How strong is the binding affinity of antibodies produced in humans?5 answersAntibodies produced in humans exhibit varying binding affinities. Research shows that during affinity maturation, the average affinity between an antibody and its ligand increases over time, leading to a decrease in heterogeneity of affinity values. Additionally, high-affinity antibodies targeting specific proteins like PD-1 and LAG-3 have been developed for cancer treatment. Techniques such as engineering bivalent antibody-binding proteins have been employed to enhance the stability and affinity of antibody complexes for functionalization purposes. Human antibodies with high affinity for nerve growth factor have been identified, showing superior binding to human NGF compared to rodent NGF. Furthermore, in the context of anti-infective therapeutics, natural human antibodies undergo in vivo affinity maturation, with the potential for further in vitro affinity enhancement when needed.
What are the recent advances in antibody drug development?5 answersRecent advances in antibody drug development include the application of artificial intelligence (AI) technology for antibody design and discovery, leading to significant progress in antibody discovery, optimization, and developability. Additionally, there has been a focus on the development of novel antibody-drug conjugate (ADC) payloads with diverse targets and reduced side effects. Computational and AI technologies are being integrated into antibody development pipelines, providing tools for predicting antibody properties, structure, and designing critical binding components. In the field of urothelial cancer treatment, ADCs targeting specific antigens have been approved by the US FDA and are being tested in different disease settings, showing promising clinical benefit. Understanding mechanisms of resistance and response, managing adverse events, and optimizing sequencing and combination strategies with existing therapies are areas of ongoing research.
What are the advantages and disadvantages of each tool?3 answersBig data tools have advantages and disadvantages. The advantages include managing complex and voluminous data, providing deep insight into big data tools' applicability in real-time environments, and helping researchers explore big data sets according to their potentiality. On the other hand, the disadvantages of big data tools include the need for in-depth knowledge of existing tools, the potential difficulty of learning how to use the tools, and the fact that some tools may have limited value in certain situations. Similarly, requirements elicitation tools have their own advantages and disadvantages. The advantages include helping systems engineers express desires and wants, and the ability to be applied to hardware, software, or soft systems. However, the disadvantages include the need for learning how to use the tools, and the fact that the tools may have varying levels of value depending on the product being developed.
Cyclic peptides bind as well as antibodies?5 answersCyclic peptides, such as those generated through directed evolution using phage display, can bind with high affinity, similar to antibodies. These peptides can be stabilized by promoting noncovalent intramolecular interactions, which contribute to their high binding affinity. Additionally, cyclic peptides can be designed to target specific proteins with high affinity and specificity, similar to monoclonal antibodies. The use of cyclic peptide scaffolds allows for precise control over their size and shape, enabling the positioning of binding residues in a preorganized manner. Therefore, cyclic peptides have the potential to serve as effective ligands with high binding affinity, comparable to antibodies, while offering advantages such as smaller size, chemical synthesis, and efficient diffusion into tissues.
What techniques can be used to understand the binding of ligand with the protein?5 answersSeveral techniques can be used to understand the binding of a ligand with a protein. High-resolution structural approaches, such as X-ray crystallography, provide a static view of the protein-ligand complex. Molecular docking simulations, based on force fields like Martini, can accurately predict binding sites and pathways, as well as binding free energies. Big Data analytics techniques, including machine learning and deep learning, can efficiently process a large number of protein-ligand complexes and identify specific protein properties responsible for correct prediction of binding. Differential scanning calorimetry (DSC) can determine the enthalpy change upon protein unfolding and the melting temperature of the protein, providing information on ligand binding. These techniques, along with thermodynamic treatments and experimental designs, can enhance our understanding of protein-ligand interactions.
Do antibodies bind B cells?3 answers