The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem — designing an amino acid sequence that will fold into a specified three-dimensional structure — has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein–protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential.
There are two general methods for predicting the structure of a target protein ("target"): template-based modeling, in which the previously determined related protein structure is used to model the unknown structure of the target; template-free modeling, which does not rely on The global similarity of structure in PDB can therefore be applied to proteins with new folds. The methods used in these two methods are completely different. Template-based modeling focuses on detecting and comparing related proteins with known structures, while template-free modeling relies on large-scale conformational sampling and physically-based energy functions.
Naturally evolved proteins have amazing molecular functional diversity due to their fine-tuned three-dimensional structure, which is determined by their genetically encoded amino acid sequence. Therefore, the predictive understanding of the relationship between amino acid sequence and protein structure will open up new ways, which can be used to predict functions based on genomic sequence data, as well as to rationally engineer new protein functions by designing amino acid sequences with specific structures. MedAI, the ability to predict and design the three-dimensional structure of proteins has been significantly improved, which has a potentially far-reaching impact on medicine and our understanding of biology. The improved protein energy function has an approximate structure prediction model used to make it possible for people to start at the first time and move it close to the experimentally determined structure through an energy-guided refinement process.
|Project name||Protein Structure Modeling Service|
|Cycle||Depends on the time you need to simulate and the time required for the system to reach equilibrium.|
|Product delivery mode||The simulation results provide you with the raw data and analysis results of molecular dynamics.|
MedAI's protein structure modeling service can reduce the cost of subsequent experiments. Protein structure modeling service is a personalized and customized innovative scientific research service. Before determining the corresponding analysis plan and price, each project needs to be evaluated. If you want to know more about service prices or technical details, please feel free to contact us. If you want to know more about service prices or technical details, please feel free to contact us.
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