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Rasa Bioinformatic CRO Lab offer high quality protein tertiary structures custom modeling services based on your own amino-acid sequences. Combination of modeling and short term simulations under controlled parameters will ensure the reliability of modeled structure for further studies RASAs protein modeling service comprise of integration of several highly recommended approaches ranging from homology, ab-initio or threading. We can customize and perform modeling as per client. Rasa Bioinformatic Lab are proud to say that we can very successfully model the complex of the proteins. One of the important parts of research in structural biology is to generate a protein structure form the genetic data or the gene.At RASA Bioinformatic Lab, our experts understand the nuances of this and are extremely capable of converting your genetic data into a protein structure using the information provided by the client or curating that information. Our team can successfully translate the genetic sequence into a protein sequence which our experts can then successfully convert into protein structure. Rasa Contract Reaserch Orgnaisation can build protein models using minimal information such as: FASTA sequence, Sequence ID or Protein name.

The term homology modeling, also called comparative modeling or sometimes template-based modelling (TBM), refers to modeling a protein 3D structure using a known experimental structure of a homologous protein (the template). We at RASA follow a comparative modelling routine needing three items of input: The sequence of the protein with unknown 3D structure, the target sequence, a 3D template is chosen by virtue of having the highest sequence identity with the target sequence. Structural information is always of great assistance in the study of protein function, dynamics, interactions with ligands and other proteins. The low-resolution structure provided by homology modeling contains sufficient information about the spatial arrangement of important residues in the protein and may guide the design of new experiments, for example site-directed mutagenesis. Even within the pharmaceutical industry homology modeling can be valuable in structure-based drug discovery and drug design.

Quantitative Structural Activity Relationship (QSAR) is frequently used methodology for ligand-similarity based lead identification which quantitatively correlates descriptors with properties (2D and 3D QSAR based on the dimensions of properties used) like physicochemical, biological, toxicity, etc for a set of similar compounds. RASAs QSAR modelling services will help both computational and experimental chemists to develop reliable QSAR models. These QSAR models generated from our QSAR Modelling services can optimally exploit the experimental data to guide future studies. In addition, our QSAR Modelling services encourage the use of high quality validated QSARs to provide reliable support for experimental study design and regulates decision making. Our QSAR modelling services can be widely applied in academy, industry, and government institutions around the world. It relates chemoinformatics with biology. Recent observations suggest that the structure-based methods & value of statistically-based QSAR approaches help to guide lead optimization. These are starting to be appreciatively reconsidered by leaders of several larger CADD groups. QSAR modelling finds broad application for assessing potential impacts of chemicals, materials, and nano-materials on human health and ecological systems. An area of active 3D QSAR expansion is in the use of predictive models for regulatory purposes by government agencies, where a still growing number of specialized regulatory tools and databases are being developed and validated. As with any scientific discipline, there have been some voices in the community questioning the viability and our QSAR modelling services tries to build newer biologically-based models using HTS data. Also, developing trends on minimizing animal use in biomedical research place additional focus on QSAR as a source of alternative predictors of in vivo effects in both animals and humans.