Recombinant Escherichia coli Uncharacterized protein ypfJ (ypfJ)

What defines an uncharacterized protein in E. coli and why are they significant research targets?

Uncharacterized proteins are those with sequenced genes but unknown physiological functions. Despite E. coli K-12 MG1655 being one of the most extensively studied bacterial genomes, approximately 30% of its genes still lack functional annotation . These proteins represent critical knowledge gaps in our understanding of bacterial physiology, regulatory networks, and potential therapeutic targets. Studying these proteins is essential for completing the functional annotation of the E. coli genome and understanding the full spectrum of bacterial processes.

The significance lies in their potential roles in previously unidentified regulatory pathways, stress responses, or metabolic processes that could elucidate bacterial adaptation mechanisms. For instance, some uncharacterized proteins may function as transcription factors (TFs), of which an estimated 50-80 remain uncharacterized in E. coli K-12 MG1655 .

How are candidate uncharacterized proteins initially identified and prioritized for experimental characterization?

Initial identification typically follows a multi-stage workflow combining computational prediction with biological knowledge. The process often includes:

• Computational screening: Machine learning algorithms like TFpredict can be applied to bacterial proteomes, generating confidence scores for each protein based on sequence homology . These algorithms analyze protein sequences to predict likelihood of specific functions.

• Domain analysis: Examination of predicted DNA-binding domains and other structural features that suggest potential function.

• Sequence homology assessment: Comparison with characterized proteins from related organisms.

• Contextual genomic analysis: Evaluating gene neighborhood and operon structure to infer functional associations.

• Expression pattern analysis: RNA-seq data can suggest conditions where the protein might be active.

Prioritization typically favors proteins with:

• High confidence scores from prediction algorithms

• Predicted interactions with DNA sequences

• Conservation across bacterial species

• Co-expression with genes of known function

For example, in one systematic study, researchers identified 16 candidate transcription factors from over a hundred uncharacterized genes in E. coli by using a combination of these approaches .

What experimental methods are commonly employed for initial characterization of uncharacterized E. coli proteins?

The initial characterization typically follows a systematic approach:

• Recombinant protein expression and purification: Production of tagged versions of the protein, similar to the recombinant YebF protein described in the search results, which can be expressed in E. coli with >90% purity and analyzed by SDS-PAGE .

• DNA-binding assays: For potential transcription factors, ChIP-exo (Chromatin Immunoprecipitation with exonuclease treatment) can identify DNA binding sites genome-wide.

• Gene deletion studies: Creating knockout mutants to observe phenotypic changes and compare gene expression profiles with wild-type strains.

• Proteomics analysis: 2D-gel electrophoresis (2-DE) coupled with mass spectrometry to analyze differential protein expression between wild-type and mutant strains .

• Transcriptional profiling: RNA-seq to determine genes whose expression changes upon deletion of the uncharacterized protein.

• Motif discovery: For DNA-binding proteins, identifying consensus binding motifs from ChIP-exo data.

These methods collectively provide insights into protein function, with DNA-binding assays and gene deletion studies being particularly informative for potential transcription factors, as demonstrated in studies capturing 255 DNA binding peaks for candidate TFs resulting in high-confidence binding motifs .

What considerations should be made when designing experiments to study uncharacterized proteins?

Experimental design for uncharacterized protein research requires careful consideration of multiple factors:

• Replication strategy: Both biological and technical replication must be adequately planned. As shown in Fig. 2 from source , different replication strategies have distinct implications:

  • Design A: One biological sample with six technical replications can lead to overestimation of precision and increased false positives

  • Design B: Three biological replications with two technical replications each provides better balance

  • Design C: Six biological replications with one technical replication prioritizes biological variability

• Randomization: To avoid systematic biases in proteomics experiments, randomization should be implemented at multiple levels:

  • Allocation of protein samples to strips

  • Strip placement in IEF apparatus

  • Strip deposit at the top of second dimension gels

  • Gel placement in migration tanks

• Blocking: When variables cannot be controlled (e.g., different experimental apparatus), blocking in the experimental design helps control for these variables.

• Statistical power considerations: Determining appropriate sample sizes to detect meaningful differences between conditions.

• Controls: Inclusion of proper positive and negative controls to validate experimental procedures.

• Condition selection: Testing multiple environmental conditions (e.g., nutrient limitation, stress) to identify conditions where the protein might be active.

As noted in the literature, establishing experimental design through collaboration between biologists and statisticians is valuable for forecasting sampling or experimental biases, limiting systematic errors, and improving precision of subsequent statistical tests .

How can computational approaches enhance the characterization of uncharacterized E. coli proteins?

Computational approaches serve as powerful complementary tools to experimental methods for uncharacterized protein characterization:

• Machine learning frameworks: Advanced algorithms like TFpredict can be trained on proteobacterial data to identify potential transcription factors with high confidence . These tools analyze protein sequences to predict functional properties based on learned patterns from known proteins.

• Regulon-based associations: Computational methods can predict regulatory networks by analyzing co-expression patterns and potential regulatory interactions, providing context for the function of uncharacterized proteins .

• Integrated analysis with metabolic models: Combining transcriptomic data with genome-scale metabolic models can predict the functional roles of uncharacterized proteins in metabolism .

• Motif discovery algorithms: For potential transcription factors, computational tools can analyze ChIP-exo data to identify consensus binding motifs and predict regulons.

• Structural modeling: Homology modeling and ab initio structure prediction can generate structural hypotheses about protein function.

• Systems biology approaches: Network analysis integrating multiple data types (transcriptomics, proteomics, metabolomics) can position uncharacterized proteins within biological pathways.

A successful example of computational integration is the workflow described in source , where machine learning predictions were combined with DNA-binding domain analysis and condition prediction to identify candidate transcription factors, leading to the discovery of regulatory roles for YiaJ, YdcI, and YeiE as regulators of L-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis, respectively .

What statistical approaches are most appropriate for analyzing differential expression data related to uncharacterized proteins?

Statistical analysis of differential expression data for uncharacterized proteins requires robust methodologies to ensure reliable results:

• Spot-by-spot analysis vs. global analysis: In 2-DE proteomics experiments, two main approaches exist:

  • Spot-by-spot analysis: Tests each protein spot independently using Gaussian distribution assumptions with Fisher or Student statistics

  • Global analysis: Uses ANOVA models that account for gel effects and spot-condition interactions across all spots simultaneously

• Transformation of spot volumes: Appropriate transformation of protein spot volumes may be necessary to satisfy statistical assumptions for ANOVA or t-tests.

• Multiple testing correction: When testing many protein spots simultaneously, methods like Benjamini-Hochberg false discovery rate (FDR) control are essential to avoid false positives.

• Variance estimation: Proper estimation of both biological and technical variance components is critical for accurate statistical inference.

The choice between spot-by-spot and global analysis approaches depends on experimental design and research questions. Global analysis typically provides better control of the false discovery rate, while spot-by-spot analysis may be more intuitive for identifying specific proteins of interest .

How can ChIP-exo and transcriptional profiling be integrated to elucidate the function of uncharacterized transcription factors?

Integration of ChIP-exo (Chromatin Immunoprecipitation with exonuclease treatment) and transcriptional profiling represents a powerful approach to characterizing transcription factors:

• Complementary information: ChIP-exo identifies genome-wide DNA binding sites, while transcriptional profiling reveals genes whose expression changes upon deletion or overexpression of the transcription factor.

• Integration workflow:

  • ChIP-exo experiments capture DNA binding peaks for candidate transcription factors

  • Motif analysis determines binding site consensus sequences

  • RNA-seq or microarray analysis of TF deletion/overexpression strains identifies differentially expressed genes

  • Comparison of binding sites with expression changes distinguishes direct from indirect regulatory effects

  • Network reconstruction based on combined datasets

• Validation strategies:

  • Testing predicted binding sites using electrophoretic mobility shift assays (EMSA)

  • Reporter gene assays to validate transcriptional regulation

  • Testing phenotypic effects of TF deletion under predicted regulatory conditions

This integrated approach has been successfully applied to discover and characterize previously uncharacterized transcription factors in E. coli. For example, researchers captured 255 DNA binding peaks for ten candidate TFs, resulting in six high-confidence binding motifs, and reconstructed the regulons of these TFs by determining gene expression changes upon TF deletion . This integrated analysis led to the identification of specific regulatory roles: YiaJ as a regulator of L-ascorbate utilization, YdcI as a regulator of proton transfer and acetate metabolism, and YeiE as a regulator of iron homeostasis under iron-limited conditions .

What are the methodological considerations for designing replication strategies in proteomics studies of uncharacterized proteins?

Replication strategy design for proteomics studies requires careful balancing of biological and technical replication to maximize statistical power while managing resources:

Key considerations include:

• Variance components: Proteomics experiments have variability in both biological (between samples) and technical (between gels) phases. Replication strategy should enable estimation of both variance components for proper statistical inference .

• Sample limitations: When sample material is limited (e.g., clinical biopsies), pooling strategies may be necessary, but pooling should maintain biological replication by using multiple pools per condition to avoid false positives .

• Randomization within replication: Even with proper replication, randomization of samples to experimental units (gels, runs) is essential to avoid systematic biases .

• Statistical power: The number of replicates should be determined based on the expected effect size and desired statistical power.

• Resource constraints: Balancing comprehensive replication with practical limitations on time, cost, and sample availability.

As noted in the literature, when protein extracts from several samples are pooled into a single sample for each condition, the differential analysis will be based on technical variance only, potentially increasing the number of false positives. Using several pools per condition avoids this problem .

What strategies can be employed to determine the physiological relevance of newly characterized proteins?

Establishing physiological relevance of newly characterized proteins requires multiple lines of evidence:

• Growth condition screening: Testing mutant strains under various environmental conditions to identify specific conditions where the protein affects fitness:

  • Nutrient limitations

  • Stress conditions (oxidative, acid, heat)

  • Alternative carbon or nitrogen sources

  • Growth phase-specific effects

• Metabolic profiling: Analyzing changes in metabolite levels in mutant strains using techniques like mass spectrometry to identify affected metabolic pathways.

• Protein-protein interaction studies:

  • Co-immunoprecipitation followed by mass spectrometry

  • Bacterial two-hybrid systems

  • Proximity-dependent biotin labeling (BioID)

• In vivo reporter systems: Using fluorescent or luminescent reporters to monitor protein activity or expression under different physiological conditions.

• Multi-omics integration: Combining transcriptomics, proteomics, and metabolomics data to place the protein within cellular networks and identify its functional context.

• Evolutionary conservation analysis: Examining the conservation pattern across bacterial species to infer functional importance.

• Complementation studies: Reintroducing the wild-type gene or homologs from other species to verify function.

For example, researchers identified the regulatory role of YiaJ in L-ascorbate utilization, YdcI in proton transfer and acetate metabolism, and YeiE in iron homeostasis under iron-limited conditions through systematic phenotypic analysis and multi-omics data integration . These findings demonstrate how comprehensive physiological testing can reveal the biological functions of previously uncharacterized proteins.

What purification approaches are most effective for recombinant uncharacterized E. coli proteins?

Purification of recombinant uncharacterized proteins requires tailored approaches depending on protein properties:

• Affinity tag selection: The choice of affinity tag impacts purification efficiency and protein function:

  • His-tags: Common for metal affinity chromatography, can be placed N- or C-terminally

  • GST-tags: Enhances solubility but adds significant size

  • MBP-tags: Improves solubility for difficult-to-express proteins

  • Small tags (FLAG, Strep): Minimal interference with protein structure

• Expression system optimization:

  • Selection of appropriate E. coli strain (BL21(DE3), Rosetta for rare codons)

  • Temperature optimization (lower temperatures for improved folding)

  • Induction conditions (IPTG concentration, induction time)

• Solubility enhancement strategies:

  • Co-expression with chaperones

  • Fusion to solubility enhancers (MBP, SUMO, thioredoxin)

  • Addition of solubilizing agents to buffers

• Purification protocol development:

  • Multi-step purification (affinity, ion exchange, size exclusion)

  • Buffer optimization to maintain stability

  • Protease inhibitor inclusion

An example from the search results shows that recombinant E. coli Protein YebF can be expressed with a tag in E. coli with >90% purity and is suitable for SDS-PAGE analysis . The protein belongs to the YebF family and consists of amino acids 22-118 of the full sequence .

For uncharacterized proteins, it's particularly important to validate proper folding and activity after purification, as the lack of functional assays makes quality assessment challenging.

How can researchers determine the optimal experimental conditions for studying the activity of uncharacterized proteins?

Determining optimal conditions for uncharacterized protein activity requires systematic exploration:

• Expression pattern analysis: Analyzing transcriptomic data across different conditions to identify when the gene is naturally expressed, suggesting activity-relevant conditions.

• Condition matrix screening: Testing protein activity across a matrix of variables:

  • pH range (typically 5.0-9.0)

  • Temperature (4-42°C)

  • Salt concentration (0-500 mM)

  • Divalent cations (Mg²⁺, Ca²⁺, Zn²⁺, Mn²⁺)

  • Cofactors and substrates

  • Redox conditions

• Thermal shift assays: Monitoring protein stability across conditions using differential scanning fluorimetry to identify stabilizing conditions that may correlate with activity.

• Growth phenotype screening: Testing knockout strains under diverse conditions to identify phenotypes that suggest protein function.

• Comparative genomics: Examining genomic context and conservation patterns across species to predict functional associations and activity conditions.

For transcription factors specifically, an effective approach involves:

• Studying expression patterns to identify inducing conditions

• Performing ChIP-exo experiments under those conditions

• Analyzing differentially expressed genes in deletion mutants

• Integrating these data to reconstruct regulons

This integrated approach has successfully elucidated the functions of previously uncharacterized transcription factors such as YiaJ, YdcI, and YeiE by identifying their optimal activity conditions and regulatory targets .

What analytical methods provide the most reliable quantification of differential expression for uncharacterized proteins?

Reliable quantification of differential protein expression requires appropriate analytical methods:

• 2D gel electrophoresis approaches:

  • Silver staining followed by image analysis using software such as ImageMaster

  • Spot volume normalization to account for gel-to-gel variations

  • Appropriate data transformation to meet statistical assumptions

  • ANOVA models incorporating experimental design factors

• Mass spectrometry-based approaches:

  • Label-free quantification

  • Stable isotope labeling (SILAC, iTRAQ, TMT)

  • Selected reaction monitoring (SRM) for targeted quantification

  • Data-independent acquisition (DIA) for comprehensive analysis

• Statistical analysis considerations:

  • Choice between spot-by-spot or global ANOVA approaches

  • Incorporation of blocking factors in statistical models

  • Multiple testing correction to control false discovery rate

  • Variance component estimation for both biological and technical sources

• Visualization and validation:

  • Volcano plots to visualize significance and fold-change

  • Heat maps for pattern recognition across multiple conditions

  • Western blotting validation of key findings

  • Orthogonal techniques to confirm discoveries

The literature highlights important considerations in the statistical analysis of differential expression data, noting that the spot-by-spot approach tests each protein independently using Gaussian distribution assumptions, while the global approach uses ANOVA models accounting for gel effects and interactions across all spots simultaneously . The choice between these approaches affects the detection of significant differences and control of false positives.

How should researchers approach the validation of predicted functions for uncharacterized proteins?

Validation of predicted functions requires a multi-faceted approach:

• Genetic validation:

  • Gene deletion and complementation studies

  • Site-directed mutagenesis of predicted functional residues

  • Suppressor mutant analysis

  • Conditional expression systems

• Biochemical validation:

  • In vitro activity assays based on predicted function

  • Substrate specificity determination

  • Kinetic characterization

  • Structural studies (X-ray crystallography, NMR)

• In vivo functional validation:

  • Reporter gene assays for transcription factors

  • Metabolite analysis for metabolic enzymes

  • Protein localization studies

  • Protein-protein interaction confirmation

• Multi-omics integration:

  • Correlation of binding sites with expression changes for transcription factors

  • Metabolic flux analysis for enzymes

  • Network perturbation analysis

• Physiological relevance testing:

  • Growth phenotypes under specific conditions

  • Stress response assessment

  • Competition assays

An example from the research literature demonstrates this integrated approach for transcription factor validation, where researchers:

• Captured DNA binding peaks using ChIP-exo

• Identified binding motifs

• Determined gene expression changes upon TF deletion

• Linked these findings to specific physiological functions (e.g., YiaJ in L-ascorbate utilization)

This comprehensive validation establishes not only the molecular function of the protein but also its biological significance in the organism.

How can the characterization of uncharacterized proteins contribute to genome-scale metabolic models?

Characterization of uncharacterized proteins enhances genome-scale metabolic models (GEMs) in several important ways:

• Filling knowledge gaps: Approximately 30% of E. coli genes still lack functional annotation . Characterizing these proteins helps complete metabolic networks and regulatory circuits in GEMs.

• Discovering new metabolic functions:

  • Identification of missing enzymes in known pathways

  • Discovery of alternative routes for metabolic processes

  • Elucidation of bypasses or shortcuts in metabolic networks

• Improving regulatory network reconstruction:

  • Integration of newly characterized transcription factors into regulatory networks

  • Identification of previously unknown regulatory mechanisms

  • Refinement of existing regulatory interactions

• Enhancing predictive accuracy:

  • Reducing the number of gap-filled reactions without genetic evidence

  • Improving flux predictions through incorporation of newly discovered constraints

  • Enabling more accurate prediction of phenotypes under various conditions

• Model refinement process:

  • Systematic reconstruction of transcriptional regulatory networks by integrating ChIP-exo data with transcriptional profiling

  • Incorporation of newly identified enzyme functions into metabolic networks

  • Validation of model predictions with experimental data from characterized proteins

The integration of experimental approaches with computational modeling creates a virtuous cycle where model predictions guide experimental characterization, and new findings improve model accuracy. For example, the characterization of YdcI as a regulator of acetate metabolism provides critical information for modeling acetate metabolism in E. coli , which is important for both basic understanding and biotechnological applications.

What approaches can address contradictory findings when characterizing novel proteins?

Resolving contradictory findings in protein characterization requires systematic investigation:

• Methodological reconciliation:

  • Comparing experimental conditions across studies

  • Evaluating differences in strain backgrounds

  • Assessing protein tags or constructs used

  • Examining purification methods and their effects on activity

• Integrated data analysis:

  • Meta-analysis combining multiple datasets

  • Statistical modeling to identify sources of variability

  • Weighting evidence based on methodological rigor

• Targeted validation experiments:

  • Designing experiments specifically to test competing hypotheses

  • Using orthogonal methods to validate findings

  • Employing controls that can distinguish between alternative explanations

• Context-dependent function assessment:

  • Testing for condition-specific activities

  • Investigating multiple potential functions

  • Exploring protein moonlighting (multiple distinct functions)

• Collaborative resolution:

  • Direct collaboration between labs with contradictory findings

  • Standardization of protocols and reagents

  • Blind replication studies

When contradictory results arise, experimental design becomes particularly important. The principles outlined in source regarding randomization, replication, and statistical analysis provide a framework for designing experiments that can resolve contradictions. Properly accounting for both biological and technical variance through appropriate replication strategies is essential for distinguishing real effects from experimental artifacts .

How can transcriptomic and proteomic data be integrated to better understand the function of uncharacterized proteins?

Integration of transcriptomic and proteomic data provides complementary insights for functional characterization:

• Multi-omics data integration approaches:

  • Correlation analysis between transcript and protein levels

  • Joint pathway enrichment analysis

  • Network reconstruction incorporating both data types

  • Machine learning methods that leverage multi-omics data

• Functional context identification:

  • Co-expression network analysis to identify functional modules

  • Protein-protein interaction network integration

  • Regulatory network inference combining ChIP-exo with RNA-seq data

• Temporal dynamics analysis:

  • Time-course experiments to track transcript and protein changes

  • Identification of delays between transcriptional and translational responses

  • Inference of regulatory cascades

• Integration strategies:

  • Early integration: combining raw data before analysis

  • Intermediate integration: analyzing each dataset separately, then combining results

  • Late integration: making biological interpretations from separate analyses

• Statistical considerations:

  • Different variance structures in transcriptomic vs. proteomic data

  • Appropriate normalization methods for each data type

  • Multiple testing correction across integrated datasets

A successful example from the literature shows how ChIP-exo data identifying DNA binding sites can be integrated with transcriptional profiling of deletion mutants to reconstruct the regulons of transcription factors . This approach led to the identification of specific regulatory roles for previously uncharacterized transcription factors YiaJ, YdcI, and YeiE .

The integration of these complementary data types provides a more comprehensive understanding of protein function than either approach alone, revealing both the mechanism of action and the biological consequences of the protein's activity.

What strategies can overcome challenges in expressing and purifying difficult uncharacterized proteins?

Difficult-to-express uncharacterized proteins require specialized strategies:

• Alternative expression systems:

  • Cell-free protein synthesis for toxic proteins

  • Baculovirus-insect cell systems for complex proteins

  • Specialized E. coli strains (C41/C43 for membrane proteins, Origami for disulfide bonds)

  • Expression temperature optimization (typical range: 16-37°C)

• Solubility enhancement approaches:

  • Fusion partners (MBP, SUMO, thioredoxin, NusA)

  • Co-expression with chaperones (GroEL/ES, DnaK/J)

  • Addition of solubility enhancers to buffers (glycerol, arginine, non-detergent sulfobetaines)

  • Truncation constructs to identify soluble domains

• Membrane protein strategies:

  • Detergent screening for extraction and stabilization

  • Amphipol or nanodisc reconstitution

  • Fusion to stabilizing membrane protein partners

• Refolding approaches:

  • Inclusion body isolation and purification

  • Systematic refolding screen (pH, ionic strength, additives)

  • Step-wise dialysis methods

  • On-column refolding techniques

• Stabilization methods:

  • Ligand or substrate addition

  • Buffer optimization using thermal shift assays

  • Directed evolution for stability

  • Surface entropy reduction

Each uncharacterized protein presents unique challenges. For instance, the recombinant YebF protein described in the search results is expressed with >90% purity , but this level of success may require optimization of expression and purification conditions, especially for proteins with unknown properties or functions.

How can researchers design experiments to identify interaction partners of uncharacterized proteins?

Identifying interaction partners requires strategic experimental design:

• Affinity purification-mass spectrometry approaches:

  • Tandem affinity purification (TAP)

  • FLAG or HA tag immunoprecipitation

  • Crosslinking immunoprecipitation (CLIP)

  • Comparative analysis with appropriate controls to filter non-specific interactions

• Proximity-based methods:

  • Bacterial two-hybrid (B2H) systems

  • Split-protein complementation assays

  • BioID or APEX2 proximity labeling

  • Photo-crosslinking with unnatural amino acids

• In vitro interaction studies:

  • Surface plasmon resonance (SPR)

  • Isothermal titration calorimetry (ITC)

  • Microscale thermophoresis (MST)

  • AlphaScreen or ELISA-based methods

• Network approaches:

  • Co-expression network analysis

  • Genetic interaction screening

  • Suppressor mutation analysis

• Experimental design considerations:

  • Multiple biological replicates to distinguish true interactions

  • Appropriate negative controls (unrelated proteins, tag-only controls)

  • Reciprocal tagging strategies

  • Condition-specific interaction mapping

• Validation strategies:

  • Orthogonal methods confirmation

  • Functional assays to test biological relevance

  • Structural studies of complexes

For transcription factors, a powerful approach combines ChIP-exo to identify DNA binding sites with RNA-seq to determine genes whose expression changes upon transcription factor deletion . This integrated approach not only identifies the direct targets of the transcription factor but also helps reconstruct its regulon and biological function.

What are the most effective approaches for determining the three-dimensional structure of uncharacterized proteins?

Structural determination of uncharacterized proteins involves multiple complementary approaches:

• X-ray crystallography workflow:

  • High-throughput crystallization screening

  • Optimization of crystal growth conditions

  • Data collection at synchrotron facilities

  • Phase determination (molecular replacement, heavy atom derivatives, selenomethionine incorporation)

  • Model building and refinement

• Cryo-electron microscopy approaches:

  • Sample preparation optimization

  • Single particle analysis

  • Data collection on high-end microscopes

  • Image processing and 3D reconstruction

  • Model building and validation

• NMR spectroscopy methods:

  • Isotopic labeling (¹⁵N, ¹³C, ²H)

  • Multidimensional NMR experiments

  • Chemical shift assignment

  • NOE-based distance restraints

  • Structure calculation and refinement

• Computational structure prediction:

  • Template-based modeling (homology modeling)

  • Ab initio structure prediction

  • Deep learning approaches (AlphaFold2, RoseTTAFold)

  • Molecular dynamics simulations for refinement

• Integrative structural biology:

  • Combining multiple experimental techniques (SAXS, HDX-MS, crosslinking-MS)

  • Hybrid modeling approaches

  • Validation across multiple methods

• Structure-guided functional studies:

  • Identification of potential active sites or binding pockets

  • Rational design of mutations for functional testing

  • Virtual screening for potential ligands or inhibitors