Trans-isomerases are enzymes that catalyze the conversion of substrate molecules from their cis-form to the trans-form, or vice versa. These enzymes play a crucial role in various biological processes and have significant applications in various industries, including pharmaceuticals, agriculture, and chemicals. The ability to efficiently design and screen trans-isomerase variants with improved properties is essential for advancing enzyme engineering and molecular design. Traditional methods for trans-isomerase design and screening are often time-consuming, labor-intensive, and yield limited success rates. However, with the advent of AI technologies, substantial progress has been made in accelerating this process and enhancing success rates.
AI Strategies for Trans-Isomerase Design
In addition to algorithm-driven approaches, our team of experts employs rational design strategies based on bioinformatics, structural modeling, and molecular dynamics simulations. These techniques enable us to gain insights into protein structure-function relationships, guiding the design of trans-isomerase variants with desired properties.
Optimized Design with a Template
This strategy uses wild enzymes as templates and known sequences and structures or predicted structures as input. New sequences are obtained using single-point, random, or automated optimized mutations.
Template-Free De Novo Design
This strategy defines the protein backbone directly from function. Find the amino acid composition of the skeleton through kinetic calculations, etc., and screen the ideal enzyme structure based on empirical scoring functions or model predictions.
Data-Driven Self-Design
It will learn directly from the natural enzyme database. Starting from a known sequence space, algorithms such as generative models are used to extend the original sequence space. The output includes all or part of sequences, structures, or a combination of sequences and structures.
Our Services
At CD ComputaBio, we offer a comprehensive range of AI-based trans-isomerase design and screening services tailored to meet your specific needs. Our services include but are not limited to:
| Trans-Isomerase Variant Design Service |
Utilizing state-of-the-art machine learning and AI algorithms, we can generate trans-isomerase variant libraries based on the native enzyme or desired target properties. It includes the identification and incorporation of key amino acid mutations, structural modifications, and active site design. |
| Trans-Isomerase Sequencing or Structure Induction Service |
AI technology can assist in the design and optimization of promoter sequences, primers and reaction conditions for trans-isomerase sequencing. By analyzing a large amount of trans-isomerase sequencing data, our scientists can use AI to analyze patterns and correlations between promoters, primers and reaction conditions. |
| Trans-Isomerase Performance or Function Prediction Service |
Our scientists can predict the secondary and tertiary structures of trans-isomerase through AI technology. Combining a large-scale trans-isomerase database with advanced deep learning algorithms, the topology and functional sites of enzymes can be predicted to infer their potential catalytic capacity, protein-ligand binding affinity or substrate specificity. |
| Trans-Isomerase Conditional Screening Service |
Our scientists build substrate prediction models to predict a trans-isomerase's affinity and catalytic efficiency for a specific substrate, guiding substrate screening and design. |
| Sequence and Structure Analysis Service |
We analyze the sequence and structure of trans-isomerases using AI-based tools to identify key functional residues, predict substrate specificities, and evaluate the impact of mutations on enzyme properties. This analysis aids in the rational design of improved trans-isomerase variants. |
Why Choose Us?
With our cutting-edge technology and extensive knowledge, CD ComputaBio excels as a prominent supplier of AI-driven services for trans-Isomerase design. If you are interested in our services or have any questions, please feel free to contact us.
Reference:
- Ming Y, Wang W, Yin R, et al. A review of enzyme design in catalytic stability by artificial intelligence[J]. Briefings in Bioinformatics, 2023, 24(3): bbad065.
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