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These scales are commonly used to predict the transmembrane alpha-helices of membrane proteins. When consecutively measuring amino acids of a protein, changes in value indicate attraction of specific protein regions towards the hydrophobic region inside lipid bilayer .
The fourth is a great example of how interactive graphical tools enable a worker involved in sequence analysis to conveniently execute a variety if different computational tools to explore an alignment's phylogenetic implications; or, to predict the structure and functional properties of a specific sequence, e.g., comparative modelling.
Protein–protein docking, the prediction of protein–protein interactions based only on the three-dimensional protein structures from X-ray diffraction of protein crystals might not be satisfactory. [44] [45] Network analysis includes the analysis of interaction networks using methods of graph theory or statistical methods.
Protein–lipid interaction is the influence of membrane proteins on the lipid physical state or vice versa.. The questions which are relevant to understanding of the structure and function of the membrane are: 1) Do intrinsic membrane proteins bind tightly to lipids (see annular lipid shell), and what is the nature of the layer of lipids adjacent to the protein?
Constituent amino-acids can be analyzed to predict secondary, tertiary and quaternary protein structure. This list of protein structure prediction software summarizes notable used software tools in protein structure prediction, including homology modeling, protein threading, ab initio methods, secondary structure prediction, and transmembrane helix and signal peptide prediction.
Protein–protein interaction prediction is a field combining bioinformatics and structural biology in an attempt to identify and catalog physical interactions between pairs or groups of proteins. Understanding protein–protein interactions is important for the investigation of intracellular signaling pathways, modelling of protein complex ...
The user provides a proteome in fasta format, and the system employs Psi-blast, Psipred and Modeller to predict protein function and subcellular localization. Proteome Analyst uses machine-learned classifiers to predict things such as GO molecular function. User-supplied training data can also be used to create custom classifiers. (bio.tools entry)
It can function independently or integrated into other software, enabling analysis of large datasets on a desktop computer. [1] Byonic Proprietary: Byonic is a database search algorithm released in 2011 by Protein Metrics Inc. with original developments at PARC. [2]