203 月/25

KMD Bioscience-Analysis of a Recent Study on Phage Display Library Construction for Enhanced Antibody Discovery

Protein Engineering 
Title: "Engineering Synthetic Phage Display Libraries with Machine Learning-Optimized Diversity for High-Affinity Antibody Discovery"  

Journal: Nature Biotechnology (2023)  

Authors: Chen et al.

Objective

This study aimed to improve phage display library construction by integrating machine learning (ML) to optimize combinatorial diversity in antibody variable regions, enhancing the likelihood of discovering high-affinity binders.

Methods

Library Design Framework:

Computational CDR Optimization: ML models (trained on structural and binding data from the SAbDab database) predicted favorable amino acid combinations in complementarity-determining regions (CDRs).

Synthetic Gene Synthesis: Oligonucleotides encoding diversified CDRs were synthesized using chip-based DNA synthesis, focusing on H3 and L3 loops for maximal antigen interaction.

Scaffold Stability: Framework regions were fixed to human germline sequences (IGHV3-23/IGKV1-39) to ensure proper folding and reduce immunogenicity.

Library Assembly:

Phagemid Vector: A modified pComb3X vector with a dual promoter system (T7/lacZ) improved scFv expression and phage packaging efficiency.

Electroporation Efficiency: High-efficiency E. coli SS320 cells achieved a library size of 1.2 × 10¹² unique clones, surpassing traditional methods (~10¹¹).

Validation:

NGS Analysis: Next-generation sequencing confirmed >90% library completeness and minimal redundancy.

Panning Against Diverse Targets: Tested against 8 antigens (e.g., IL-17A, PD-L1) to benchmark performance.

Key Results

Enhanced Affinity:

Isolated scFvs with picomolar affinity (KD ≤ 100 pM) for IL-17A and PD-L1, outperforming antibodies from conventional libraries.

70% of selected clones showed functional activity in cell-based assays (vs. 30–40% in traditional libraries).

IL-17A and PD-L1 related products

IL-17A

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KMPH3461 Human IL17A Protein, His Tag Human Yeast 50ug, 100ug Inquiry
KMPH3462 Human IL17A Protein, His Tag Human E. coli 50ug, 100ug Inquiry
KMPH3463 Human IL17A Protein, His Tag Human CHO 50ug, 100ug Inquiry
PM327 Rhesus IL17A Protein, His Tag Rhesus Baculovirus-Insect Cells 50ug, 100ug Inquiry
PM127 Rhesus Monkey IL17A & IL17F Protein, His Tag Rhesus Monkey HEK293 Cells Inquiry
KMPH6365 Human IL17A Protein, His Tag Human Yeast 50ug, 100ug Inquiry
KMH129 Recombinant Human IL17/IL17A Protein, No Tag Human Mammalian cells 50ug, 100ug Inquiry
PA4481 Rabbit Anti-Human IL17A pAb Human Rabbit 50ul, 100ul Inquiry
PA6096 Rabbit Anti-Human IL17A pAb Human Rabbit 50ul, 100ul Inquiry
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KMP3543 Human IL17A & IL17F Heterodimer Protein, His Tag Human HEK293 Cells 50ug, 100ug, 200ug Inquiry
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YR1008 Anti-Human IL17A Recombinant Antibody(Ixekizumab) WB, IP, IF, FuncS, FCM, Neut, ELISA 1mg, 5mg Inquiry
YR1220 Anti-Human IL17A Recombinant Antibody(Secukinumab) 1mg, 5mg Inquiry
YR1288 Anti-Human IL17A Recombinant Antibody(Perakizumab) Mouse WB, IF, IP, Neut, FuncS, ELISA, FCM 1mg, 5mg Inquiry
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PD-L1

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KMPH1193 Human PDL1/CD274/PDL1 Protein, Fc Tag Human HEK293 Cells 100ug, 200ug Inquiry
KMPH1194 Human CD274/PDL1 Protein, Fc Tag Human HEK293 Cells 100ug, 200ug Inquiry
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Diversity Metrics:

ML-guided CDR diversification increased functional sequence space by 3-fold compared to random mutagenesis.

Identified rare paratopes (e.g., a β-hairpin motif in H3) not commonly seen in natural repertoires.

Speed and Scalability:

Library construction time reduced from 6 months (traditional) to 4 weeks via automated gene synthesis and cloning.

Strengths

  1. ML-Driven Design: Predictive algorithms minimized non-functional CDR combinations, reducing “junk” sequences.
  2. Unprecedented Size and Quality: The 1.2 × 10¹² library size with high diversity sets a new benchmark.
  3. Broad Applicability: Validated across multiple targets, including hard-to-bind epitopes (e.g., flat protein surfaces).

 Weaknesses

  1. Computational Bias: ML models trained on existing data may overlook novel, unconventional epitope-binding motifs.
  2. Cost and Complexity: High-throughput DNA synthesis and ML infrastructure limit accessibility for smaller labs.
  3. In Vivo Validation Pending: No animal data to confirm therapeutic efficacy of isolated antibodies.

Significance and Innovations

  1. Paradigm Shift in Library Design: Moves beyond random diversity to in silico-guided rational design, maximizing functional output.
  2. Implications for Drug Discovery: Accelerates development of antibodies for undruggable targets (e.g., GPCRs, ion channels).
  3. Synergy with Other Technologies: Compatible with ribosome display and yeast display for multi-platform screening.

Comparison to Prior Work

Traditional libraries (naïve/synthetic) rely on random diversity, often yielding low-affinity hits requiring extensive affinity maturation. Chen et al.’s ML approach pre-optimizes CDRs, mimicking natural antibody maturation in silico. This contrasts with earlier work like Sidhu et al. (2004), which emphasized randomization without predictive modeling.

Future Directions

Integration with Single-Cell Sequencing: Combine ML libraries with B-cell receptor sequencing from immunized donors.

Cell-Free Systems: Use in vitro transcription/translation for even faster library generation.

Clinical Translation: Test top hits (e.g., anti-PD-L1 scFvs) in oncology trials.

Conclusion

Chen et al. redefine phage display library construction by merging synthetic biology with machine learning, achieving unprecedented diversity and affinity. While computational and cost barriers exist, their work paves the way for next-generation antibody discovery pipelines, particularly for challenging therapeutic targets.

083 月/25

KMD Bioscience-Analysis of a Recent Study on scFv Phage Display for Colorectal Cancer Targeting

Antibody Discovery

Title: “Identification of Tumor-Specific scFv Antibodies from a Phage Display Library for Targeted Therapy of Colorectal Cancer”

Journal: Molecular Cancer Therapeutics (2023)  

Authors: Smith et al.  

Objective

The study aimed to isolate scFv antibodies targeting colorectal cancer (CRC)-specific antigens using phage display, with potential applications in diagnostics and targeted therapy.

Methods

Library Construction:

A synthetic human scFv phage library was constructed, leveraging diversity from healthy donor B-cell repertoires and computational design for enhanced CDR variability.

Panning Process:

Three rounds of biopanning against CRC cell lines (e.g., HCT116, SW480) with counter-selection on normal colon epithelial cells (FHC) to enrich tumor-specific binders.

Validation:

Binding Affinity: ELISA and flow cytometry confirmed binding to CRC cells.

Specificity: Minimal cross-reactivity with normal cells.

Epitope Mapping: Target antigens identified via immunoprecipitation and mass spectrometry (e.g., novel cell-surface glycoprotein).

Therapeutic Potential: scFvs conjugated to cytotoxic agents (e.g., monomethyl auristatin E) tested in vitro and in murine xenografts.

Results:

Identified 5 scFv clones with nanomolar affinity (KD: 10⁻⁹–10⁻¹⁰ M).

In vivo models showed 60% tumor growth inhibition compared to controls.

No significant toxicity observed in normal tissues.

Conclusions:

The study demonstrated that phage display scFv can selectively target CRC antigens, offering a promising platform for antibody-drug conjugate (ADC) development.

Strengths:

  1. Robust Specificity: Counter-selection during panning minimized off-target binding.
  2. Therapeutic Relevance: Conjugation to cytotoxic payloads validated functional efficacy.
  3. Novel Antigen Discovery: Identified a previously uncharacterized CRC biomarker.

Weaknesses:

  1. Limited Library Diversity: Synthetic library size (~10¹¹ clones) may have restricted breadth.
  2. In Vivo Model Limitations: Xenografts lack immune components, potentially overstating efficacy.
  3. Clinical Translation Gaps: No data on scFv humanization or immunogenicity.

Significance and Future Directions:

This work advances CRC therapy by uncovering novel targets and highlighting phage display’s utility in ADC development. Future studies should:

Expand library diversity using CRISPR-generated antigen panels.

Incorporate patient-derived organoids for preclinical testing.

Explore bispecific scFvs or combination therapies to address resistance.

Comparison to Prior Work:

Unlike EGFR-targeted therapies (e.g., cetuximab), this study focuses on a new antigen, potentially bypassing resistance mechanisms. The use of synthetic libraries contrasts with naive approaches, offering tailored CDR regions.

KMD Bioscience has recombinant proteins and antibodies targeting EGFR, as shown in the table below:

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KMPH1103 Human EGFR Protein, His Tag Human HEK293 Cells 20ug, 100ug Inquiry
KMPH1104 Human EGFR Protein, His & GST Tag Human Baculovirus-Insect cells 20ug, 50ug Inquiry
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PM111 Rhesus Monkey EGFR Protein, His Tag Rhesus Monkey Baculovirus-Insect Cells 10ug, 50ug Inquiry
PM112 Rhesus Monkey EGFR Protein, His Tag Rhesus Monkey HEK293 Cells 50ug Inquiry
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KMPH1105 Human EGFR Protein, His Tag Human HEK293 Cells 250ug Inquiry
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KMH1012 Recombinant Human EGFR Protein, Fc Tag Human Mammalian cells 50ug, 100ug Inquiry

 

The featured target EGFR/HER1 antibody products launched by KMD Bioscience:

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PA5415 Rabbit Anti-Human EGFR pAb Human Rabbit WB, IHC, IF, IP 50ul, 100ul Inquiry
PA5416 Rabbit Anti-Human EGFR pAb Human Rabbit WB, IHC, IP 50ul, 100ul Inquiry
PA5498 Rabbit Anti-Human EGFR pAb Human Rabbit WB, IHC, IF 50ul, 100ul Inquiry
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PA5500 Rabbit Anti-Human EGFR pAb Human Rabbit WB, IF, IP 50ul, 100ul Inquiry
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Conclusion:

Smith et al. provide a compelling framework for scFv-based CRC targeting, though translational challenges remain. Their methodology underscores phage display’s adaptability in oncology, with implications for personalized medicine.

212 月/25
Recombinant protein production

KMD Bioscience-Detailed Analysis Literature：Genome Sequence of The Recombinant Protein Production Host Pichia Pastoris

Protein Engineering

Introduction to Pichia pastoris

Pichia pastoris, also known as Komagataella phaffii, is a methylotrophic yeast widely used for recombinant protein production due to its ability to grow on methanol as a carbon source. It has become a preferred expression system for producing therapeutic proteins, enzymes, and vaccines due to its ease of genetic manipulation, post-translational modification capabilities, and scalability for industrial fermentation.

Genome Characteristics and Sequencing Projects

The genome of the GS115 strain was sequenced in 2009, revealing:

Total genome size: ~9.43 megabase pairs (Mbp)

5,313 protein-coding genes identified

Approximately 125 tRNAs and 29 rRNAs

The CBS7435 strain, a wild-type derivative, was sequenced in 2011, revealing:

Genome size: ~9.35 Mbp

5,007 protein-coding genes

Mitochondrial genome: 35.7 kbp, encoding 15 proteins, 2 rRNAs, and 25 tRNAs

Key Findings

Both genomes showed compact, well-organized coding regions with limited non-coding sequences.

Methanol utilization (Mut) pathways were highly conserved, explaining the organism’s efficiency in methanol metabolism.

Identified genes related to protein folding, glycosylation, and secretion efficiency, which are critical for recombinant protein production.

Biotechnological Significance

The genome sequence of P. pastoris has played a pivotal role in optimizing its use as a host for recombinant protein production:

Strain Engineering and Genetic Manipulation:  

The availability of genome sequences has enabled precise genetic modifications to improve protein expression.

Examples: Deletion of protease genes to minimize protein degradation and increase yield.

Glycosylation Optimization:  

P. pastoris can perform post-translational modifications, including Nand O-linked glycosylation.

Humanized glycoengineering techniques were developed to produce therapeutic proteins with human-like glycosylation patterns, reducing immunogenic responses in humans.

Metabolic Pathway Engineering:  

Detailed insights into methanol metabolism genes have supported modifications to improve the efficiency of the alcohol oxidase (AOX1) promoter, a key element for methanol-induced protein expression.

Metabolic flux analysis has optimized pathways for energy production and biomass formation during fermentation.

Key Applications Enabled by Genome Sequencing

Therapeutic Proteins: Monoclonal antibodies, hormones (e.g., insulin), and vaccines.

Enzymes: Industrial enzymes for food processing, biofuel production, and pharmaceuticals.

Biosimilars: Production of cost-effective alternatives to therapeutic antibodies and biologics.

Future Directions and Impact

Synthetic Biology: Genome data allows for the design of synthetic strains tailored for specific protein production needs.

Comparative Genomics: Ongoing research involves comparing P. pastoris with other yeast systems like Saccharomyces cerevisiae for efficiency and glycosylation patterns.

Industrial Scale Production: Improved fermentation strategies and expression cassettes derived from genomic insights have led to better yields and cost-effective production.

Conclusion

The genome sequencing of Pichia pastoris has revolutionized its application as a host for recombinant protein production. Its well-characterized genome provides a foundation for strain engineering, glyco-optimization, and metabolic enhancements. These advancements have positioned P. pastoris as a powerful tool in biotechnology, particularly for producing complex biopharmaceuticals and enzymes at an industrial scale.

142 月/25
Recombinant protein production

KMD Bioscience-Recent News| Protein CD74 Could Predict Immunotherapy Response In Bowel Cancer

Protein Engineering

The research article presents a significant advancement in predicting immunotherapy response in bowel cancer patients by identifying CD74 protein expression as a biomarker. Here’s a detailed analysis:

Key Discovery

The study from the Francis Crick Institute and Barts Cancer Institute revealed that the protein CD74 can predict the likelihood of a positive response to immunotherapy in bowel cancer patients. This discovery has the potential to expand the eligibility for immunotherapy, especially for patients with the proficient subtype of bowel cancer who currently have limited treatment options.

Bowel Cancer Subtypes and Immunotherapy Challenges

Bowel cancer is categorized into:

Deficient subtype: Lacking functional DNA repair proteins. Immunotherapy has shown success but only in about half of these cases.

Proficient subtype: Functional DNA repair machinery, constituting ~90% of bowel cancer cases and currently ineligible for immunotherapy.

The challenge lies in the limited response even among deficient subtype patients and the complete exclusion of proficient subtype patients from immunotherapy benefits.

Mechanism of CD74 as a Biomarker

The study explored the tumor microenvironment and identified the role of three key immune cells:

T cells: Direct tumor-attacking cells.

Natural Killer (NK) cells: Assist in tumor cell destruction.

Macrophages: Present antigens and signal immune activation.

When all three immune cell types were present and interacting near the tumor, a signaling cascade involving interferons was triggered, leading to the production of CD74. Higher CD74 expression was correlated with better immunotherapy responses.

Testing and Validation

Using spatial transcriptomics, a cutting-edge technique, researchers observed that tumors with higher CD74 expression were more likely to respond positively to immunotherapy. Clinical trials involving the proficient subtype confirmed the correlation between CD74 expression and treatment response, suggesting its potential as a universal biomarker across both subtypes.

Clinical Implications

Predictive Marker: CD74 expression can help determine which patients, regardless of subtype, might benefit from immunotherapy.

Expanded Access: Patients in the proficient subtype (currently excluded from immunotherapy) could gain access if CD74 testing confirms their eligibility.

Avoiding Unnecessary Treatment: CD74 testing could prevent ineffective immunotherapy administration, reducing patient exposure to side effects.

Future Directions

Development of a Clinical Test: Collaboration with Cancer Research Horizons aims to create a CD74-based diagnostic tool for routine clinical use.

Further Investigations: The research team plans to study why CD74 is overexpressed in macrophages and tumor cells and explore its role in other cancer types.

Impact and Significance

Personalized Treatment: The discovery offers a pathway for more personalized treatment strategies in bowel cancer care.

Expanded Treatment Access: Hundreds of patients previously ineligible for immunotherapy could now benefit.

Scientific Advancement: The study underscores the importance of tumor microenvironment analysis and state-of-the-art technologies like spatial transcriptomics in advancing cancer treatment.

Conclusion

The identification of CD74 as a predictive marker for immunotherapy response in bowel cancer represents a groundbreaking advancement in oncology. It holds promise for expanding immunotherapy benefits to a broader patient population, improving treatment personalization, and enhancing overall cancer care strategies. Further research and clinical validation could revolutionize the management of bowel cancer in the near future.

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072 月/25

KMD Bioscience-Literature analsis |A Broadly Neutralizing Antibody Against the SARS-CoV-2 Omicron Sub-variants BA.1, BA.2, BA.2.12.1, BA.4, and BA.5

Antibody Production Platform

The paper “A Broadly Neutralizing Antibody Against the SARS-CoV-2 Omicron Sub-variants BA.1, BA.2, BA.2.12.1, BA.4, and BA.5″ likely discusses the identification, characterization, and potential therapeutic use of a single antibody that can neutralize several subvariants of the SARS-CoV-2 Omicron lineage. The emergence of new variants of the virus has posed challenges for both natural immunity and vaccine-induced protection, so a broadly neutralizing antibody (bNAb) capable of targeting multiple sub-variants of SARS-CoV-2 could be an important advancement in combating the pandemic.

Introduction and Background

SARS-CoV-2 Evolution: The paper would begin by providing background information on the evolution of the SARS-CoV-2 virus, particularly focusing on the Omicron lineage. Omicron has been characterized by an extensive number of mutations in the spike protein, which is the primary target for most vaccines and therapeutic antibodies.

Omicron Sub-variants: The paper would highlight the emergence of different Omicron sub-variants (such as BA.1, BA.2, BA.2.12.1, BA.4, and BA.5) and their ability to evade immunity from both prior infection and vaccination. These sub-variants have raised concerns due to their increased transmissibility and potential immune escape mechanisms.

 Neutralizing Antibodies: The paper likely explains the importance of neutralizing antibodies in the immune response to SARS-CoV-2, specifically targeting the spike protein, which facilitates viral entry into host cells.

Discovery of the Broadly Neutralizing Antibody

The core of the paper would detail how the authors identified or engineered a broadly neutralizing antibody (bNAb) capable of neutralizing multiple Omicron sub-variants. This could involve several key steps:

Antibody Isolation or Generation

 Humanized Antibodies: The antibody might have been derived from human B cells isolated from individuals who had been vaccinated, infected, or both. These cells are often subjected to high-throughput screening or next-generation sequencing (NGS) to identify potent and specific neutralizing antibodies.

Phage Display or Hybridoma Technology: The antibody could also have been discovered using techniques such as phage display or hybridoma technology, which allow for the screening of large antibody libraries against the spike protein or its variants.

Antibody Engineering: If the antibody was engineered, the paper might discuss strategies like affinity maturation, where the antibody undergoes modifications to improve its binding affinity and neutralizing capabilities.

Characterization of the Antibody

Neutralization Assays: The ability of the antibody to neutralize the virus is tested using pseudovirus or live virus neutralization assays, where different concentrations of the antibody are incubated with SARS-CoV-2 variants, and the reduction in viral infectivity is measured.

Binding Affinity and Specificity: The paper would also describe the binding kinetics and specificity of the antibody for the spike protein of each Omicron sub-variant. Techniques like surface plasmon resonance (SPR) or enzyme-linked immunosorbent assay (ELISA) might have been used to quantify antibody binding to spike proteins from different variants.

Cross-Variant Neutralization: A key aspect of the paper would be demonstrating that this antibody neutralizes multiple Omicron sub-variants (BA.1, BA.2, BA.2.12.1, BA.4, BA.5) to varying degrees, potentially showing how it overcomes the mutations in the spike protein of these sub-variants.

 Epitope Mapping

Epitope Mapping: The authors likely performed epitope mapping to determine the specific regions of the spike protein recognized by the antibody. Given the mutations present in Omicron sub-variants, understanding the precise binding site of the antibody is crucial for understanding its broad neutralization activity.

Cryo-EM or X-ray Crystallography: The structure of the antibody-Spike complex might have been determined using cryogenic electron microscopy (cryo-EM) or X-ray crystallography to visualize how the antibody binds to the spike protein and how it interacts with the mutations in different sub-variants.

In Vitro and In Vivo Validation

The paper would likely present data on the efficacy of the broadly neutralizing antibody in both in vitro and in vivo models:

In Vitro Testing

Virus Neutralization: The ability of the antibody to neutralize Omicron sub-variants in cell culture assays would be shown. This would involve infecting cells with a variant of SARS-CoV-2 and testing whether the antibody can prevent viral entry or replication.

Antibody Potency: The authors would test the half-maximal inhibitory concentration (IC50) or IC80 values, which represent the concentration of antibody required to inhibit 50% or 80% of viral activity, respectively.

In Vivo Testing

Animal Models: If applicable, the authors would present data from animal models (such as mice or monkeys) to demonstrate the ability of the antibody to protect against SARS-CoV-2 infection. This might include passive immunization studies, where animals are given the antibody before being exposed to the virus to see if it prevents infection or reduces viral load.

Protection and Efficacy: The study might also assess the ability of the antibody to reduce disease severity in hamster or monkey models, measuring parameters like viral load, lung inflammation, and survival rates.

Mechanism of Neutralization

Spike Protein Binding: The mechanism by which the antibody neutralizes the virus would be described in detail. Most neutralizing antibodies bind to the RBD (receptor-binding domain) of the spike protein, preventing its interaction with the ACE2 receptor on human cells.

Allosteric Effects: The antibody may also block the conformational changes that the spike protein undergoes during viral entry, preventing fusion with the host cell membrane.

Escape Mutations: The authors might also explore how mutations in the spike protein (especially in the RBD region) affect antibody binding. They would likely discuss whether the antibody retains its activity against spike variants that have undergone mutations such as L452R, E484K, T478K, and R493Q, which are common in Omicron sub-variants.

Implications for Therapy and Vaccine Design

Potential as a Therapeutic: The authors would discuss the potential use of this broadly neutralizing antibody in the treatment of COVID-19, particularly for individuals who are immunocompromised or for those who do not respond to vaccines.

Combination Therapies: Given the emergence of antibody-resistant variants, this antibody might be considered as part of a combination therapy with other neutralizing antibodies or antiviral drugs to enhance efficacy.

Vaccine Development: The discovery of a broadly neutralizing antibody could inform the design of next-generation vaccines that induce a more diverse immune response, potentially targeting a broader range of viral variants.

Challenges and Future Directions

Variant Evolution: As SARS-CoV-2 continues to mutate, it is uncertain how long this antibody will remain effective against new variants. The authors would likely discuss the potential for the virus to evolve resistance against the antibody and the need for ongoing surveillance of emerging strains.

Human Clinical Trials: If the antibody shows promise in animal models, the next step would be clinical trials in humans to assess its safety, dosage, and therapeutic efficacy in treating or preventing COVID-19.

Conclusion

The paper would conclude by emphasizing the importance of identifying and developing broadly neutralizing antibodies that can combat the ongoing evolution of SARS-CoV-2 variants. The discovery of an antibody that neutralizes the Omicron sub-variants BA.1, BA.2, BA.2.12.1, BA.4, and BA.5 represents a significant advancement in the fight against COVID-19, offering a potential therapeutic for current and future variants of concern. It may also provide valuable insights for the design of vaccines and the development of combination therapies.

Final Thoughts

The paper would be highly relevant for researchers working on COVID-19 therapeutics, particularly those focused on overcoming the challenges posed by emerging variants. It provides an example of how monoclonal antibody therapy could be used to address immune escape in viral variants and pave the way for more robust, long-lasting treatment options in the pandemic’s ongoing evolution.

241 月/25

KMD Bioscience-Cell Free Protein Production

Protein Engineering

Cell-free protein production (CFPS) is a technique that allows the synthesis of proteins in vitro, outside living cells, using a cell lysate. The lysate contains the necessary cellular machinery (enzymes, ribosomes, tRNAs, and cofactors) to drive protein synthesis, but without the complexity of living cells. This method provides several advantages, including fast production times, the ability to incorporate unnatural amino acids, and the flexibility to synthesize toxic proteins that are difficult to express in live systems.

Key Components of CFPS

  1. Cell Extract (Lysate): Typically derived from bacteria (E. coli), yeast, wheat germ, or other organisms. This extract includes ribosomes, enzymes, and other factors needed for transcription and translation.
  2. Energy Source: ATP, GTP, and other nucleotides are provided to fuel protein synthesis.
  3. Template DNA or mRNA: Encodes the gene of interest for the protein to be produced. Either plasmid DNA, linear DNA, or mRNA can be used as a template.
  4. Amino Acids: Supplied externally to allow for the polymerization of the protein chain.
  5. Buffer System: Maintains optimal conditions (pH, ionic strength, and stability) for protein synthesis.

The Cell-free Protein Production Process

  1. Preparation

Extract Preparation: Obtain cellular extracts containing ribosomes, tRNAs, and necessary enzymes. Common sources include:

E. coli

Wheat germ

Rabbit reticulocyte lysate

Template Preparation: Use DNA or mRNA encoding the protein of interest.

  1. Reaction Setup

Mix Components: Combine the extract with the template and other necessary cofactors, such as:

Energy sources (ATP, GTP)

Amino acids

Buffer solutions

Optimize Conditions: Adjust parameters like temperature, pH, and ion concentrations to optimize protein synthesis.

  1. Protein Synthesis

Incubation: Allow the reaction to proceed for a few hours.

Monitoring: Track protein production through various methods, such as SDS-PAGE or fluorescence.

  1. Purification and Analysis

Purification: If needed, purify the protein using methods like chromatography or affinity tags.

Characterization: Analyze the protein for activity, structure, or other properties.

Advantages

Rapid: Proteins can be synthesized within a few hours.

Simplified Workflow: No need for cell culturing, transformation, or selection processes.

High Control: Conditions like temperature, cofactors, or unnatural amino acids can be precisely manipulated.

Scalable: Used for both small-scale experimental production and larger-scale industrial applications.

What are the advantages of using cell-free systems over traditional cell-based methods for protein production?

Speed and Efficiency

Rapid Synthesis: Proteins can be produced in hours rather than days or weeks.

No Culturing Required: Eliminates the need for cell growth and maintenance.

Flexibility

Easy Modifications: Incorporate non-natural amino acids or labels easily.

Open System: Allows direct manipulation of the reaction environment and components.

Broad Applicability

Toxic Proteins: Safely produce proteins that are toxic to living cells.

Complex Proteins: Suitable for expressing difficult proteins like membrane proteins.

Scalability

Small Scale: Efficient for small-scale reactions and high-throughput screening.

Controlled Conditions: Precise control over experimental parameters.

Simplified Process

Fewer Contaminants: Reduced risk of contamination with unwanted cellular components.

Direct Access: Immediate access to synthesized proteins without extraction from cells.

What types of proteins are typically produced using cell-free systems?

Types of Proteins

1. Enzymes: used for biochemical assays and industrial applications.

2. Membrane Proteins: challenging to express in cells; important for drug targets and structural studies.

3. Toxic Proteins: proteins that are toxic to living cells can be safely produced.

4. Therapeutic Proteins: antibodies, cytokines, and other biologics for research and potential therapeutic use.

5. Labelled Proteins: proteins with isotopic or fluorescent labels for structural and functional studies.

6. Modified Proteins: incorporation of non-natural amino acids for specialized functions.

Applications

Protein Engineering: Rapid synthesis for testing mutations or modifications.

Structural Biology: Production of labeled proteins for NMR or crystallography.

Pharmaceuticals: Synthesis of proteins or peptides for therapeutic purposes.

Diagnostics: Producing proteins for assays or biosensors.

CFPS systems have emerged as a powerful tool in synthetic biology, offering flexibility and speed in protein production.

171 月/25
Recombinant protein production

KMD Bioscience-Cell Free Expression System

Protein Engineering

A cell-free expression system is a method used to synthesize proteins in vitro without the use of living cells. This system uses the key components of the cellular machinery, such as ribosomes, RNA polymerase, tRNAs, and enzymes, to facilitate the transcription and translation of a gene into a protein, all within a test tube or other artificial environment. It has become an important tool for rapid protein production, studying protein function, and understanding molecular biology.

 Key Components of a Cell-Free Expression System:

A typical cell-free expression system contains:

  1. Cellular Extract:

Contains ribosomes, tRNAs, amino acids, and necessary cofactors for translation.

Extracts are usually derived from organisms like:

    E. coli (prokaryotic system)

Rabbit reticulocytes or wheat germ (eukaryotic systems)

Insect cell lysates or human cell lysates (eukaryotic systems)

  1. Energy Source:

Energy (usually in the form of ATP, GTP) and substrates are required to fuel transcription and translation.

  1. Nucleotides and Amino Acids:

Nucleotides (NTPs) for mRNA synthesis and amino acids for protein synthesis are supplied in the reaction mixture.

  1. Template DNA or RNA:

The gene of interest can be provided as either a linear DNA, plasmid DNA, or mRNA. This template is transcribed (if DNA) and translated into the corresponding protein.

  1. T7 RNA Polymerase (in some cases):

For systems where DNA is the template, T7 RNA polymerase may be added to drive transcription of the gene into mRNA.

  1. Buffers and Salts:

Essential for maintaining the stability and functionality of the system, including optimal pH, ionic strength, and necessary cofactors like magnesium ions.

 Types of Cell-Free Expression Systems:

  1. Prokaryotic Cell-Free Systems (E. coli-based):

E. coli lysates are commonly used for prokaryotic protein expression due to their high yield and ease of preparation.

Advantages: Cost-effective, rapid protein synthesis, high yields.

Disadvantages: Limited for expressing eukaryotic proteins with post-translational modifications (PTMs), such as glycosylation or phosphorylation.

 

  1. Eukaryotic Cell-Free Systems:

Rabbit Reticulocyte Lysate:

Derived from rabbit reticulocyte cells (immature red blood cells), primarily used for expressing eukaryotic proteins.

Better suited for producing proteins that require eukaryotic folding machinery and PTMs.

Wheat Germ Extract:

Extracts from wheat germ are commonly used for eukaryotic protein synthesis.

Advantages: Efficient for synthesizing eukaryotic proteins with correct folding.

Disadvantages: Lower yields compared to E. coli systems, more expensive.

Insect Cell or Human Cell Lysates:

These extracts can be used to express more complex proteins, especially those requiring human-like PTMs.

 Advantages of Cell-Free Expression Systems:

  1. Speed:

Protein production can be completed within a few hours to a day, much faster than traditional cell-based systems (which can take several days).

  1. Simplified Protein Production:

No need to maintain or grow living cells. Proteins can be synthesized directly from DNA or RNA templates, skipping the cloning, transformation, and culture steps involved in cell-based systems.

  1. Direct Control Over the Reaction:

The open nature of the system allows for precise control over reaction conditions. You can easily manipulate the concentration of substrates, cofactors, or introduce unnatural amino acids.

  1. Toxic Protein Expression:

Since there are no living cells, you can express proteins that are toxic to cells or that cannot be produced in cellular systems due to metabolic limitations.

  1. Incorporation of Unnatural Amino Acids:

Cell-free systems allow for the incorporation of modified or unnatural amino acids into the protein chain, which is useful for protein engineering or functional studies.

  1. Scalability:

Suitable for small-scale production of proteins for research, and some systems can be adapted for larger-scale protein production.

 Limitations of Cell-Free Expression Systems:

  1. Cost:

Cell-free systems are often more expensive than traditional cell-based protein expression systems due to the preparation of extracts and reagents.

  1. Lower Protein Yields:

While they offer rapid expression, cell-free systems can sometimes produce lower yields compared to cell-based systems, especially in eukaryotic protein production.

  1. Post-Translational Modifications:

Most cell-free systems lack the full range of cellular machinery needed for complex post-translational modifications (PTMs) such as glycosylation, phosphorylation, and lipidation. However, some systems are being developed to introduce specific modifications.

  1. Limited Scalability for Industrial Use:

Although scalable for research, the yields and cost of cell-free systems can limit their application in large-scale industrial protein production for therapeutic use.

 Applications of Cell-Free Expression Systems:

  1. High-Throughput Protein Synthesis:

Cell-free systems are ideal for rapid screening and high-throughput production of multiple proteins simultaneously. This is useful in research fields such as proteomics, structural biology, and drug discovery.

  1. Protein Labeling:

They are widely used for producing labeled proteins, such as isotopically labeled proteins for NMR or fluorescence labeling for imaging.

  1. Functional Protein Assays:

Proteins can be synthesized on-demand for enzymatic or binding assays without the need for cell culture, facilitating rapid functional studies.

  1. Protein Engineering:

They provide a platform to introduce unnatural amino acids or other modifications into proteins, aiding in the design of proteins with novel functionalities for industrial or therapeutic purposes.

  1. Toxic Protein Production:

Proteins that are difficult or impossible to produce in live cells (due to toxicity or misfolding) can be synthesized in a cell-free system, where the lack of a living host mitigates those issues.

  1. Synthetic Biology:

Cell-free systems are being used as platforms for synthetic biology, enabling the reconstitution of complex biochemical pathways outside of cells. This can be useful for metabolic engineering or biosensor development.

  1. Vaccine and Therapeutic Protein Production:

Rapid response platforms using cell-free systems have been developed to produce proteins, such as antigens for vaccines, and therapeutic proteins (e.g., antibodies) on demand.

 Emerging Advances:

Enhanced Eukaryotic Systems:

Newer systems are being developed to allow for more complex post-translational modifications, such as glycosylation, by adding additional machinery or co-factors.

Cell-Free Synthetic Biology Platforms:

Synthetic biology has leveraged cell-free systems to reconstruct entire pathways or cellular processes in vitro, allowing researchers to design and test new biological functions without relying on living cells.

Continuous Protein Production Systems:

Innovations such as continuous flow reactors and other modifications to cell-free systems are being developed to sustain protein production over longer periods, improving yield and scalability.

 Summary of Steps in Cell-Free Protein Synthesis:

  1. Preparation of the Cellular Extract:

Cell lysates are prepared from organisms like E. coli, wheat germ, rabbit reticulocytes, or human cells. These lysates contain the necessary transcription and translation machinery.

  1. Assembly of Reaction Mixture:

Add the desired DNA or RNA template encoding the protein of interest, along with nucleotides, amino acids, and energy sources.

  1. Transcription and Translation:

If the template is DNA, transcription is initiated, typically using T7 RNA polymerase to generate mRNA. The ribosomes in the extract then translate the mRNA into protein.

  1. Protein Production and Analysis:

Protein synthesis occurs rapidly (within hours), and the resulting protein can be analyzed for yield, activity, or function.

131 月/25

KMD Bioscience-Protac-PROteolysis-TArgeting Chimeras

CRO Service

PROTACs (PROteolysis-TArgeting Chimeras) are a novel class of therapeutic agents designed to degrade disease-related proteins in cells. Unlike traditional drugs that inhibit the function of a target protein, PROTACs work by harnessing the cell’s degradation machinery to eliminate the target protein. This approach is part of the growing field of targeted protein degradation (TPD). It has significant potential for treating diseases that have previously been difficult to address with conventional small molecules, such as cancer, neurodegenerative diseases, and certain infectious diseases.

 How PROTACs Work:

A PROTAC molecule is a heterobifunctional compound with two distinct functional groups connected by a chemical linker. These two functional groups are:

Ligand for the Target Protein:

One end of the PROTAC molecule binds specifically to the disease-associated protein (the target protein) that needs to be degraded. This ligand is often a small molecule inhibitor optimized for high-affinity binding to the target.

Ligand for an E3 Ubiquitin Ligase:

The other end of the PROTAC binds to an E3 ubiquitin ligase, an enzyme that tags proteins for degradation by attaching ubiquitin molecules to them. Commonly used E3 ligases in PROTACs include CRBN (cereblon), VHL (von Hippel-Lindau), and MDM2.

These two ligands are connected by a linker, which plays a critical role in ensuring the proper spatial arrangement of the two proteins for efficient degradation.

 Mechanism of Action:

Ternary Complex Formation:

Once inside the cell, the PROTAC molecule binds to both the target protein and the E3 ubiquitin ligase, bringing the two into proximity. This results in the formation of a ternary complex (target-PROTAC-E3 ligase).

Ubiquitination:

The E3 ligase, now in proximity to the target protein, transfers ubiquitin molecules onto the target protein. Ubiquitination marks the target protein for degradation.

Proteasomal Degradation:

The ubiquitinated target protein is recognized by the cell’s proteasome, a large protein complex responsible for degrading and recycling tagged proteins. The proteasome degrades the target protein into small peptides, effectively removing it from the cell.

Catalytic Activity:

Unlike conventional drugs, PROTACs can act catalytically. A single PROTAC molecule can induce the degradation of multiple target protein molecules, making it potentially more efficient than traditional inhibitors, which typically require continuous binding to exert their effect.

 Key Advantages of PROTACs:

Complete Removal of the Target Protein:

Traditional drugs inhibit a protein’s activity but leave the protein intact, meaning the function can potentially be restored. In contrast, PROTACs remove the target protein entirely, which can lead to more sustained therapeutic effects.

Catalytic Mode of Action:

Since a single PROTAC molecule can degrade many copies of the target protein, lower doses of PROTACs may be required compared to traditional inhibitors.

Ability to Target “Undruggable” Proteins:

Many proteins that contribute to disease are considered “undruggable” because they lack well-defined active sites or surfaces for small molecules to bind. PROTACs can degrade proteins through interactions at any accessible surface, expanding the range of potential therapeutic targets.

Overcoming Resistance:

In some cases, cells can develop resistance to traditional inhibitors by mutating the binding site of the target protein. Since PROTACs rely on protein degradation rather than inhibition, they may help overcome resistance mechanisms by removing the mutated protein altogether.

Targeting Multiple Protein Isoforms:

PROTACs can potentially degrade different isoforms of the same protein, allowing for more comprehensive control of protein function in diseases where multiple isoforms are implicated.

Limitations and Challenges of PROTACs:

Target and E3 Ligase Selection:

The effectiveness of a PROTAC depends on the availability of a suitable E3 ligase for a specific target. Not all E3 ligases are expressed in all cell types, and the wrong choice of ligase can reduce the efficacy of degradation.

Linker Optimization:

The design of the chemical linker between the target-binding ligand and the E3 ligase ligand is critical. The linker must allow proper formation of the ternary complex but can also affect the drug’s pharmacokinetics and cell permeability.

Pharmacokinetics and Bioavailability:

PROTAC molecules tend to be larger and more complex than traditional small-molecule drugs, which can present challenges in terms of drug delivery, stability, and cellular permeability.

Potential Off-Target Effects:

Since PROTACs can lead to protein degradation, careful design is needed to ensure that they do not inadvertently degrade other proteins, which could lead to unwanted side effects.

Proteasome Dependency:

PROTACs rely on the cellular proteasome machinery for degradation. If proteasome function is impaired (e.g., in certain cancer cells), the effectiveness of PROTACs may be reduced.

Therapeutic Applications of PROTACs:

Cancer Therapy:

Many cancers are driven by the overexpression or abnormal function of certain proteins (e.g., oncogenes, transcription factors). PROTACs can target these proteins for degradation, potentially offering a new approach to cancer treatment. Examples include PROTACs that degrade BRD4 (a bromodomain-containing protein involved in cancer) and androgen receptor (AR) in prostate cancer.

Neurodegenerative Diseases:

Neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s diseases are often associated with the accumulation of misfolded or aggregated proteins. PROTACs could potentially be used to degrade these toxic proteins and prevent their buildup in neurons.

Infectious Diseases:

PROTACs have the potential to degrade viral or bacterial proteins essential for pathogen survival, offering a novel approach to treating infections.

Autoimmune and Inflammatory Diseases:

By degrading key proteins involved in the immune response, PROTACs could be used to modulate overactive immune signaling in diseases like rheumatoid arthritis or lupus.

Examples of PROTACs in Development

  1. ARV-110 (Arvinas): A PROTAC designed to target the androgen receptor (AR) for degradation in prostate cancer. ARV-110 is currently in clinical trials for metastatic castration-resistant prostate cancer.

 

  1. ARV-471 (Arvinas): A PROTAC targeting the estrogen receptor (ER) in breast cancer, currently in clinical trials for ER-positive breast cancer.

 

  1. DT2216 (Dialectic Therapeutics): Targets BCL-XL, a pro-survival protein overexpressed in many cancers. BCL-XL inhibitors are associated with toxicity, but PROTAC-mediated degradation of BCL-XL is expected to have fewer side effects.

 

 Conclusion:

PROTACs represent a promising new class of drugs that can degrade disease-causing proteins, offering an innovative approach to treating a variety of diseases. By using the cell’s natural protein degradation machinery, PROTACs can potentially overcome many of the limitations associated with traditional drug therapies, such as resistance and off-target effects. As research progresses, PROTACs are poised to transform the landscape of drug development, particularly in areas like oncology, neurodegenerative diseases, and beyond.

031 月/25

KMD Bioscience-Antibody Affinity Measurement

Antibody Discovery

Antibody affinity measurement is critical for understanding how tightly an antibody binds to its target antigen, which is key to determining its effectiveness in biological assays, therapeutic applications, and vaccine development. Several techniques can be used to measure the affinity of antibodies for their antigens, with each method offering different advantages depending on the type of experiment, the nature of the antibody, and the binding conditions.

Key Terminology

Affinity: The strength of the interaction between a single antigen-binding site of an antibody and its corresponding epitope on an antigen.

KD (Dissociation Constant): A quantitative measure of antibody affinity. Lower KD values indicate higher affinity (stronger binding), while higher KD values indicate lower affinity (weaker binding).

Techniques for Measuring Antibody Affinity

Surface Plasmon Resonance (SPR)

Principle: SPR measures real-time binding interactions between an antibody and an antigen immobilized on a sensor chip. As the antibody binds to the antigen, changes in the refractive index on the chip surface are detected, providing information on both the association (on-rate) and dissociation (off-rate) of the antibody-antigen interaction.

KD Calculation: The KD value is calculated as the ratio of the dissociation rate constant (k_off) to the association rate constant (k_on):

KD = Koff \ Kon

 Advantages

Provides both kinetic and equilibrium affinity data.

Can measure interactions in real-time without labeling.

Suitable for a wide range of affinities, including very low KD values.

 Disadvantages

Requires specialized equipment (e.g., Biacore).

Immobilization of antigens may affect the interaction depending on orientation and density.

Applications

Frequently used in drug development, therapeutic antibody screening, and protein-protein interaction studies.

Biolayer Interferometry (BLI)

Principle: Similar to SPR, BLI measures the interference pattern of light reflected from the surface of a sensor where an antigen is immobilized. When an antibody binds to the antigen, the interference pattern shifts, and both association and dissociation rates can be determined.

KD Calculation: Like SPR, KD is calculated as the ratio of the dissociation rate (k_off) to the association rate (k_on).

  Advantages

Label-free, real-time measurement.

Does not require extensive sample preparation.

Works well in a wide variety of buffers and experimental conditions.

 Disadvantages

Immobilization on the sensor surface can affect binding properties.

  Applications

Used for antibody affinity measurement in drug discovery, protein engineering, and structural biology.

Enzyme-Linked Immunosorbent Assay (ELISA) for Affinity

Principle: ELISA can be adapted to estimate antibody affinity by measuring the interaction between an antigen and antibody under equilibrium conditions. A high-affinity antibody will remain bound to its antigen even at low concentrations of antigen, while low-affinity antibodies will dissociate more easily.

KD Estimation:

One common approach is to coat an antigen on an ELISA plate, incubate with various concentrations of the antibody, and then detect bound antibodies with a labeled secondary antibody.

A Scatchard plot or nonlinear regression analysis can be used to estimate the KD from the ELISA binding curves.

 Advantages

Simple and widely available method.

Does not require specialized equipment.

Disadvantages

Does not provide real-time kinetic data (association/dissociation rates).

The affinity measurements are approximate, and this method is less sensitive for very high-affinity antibodies.

Applications

Used for routine affinity estimation in antibody development, including hybridoma screening.

Equilibrium Dialysis

Principle: Equilibrium dialysis is a classic method where an antibody and its antigen are separated by a semipermeable membrane that allows free diffusion of the antigen. The system is allowed to reach equilibrium, and the concentration of free antigen and antigen-bound antibody is measured on both sides of the membrane.

KD Calculation: The KD is determined from the ratio of bound to free antigen at equilibrium.

Advantages

Provides an accurate measure of KD, particularly for small antigens such as peptides or small molecules.

Disadvantages

Time-consuming and requires significant sample material.

Not suitable for real-time measurement or large antigens.

Applications

Primarily used for small molecules and peptide-antibody interactions.

Isothermal Titration Calorimetry (ITC)

Principle: ITC measures the heat released or absorbed during the interaction between an antibody and an antigen. The heat change is directly proportional to the amount of binding, allowing for the calculation of the binding affinity.

KD Calculation: The ITC experiment provides a direct measure of the binding constant (KD), as well as the stoichiometry (n), enthalpy (ΔH), and entropy (ΔS) of the interaction.

Advantages

Label-free and provides thermodynamic information (ΔH, ΔS).

Can measure very weak to strong binding affinities.

Disadvantages

Requires a large amount of sample (milligram quantities).

Less suitable for very high-affinity interactions (low KD values).

 Applications

Widely used in biophysics to study antibody-antigen interactions and in the development of monoclonal antibodies.

Fluorescence Polarization (FP)

Principle: Fluorescence polarization measures the binding affinity between a fluorescently labeled antigen and an antibody. When the fluorescent antigen is free in solution, it rotates quickly, resulting in low polarization of emitted light. When the antigen binds to an antibody, the rotation slows, leading to higher polarization.

KD Calculation: The KD is determined by measuring the polarization of light as a function of increasing antibody concentration.

Advantages

Fast and suitable for high-throughput screening.

Works well for small molecules and peptides.

 Disadvantages

Requires labeling of the antigen, which may affect binding.

Limited to interactions with small or medium-sized antigens.

 Applications

Often used for small molecule-protein interactions, peptide-antibody interactions, and screening assays.

Flow Cytometry for Affinity Measurement (FACS-Based Binding Assays)

Principle: Flow cytometry can be adapted to measure the binding of antibodies to cell-surface antigens by incubating cells with varying concentrations of fluorescently labeled antibodies. The mean fluorescence intensity (MFI) is proportional to the binding of the antibody to the antigen.

KD Calculation: By plotting MFI as a function of antibody concentration, the KD can be estimated using nonlinear regression analysis.

Advantages

Allows measurement of antibody binding in the context of live cells and membrane-bound antigens.

Provides single-cell resolution and can be used for different cell populations simultaneously.

Disadvantages

Limited to membrane-bound antigens or antigens expressed on the surface of cells.

 Applications

Used in immunology for measuring antibody affinities to cell-surface receptors or other membrane-associated antigens.

 Comparison of Techniques for Antibody Affinity Measurement

Method KD Range Kinetics Sample Size Real-Time Labeling Applications
SPR (Surface Plasmon Resonance) pM–mM Yes Low Yes No Kinetic analysis, therapeutic antibody screening
BLI (Biolayer Interferometry) pM–mM Yes Low Yes No Drug discovery, protein interaction studies
ELISA (for affinity) nM–μM No Low No No Routine affinity screening
Equilibrium Dialysis pM–mM No High No No Peptide-antibody interactions
Isothermal Titration Calorimetry (ITC) nM–mM No High No No Thermodynamic studies, antibody development
Fluorescence Polarization nM–μM No Low No Yes Small molecule-protein interactions
Flow Cytometry (FACS-based) nM–μM No Medium No Yes Antibody binding to cell-surface antigens

Conclusion

The choice of technique for measuring antibody affinity depends on the nature of the interaction, the type of antigen (soluble or membrane-bound), and the resources available. SPR and BLI are popular for real-time kinetic studies, while ELISA and ITC are commonly used for equilibrium affinity measurements. Each method has its strengths and limitations, so researchers often choose the technique that best fits their experimental design and desired information.

2712 月/24

KMD Bioscience-Affinity Drug Screening

Antibody Discovery

Affinity drug screening is a process used to identify and evaluate the binding strength of small molecules (potential drugs) to biological targets, such as proteins, enzymes, or receptors. The goal of affinity drug screening is to discover compounds that exhibit high affinity and specificity for the target, which is a key indicator of their potential effectiveness as drugs. This method is widely used in drug discovery and development to identify lead compounds for further testing and optimization.

 Key Concepts in Affinity Drug Screening:

Affinity:

Refers to how tightly a drug (ligand) binds to its target (usually a protein or receptor). High-affinity binding means the drug stays attached to the target for a longer time, increasing its potential efficacy.

Target:

Typically a protein (e.g., enzyme, receptor, or transporter) associated with a disease. The drug must bind specifically to the target to modulate its activity, either by inhibiting (antagonist) or enhancing (agonist) the target’s function.

Ligand:

The small molecule or drug candidate being tested for its ability to bind to the target.

Binding Kinetics:

Association rate (kon): The speed at which the drug binds to the target.

Dissociation rate (koff): The speed at which the drug unbinds from the target.

Dissociation constant (KD): A measure of binding affinity, calculated as KD = Koff\Kon. Lower ( KD ) values indicate higher affinity.

 Methods Used for Affinity Drug Screening:

Surface Plasmon Resonance (SPR)

Principle: SPR detects changes in refractive index as a drug binds to its target protein immobilized on a sensor chip. This method provides real-time data on the association and dissociation rates, allowing for the calculation of binding affinity and kinetics.

Application: SPR is used to screen large libraries of drug candidates, helping identify molecules with high affinity and optimal binding kinetics.

Advantages: Label-free and provides detailed kinetic information.

Disadvantages: Requires specialized equipment and can be costly.

Isothermal Titration Calorimetry (ITC)

Principle: ITC measures the heat released or absorbed when a drug binds to its target. This method provides thermodynamic data, including binding affinity, stoichiometry, and enthalpy/entropy changes.

Application: ITC is useful for studying the thermodynamic properties of drug binding, offering insight into the molecular forces driving the interaction.

Advantages: Label-free, no immobilization required, and provides comprehensive thermodynamic profiles.

Disadvantages: Requires high sample concentrations and is less suitable for high-throughput screening.

Fluorescence Polarization (FP)

Principle: FP measures the binding affinity of a drug to its target by monitoring changes in the polarization of fluorescence emitted from a labeled ligand. When a small, fluorescently labeled ligand binds to a larger target protein, its rotation slows, increasing the polarization of the emitted light.

Application: Widely used for high-throughput screening (HTS) to quickly assess the binding affinity of many drug candidates.

Advantages: High-throughput, sensitive, and suitable for screening large libraries.

Disadvantages: Requires fluorescent labeling of the ligand.

Biolayer Interferometry (BLI)

Principle: BLI is similar to SPR, where drug-target interactions are measured by changes in interference patterns of light. The target is immobilized on a biosensor, and drug binding is monitored in real-time.

Application: Used for kinetic studies and affinity screening, especially for identifying strong binders in a drug library.

Advantages: Label-free, real-time measurements, and applicable for HTS.

Disadvantages: Requires specialized equipment and may have lower sensitivity compared to SPR.

Differential Scanning Calorimetry (DSC)

Principle: DSC measures changes in the thermal stability of a protein when a drug binds to it. Binding often stabilizes the protein, leading to a shift in the temperature at which the protein unfolds (denatures).

Application: Used to screen for binding interactions and assess drug-induced stabilization of the target protein.

Advantages: Label-free and provides information on the thermal stability of the protein-ligand complex.

Disadvantages: Requires large amounts of protein and is less suited for high-throughput screening.

Equilibrium Dialysis

Principle: A semipermeable membrane separates a small molecule drug from its target. The drug is allowed to diffuse across the membrane, and after equilibrium is reached, the concentration of bound and free drug is measured.

Application: Used to calculate binding constants for drug-target interactions.

Advantages: Simple and does not require specialized equipment.

Disadvantages: Time-consuming and not suitable for real-time analysis or HTS.

High-Throughput Screening (HTS) Using ELISA-Based Methods

Principle: ELISA (enzyme-linked immunosorbent assay) detects drug-target interactions using a colorimetric, fluorescent, or luminescent readout. Targets are immobilized on a surface, and drug binding is detected using enzyme-linked secondary antibodies.

Application: Commonly used in HTS campaigns to screen large libraries of compounds for those that bind to the target of interest.

Advantages: Suitable for screening large numbers of compounds quickly and relatively inexpensive.

Disadvantages: Indirect, requires labeling, and provides less information about binding kinetics.

Nuclear Magnetic Resonance (NMR)

Principle: NMR spectroscopy can detect direct binding of small molecules to target proteins by monitoring changes in the chemical shifts of nuclei (usually hydrogen) within the drug or protein.

Application: NMR is useful for detecting weak binding interactions and for structural analysis of drug-target complexes.

Advantages: Provides structural information about the binding site and affinity.

Disadvantages: Requires high concentrations of the drug and protein, and it’s less suited for HTS.

Factors Affecting Drug Affinity:

  1. Molecular Structure: Small changes in the structure of the drug can significantly impact binding affinity. Structure-activity relationship (SAR) studies help optimize these interactions.
  2. Binding Site Flexibility: The dynamics of the protein’s binding site can influence how well a drug fits and binds, affecting the overall affinity.
  3. Non-Covalent Interactions: Hydrogen bonding, van der Waals forces, electrostatic interactions, and hydrophobic effects all contribute to binding affinity.
  4. Solvent Effects: The surrounding solvent, such as water or ions in solution, can alter the binding affinity by stabilizing or destabilizing the drug-target complex.

 Applications of Affinity Drug Screening:

  1. Lead Compound Identification: Screening libraries of small molecules to find those with high affinity for a disease-related target.
  2. Optimization of Drug Candidates: Modifying the structure of identified leads to improve affinity, specificity, and pharmacokinetics.
  3. Selectivity Studies: Screening for affinity to off-targets to assess potential side effects and drug safety.
  4. Biophysical Characterization: Understanding the thermodynamics and kinetics of drug binding to optimize drug design.

Affinity drug screening is a critical step in the drug discovery pipeline, helping to identify and optimize compounds that may become therapeutic agents. The use of advanced technologies like SPR, ITC, and BLI provides detailed insights into drug-target interactions, allowing researchers to develop more effective and selective drugs.