Protein Informatics and Cheminformatics — NCERT Solutions

Given/Concept: Information technology (IT) provides computational tools and databases that allow scientists to analyse protein sequences and structures without performing every experiment in the laboratory.

Answer:

Information technology plays a crucial role in the determination of protein properties in the following ways:

1. Sequence Analysis: IT tools allow rapid analysis of amino acid sequences to determine physicochemical properties such as molecular weight, isoelectric point (pI), instability index, aliphatic index, and GRAVY (Grand Average of Hydropathy) value. Servers like ProtParam (ExPASy) perform these calculations automatically from raw sequence data.

2. Secondary Structure Prediction: Tools such as APSSP, CPSSP, SOPMA, and GOR predict the secondary structural elements (α-helices, β-sheets, coils) of a protein from its primary sequence.

3. Domain and Motif Prediction: IT-based tools (e.g., PROSITE, Pfam, InterPro) identify functional domains and conserved motifs within a protein sequence.

4. 3D Structure Prediction: Computational methods like homology modelling, fold prediction, and de novo prediction use IT to build three-dimensional models of proteins whose structures have not been experimentally determined.

5. Database Management: Large biological databases (UniProt, PDB, NCBI) store and organise vast amounts of protein data, making it accessible for analysis worldwide.

Conclusion: Thus, information technology accelerates protein characterisation, reduces experimental cost and time, and enables large-scale proteomics studies.

Given/Concept: Computational analysis of proteins begins with raw data that is fed into bioinformatics tools and servers.

Answer:

The primary types of protein raw data used for computationally extracting information are:

1. Amino Acid Sequence (Primary Sequence Data): The most fundamental raw data is the linear sequence of amino acids (in single-letter or three-letter code) obtained from gene sequencing or direct protein sequencing. This is used to calculate physicochemical properties, predict secondary structure, identify domains, and perform homology modelling.

2. Nucleotide Sequence Data: The coding DNA/mRNA sequence can be translated in silico to obtain the protein sequence, which is then used for further analysis.

3. 3D Coordinate Data (PDB files): Experimentally determined three-dimensional atomic coordinates from X-ray crystallography, NMR spectroscopy, or cryo-EM, stored in Protein Data Bank (PDB) format, serve as raw data for structural analysis and comparison.

4. Mass Spectrometry Data: Peptide mass fingerprinting data from mass spectrometry is used to identify proteins and post-translational modifications.

Conclusion: Among all these, the amino acid sequence is the most commonly used raw data for computational extraction of protein information, as it is the starting point for most bioinformatics analyses.

Given/Concept: Domain prediction tools identify conserved functional or structural regions within a protein sequence.

Answer:

Two common tools used for domain prediction are:

1. PROSITE — A database and tool that identifies protein domains, families, and functional sites using patterns and profiles derived from known protein sequences.

2. Pfam — A large database of protein families and domains. It uses Hidden Markov Models (HMMs) to detect conserved domains in a query protein sequence.

Additional examples (for reference): InterPro, SMART, CDD (Conserved Domain Database).

Conclusion: PROSITE and Pfam are two widely used bioinformatics tools for predicting and identifying functional domains in proteins.