In the omics revolution, proteome analysis techniques were also evolved immensely. Proteomics is used to analyse the entire protein complement of a cell, tissue or organism under specific conditions. Nowadays, protein abundances, modifications, and interacting partners and networks are standard in advanced research studies performed at a large scale. Bottom-up and top-down two strategies are common in discovery proteome analysis. In the bottom-up approach, a crude protein mixture first undergoes protease digestion and then separation by liquid chromatography, followed by MS analysis. In the top-down method, proteins are characterised by MS without prior proteolysis and have the advantage of providing more excellent sequence coverage, resolution of sequence ambiguities, and preservation of PTMs. Bioinformatics is an integral part of protein studies, such as sequencing, structure, function, evolution and comparative proteomics analysis of organisms. Many databases and bioinformatics tools and techniques are available to aid in discovering factors influencing protein folding, stability and structures from the amino acid sequence. Hap Genomics offers proteomic data analysis services for advanced research projects and publications. 

We have substantial experience in resolving a wide range of bioinformatics issues associated with proteomics data analysis.

1. Three-dimensional structure prediction and validation

2. Molecular docking

      Protein – protein docking

      Protein – ligand docking

      Protein – aptamer docking

      Protein – peptide docking

       Antigen – antibody docking

3. Molecular dynamic simulation (MD)

        Protein-protein complex MD

        Protein – ligand complex MD

        Protein – peptide complex MD

        Protein – aptamer complex MD

4. Computational proteomics data analysis (LC-MS data)

5. Epitope and paratope prediction

6. Compound screening for drug discovery

7. Diagnostics biomarkers prediction

8. Protein-based evolution analysis

9. Post-translation modification prediction

10. Evaluation of  impact of SNP on protein structure and function