Prediction of protein structure

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Prediction of protein structure

Proteins build their three-dimensional structures from the bottom up, utilizing bonding interactions between atoms in the backbone, the sidechains and water. And they do it with astonishing speed and efficiency. Although protein chemists would love to understand and model protein folding at this detailed level of physical chemistry, it is simply too formidable a challenge — now and for the fore...

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In Silico Prediction and Docking of Tertiary Structure of Multifunctional Protein X of Hepatitis B Virus

Hepatitis B virus (HBV) infection is a universal health problem and may result into acute, fulminant, chronic hepatitis liver cirrhosis, or hepatocellular carcinoma. Sequence for protein X of HBV was retrieved from Uniprot database. ProtParam from ExPAsy server was used to investigate the physicochemical properties of the protein. Homology modeling was carried out using Phyre2 server, and refin...

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Bayesian Protein Structure Prediction

An important role for statisticians in the age of the Human Genome Project has developed in the emerging area of “structural bioinformatics”. Sequence analysis and structure prediction for biopolymers is a crucial step on the path to turning newly sequenced genomic data into biologically and pharmaceutically relevant information in support of molecular medicine. We describe our work on Bayesian...

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Protein Structure Prediction

Protein structure prediction from sequence remains a premiere computational problem for modern molecular biology. The papers in this session address ab initio folding simulations and theoretical studies, the use of known structures as a basis for prediction, and analysis of how and what we know (or don't) about those known structures. The \thermodynamic hypothesis" asserts that proteins fold to...

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Protein secondary structure prediction.

The past year has seen a consolidation of protein secondary structure prediction methods. The advantages of prediction from an aligned family of proteins have been highlighted by several accurate predictions made 'blind', before any X-ray or NMR structure was known for the family. New techniques that apply machine learning and discriminant analysis show promise as alternatives to neural networks.

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ژورنال

عنوان ژورنال: Current Biology

سال: 2000

ISSN: 0960-9822

DOI: 10.1016/s0960-9822(00)00290-6