Evolutionary Strategy for Prediction of Protein Native Conformations

Through our comprehensive protein engineering services, Creative Biolabs is committed to developing the most promising evolutionary strategy for predicting a series of protein structures. With in-depth expertise in protein model design, model construction, as well as genetic algorithms, we can provide a full range of protein native conformation services basing on many state-of-art technologies. Nowadays, we provide high-quality customized assays, with different formats, endpoints, parameters, to satisfy any specific requirement with a most competitive price.

Introduction to Protein Native Conformations

Protein native conformation has been regarded as an important element of protein properties and plays an important role in mediating human diseases. Previous studies have revealed that the native conformation is strongly associated with the amino acid sequence of targeted proteins. A series of changes in protein conformations can lead to the occurrence of various diseases in humans. As a consequence, many attempts have been made to predict the amino acid sequence of proteins in different disease states. α -helices and β -sheets are most studied in protein conformation prediction. The data of X-ray crystallography suggest that they are essential to the spatial structure of proteins. In addition, a wide variety of statistical analysis strategies of protein conformation have been developed for evaluating various conformational states of amino acids. Recent researchers have illustrated that the recognition of homologous sequences in structural domains is critical for understanding specific biological functions of various proteins in different disease models. Among them, a number of evolution strategy algorithms have been generated and widely used for predicting protein conformations, such as Pareto archived evolution strategy (PAES) and hydrophobic and polar residues (HP) models.

Fig.1 Evolutionary Strategy for Prediction of Protein Native Conformations.

Evolutionary Strategy

In the past few years, many studies conducted on protein conformations suggest that they are important to applications in various fields, including candidate drug generation, disease prevention, and treatment. As a result, Creative Biolabs has built up a wide spectrum of services to suit any requirement of our clients on the prediction of protein native conformations. Currently, we have established a number of structural models and have equipped with many optimization technologies to predict protein conformations for discovering novel strategies of disease therapy. For instance, a variety of three-dimensional off-lattice AB model proteins have been constructed by using the hydrophobic (A) and the hydrophilic (B) residues in our company. Meanwhile, many optimization technologies have been widely used for screening the native conformation of a protein sequence based on specific structure models. In addition, a group of algorithms basing on evolutionary strategy has also been developed to predict or optimize protein native conformations. In particular, genetic algorithm and tabu search (TS) algorithm are commonly used, and the results have shown that our algorithms have better performance in global prediction and can effectively display 3D protein structure on the computer.

Fig.2 Graphical representation of the genetic codes in the three-dimensional space of objective function. (Wnetrzak, 2018)

As a global contract research organization, Creative Biolabs works on a wide range of protein conformation prediction services for various protein types. Our goal is to promote the development of protein engineering for a wide range of disease treatments. We have built a team of talented and motivated scientists and technicians to pursue our commitment. If you are interested in our services, please feel free to contact us for closer communication to learn how we can be involved in your project. Separate services or integrated end-to-end solutions are all welcomed.

Reference

Wnetrzak, M.; et al. Optimization of the standard genetic code according to three codon positions using an evolutionary algorithm. PLoS One. 2018, 13(8): e0201715.

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