With the improvement of prediction techniques, protein models are becoming very useful in biology and medicine, including drug design. There are numerous examples where in silico models were used to infer protein function, hint at protein interaction partners and binding site locations, design or improve novel antibodies. As a world-class provider of biotechnology, we are confident in offering a variety of protein structure assessment services to meet the diverse needs of our customers.
Protein structures have proven to be a crucial piece of information for biomedical research. In the millions of currently sequenced proteins, only a small fraction has been experimentally solved for structure and the only feasible way to bridge the gap between sequence and structure data is computational modeling. As defined by the current CASP (critical assessment of techniques for protein structure prediction) classification, protein structure prediction methods can be divided into two broad categories: template-based modeling (TBM) and free modeling (FM). Each of these categories can be further split into two subcategories: TBM-into comparative modeling and fold recognition; and FM-into knowledge-based de novo modeling and ab initio modeling from first principles.
The template-based methods currently offer the most reliable prediction results but their applicability is limited to cases where it is possible to identify a structurally similar protein that can be used as a template for building the model. If the sequence similarity between the target and the available templates falls into the twilight zone (sequence identity <25%), the resulting models become less accurate.
For the sequences without appropriate template structures, the so-called free modeling methods are required. CASP experiments demonstrate that the quality of free modeling predictions remains in general poor compared with predictions that are based on templates and insufficient for many biomedical applications.
Currently available template-based methods can generate models with the level of detail that, in cases of high homology, is sufficient for applications as demanding as drug design. Free modeling methods are not mature enough for biomedical applications, but the first instances of high-resolution structure prediction in the absence of templates have now been reported.
Fig.1 The simplified steps of protein homology modeling.
We have developed a number of approaches to assess the correctness of protein structures and models. These methods use stereochemistry checks, molecular mechanics energy-based functions, statistical potentials, and machine learning approaches to tackle the problem. Typically, the features taken into account are the molecular environment, hydrogen bonding, secondary structure, solvent exposure, pair-wise residue interactions, and molecular packing. We can provide various protein structure assessment services to meet the specific requirements of our customers.
Creative Biolabs has focused on the development of protein engineering for years, we whole-heartedly cooperate with you to accomplish our shared goals. Our team provides you with outstanding support and meets your specific needs with a professional technology platform. If you are interested in our services, please contact us for more details.
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