Enzyme De Novo Design

Enzyme De Novo Design

Inquiry

Welcome to CD ComputaBio, your premier destination for cutting-edge computational modeling services in Enzyme De Novo Design. At CD ComputaBio, we specialize in leveraging advanced algorithms and techniques to design novel enzymes tailored to meet specific client needs. Our team of experienced scientists and analysts are dedicated to delivering high-quality, customized solutions to advance research and innovation in the field of enzyme design.

Backgroud

Enzymes play a crucial role in various biological processes and industrial applications. Enzyme engineering, particularly De Novo Enzyme Design, offers the opportunity to create enzymes with enhanced properties, such as improved catalytic activity, stability, and substrate specificity. At CD ComputaBio, we harness the power of computational modeling to facilitate the rational design of enzymes with desired functionalities, providing our clients with tailored solutions to their enzyme engineering challenges.

Figure 1. Enzyme De Novo Design. Figure 1. Enzyme De Novo Design. (Zanghellini A.2014)

Our Service

Our Enzyme De Novo Design process is powered by a sophisticated algorithm that integrates multiple computational techniques:

Services Description
Enzyme De Novo Design Our core service involves the design of novel enzymes from scratch using advanced computational tools and algorithms.
Enzyme Engineering We offer customized enzyme engineering solutions to optimize enzyme properties for specific applications.
Virtual Screening Using molecular docking and simulation techniques, we identify potential enzyme candidates for further evaluation.
Structural Analysis Our experts conduct in-depth structural analysis of enzymes to understand their functions and improve their performance.

Applications

Enzyme De Novo Design has a wide range of applications across various industries, including:

  • Biocatalysis: Designing enzymes for sustainable chemical synthesis and biotransformation processes.
  • Pharmaceuticals: Developing enzymes for drug discovery and biopharmaceutical production.
  • Food and Beverage: Engineering enzymes for improved food processing and quality control.
  • Environmental Remediation: Designing enzymes for environmental cleanup and waste treatment applications.

Our Algorithm

Figure 4. Structural Modeling

Structural Modeling

Using homology modeling, ab initio modeling, and molecular dynamics simulations to predict the 3D structure of the target enzyme.

Figure 3. Active Site Identification

Active Site Identification

Detecting the active site and key residues using site-directed mutagenesis and computational docking studies.

Figure 2. In Silico Mutagenesis

In Silico Mutagenesis

Generating and screening variants through computational methods to identify beneficial mutations.

Enzyme Design Tools

Category Name Description Features and limitations
ASR Bali-phy A standalone tool for the estimation of multiple sequence alignments and evolutionary trees Features: Excellent performance on tested protein data sets
Limitations: Tends to systematically under-align on the biological sequence data
IQ-TREE Standalone software and web tool for the inference of phylogenetic trees using the maximum-likelihood approach Features: It is an integral component of many biomedical research open-source applications such as Galaxy, Nextstrain, and QIIME 2
Limitations: Both IQ-Tree- and RaxML-NG-inferred maximum-likelihood gene trees have been suggested to have reproducibility issues
Molecular Evolutionary Genetics Analysis (MEGA X) User-friendly software for molecular evolution analysis and construction of phylogenetic trees Features: Easy-to-use graphical user interface (GUI) with good documentation available; works across all platforms
Limitations: Works like a black box for some parameters, with no user control
MrBayes A program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models Features: GPU acceleration available
Limitations: Requires quite complex input data, which can hinder non-experts from performing ASR successfully
Phylogenetic Analysis by Maximum Likelihood (PAML) Standalone software and web tool to perform phylogenetic analysis using the maximum likelihood approach Features: Has a GUI version; very customizable
Limitations: Steep learning curve for novice users
Randomized Axelerated Maximum Likelihood (RAxML) A standalone tool for ASR using the maximum-likelihood approach; a GUI is being developed Features: Has a GUI (under development but already available for use)
Limitations: Both IQ-Tree- and RaxML-NG-inferred maximum-likelihood gene trees have been suggested to have reproducibility issues
The FastML Server (FastML) A web tool for ASR using the maximum-likelihood approach Features: Easy-to-use web tool; can take unaligned sequences as input
Limitations: Accepts only the FASTA file format as input
Allostery DFI A protocol developed to identify per-residue contributions to a protein's overall dynamical profile Features: Code easily available and ready to use
Limitations: Requires atomic coordinates
Ohm Web tool that predicts allosteric sites and inter-residue correlation and identifies the allosteric pathways between them Features: Easy-to-use web tool requiring only the insertion of a 3D structure
Limitations: Structure-based predictions do not take dynamics into account
SPM Tool developed for the identification of distal mutations affecting function, based on the shortest-path-map algorithm Features: Uses long dynamic simulations to predict allosteric sites
Limitations: Poor availability of the code
Databases (assorted) BRENDA Electronic repository containing molecular and biochemical information on enzymes Features: Several different queries are available as examples; integrates CATH and SCOPe
Limitations: Requires login and there is a 'professional' version
PDB A protein databank containing more than 179 000 macromolecular structures Features: General database for protein structures obtained through NMR, X-ray crystallography, and cryoelectron microscopy
Limitations: Contains redundant data and the search function could be better
UniProt A central repository of protein data created by combining the Swiss-Prot, TrEMBL, and PIR-PSD databases Features: De facto go-to database for general protein information
Limitations: Overwhelming amount of data for newcomers
Databases (family information) CATH Protein Structure Classification database A database containing information about the evolutionary relationships of protein domains Features: Huge open-source database helps with automated implementation in one's own workflows
Limitations: Drug compound information is still being developed
Fuzzle A database containing evolutionary information about protein fragments Features: Evolutionary information on fragments as opposed to full proteins only
Limitations: Hierarchical databases can lead to misclassification when slight differences are present in the sequences
Pfam A database with information about protein families, represented by multiple sequence alignments generated using Hidden Markov models Features: Constant development and integration with other European Bioinformatics Institute (EBI) tools
Limitations: There is a high-quality database and a low-quality database that can lead to errors
SCOP A database of proteins classified based on structural relatedness, such as superfamilies, families, and folds Features: Uses data deposited in the PDB to group proteins; curated to be non-redundant
Limitations: New version is not totally backwards compatible
Modeling AlphaFold Protein prediction tool using neural networks; achieved a score twice as good as the second-best protein predictor in CASP14 Features: New gold standard for protein structure prediction; makes use of a newly developed neural network
Limitations: It is better where others were good, but still lacks good loop region predictions.
iTasser Both a web tool and a standalone tool, it predicts protein structure using a hierarchical approach; runs iteratively until the lowest-energy structures are achieved and then uses publicly available function information to identify closely related templates with the same function Features: Good and easy-to-use web tool and a powerful standalone tool
Limitations: Lacks accuracy when few templates are available and is slower than similar options available
Modeller Protein structure modeling tool that predicts structures by the satisfaction of spatial restraints obtained from a sequence alignment and shown as a probability-density function Features: Can be installed on any platform and is very fast in creating a new model
Limitations: Speed comes at the cost of accuracy for models where slower tools yield better results
Multiple sequence alignment (MSA) Clustal Omega Multiple sequence alignment web tool Features: Has been constantly developed since the 1980s for MSA
Limitations: Does not yield good results when a large amount of sequences is provided as input
Multiple Alignment Using Fast Fourier Transform (MAFFT) Multiple sequence alignment web tool; can also be used locally as a standalone tool Features: Users can choose between various multiple alignment methods
Limitations: It requires more memory to run
Multiple Sequence Comparison by Log-Expectation (MUSCLE) Multiple sequence alignment web tool; as with Clustal Omega, it is integrated in the EBI ecosystem Features: Can achieve better average accuracy and speed than ClustalW2 or T-Coffee
Limitations: The Kimura distance used at the second stage, although fast, does not consider which changes of amino acids occur between sequences
Protein design AbDesign An algorithm for backbone design using structure- and sequence-based information Features: Stepwise workflow for the design of antibodies that focuses on stability and binding affinity
Limitations: Requires sufficient sequence data on homologs as well as atomic coordinates
FuncLib Web tool to design and rank multiple point mutations based on evolutionary information and protein-folding stability calculations Features: Multipoint variant design tool with an easy-to-use web server
Limitations: Works better with a pre-stabilized protein scaffold; poor knowledge of one's system may lead to poor results
Loop Grafter A web tool with a workflow to compare loop dynamics between proteins and transplant loops from one protein to another Features: Automated way to transfer loops from one protein to the other
Limitations: Works only with the input of both the template and the scaffold proteins
PROSS A user-friendly web tool to predict protein amino acid substitutions that yield higher-stability variants Features: Automated way to stabilize proteins by inserting mutations in the original protein; the method is reliable enough that only a more limited number of designs as output is sufficient
Limitations: Requires a structure (which may not always be available) for stability calculations
ProtLego A Python library for chimera design and analysis Features: Automatic construction and ranking of chimeras
Limitations: The correlation between structural features and experimental success is not yet clear
Rosetta A comprehensive software suite with several algorithms that can be used for the modeling and analysis of proteins Features: Encompasses many modules under the same umbrella name
Limitations: Not unified and developed by many people through the years; can be hard to implement and use different modules
SEWING A protocol to design new tertiary protein structures by 'sewing' together secondary-structure building blocks Features: Continuous and discontinuous SEWING can be merged to create additional diversity
Limitations: At present, it appears to have been applied only to the construction of all-α-helix chimeras
Tunnels and cavities CAVER Software tool for the analysis and visualization of tunnels and channels in protein structure Features: Integration with other CaverSuite tools allows deeper analysis
Limitations: Still lacks the possibility of calculating pores
POVME Standalone tool for ligand-binding pocket calculations Features: Calculates ligand-binding pockets using MD snapshots
Limitations: The lack of a GUI makes it less accessible to non-bioinformaticians
Similarity search BLAST A tool to calculate statistical significance between biological sequences Features: One of the most-used tools for local alignment search; fast and easy to use
Limitations: Developed in 1990 and has not changed substantially since then
FASTA A web tool to provide a heuristic local search with a protein query Features: First tool in the field; as with any other tool from the EBI, it is integrated with many other tools and analyses; newer tools exist that have evolved from this
Limitations: As opposed to BLAST, it does not remove low-complexity regions
Structure and trajectory data Bio3D R package for the analysis of protein structure and trajectory data; provides a variety of approaches for conformational analysis of a protein Features: Comprehensive tool with many tutorials and easy installation
Limitations: Lack of GUI and a webserver makes for a steep learning curve for non-bioinformaticians

Sample Requirements

To initiate an Enzyme De Novo Design project with us, we typically require the following samples and information:

  • Target Enzyme: A detailed description of the target enzyme and its intended application.
  • Substrate Information: Information about the substrate(s) the enzyme will interact with.
  • Desired Properties: Specific characteristics and functions desired from the designed enzyme.

Results Delivery

Upon completion of the Enzyme De Novo Design process, we provide our clients with:

  • Comprehensive Report: A detailed report outlining the design process, results, and recommendations.
  • Enzyme Candidates: High-quality enzyme candidates with predicted properties and structural information.
  • Consultation: Expert consultation to discuss and interpret the results and plan further steps.

Our Advantages

Expertise

Our team consists of experienced scientists and analysts with expertise in computational modeling and enzyme design.

Efficiency

Our algorithm-driven approach accelerates the enzyme design process, delivering results in a timely manner.

Quality Assurance

We uphold high standards of quality and accuracy in our computational predictions and analyses.

Enzyme De Novo Design holds immense potential for revolutionizing enzyme engineering and biocatalysis, opening new avenues for innovation and sustainability. At CD ComputaBio, we are committed to providing top-notch computational modeling services to support our clients' enzyme design endeavors. With our expertise, cutting-edge technology, and dedication to excellence, we are your trusted partner in shaping the future of enzyme engineering.

Frequently Asked Questions

How does enzyme de novo design work?

The process of enzyme de novo design generally follows several key steps:

  1. Target Identification: Define the specific function or reaction that the desired enzyme should catalyze.
  2. Computational Modeling: Use computational tools (like molecular dynamics simulations, machine learning, and structure prediction algorithms) to design and predict the amino acid sequence that would fold into a stable structure with catalytic capability.
  3. Synthesis and Expression: Synthesize the designed DNA sequence to produce the enzyme in a suitable expression system (like bacteria or yeast).
  4. Characterization: Test the new enzyme's activity, stability, and specificity to ensure it meets the desired criteria.
  5. Optimization: Iterate the design based on performance to enhance functionality and stability through further computational and experimental feedback.

What computational methods are commonly used in enzyme de novo design?

Various computational methods are employed in enzyme de novo design, each serving distinct purposes:

  • Molecular Dynamics Simulations: These simulations help in understanding how proteins behave over time and can inform about stability and flexibility.
  • Monte Carlo Simulations: This probabilistic method helps explore the conformational space of proteins to find stable states that may correlate with ideal functional conformations.
  • Machine Learning Approaches: Recently, machine learning has been increasingly utilized to predict protein folding, stability, and even evolutionarily conserved features that might influence design.
  • Rosetta Software Suite: A popular platform for computational protein design, it uses powerful algorithms to predict and design new protein structures and functions.

What are the applications of enzyme de novo design?

Enzyme de novo design has a broad range of applications, including:

  • Biocatalysis: Designed enzymes can catalyze industrial chemical reactions under mild conditions, which are often greener and more sustainable than traditional chemical processes.
  • Pharmaceutical Development: Novel enzymes can be engineered to synthesize pharmaceuticals more efficiently or develop drugs that target specific sites within pathogens.
  • Environmental Applications: Designed enzymes can help tackle environmental issues, such as breaking down pollutants or facilitating bioremediation processes.

How is enzyme de novo design changing the field of biotechnology?

Enzyme de novo design is revolutionizing biotechnology by allowing for:

  • Customization and Enhanced Specificity: Unlike traditional methods, where natural enzymes may not fit perfectly for specific reactions, de novo design allows precise customization of enzymatic activity.
  • Innovation Capability: Scientists can now create enzymes with novel functionalities that do not exist in nature, expanding the toolbox for biotechnology applications.
  • Sustainability Efforts: By designing more efficient biocatalysts, researchers are contributing to more sustainable industrial practices that reduce waste and energy consumption.

Reference

  1. Zanghellini A. De Novo computational enzyme design. Current opinion in biotechnology, 2014, 29: 132-138.
For research use only. Not intended for any clinical use.

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