IDE Human, Active

What is human Insulin-Degrading Enzyme and what is its primary function?

Insulin-Degrading Enzyme (IDE), also known as insulysin or insulin protease, is a large zinc-binding protease belonging to the M16 metalloprotease family. It is primarily recognized for its ability to cleave multiple short polypeptides with varied sequences. IDE was initially identified for its capability to degrade the B chain of insulin, an activity observed over sixty years ago, though the specific enzyme responsible was identified more recently . Beyond insulin, IDE has demonstrated the ability to hydrolyze several other peptide hormones, including glucagon, amylin, TGF alpha, β-endorphin, and the amyloid β-protein, making it a multifunctional enzyme with significant physiological implications .

What is the structural composition of human IDE?

The human gene IDE encodes the Insulin-degrading enzyme protein and is located at chromosome band 10q23-q25, containing 28 exons . Due to alternative splicing, human IDE exists in two isoforms. Isoform 1 is approximately 118 kDa in size and composed of 1019 amino acids, while isoform 2 is smaller at approximately 54.2 kDa and consists of 464 amino acids (missing amino acids 1-555). The calculated theoretical isoelectric point (pI) of this protein isoform is 6.26 .

Structurally, IDE features defined N and C terminal units that form a proteolytic chamber containing the zinc-binding active site. The enzyme can exist in two conformational states: an open conformation allowing substrates to access the active site, and a closed state where the active site is contained within the chamber formed by two concave domains .

What role does zinc play in IDE's mechanism of action?

The catalytic mechanism of IDE involves a zinc-bound hydroxide group performing a nucleophilic attack on a carbon substrate, forming an intermediate (INT1). In this species, the zinc-bound hydroxide is critical to the enzyme's proteolytic activity . As a mononuclear Zn²⁺-dependent metalloenzyme, the zinc ion is essential for IDE's function in hydrolyzing various peptide hormones . The zinc-binding active site is located within the proteolytic chamber formed by the N and C terminal units of the enzyme structure .

How do conformational changes in IDE affect its catalytic activity, and what are the implications for therapeutic development?

Research has revealed that IDE undergoes significant conformational changes between open and closed states, directly impacting its catalytic efficiency. Structural studies by Shen et al. have demonstrated that targeted mutations favoring the open conformation result in a remarkable 40-fold increase in catalytic activity . This finding has substantial implications for therapeutic development, particularly for conditions like Alzheimer's disease.

The therapeutic approach proposed involves shifting IDE's conformational preference toward the open state, thereby increasing its ability to degrade amyloid β-protein (Aβ), preventing aggregation, and potentially mitigating the neuronal loss characteristic of Alzheimer's disease . This strategy represents a novel approach to addressing neurodegenerative conditions through enzyme conformational modulation rather than traditional inhibitory mechanisms.

What is the selective inhibition profile of hydroxypyridinethione derivatives against IDE, and how might this inform substrate-selective therapeutic approaches?

Recent research has identified 1-hydroxypyridine-2-thione (1,2-HOPTO) as an effective Zn²⁺-binding scaffold for developing IDE inhibitors, with 3-sulfonamide derivatives demonstrating Ki values of approximately 50 μM . Further structure-activity relationship (SAR) studies yielded several thiophene-sulfonamide HOPTO derivatives with broad-spectrum activity against IDE.

Most significantly, screening of these compounds against multiple IDE substrates revealed varied degrees of substrate selectivity, including:

• Three compounds showing amylin-sparing activity

• Two compounds demonstrating selectivity for glucagon

• Five compounds that actually activated the degradation of substrate V

This substrate-selective inhibition profile opens avenues for developing therapeutic agents that could inhibit insulin degradation while preserving IDE activity against other substrates. Such an approach could theoretically provide a novel pharmacological strategy to boost insulin signaling for treating Type 2 Diabetes Mellitus (T2DM) without compromising IDE's activity against other physiologically relevant substrates like amyloid β-protein .

What methodological approaches are most effective for screening zinc-targeting IDE inhibitors?

A successful methodological approach for screening zinc-targeting IDE inhibitors involves a multi-step process:

• Initial library screening: A library of ~350 metal-binding pharmacophores was screened against IDE to identify effective Zn²⁺-binding scaffolds .

• Multi-substrate screening protocol: Compounds were tested against five different substrates (insulin, glucagon, amylin, Aβ, and the synthetic fluorogenic peptide, substrate V) at fragment concentrations of 500 and 50 μM .

• Dose-response experiments: For compounds showing good inhibitory activity, Ki values were established through dose-response experiments across all five substrates .

• Chemical synthesis and modification: For lead compounds like 1,2-HOPTO (compound 5), focused libraries were created through systematic modifications. For instance, sulfonamide derivatives were synthesized, and various substituents were explored to establish structure-activity relationships .

• Consideration of oxidation states: Some HOPTO products were isolated as mixtures with oxidized dimers (through the sulfur atom). These dimers require reversion to the active thione monomer in the presence of DTT as a reductant, which conveniently serves the dual function of also ensuring reduction of IDE's 13 Cys residues in biochemical assays .

What are the key user expectations for AI-driven tools within Integrated Development Environments?

Research indicates that users expect AI-driven tools integrated within Integrated Development Environments (IDEs) to possess several essential qualities. These include efficiency, context awareness, accuracy, user-friendliness, customizability, and security . These expectations form the foundation for successful human-AI interaction within development environments.

Understanding these expectations is crucial for designing effective AI systems that improve developer productivity without introducing friction into established workflows. Research methodologies targeting these aspects typically involve structured interviews, user surveys, and observational studies to gather comprehensive insights into developer needs and preferences .

How are different user groups characterized in their interaction with AI in IDEs?

Research has identified three distinct user groups in relation to AI in IDEs:

• Adopters: Users who have integrated AI tools into their regular development workflow. This group appreciates advanced features and non-interruptive integration of AI systems with their existing processes .

• Churners: Users who have tried AI tools but subsequently abandoned them. Their primary concerns focus on the need for improved reliability and privacy protections in AI systems .

• Non-Users: Individuals who have not incorporated AI tools into their development processes. Their resistance often stems from skill development concerns and ethical considerations regarding AI implementation .

These categorizations provide a framework for understanding the varied perspectives and requirements across the developer spectrum, informing more targeted design and implementation strategies for AI in IDEs .

What are the key dimensions of the design space for in-IDE Human-AI experiences, and how do they impact developer productivity?

Research has identified several critical dimensions of the design space for in-IDE Human-AI experiences:

• Technology Improvement: This dimension encompasses advancements in the underlying AI technologies that power in-IDE tools, including performance optimization and feature enhancement .

• Interaction: This dimension focuses on how developers engage with AI systems, including interface design, feedback mechanisms, and command structures .

• Alignment: This dimension addresses how well AI tools integrate with existing workflows, project requirements, and team dynamics .

• Skill Building Simplification: This dimension examines how AI tools can facilitate learning and skill development among developers .

Each of these dimensions significantly impacts developer productivity through various mechanisms. For instance, well-aligned AI tools that integrate seamlessly with existing workflows reduce cognitive load and context switching, while interactive systems with appropriate feedback loops enhance developer learning and confidence in AI-generated solutions .

What methodological approaches effectively capture the nuanced requirements of different user groups interacting with AI in IDEs?

Research into human-AI interaction in IDEs employs several methodological approaches to capture the nuanced requirements of different user groups:

• Structured interviews: Conducting in-depth interviews with developers from different user categories (Adopters, Churners, and Non-Users) provides qualitative insights into preferences, pain points, and expectations .

• Comparative analysis: Systematically analyzing responses across user groups to identify commonalities and differences in requirements and concerns helps tailor solutions to specific user segments .

• Design space mapping: Creating comprehensive frameworks that categorize findings into key areas such as Technology Improvement, Interaction, Alignment, and Skill Building provides a structured approach to understanding the multifaceted nature of human-AI interaction in development environments .

• Contextual inquiry: Observing developers in their natural work environment while they interact with AI tools provides insights that might not emerge through direct questioning .

These approaches collectively enable researchers to develop a holistic understanding of the complex interactions between developers and AI systems in IDEs, accounting for the diverse needs of different user populations .

How can structure-function relationship studies be designed to inform both IDE inhibitor development and human-AI interaction improvements?

Structure-function relationship studies can be designed to inform both research areas through parallel methodological approaches:

For IDE inhibitor development:

• Systematic modification of inhibitor scaffolds (such as the HOPTO derivatives) to establish structure-activity relationships

• Evaluation across multiple substrates to identify selective inhibition profiles

• Analysis of how structural modifications influence binding to the active site zinc ion

For human-AI interaction improvements:

• Systematic modification of AI interface components to establish structure-usability relationships

• Evaluation across multiple user groups (Adopters, Churners, Non-Users) to identify group-specific preferences

• Analysis of how structural modifications in AI systems influence user engagement and productivity

Both approaches benefit from iterative design-test-refine cycles, where initial findings inform subsequent modifications, creating a progressive improvement path driven by empirical evidence. This methodological parallel highlights how principles of structure-function analysis can be applied across distinctly different research domains.

What analytical frameworks can be applied to both IDE conformational studies and AI system adaptation studies?

Several analytical frameworks can be effectively applied to both research areas:

• State-transition analysis: In IDE research, this involves studying transitions between open and closed conformational states and their impact on catalytic activity . In AI systems research, this involves analyzing transitions between different operational modes and their impact on user productivity .

• Adaptive response evaluation: For IDE, this examines how the enzyme responds to different substrates by adapting its conformation . For AI systems, this examines how the system adapts to different user inputs and contexts .

• Intervention efficacy assessment: In IDE research, this measures how interventions (like mutations) that favor specific conformations affect enzymatic activity . In AI research, this measures how design interventions that favor specific interaction modes affect user experience and productivity .

These shared analytical frameworks demonstrate the conceptual parallels between biomolecular and computational systems, both of which exhibit complex adaptive behaviors in response to inputs. By applying similar analytical lenses to these distinct domains, researchers can potentially transfer insights between fields and accelerate progress in both areas.