone interested in understanding the dynamic and growing field of the protein structure prediction.

Produktinformation

Utgivningsdatum:2024-11-23
Mått:178 x 254 x 26 mm
Vikt:887 g
Format:Inbunden
Språk:Engelska
Serie:Methods in Molecular Biology
Antal sidor:353
Upplaga:2
Förlag:Springer-Verlag New York Inc.
ISBN:9781071641958

Utforska kategorier

Biokemi inom Naturvetenskap och teknik

Innehållsförteckning

The Iconic a-Helix: From Pauling to the Present.- Secondary Structure in Free and Assisted Modeling of Proteins with the Coarse-Grained UNRES Force Field.- Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks.- Importance of Secondary Structure Data in Large Scale Protein Modeling using Low-Resolution SURPASS Method.- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.- Protein Secondary Structure and DNA/RNA Detection for Cryo-EM and Cryo-ET Using Emap2sec and Emap2sec+.- Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.- Alphafold2, SPINE-X, and Seder on Four Hard CASP Targets.- Improving the Annotations of JCVI-Syn3a Proteins.- DESCRIBEprot Database of Residue-Level Protein Structure and Function Annotations.- Structural and Functional Studies on HIV Protease: Mechanism of Action, Subtypes, Inhibitors, and Drug Resistance.- Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn.- Prediction of Disordered Linkers Using APOD.- Computational Prediction of Linear Interacting Peptides.- Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER.- LMPTMSite: A Platform for PTM Sites Prediction in Proteins Leveraging Transformer-Based Protein Language Models.- Prediction and Evaluation of Protein Aggregation with Computational Methods.- Bioinformatics Approaches for Understanding the Binding Affinity of Protein-Nucleic Acid Complexes.- A Glimpse of Noncoding RNAs: Secondary Structure, Emerging Trends, and Potential Applications in Human Diseases.