Peptide Hormone Binding Protein Design
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Questions and Answers

What are peptide hormones known to adopt upon binding their receptors?

  • α-helical structures (correct)
  • Non-helical structures
  • Random coils
  • β-sheets

The RFdiffusion method can only generate binders with high affinity for structured proteins.

False (B)

What is the main challenge in designing interactions between proteins and short peptides?

The design of interactions is an unmet challenge due to their helical propensity.

Peptide hormones, such as parathyroid hormone (PTH) and neuropeptide Y (NPY), play key roles in _____ biology.

<p>human</p> Signup and view all the answers

Match the peptide hormone with its correct description:

<p>PTH = Regulates calcium levels NPY = Involved in appetite regulation GCG = Increases blood sugar levels SCT = Stimulates bile secretion</p> Signup and view all the answers

Which method is mentioned for the sensitive detection of peptide hormones?

<p>Mass spectrometry (B)</p> Signup and view all the answers

Antibodies are considered highly effective for quantifying all types of peptide hormones.

<p>False (B)</p> Signup and view all the answers

What is a disadvantage of using antibodies for peptide hormone detection?

<p>They require substantial resources to generate and often have less-than-desirable stability and reproducibility.</p> Signup and view all the answers

What is a challenge in designing proteins that bind helical peptides?

<p>Peptides are often unstructured in isolation (A)</p> Signup and view all the answers

The design of proteins that bind folded proteins is less challenging than that of those binding helical peptides.

<p>True (A)</p> Signup and view all the answers

Name one method used for designing proteins that bind helical peptides.

<p>Parametric generation or deep learning methods</p> Signup and view all the answers

Peptides with _____ residues are often challenging to design binding proteins for due to their flexibility.

<p>fewer</p> Signup and view all the answers

What principle has been applied to design binders for calmodulin peptides?

<p>Coiled-coil assemblies (D)</p> Signup and view all the answers

Hydrophobic surfaces in proteins increase the binding affinity for target peptides.

<p>False (B)</p> Signup and view all the answers

What are the internal interactions of α-helical peptides primarily composed of?

<p>Backbone-hydrogen bonding</p> Signup and view all the answers

The binding affinity of designed proteins to targets was measured using a _____ assay.

<p>NanoBiT split-luciferase</p> Signup and view all the answers

Which method was shown to rapidly generate tight affinity binders with picomolar Kd values?

<p>RFdiffusion (D)</p> Signup and view all the answers

The Hallucination approach requires pre-specification of the bound structures to generate high-affinity binders.

<p>False (B)</p> Signup and view all the answers

Match the following peptide binding approaches with their descriptions:

<p>Parametric generation = Sampling designed scaffolds with specified shapes Deep learning = Exploring sequences without pre-specified geometries Hallucination methods = Optimizing sequences for desired structural properties Threading approach = Adapting an existing structure to fit a new target</p> Signup and view all the answers

Which of the following peptide binding targets was mentioned in the content?

<p>Gastric inhibitory peptide (D)</p> Signup and view all the answers

What key strategy was used to achieve a more uniform distribution of supercoiling in the scaffold library?

<p>biased sampling strategy</p> Signup and view all the answers

High affinity for designed proteins was achieved in all peptide binding cases mentioned.

<p>False (B)</p> Signup and view all the answers

RFjoint designs were validated and filtered in silico by _____ with an initial guess.

<p>AF2</p> Signup and view all the answers

What characterizes the proteins designed to bind helical peptides?

<p>Helical bundles with an open groove</p> Signup and view all the answers

Match the following methods with their applications:

<p>RFjoint = Designing diversity in peptide binders RFdiffusion = Generating tight affinity binders Hallucination = Creating binders without pre-specification Rosetta = Filtering and scoring designs</p> Signup and view all the answers

_____ measures the predicted alignment error across the interface in the Hallucination methods.

<p>pAE</p> Signup and view all the answers

What kind of library was sampled using a random sampling approach?

<p>Parametric groove-shaped scaffold library (D)</p> Signup and view all the answers

Which of the following factors complicates the design of peptide-binding proteins?

<p>Limited interaction sites on peptides (A)</p> Signup and view all the answers

Heterogeneous amino acid selections were achieved by independent random selections of helix distances.

<p>True (A)</p> Signup and view all the answers

The method of sampling scaffolds allows for exploring variations in target binding designs.

<p>True (A)</p> Signup and view all the answers

What specific method was used to extend the binding interface of the putative binder?

<p>RFjoint Inpainting network</p> Signup and view all the answers

The RFdiffusion method improves upon starting designs by partial ______ and denoising.

<p>noising</p> Signup and view all the answers

Which proteins were used as input for the RFjoint design?

<p>PTH, GCG and NPY (D)</p> Signup and view all the answers

The Rosetta-designed binders were filtered on metrics that included buried polar surface area per residue.

<p>False (B)</p> Signup and view all the answers

What is the purpose of the Rosetta ConnectChainsMover in the design process?

<p>To loop scaffold backbones</p> Signup and view all the answers

The resulting designs were influenced by the threaded target sequence and the _______ filtering process.

<p>gate</p> Signup and view all the answers

What was the primary outcome of using RFdiffusion in the study?

<p>Creating high-affinity binders from random noise (B)</p> Signup and view all the answers

What percentage did the average contact molecular surface for the partially diffused GCG binders increase?

<p>33% (C)</p> Signup and view all the answers

What was the limit of detection (LOD) for the lucCagePTH biosensor?

<p>10 nM (C)</p> Signup and view all the answers

The designed GCG binder exhibited lower recovery rates than the monoclonal antibody when normalized to 100%.

<p>False (B)</p> Signup and view all the answers

What advantage do the RFdiffusion-generated binders offer over traditional antibodies?

<p>Greater robustness and stability (A)</p> Signup and view all the answers

What approach provides a general route for creating high-shape-complementary binders?

<p>RFdiffusion de novo design</p> Signup and view all the answers

The PTH lucCage biosensor underwent a conformational change in the presence of _____ peptide target.

<p>PTH</p> Signup and view all the answers

The lucCagePTH sensor is designed for immunosensor applications.

<p>False (B)</p> Signup and view all the answers

The designed binders provide a more robust platform for diagnostics compared to traditional _____ sensors.

<p>immuno</p> Signup and view all the answers

Match the following peptide binders with their corresponding recovery rates:

<p>PTH binder = 53% GCG binder = 91.1% Antibody beads = 100% Unrelated binder = Lower recovery</p> Signup and view all the answers

Which property of designed binders contributes to cost reduction?

<p>Lower production costs (D)</p> Signup and view all the answers

What computational method is mentioned for designing helical peptide binders?

<p>Rosetta ddG</p> Signup and view all the answers

Designed scaffolds are less amenable to incorporation into sensors than antibodies.

<p>False (B)</p> Signup and view all the answers

During the experimentation, the trypsin digestion step was _____ for the GCG binder.

<p>skipped</p> Signup and view all the answers

What platform is used for detecting low-abundance protein biomarkers?

<p>LC–MS/MS (D)</p> Signup and view all the answers

What were the recovery rates of PTH in buffer and human plasma?

<p>53% in buffer and 43% in human plasma</p> Signup and view all the answers

What does the term 'partial diffusion' refer to in the context of protein design?

<p>A technique for enhancing structural diversity and binding affinity (C)</p> Signup and view all the answers

The GCG binder showed comparable binding affinities to both GCG and SCT.

<p>False (B)</p> Signup and view all the answers

What is the significance of adding noise during the partial diffusion process?

<p>It allows for diversification of the starting protein fold and improves binding affinities.</p> Signup and view all the answers

The binding affinity of the final designs to NPY was measured to be ______ nM.

<p>5.6</p> Signup and view all the answers

What is a primary advantage of using Hallucination for binder design?

<p>It enables the design of binders to peptides in different conformations. (B)</p> Signup and view all the answers

The Bid peptide is structured even in isolation.

<p>False (B)</p> Signup and view all the answers

Match the following terms with their descriptions:

<p>Kd = Dissociation constant reflecting binding affinity AF2 = A model used for protein structure prediction FP = Fluorescence polarization, a method to measure binding Ile13 = An isoleucine residue that enhances binding through shape complementarity</p> Signup and view all the answers

Which of the following statements accurately describes the outcomes of applying partial diffusion?

<p>It allows small structural modifications that enhance protein interaction. (A)</p> Signup and view all the answers

What was the purpose of optimizing for confident binding to the target peptide during the design process?

<p>To ensure that the designed binders effectively bind to the Bid peptide.</p> Signup and view all the answers

The Bid peptide showed increased __________ on interaction with Hallucinated proteins.

<p>helicity</p> Signup and view all the answers

The designs created from the original Inpainted binders showed poorer binding efficacy after undergoing partial diffusion.

<p>False (B)</p> Signup and view all the answers

Match the following terms with their descriptions:

<p>Hallucination = Design method optimizing for binding metrics Bid peptide = Apoptosis-related protein subject to binding designs RFdiffusion = Generative process for protein structure refinement Monte Carlo search = Method used for exploring sequence space</p> Signup and view all the answers

What does RMSD stand for, and how was it relevant in assessing the binding models?

<p>RMSD stands for Root Mean Square Deviation; it was used to measure the alignment accuracy of the modeled structures against crystal structures.</p> Signup and view all the answers

What was the observed result of the binding affinity measurements?

<p>The binder had a binding affinity of 7 nM to the Bid peptide. (C)</p> Signup and view all the answers

The crystal structure alignments for the GCG binder indicated a shift of _____ Ã… towards the target in the binder backbone.

<p>2.7</p> Signup and view all the answers

What percentage of the highest-affinity designs was successful in binding GCG in initial screening?

<p>25% (C)</p> Signup and view all the answers

Four of the Hallucinated designs were further characterized and showed soluble, monomeric expression.

<p>True (A)</p> Signup and view all the answers

What is one of the challenges associated with traditional Rosetta-based design approaches?

<p>Deep conformational sampling is very compute intensive.</p> Signup and view all the answers

The binding affinity of the original Inpainted GCG binder was higher than that of the partially diffused binder.

<p>False (B)</p> Signup and view all the answers

What structural feature was introduced at residue 13 to improve shape complementarity in the GCG binder?

<p>Isoleucine was introduced to replace phenylalanine.</p> Signup and view all the answers

During the reverse diffusion process of RFdiffusion, random __________ is taken as input.

<p>Gaussian noise</p> Signup and view all the answers

The analysis showed a contact molecular surface increase from _____ Ų to _____ Ų due to partial diffusion.

<p>431, 522</p> Signup and view all the answers

What unique feature do many of the binders from this method contain?

<p>A well-defined groove (D)</p> Signup and view all the answers

RFdiffusion is less compute efficient than Hallucination.

<p>False (B)</p> Signup and view all the answers

Which of the following proteins did the study utilize to understand specificity changes mediated by partial diffusion?

<p>Colicin–immunity protein system (B)</p> Signup and view all the answers

What methodology was used to experimentally test the Hallucinated designs?

<p>Co-expression of a GFP-tagged Bid peptide and His-tagged binders.</p> Signup and view all the answers

Partial diffusion can only be applied to existing crystal structures.

<p>False (B)</p> Signup and view all the answers

Many designed binders were predicted to bind to Bid in a predominantly __________ conformation.

<p>helical</p> Signup and view all the answers

Which protein was used as a comparison for binding affinity?

<p>Bcl-2 protein Mcl-1 (C)</p> Signup and view all the answers

What is the primary goal of RFdiffusion in the binder design process?

<p>To design binders completely de novo (C)</p> Signup and view all the answers

RFdiffusion consistently requires the topology of binding proteins to be specified.

<p>False (B)</p> Signup and view all the answers

What type of secondary structure do the designed binders exhibit according to circular dichroism data?

<p>Helical secondary structure</p> Signup and view all the answers

RFdiffusion showed high specificity, as no binding was observed to ______.

<p>PTHrp</p> Signup and view all the answers

Match the binder types with their corresponding affinity measurements:

<p>PTH binder = &lt; 500 pM Bim binder = &lt; 500 pM PYY binder = 24.5 nM GCG binder = lower affinity</p> Signup and view all the answers

What information was initially provided to RFdiffusion for designing binders?

<p>Sequence and structures of peptides (D)</p> Signup and view all the answers

RFdiffusion can generate folded structures using only the amino acid sequence as input.

<p>True (A)</p> Signup and view all the answers

What feature of the designed binders indicates that they are structurally stable?

<p>Stability at 95 °C</p> Signup and view all the answers

The designed PTH and Bim binders were found to have a ______ affinity.

<p>picomolar</p> Signup and view all the answers

Match the design methodologies with their outcomes:

<p>Fixed target structure RFdiffusion = Designed high-affinity binders Flexible backbone RFdiffusion = Lower affinity than fixed structure Deep learning methods = Converged on helical peptide-binding designs Yeast surface display = Tested binding of designed peptides</p> Signup and view all the answers

What aspect did RFdiffusion incorporate to increase its effectiveness?

<p>Training on multi-chain systems from PDB (D)</p> Signup and view all the answers

The X-ray crystal structure of the Bim binder closely matched its design model.

<p>True (A)</p> Signup and view all the answers

What peptide was used to demonstrate flexibility in the binding process with RFdiffusion?

<p>PYY</p> Signup and view all the answers

The approach used to design binders to PYY resulted in ______ affinity than that in the fixed structure case.

<p>lower</p> Signup and view all the answers

Flashcards

Alpha-helix formation in peptide hormones

Peptide hormones like parathyroid hormone (PTH), neuropeptide Y (NPY), glucagon (GCG), and secretin (SCT) often adopt an alpha-helix shape when they bind to their receptors. This helical structure is important for their function and recognition by the receptors.

Biological importance of peptide hormones

Peptide hormones play crucial roles in human biology, acting as messengers in various processes. They are also used as biomarkers in clinical care and biomedical research, providing insights into disease states.

Importance of sensitive peptide hormone detection

Sensitive and specific methods for detecting and quantifying peptide hormones are in demand. This is because these hormones can inform us about various biological processes and diseases. Current methods often rely on antibodies, which have limitations.

Limitations of antibodies in peptide hormone detection

Antibodies, which are used to detect and bind to specific molecules, can be challenging to produce with high affinity, stability, and reproducibility for peptide hormones. They often bind to these hormones in non-helical forms, which may not reflect the functional state of the hormone.

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De novo protein design

De novo protein design is a powerful technique where scientists create new proteins with desired properties. It's a rapidly developing field with the potential to generate novel tools for research and medicine.

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RFdiffusion in protein design

RFdiffusion is a computational method used for designing proteins that bind to specific target molecules. It's particularly helpful because it can handle flexible targets, which means it can work with molecules that can change shape.

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Partial diffusion in protein design

Partial diffusion is a technique where the structure of a protein is repeatedly disturbed and then reconstructed. This process helps refine the design of the protein, leading to better binding properties.

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Designing binders for conformationally variable targets

The ability to design binders that can recognize and bind to conformationally variable molecules, like peptide hormones, is a significant breakthrough. This opens doors for developing better diagnostic and therapeutic tools.

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High Stability Protein Production

Designed proteins with high stability can be readily produced with high yield and low cost in Escherichia coli.

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Challenge of Binding Helical Peptides

Designing proteins that bind helical peptides with high affinity and specificity remains a challenge.

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Binding Folded vs. Extended Peptides

Proteins designed to bind folded proteins often have shapes suitable for binding rigid concave targets, not for cradling extended peptides.

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Coiled-Coil Assembly

Helical peptides can readily associate to form coiled-coil assemblies, but coiled-coil subunits generally self-associate in the absence of binding partners, reducing target-binding affinity.

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Entropic Cost of Peptide Binding

Peptides have fewer residues to interact with and are often partially or entirely unstructured in isolation, leading to an entropic cost of structuring the peptide into a specific conformation.

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Limitations of Alpha-Helix Binding

The extensive internal backbone-backbone hydrogen bonding in alpha-helical peptides makes it difficult to use protein side chains to interact with the peptide backbone.

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Parametric Approach to Peptide Binding

A parametric approach using helical bundle scaffolds with an open groove for a helical peptide could provide a general solution for designing helical peptide binders.

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Helical Bundle Scaffold Library

A library of helical bundle scaffolds with varying supercoiling and helix-helix spacings was created to accommodate a variety of helical peptide targets.

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Deep Learning Hallucination for Peptide Binding

Deep learning hallucination methods can generate helical peptide binders de novo, without pre-specifying the binder or peptide geometry.

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Hallucination Approach

Hallucination or 'activation maximization' approaches start from a network that predicts protein structure from sequence and optimize sequences for desired properties.

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Applications of Hallucination

Hallucination has been used to generate new monomers, functional-site scaffolds, and cyclic oligomers.

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Parametric Approach Results

The parametric approach successfully designed groove-shaped scaffolds that bind helical peptides, but improvements were needed for higher affinity binding.

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Deep Learning Hallucination Potential

Deep learning hallucination methods offer a promising avenue for designing helical peptide binders with high affinity and specificity.

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Combined Approaches for Peptide Binder Design

The combination of parametric and deep learning approaches can lead to more efficient and effective design of helical peptide binders.

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Implications of Helical Peptide Binders

The development of helical peptide binders with high affinity and specificity has important implications for drug discovery and other biomedical applications.

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De Novo Binder Design

The process of generating new protein designs from scratch, using only the sequence and structure of the target molecule as input.

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RFdiffusion

A deep learning method based on denoising diffusion processes, used for designing proteins.

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AlphaFold2 (AF2)

A computational method for predicting protein structure and stability.

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Fluorescence Polarization (FP)

A technique for measuring protein binding affinities by observing a change in fluorescence.

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Alpha Helix

A type of secondary protein structure characterized by a spiral shape.

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Binding Site

The part of a protein that interacts with a binding partner.

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Stability

The ability of a protein to retain its structure and activity under various conditions.

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Yeast Surface Display

A method for displaying proteins on the surface of yeast cells, allowing for selection and screening of binders.

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Dissociation Constant (Kd)

The strength of the interaction between two molecules, quantified as the concentration needed for half of the molecules to be bound.

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Circular Dichroism (CD)

A method for studying protein structure and dynamics using circularly polarized light.

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Protein Structure

The three-dimensional arrangement of atoms in a protein.

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Sequence-Based Design

Providing only the amino acid sequence of a target molecule as input for a protein design algorithm.

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Denoising Diffusion Process

A process that progressively removes noise from a noisy signal, ultimately revealing a clear signal.

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Binder

A protein that binds to and interacts with a specific target molecule.

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Design Model

A structural model that represents possible configurations of a molecule.

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RFjoint Inpainting

A deep learning method that helps design proteins by iteratively improving an initial design through a process resembling image denoising.

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Hallucination

A computational method for protein design that can generate high-affinity binders to various targets without prior knowledge of the bound structure.

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Parametric Groove-shaped Scaffold

A specific type of protein scaffold characterized by a groove-shaped structure formed by multiple helices.

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Supercoiling

A key parameter used to characterize the overall shape of helical bundles.

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Helix Neighbor Distance

The distance between neighboring helices in a helical bundle.

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Rosetta ConnectChainsMover

A computational tool used to design protein backbones and optimize their structure.

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Rosetta

A suite of computational tools used to design and evaluate protein structures.

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AF2

A computational method used for predicting protein structures and evaluating their stability.

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pLDDT

A measure of how well a protein design adheres to known structural principles.

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pTM

A metric used to predict the quality of protein models generated by computational methods.

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Sequence Redesign

The process of modifying a protein sequence to introduce or remove specific amino acids, often to improve its function or stability.

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Threading

A computational method used to predict protein-peptide interactions based on hydrophobic interactions between the two.

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Kd values

A measure of the binding affinity between a protein and its target molecule. Lower Kd values indicate higher affinity.

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Extending Binding Interface

A computational approach that enhances the binding interface between a protein and its target molecule by making additional contacts.

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Partial Diffusion

A technique used to improve the designability of protein backbones by introducing noise and then denoising the structure. This process can diversify the protein fold and refine its interactions.

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Structure Recapitulation (Self-Consistency)

A measure of how well a protein model fits its target sequence. A higher self-consistency score means the protein backbone is more stable and predictable.

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Shape Complementarity

A measure of how well a protein's shape complements its target molecule. A higher shape complementarity means better interaction.

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Contact Molecular Surface

A measure of the surface area of a protein that interacts with its target molecule. A larger contact molecular surface indicates stronger binding.

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Parametrically Designed Binder

A protein engineered to bind specifically to a target molecule. This involves selecting the right amino acids and arranging them to create the desired binding site.

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Inpainting

A type of protein engineering technique used to design binders that bind to specific target molecules, usually peptides. This technique involves inserting specific amino acids into a defined framework based on the target's structure.

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High-Affinity Binder

A protein that binds specifically to a target molecule with a high affinity. The binding affinity is measured by the dissociation constant (Kd).

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Binding Affinity

A measure of how strongly a protein binds to its target molecule. Lower Kd values indicate stronger binding.

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Crystallography Alignment

A technique used to study protein structures by comparing the experimental data from X-ray crystallography to a computer model.

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Structural Motif

A common structural motif found in proteins that involves a specific arrangement of amino acids. These motifs often contribute to the protein's function or stability.

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Specificity

A type of protein-protein interaction that is specific to the structure and sequence of the proteins involved. This type of interaction is often crucial for biological processes.

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Molecular Dynamics Simulation

A technique used to study protein structure and dynamics by simulating the movement of atoms in a protein molecule. This method can be used to predict the protein's function and make predictions about its stability or binding affinity.

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Protein Structure Prediction

The technique of predicting the 3D structure of a protein from its amino acid sequence. Different methods of structure prediction exist, each with its own strengths and limitations.

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Flexible Backbone Protein Design

A type of protein design approach that aims to generate optimized protein structures that bind to a target peptide, even if that peptide is unstructured.

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Groove Scaffold Design

A protein design strategy that involves generating new sequences for binding to helical target peptides. It specifically focuses on creating a deep groove in the binder structure to accommodate and interact with the target helix.

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Bcl-2 family

A family of proteins that play a vital role in regulating apoptosis, or cell death.

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BH3 Domain

A domain within the Bid protein that is important for its role in apoptosis, specifically for binding to other Bcl-2 family proteins.

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Mcl-1

A key binding partner in the apoptotic pathway, specifically in the regulation of mitochondrial outer membrane permeabilization (MOMP). It can directly bind to the BH3 domain of Bid.

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Bcl-2

A protein that is a founding member of the Bcl-2 family and is crucial for regulating the apoptotic pathway.

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Bid

A protein that plays a vital role in the apoptotic pathway by regulating the release of cytochrome c from mitochondria, a step that initiates cell death.

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NPY (neuropeptide Y)

A peptide that is a naturally occurring neurotransmitter that is important for appetite regulation.

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ProteinMPNN

A method used to enhance the binding affinity of a protein to its target ligand by introducing small, localized changes to the protein's structure.

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Co-expression

This refers to the process of expressing two proteins (or peptides) in the same cell, often for the purpose of studying their interaction. It helps researchers investigate how these proteins interact and bind to each other.

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What is lucCagePTH biosensor?

A method for creating protein-based biosensors using computational design. It involves designing a protein scaffold that specifically binds to a target peptide, such as PTH, and then incorporating this scaffold into a luminescence-based detection system.

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What does 'build to fit' mean in the context of protein design?

The ability of a new design method to quickly create protein binders that perfectly complement the shape of a target molecule.

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What is 'partial diffusion' in protein design, and how does it affect binding affinity?

Small changes in the protein backbone that allow for the placement of larger side chains, leading to increased protein affinity. This is achieved by partially diffusing the protein structure during design.

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What is immunoaffinity enrichment and how does it work?

A technique used to enrich specific protein components from a complex mixture, such as human serum, using protein binders attached to small beads. This allows for the detection of low-abundance protein biomarkers.

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What is 'recovery percentage' in the context of protein enrichment?

A measure of the amount of target protein that is successfully captured and retained by a designed binder during the enrichment process.

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What are 'de novo binders' in the context of protein design?

A protein designed to bind specifically to a particular peptide, such as PTH or GCG. These binders are often created using computational design methods.

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What are the advantages of using de novo binders over antibodies?

De novo protein binders offer advantages over antibodies in terms of their stability, cost-effectiveness, and tunability. They can be created using computational design, eliminating the need for animal immunization.

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What is 'high affinity' in the context of protein binding?

The ability of a protein to bind to a target molecule with high affinity, meaning they bind tightly and with high specificity. This is essential for creating effective biosensors and enrichment reagents.

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What is 'rapid build to fit' in protein design?

The ability of a design method to quickly and efficiently create new proteins with desirable properties, such as high affinity binding to a target molecule.

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What is 'Rosetta ddG' in protein design?

The computational prediction of whether a protein will be more or less stable after a designed change. It provides a way to assess the stability and functionality of a protein prior to its synthesis.

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What is 'contact molecular surface' in protein design?

A measure of the surface area of a protein that is in contact with another molecule, like a target peptide. Higher contact surface area often indicates stronger binding.

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What is 'shape complementarity' in protein design?

The ability of a protein to bind to a target molecule, which is often determined by the shape and chemical properties of the binding site. Higher shape complementarity often leads to stronger binding.

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What are 'computational design helical scaffolds'?

A type of protein scaffold that uses a groove-like structure to bind extended alpha-helical peptides. It has an advantage over antibodies in terms of binding stability and flexibility for sensor applications.

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What is 'computational design' in the context of protein engineering?

The use of computational methods to create new proteins with specific desired functions, such as binding to a target molecule.

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Study Notes

Peptide Hormone Binding Protein Design

  • Peptide hormones, like parathyroid hormone (PTH), neuropeptide Y (NPY), glucagon (GCG), and secretin (SCT), adopt α-helical structures upon binding receptors and are crucial biomarkers.
  • Sensitive and specific quantification of these hormones currently relies on antibodies, which can be problematic due to resource intensity, low affinity, instability, and reproducibility.
  • Antibodies are not well-suited for binding extended helical peptides; anti-peptide antibodies usually bind in non-helical conformations.

Computational Design Approaches

  • Parametric approach: Designs helical bundle scaffolds with an open groove to fit helical peptides. This helps achieve high-affinity and specific binding, with helices limiting self-association. The method samples various supercoiling and helix-helix spacings.
  • Deep learning (Hallucination): Generates helical peptide binders without pre-specifying scaffold geometries. Optimized metrics for binding, though adversarial protein sequences can be a concern. This allows design to various conformations.
  • RFdiffusion: An extension of the denoising diffusion method that directly generates protein structures. It's more efficient. Partial diffusion allows backbone resampling and refinement. This can enhance binding affinity, enabling subtle adjustments to match target shape.

Design Method Details

  • Parametric designs: Random sampling of helical bundle scaffold parameters (supercoiling, helix distances) creates diverse models.
  • Hallucination: Initial structure predicted from sequence, optimizing in sequence space for desired binding properties with AF2 or RoseTTAFold. Refinement using ProteinMPNN to avoid unwanted sequences.
  • RFdiffusion: Starting from random noise, iteratively refines to a new protein structure to match the target (or refined using partial noise).

Experimental Validation

  • High-affinity binders (picomolar range): Achieved through parametric, deep learning, and RFdiffusion approaches for various peptide targets (PTH, GCG, NPY, Bim).
  • Binding assays: NanoBiT split-luciferase, fluorescence polarization (FP), yeast surface display, and LC-MS/MS.

Biosensors and Detection Tools

  • PTH lucCage biosensor: Grafting the binder into a lucCage system creates a sensitive biosensor (10 nM limit of detection) with a ≈21-fold luminescence activation in the presence of PTH.
  • LC-MS/MS enrichment: Successfully enriched PTH and GCG from buffer and human plasma. Designed binders showed high and maintained binding activity in repeated experiments avoiding antibody limitations.

Overall Significance

  • Computationally designed helical scaffolds surpass the typical limitations of antibodies for binding helical peptides: wider surface area, easier integration with sensors, more stability, and low cost using E. coli.

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Description

Explore the cutting-edge methods in designing peptide hormone binding proteins. This quiz covers the significance of peptide hormones, the limitations of traditional antibody binding, and innovative computational design approaches like parametric design and deep learning. Test your knowledge on these key concepts in biochemistry.

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