Creative BioMart is a well-known expert in the development of a variety of computational analysis methods to predict the three-dimensional structure of proteins, and predict its functional conformation from the amino acid sequence of the protein. With years of experience, we provide customized de novo protein structure prediction service to precisely meet customer requirements.

Introduction of De Novo Prediction

De novo protein structure prediction uses algorithms to determine the tertiary structure of a protein from the primary sequence without the need for a starting template, based on conformational features obtained from governing protein folding energetics and native structure. Research on de novo structure prediction has focused on three areas: alternative low-resolution representations of proteins, accurate energy functions, and efficient sampling methods. De novo methods are suitable for relatively small proteins. De novo prediction of the protein structure of larger proteins will require better algorithms and larger computational resources (e.g. Blue Gene, MDGRAPE-3, the Human Proteome Folding Project, etc.). Despite considerable computational barriers, the potential benefits of structural genomics to fields such as medicine and drug design have made de novo structure prediction an active area of research.

Fig 1. Major aspects of the de novo protein design. (Pan X, et al., 2021)

Services

Currently, the gap between known protein sequences and confirmed protein structures is enormous. Given experimental limitations, designing efficient computer programs to close the gap between known sequences and structures is considered the only viable option. As a leading service provider of protein engineering, Creative BioMart has developed a number of successful algorithms to accurately predict the folding of small single-domain proteins at atomic resolution, thereby determining the protein whose amino acid sequence folds into the desired function. Our de novo prediction service includes protein secondary structure prediction, hyper-bistructure prediction, structure type prediction, folding pattern prediction, direct prediction of detailed 3D structures, etc.

A major limitation of de novo protein prediction methods is the large amount of computer time required to successfully resolve the native conformation of the protein. Our scientists develop a variety of computational methods for de novo protein structure prediction, including rosetta methods, distributed methods, markov models, coarse-grained modeling, etc. These methods allow for de novo structure prediction of small proteins or large protein fragments in a very short computational time.

First we spatially organize the amino acids from the protein of interest. This process is guided by several functions and sequence-dependent biases and constraints to generate a range of possible candidate structures. Next, from these candidate structures, a scoring function is used to select the structure that is closest to the native. Scoring functions include:

(1) Physics-based functions, which use mathematical methods to model physics-based molecular interactions.

(2) Knowledge-based functions, which are based on statistical models that define properties of native-like conformations.

Features of Fold Recognition

You can choose this method for protein structure prediction when experimental structural information for similar proteins is not available. Our ab initio prediction model has the following characteristics:

• An accurate energy function that maps the most thermodynamically stable state to the native structure of the protein.
• An efficient search method capable of rapidly identifying low-energy states by conformational search.
• Ability to select native-like models from a collection of candidate conformational structures.
• De novo modeling is limited to relatively small protein sequences (<100-150 residues).
• Unlike homology modeling and fold recognition, de novo functional prediction does not require a template.

Creative BioMart is committed to providing high-quality de novo protein structure modeling services to global customers for solving many key challenges in biomedicine and bioengineering. We will work with you to develop the most appropriate strategy and provide the most meaningful data for your research for accelerating the research of life sciences. If you are interested in our services, please do not hesitate to contact us for more information.

Reference

1. Pan X, Kortemme T. (2021) Recent advances in de novo protein design: Principles, methods, and applications[J]. Journal of Biological Chemistry. 100558.

• Fold Recognition Service
• Homology Modeling Service