Entropy of stapled peptide inhibitors in free state is the major contributor to the improvement of binding affinity with the GK domain

Structure-guided design and cloning of peptide inhibitors targeting CDK9/cyclin T1 protein-protein interaction

CDK9 (cyclin-dependent kinase 9) plays a significant role in numerous pathological conditions, such as HIV-1 infection and cancer. The interaction between CDK9 and cyclin T1 is crucial for maintaining the kinase's active state. Therefore, targeting this protein-protein interaction offers a promising strategy for inhibiting CDK9. In this study, we aimed to design and characterize a library of mutant peptides based on the binding region of cyclin T1 to CDK9. Using Osprey software, a total of 7,776 mutant peptides were generated. After conducting a comprehensive analysis, three peptides, namely, mp3 (RAADVEGQRKRRE), mp20 (RAATVEGQRKRRE), and mp29 (RAADVEGQDKRRE), were identified as promising inhibitors that possess the ability to bind to CDK9 with high affinity and exhibit low free binding energy. These peptides exhibited favorable safety profiles and displayed promising dynamic behaviors. Notably, our findings revealed that the mp3 and mp29 peptides interacted with a conserved sequence in CDK9 (residues 60-66). In addition, by designing the structure of potential peptides in the plasmid vector pET28a (+), we have been able to pave the way for facilitating the process of their recombinant production in an Escherichia coli expression system in future studies. Predictions indicated good solubility upon overexpression, further supporting their potential for downstream applications. While these results demonstrate the promise of the designed peptides as blockers of CDK9 with high affinity, additional experimental studies are required to validate their biological activity and assess their selectivity. Such investigations will provide valuable insights into their therapeutic potential and pave the way for the future development of peptide-based inhibitors targeting the CDK9-cyclin T1 complex.

Dissecting the geometric and hydrophobic constraints of stapled peptides

Stapled peptides are a promising class of molecules with potential as highly specific probes of protein-protein interactions and as therapeutics. Hydrocarbon stapling affects the peptide properties through the interplay of two factors: enhancing the overall hydrophobicity and constraining the conformational flexibility. By constructing a series of virtual peptides, we study the role of each factor in modulating the structural properties of a hydrocarbon-stapled peptide PM2, which has been shown to enter cells, engage its target Mouse Double Minute 2 (MDM2), and activate p53. Hamiltonian replica exchange molecular dynamics (HREMD) simulations suggest that hydrocarbon stapling favors helical populations of PM2 through a combination of the geometric constraints and the enhanced hydrophobicity of the peptide. To further understand the conformational landscape of the stapled peptides along the binding pathway, we performed HREMD simulations by restraining the peptide at different distances from MDM2. When the peptide approaches MDM2, the binding pocket undergoes dehydration which appears to be greater in the presence of the stapled peptide compared with the linear peptide. In the binding pocket, the helicity of the stapled peptide is increased due to the favorable interactions between the peptide residues as well as the staple and the microenvironment of the binding pocket, contributing to enhanced affinity. The dissection of the multifaceted mechanism of hydrocarbon stapling into individual factors not only deepens fundamental understanding of peptide stapling, but also provides guidelines for the design of new stapled peptides.

The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein-ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models' decision-making processes and accurately compare the performance of models.

Structural factors governing binding of curvature-sensing peptides to bacterial extracellular vesicles covered with hydrophilic polysaccharide chains

Extracellular vesicles (EVs) have attracted an attention as important targets in the fields of biology and medical science because they contain physiologically active molecules. Curvature-sensing peptides are currently used as novel tools for marker-independent EV detection techniques. A structure-activity correlation study demonstrated that the α-helicity of the peptides is prominently involved in peptide binding to vesicles. However, whether a flexible structure changing from a random coil to an α-helix upon binding to vesicles or a restricted α-helical structure is an important factor in the detection of biogenic vesicles is still unclear. To address this issue, we compared the binding affinities of stapled and unstapled peptides for bacterial EVs with different surface polysaccharide chains. We found that unstapled peptides showed similar binding affinities for bacterial EVs regardless of surface polysaccharide chains, whereas stapled peptides showed substantially decreased binding affinities for bacterial EVs covered with capsular polysaccharides. This is probably because curvature-sensing peptides must pass through the layer of hydrophilic polysaccharide chains prior to binding to the hydrophobic membrane surface. While stapled peptides with restricted structures cannot easily pass through the layer of polysaccharide chains, unstapled peptides with flexible structures can easily approach the membrane surface. Therefore, we concluded that the structural flexibility of curvature-sensing peptides is a key factor for governing the highly sensitive detection of bacterial EVs.

Rapid Evaluation of Staple Placement in Stabilized α Helices Using Bacterial Surface Display

There are a wealth of proteins involved in disease that cannot be targeted by current therapeutics because they are inside cells, inaccessible to most macromolecules, and lack small-molecule binding pockets. Stapled peptides, where two amino acids are covalently linked, form a class of macrocycles that have the potential to penetrate cell membranes and disrupt intracellular protein-protein interactions. However, their discovery relies on solid-phase synthesis, greatly limiting queries into their complex design space involving amino acid sequence, staple location, and staple chemistry. Here, we use stabilized peptide engineering by Escherichia coli display (SPEED), which utilizes noncanonical amino acids and click chemistry for stabilization, to rapidly screen staple location and linker structure to accelerate peptide design. After using SPEED to confirm hotspots in the mdm2-p53 interaction, we evaluated different staple locations and staple chemistry to identify several novel nanomolar and sub-nanomolar antagonists. Next, we evaluated SPEED in the B cell lymphoma 2 (Bcl-2) protein family, which is responsible for regulating apoptosis. We report that novel staple locations modified in the context of BIM, a high affinity but nonspecific naturally occurring peptide, improve its specificity against the highly homologous proteins in the Bcl-2 family. These compounds demonstrate the importance of screening linker location and chemistry in identifying high affinity and specific peptide antagonists. Therefore, SPEED can be used as a versatile platform to evaluate multiple design criteria for stabilized peptide engineering.

High-Throughput Screening of Stapled Helical Peptides in Drug Discovery

Therapeutic peptides have revolutionized treatment for a number of human diseases. In particular, the past two decades have witnessed rapid progress of stapled helical peptides in drug discovery. Stapled helical peptides are chemically modified and constrained in their bioactive α-helical conformation. Compared to unstabilized linear peptides, stapled helical peptides exhibit superior binding affinity and selectivity, enhanced membrane permeability, and improved metabolic stability, presenting exciting promise for targeting otherwise challenging protein-protein interfaces. In this Perspective, we summarize recent applications of high-throughput screening technologies for identification of potent stapled helical peptides with optimized binding properties. We expect to provide a broad reference to accelerate the development of stapled helical peptides as the next generation of therapeutic peptides for various human diseases.

Application of per-Residue Energy Decomposition to Design Peptide Inhibitors of PSD95 GK Domain

Specific interaction between the postsynaptic density protein 95 (PSD95) and synapse-associated protein 90/postsynaptic density 95–associated protein (SAPAP) is crucial for excitatory synaptic development and plasticity. Designing inhibitors that target the guanylate kinase (GK) domain of PSD95, which is responsible for the interaction, is a promising manipulation tool for the investigation of the function of PSD95 GK and the etiology of its related psychiatric disorders. Herein, we designed new peptide inhibitors of PSD95 GK/SAPAP with higher binding affinity by using molecular dynamics simulations. First, the interactions between PSD95 GK and their reported phosphorylated and unphosphorylated peptides were explored by molecular dynamics simulations. Besides the hydrogen bonding interactions mediated by the phospho-serine (p-Ser) or corresponding phosphomimic residue Asp/Glu, the hydrophobic interactions from the other amino acids also contribute to the PSD95 GK/SAPAP interaction. As an unphosphorylated synthetic peptide with moderate binding affinity and relatively lower molecular weight, the QSF inhibitory peptide was selected for further modification. Based on per-residue energy decomposition results of the PSD95 GK/QSF complex, ten peptides were designed to enhance the binding interactions, especially the hydrophobic interactions. The top-ranked five peptides with lower binding energy were eventually synthesized. The binding affinities of the synthesized peptides were determined using fluorescence polarization (FP) assay. As expected, all peptides have higher binding affinity than the QSF peptide (Ki = 5.64 ± 0.51 μM). Among them, F10W was the most potent inhibitor (Ki = 0.75 ± 0.25 μM), suggesting that enhancement of the hydrophobic interactions is an important strategy for the design of new inhibitory peptides targeting PSD95 GK.