Protein Informatics and Cheminformatics
• Protein informatics is a growing field of information technology in which information about any protein is collected using sophisticated techniques. Raw data of proteins collected through various means is used to retrieve crucial information about the protein of interest.
• Primary structure of a protein can be analysed using ProtParam tool of ExPASy Proteomics Server. Isoelectric point, aliphatic Index, instability index and Grand AverageHydropathy (GRAVY) value of a protein are calculated using this server. Secondary structure of a protein is predicted using APSSP, CPSSP, SOPMA and GOR.
• Homology modelling, fold prediction and de novo protein structure prediction are common computational methods used to predict protein 3D structure.
• Cheminformatics combines the computational and informational techniques to understand the problems related to chemistry. The information used in cheminformatics includes information on physical properties, 3-D molecular crystal structures, chemical reaction pathways, etc.
• Pharmacophore modelling is a method which gives description about the molecular features that define molecular recognition of a ligand. Lipinski’s rule of five (RO5) outlines key molecular properties of compounds which is helpful in choosing potential drug compounds.
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Programming and Systems Biology
• With increasing amount of data produced everyday by biologists, it has become all the more important to competently handle complex datasets in order to generate and explore hypotheses. Programming languages make it easier for the scientists to access, filter and manipulate the biological data.
• Some of the most advanced programming languages include Python and R. Python can run on unix, mac and windows, and is used for visualisation and analysis of sequence and structure datasets. The R language provides facilities of statistical tools, and is suitable for high volume analysis and visualisation.
•Systems biology utilises computational methods to analyse complex biological datasets. Examples of systems models are metabolic and signaling network.
• Data management, optimisation of network development parameters, performance analysis and evaluation are some of the important requirements of computation for systems biology. There are three aspects of data management in systems biology, namely minimum information, file formats and ontologies.