Target-templated de novo design of macrocyclic d-/l-peptides: discovery of drug-like inhibitors of PD-1

Miniprotein engineering for inhibition of PD-1/PD-L1 interaction

Miniproteins constitute an excellent basis for the development of structurally demanding functional molecules. The engrailed homeodomain, a three-helix-containing miniprotein, was applied as a scaffold for constructing programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) interaction inhibitors. PD-L1 binders were initially designed using the computer-aided approach and subsequently optimized iteratively. The conformational stability was assessed for each obtained miniprotein using circular dichroism spectroscopy, indicating that numerous mutations could be introduced. The formation of a sizable hydrophobic surface at the inhibitor that fits the molecular target imposed the necessity for the incorporation of additional charged amino acid residues to retain its appropriate solubility. Finally, the miniprotein effectively binding to PD-L1 (KD = 51.4 nM) that inhibits PD-1/PD-L1 interaction in cell-based studies with EC50 = 3.9 μM, was discovered.

Integrated Anchored Stapling and Hierarchical Dynamics: MSICDA-Driven CREBBP Bromodomain Inhibition

Despite recent success in the computational approaches of cyclic peptide design, current studies face challenges in modeling noncanonical amino acids and nonstandard cyclizations due to limited data. To address this challenge, we developed an integrated framework for the tailored design of stapled peptides (SPs) targeting the bromodomain of CREBBP (CREBBP-BrD). We introduce a powerful combination of anchored stapling and hierarchical molecular dynamics to design and optimize SPs by employing the MultiScale integrative conformational dynamics assessment (MSICDA) strategy, which involves an initial virtual screening of over 1.5 million SPs, followed by comprehensive simulations amounting to 154.54 μs across 5418 of instances. The MSICDA method provides a detailed and holistic stability view of peptide-protein interactions, systematically isolated optimized peptides and identified two leading candidates, DA#430 and DA#99409, characterized by their enhanced stability, optimized binding, and high affinity toward the CREBBP-BrD. In cell-free assays, DA#430 and DA#99409 exhibited 2- to 12-fold greater potency than inhibitor SGC-CBP30. Cell studies revealed higher peptide selectivity for cancerous versus normal cells over small molecules. DA#430 combined with (+)-JQ-1 showed promising synergistic effects. Our approach enables the identification of peptides with optimized binding, high affinity, and enhanced stability, leading to more precise and effective cyclic peptide design, thereby establishing MSICDA as a generalizable and transformative tool for uncovering novel targeted drug development in various therapeutic areas.

KCD: A prediction web server of knowledge-based circular dichroism

We present a web server that predicts the far-UV circular dichroism (CD) spectra of proteins by utilizing their three-dimensional (3D) structures from the Protein Data Bank (PDB). The main algorithm is based on the classical theory of optical activity together with a set of atomic complex polarizabilities, which are obtained from the analysis of a series of synchrotron radiation CD spectra and their related 3D structures from the PDB. The results of our knowledge-based CD method (KCD) are in good agreement with measured spectra that could include the effect of D-amino acids. Our method also delivers some of the most accurate predictions, in comparison with the calculated spectra from well-established models. Specifically, using a metric of closeness based on normalized absolute deviations between experimental and calculated spectra, the mean values for a series of 57 test proteins give the following figures for such models: 0.26 KCD, 0.27 PDBMD2CD, 0.30 SESCA, and 0.47 DichroCalc. From another point of view, it is worth mentioning the remarkable capabilities of the recent approaches based on artificial intelligence, which can precisely predict the native structure of proteins. The structure of proteins, however, is flexible and can be modified by a diversity of environmental factors such as interactions with other molecules, mechanical stresses, variations of temperature, pH, or ionic strength. Experimental CD spectra together with reliable predictions can be utilized to assess eventual secondary structural changes. A similar kind of evaluation can be done for the case of an incomplete protein structure that has been reconstructed by using different approaches. The KCD method can be freely accessed from: https://kcd.cinvestav.mx/.

A high-throughput molecular dynamics screening (HTMDS) approach to the design of novel cyclopeptide inhibitors of ATAD2B based on the non-canonical combinatorial library

Cyclic peptides (CPs) are a promising class of drugs because of their high biological activity and specificity. However, the design of CP remains challenging due to their conformational flexibility and difficulties in designing stable binding conformation. Herein, we present a high-throughput MD screening (HTMDS) process for the iterative design of stable CP binders with a combinatorial CP library composed of canonical and non-canonical amino acids. As a proof of concept, we apply our methods to design CP inhibitors for the bromodomain (BrD) of ATAD2B. 698,800 CP candidates with a total of 25,570 ns MD simulations were performed to study the protein-ligand binding interactions. The binding free energies (ΔGbind) estimated by MM/PBSA approach for eight lead CP designs were found to be low. CP-1st.43 was the best CP candidate with an estimated ΔGbind of -28.48 kcal/mol when compared to the standard inhibitor C-38 which has been experimentally validated and shown to exhibit ΔGbind of -17.11 kcal/mol. The major contribution of binding sites for BrD of ATAD2B involved the hydrogen-bonding anchor within the Aly-binding pocket, salt bridging, and hydrogen-bonding mediated stabilization of the ZA loop and BC loop, and the complementary Van der Waals attraction. Our methods demonstrate encouraging results by yielding conformationally stable and high-potential CP binders that should have potential applicability in future CP drug development.Communicated by Ramaswamy H. Sarma.

Macrocycles and macrocyclization in anticancer drug discovery: Important pieces of the puzzle

Increasing disease-related proteins have been identified as novel therapeutic targets. Macrocycles are emerging as potential solutions, bridging the gap between conventional small molecules and biomacromolecules in drug discovery. Inspired by successful macrocyclic drugs of natural origins, macrocycles are attracting more attention for enhanced binding affinity and target selectivity. Due to the conformation constraint and structure preorganization, macrocycles can reach bioactive conformations more easily than parent acyclic compounds. Also, rational macrocyclization combined with sequent structural modification will help improve oral bioavailability and combat drug resistance. This review introduces various strategies to enhance membrane permeability in macrocyclization and subsequent modification, such as N-methylation, intramolecular hydrogen bonding modulation, isomerization, and reversible bicyclization. Several case studies highlight macrocyclic inhibitors targeting kinases, HDAC, and protein-protein interactions. Finally, some macrocyclic agents targeting tumor microenvironments are illustrated.

Selective inhibitors targeting Fis1/Mid51 protein-protein interactions protect against hypoxia-induced damage in cardiomyocytes

Cardiovascular diseases (CVDs) are the most common non-communicable diseases globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Mitochondria play critical roles in cellular metabolic homeostasis, cell survival, and cell death, as well as producing most of the cell's energy. Protein-protein interactions (PPIs) have a significant role in physiological and pathological processes, and aberrant PPIs are associated with various diseases, therefore they are potential drug targets for a broad range of therapeutic areas. Due to their ability to mimic natural interaction motifs and cover relatively larger interaction region, peptides are very promising as PPI inhibitors. To expedite drug discovery, computational approaches are widely used for screening potential lead compounds. Here, we developed peptides that inhibit mitochondrial fission 1 (Fis1)/mitochondrial dynamics 51 kDa (Mid51) PPI to reduce the cellular damage that can lead to various human pathologies, such as CVDs. Based on a rational design approach we developed peptide inhibitors of the Fis1/Mid51 PPI. In silico and in vitro studies were done to evaluate the biological activity and molecular interactions of the peptides. Two peptides, CVP-241 and CVP-242 were identified based on low binding energy and molecular dynamics simulations. These peptides inhibit Fis1/Mid51 PPI (-1324.9 kcal mol-1) in docking calculations (CVP-241, -741.3 kcal mol-1, and CVP-242, -747.4 kcal mol-1), as well as in vitro experimental studies Fis1/Mid51 PPI (KD 0.054 µM) Fis1/Mid51 PPI + CVP-241 (KD 3.43 µM), and Fis1/Mid51 PPI + CVP-242 (KD 44.58 µM). Finally, these peptides have no toxicity to H9c2 cells, and they increase cell viability in cardiomyocytes (H9c2 cells). Consequently, the identified inhibitor peptides could serve as potent molecules in basic research and as leads for therapeutic development.

Inside PD-1/PD-L1,2 with their inhibitors

This review summarizes current knowledge in the development of immune checkpoint inhibitors, including antibodies and small molecules.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics Implementation in GROMACS

The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy and is represented by the ensemble of its sampled conformations. Although some algorithms excel at creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations and often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, namely, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS ver. 4.6.7 and benchmarked on two small cyclic peptide model systems: a cyclized furin cleavage site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (cyclo-(CGPRRARSG)) and oxytocin (disulfide-bonded CYIQNCPLG). Additionally, we also benchmarked Res-REMD on alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. For Trpzip2, Res-REMD coupled with an umbrella-sampling-derived reservoir generated similar folded fractions as regular REMD but on a much faster time scale. For cyclic peptides, Res-REMD appeared to be marginally faster than REMD in ensemble generation. Finally, Res-REMD was more effective in sampling rare events such as trans to cis peptide bond isomerization. We provide a GitHub page with the modified GROMACS source code for running Res-REMD at https://github.com/PlotkinLab/Reservoir-REMD.

Utilization of macrocyclic peptides to target protein-protein interactions in cancer

Protein-protein interactions (PPIs) play vital roles in normal cellular processes. Dysregulated PPIs are involved in the process of various diseases, including cancer. Thus, these PPIs may serve as potential therapeutic targets in cancer treatment. However, despite rapid advances in small-molecule drugs and biologics, it is still hard to target PPIs, especially for those intracellular PPIs. Macrocyclic peptides have gained growing attention for their therapeutic properties in targeting dysregulated PPIs. Macrocyclic peptides have some unique features, such as moderate sizes, high selectivity, and high binding affinities, which make them good drug candidates. In addition, some oncology macrocyclic peptide drugs have been approved by the US Food and Drug Administration (FDA) for clinical use. Here, we reviewed the recent development of macrocyclic peptides in cancer treatment. The opportunities and challenges were also discussed to inspire new perspectives.

Design of Protein Segments and Peptides for Binding to Protein Targets

Recent years have witnessed a rise in methods for accurate prediction of structure and design of novel functional proteins. Design of functional protein fragments and peptides occupy a small, albeit unique, space within the general field of protein design. While the smaller size of these peptides allows for more exhaustive computational methods, flexibility in their structure and sparsity of data compared to proteins, as well as presence of noncanonical building blocks, add additional challenges to their design. This review summarizes the current advances in the design of protein fragments and peptides for binding to targets and discusses the challenges in the field, with an eye toward future directions.

Traversing through the Dynamic Protein-Protein Interaction Landscape and Conformational Plasticity of PD-1 for Small-Molecule Discovery

Monoclonal antibodies (mAbs) blocking the PD-1/PD-L1 interface have shown remarkable success in treating malignancies, but they may also initiate lethal immune-related adverse events. Small molecules may circumvent the mAb limitations; however, none has entered clinical trials targeting PD-1. Its complex protein-protein interaction interfaces necessitate an atomic-level understanding of recognition and binding mechanisms. Hence, we have aimed to highlight the PD-1's sequence-structure-dynamic-function link with its cognate ligands and diversely reported inhibitors. We focus primarily on the anti-PD-1 mAbs, their mode of actions, and interactions with PD-1 epitopes. The comparison of co-crystals showed that these ligands/inhibitors harness the PD-1's conformational plasticity and structural determinants differentially. The relationship between modulator binding patterns and biological activity is demonstrated using interaction fingerprinting of all reported human PD-1 co-crystals. The significant dynamical events and hot-spot residues underpinned from crystallographic wealth and computational studies have been highlighted to expedite small-molecule discovery.

Proteomic tools for the quantitative analysis of artificial peptide libraries: detection and characterization of target-amplified PD-1 inhibitors

We report a quantitative proteomics data analysis pipeline which, coupled to protein-directed dynamic combinatorial chemistry (DDC) experiments, enables the rapid discovery and direct characterization of protein-protein interaction (PPI) modulators. A low-affinity PD-1 binder was incubated with a library of >100 D-peptides under thiol-exchange favoring conditions, in the presence of the target protein PD-1, and we determined the S-linked dimeric species that resulted amplified in the protein samples versus the controls. We chemically synthesized the target dimer candidates and validated them by thermophoresis binding and protein-protein interaction assays. The results provide a proof-of-concept for using this strategy in the high-throughput search of improved drug-like peptide binders that block therapeutically relevant protein-protein interactions.

Targeting the HVEM protein using a fragment of glycoprotein D to inhibit formation of the BTLA/HVEM complex

Cancer immunotherapy using blockade of immune checkpoints is mainly based on monoclonal antibodies. Despite the tremendous success achieved by using those molecules to block immune checkpoint proteins, antibodies possess some weaknesses, which means that there is still a need to search for new compounds as alternatives to antibodies. Many current approaches are focused on use of peptides/peptidomimetics to destroy receptor/ligand interactions. Our studies concern blockade of the BTLA/HVEM complex, which generates an inhibitory effect on the immune response resulting in tolerance to cancer cells. To design inhibitors of such proteins binding we based our work on the amino acid sequence and structure of a ligand of HVEM protein, namely glycoprotein D, which possesses the same binding site on HVEM as BTLA protein. To disrupt the BTLA and HVEM interaction we designed several peptides, all fragments of glycoprotein D, and tested their binding to HVEM using SPR and their ability to inhibit the BTLA/HVEM complex formation using ELISA tests and cellular reporter platforms. That led to identification of two peptides, namely gD(1-36)(K10C-D30C) and gD(1-36)(A12C-L25C), which interact with HVEM and possess blocking capacities. Both peptides are not cytotoxic to human PBMCs, and show stability in human plasma. We also studied the 3D structure of the gD(1-36)(K10C-D30C) peptide using NMR and molecular modeling methods. The obtained data reveal that it possesses an unstructured conformation and binds to HVEM in the same location as gD and BTLA. All these results suggest that peptides based on the binding fragment of gD protein represent promising immunomodulation agents for future cancer immunotherapy.

Knowledge-Based Atomic Polarizabilities Used to Model Circular Dichroism Spectra of Proteins

We present a model of circular dichroism for proteins, which is mainly based on both the classical theory of optical activity and a series of effective atomic polarizabilities. Such polarizabilities are extracted from the analysis of a set of synchrotron radiation circular dichroism spectra and their corresponding three-dimensional structures from the Protein Data Bank. Each modeled spectrum is obtained from the protein atomic coordinates and the identification of its secondary structure elements. The resulting spectra are in good agreement with additional experimental data and also with the predictions of some other models. Among them, only our approach is able to describe the effect of d-amino acids. Moreover, our model is also utilized to evaluate protein reconstructions as well as structural changes.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.