Computational Design of Helical Peptides Targeting TNFα

Simplified α2-macroglobulin as a TNF-α inhibitor for inflammation alleviation in osteoarthritis and myocardial infarction therapy

Tumor necrosis factor α (TNF-α) is a leading proinflammatory cytokine as the master regulator of inflammation in chronic inflammation diseases. Although TNF-α antagonists such as small molecules and peptides are in development, comparable effectiveness in TNF-α neutralization is hardly achieved only with TNF-α capture. In this study, simplified α2-macroglobulin (SM) as a novel TNF-α inhibitor was fabricated to relieve inflammation response by TNF-α capture and internalization with lysosomal degradation. SM was prepared by conjugating a TNF-α-targeting peptide with a receptor binding domain (RBD) derived from α2-macroglobulin through a synthetic biology strategy. SM exhibited effective capture and bioactivity inhibition of TNF-α. Improved endocytosis of TNF-α into lysosomes was observed with SM in macrophages. Even challenged with LPS/IFNγ, the macrophages showed relieved inflammation response with SM treatment. When administrated in chronic inflammation injury in vivo, SM achieved comparable therapeutic efficacy with Infliximab, showing ameliorated cartilage degeneration with relieved inflammation in osteoarthritis (OA) and preserved cardiac function with mitigated myocardium injury in myocardial infarction (MI). These results suggest that SM functioning in TNF-α capture-internalization mechanism might be promising therapeutic alternatives of TNF-α antibodies.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Discovery of novel helix binding sites at protein-protein interfaces

Protein-protein interactions (PPIs) play a key role in numerous biological processes. Many efforts have been undertaken to develop PPI modulators for therapeutic applications; however, to date, most of the peptide binders designed to target PPIs are derived from native binding helices or using the native helix binding site, which has limited the applications of protein-protein interface binding peptide design. Here, we developed a general computational algorithm, HPer (Helix Positioner), that locates single-helix binding sites at protein-protein interfaces based on the structure of protein targets. HPer performed well on known single-helix-mediated PPIs and recaptured the key interactions and hot-spot residues of native helical binders. We also screened non-helical-mediated PPIs in the PDBbind database and identified 17 PPIs that were suitable for helical peptide binding, and the helical binding sites in these PPIs were also predicted for designing novel peptide ligands. The L2 domain of EGFR, which was the top ranked, was selected as an example to show the protocol and results of designing novel helical peptide ligands on the searched binding site. The binding stability of the designed sequences were further investigated using molecular dynamics simulations.

Design, Synthesis, and Evaluation of Dihydrobenzo[cd]indole-6-sulfonamide as TNF-α Inhibitors

Tumor necrosis factor-α (TNF-α) plays a pivotal role in inflammatory response. Dysregulation of TNF can lead to a variety of disastrous pathological effects, including auto-inflammatory diseases. Antibodies that directly targeting TNF-α have been proven effective in suppressing symptoms of these disorders. Compared to protein drugs, small molecule drugs are normally orally available and less expensive. Till now, peptide and small molecule TNF-α inhibitors are still in the early stage of development, and much more efforts should be made. In a previously study, we reported a TNF-α inhibitor, EJMC-1 with modest activity. Here, we optimized this compound by shape screen and rational design. In the first round, we screened commercial compound library for EJMC-1 analogs based on shape similarity. Out of the 68 compounds tested, 20 compounds showed better binding affinity than EJMC-1 in the SPR competitive binding assay. These 20 compounds were tested in cell assay and the most potent compound was 2-oxo-N-phenyl-1,2-dihydrobenzo[cd]indole-6-sulfonamide (S10) with an IC₅₀ of 14 μM, which was 2.2-fold stronger than EJMC-1. Based on the docking analysis of S10 and EJMC-1 binding with TNF-α, in the second round, we designed S10 analogs, purchased seven of them, and synthesized seven new compounds. The best compound, 4e showed an IC₅₀-value of 3 μM in cell assay, which was 14-fold stronger than EJMC-1. 4e was among the most potent TNF-α organic compound inhibitors reported so far. Our study demonstrated that 2-oxo-N-phenyl-1,2-dihydrobenzo[cd]indole-6-sulfonamide analogs could be developed as potent TNF-α inhibitors. 4e can be further optimized for its activity and properties. Our study provides insights into designing small molecule inhibitors directly targeting TNF-α and for protein–protein interaction inhibitor design.

Efficient conformational ensemble generation of protein-bound peptides

Conformation generation of protein-bound peptides is critical for the determination of protein–peptide complex structures. Despite significant progress in conformer generation of small molecules, few methods have been developed for modeling protein-bound peptide conformations. Here, we have developed a fast de novo peptide modeling algorithm, referred to as MODPEP, for conformational sampling of protein-bound peptides. Given a sequence, MODPEP builds the peptide 3D structure from scratch by assembling amino acids or helix fragments based on constructed rotamer and helix libraries. The MODPEP algorithm was tested on a diverse set of 910 experimentally determined protein-bound peptides with 3–30 amino acids from the PDB and obtained an average accuracy of 1.90 Å when 200 conformations were sampled for each peptide. On average, MODPEP obtained a success rate of 74.3% for all the 910 peptides and ≥ 90% for short peptides with 3–10 amino acids in reproducing experimental protein-bound structures. Comparative evaluations of MODPEP with three other conformer generation methods, PEP-FOLD3, RDKit, and Balloon, have also been performed in both accuracy and success rate. MODPEP is fast and can generate 100 conformations for less than one second. The fast MODPEP will be beneficial for large-scale de novo modeling and docking of peptides. The MODPEP program and libraries are available for download at http://huanglab.phys.hust.edu.cn/.

Rational design of TNFα binding proteins based on the de novo designed protein DS119

De novo protein design offers templates for engineering tailor-made protein functions and orthogonal protein interaction networks for synthetic biology research. Various computational methods have been developed to introduce functional sites in known protein structures. De novo designed protein scaffolds provide further opportunities for functional protein design. Here we demonstrate the rational design of novel tumor necrosis factor alpha (TNFα) binding proteins using a home-made grafting program AutoMatch. We grafted three key residues from a virus 2L protein to a de novo designed small protein, DS119, with consideration of backbone flexibility. The designed proteins bind to TNFα with micromolar affinities. We further optimized the interface residues with RosettaDesign and significantly improved the binding capacity of one protein Tbab1-4. These designed proteins inhibit the activity of TNFα in cellular luciferase assays. Our work illustrates the potential application of the de novo designed protein DS119 in protein engineering, biomedical research, and protein sequence-structure-function studies.

De novo design of helical peptides to inhibit tumor necrosis factor-α by disrupting its trimer formation

Helical peptide TNFα inhibitors were designed by targeting their dimer structure.

Break CDK2/Cyclin E1 interface allosterically with small peptides

Most inhibitors of Cyclin-dependent kinase 2 (CDK2) target its ATP-binding pocket. It is difficult, however, to use this pocket to design very specific inhibitors because this catalytic pocket is highly conserved in the protein family of CDKs. Here we report some short peptides targeting a noncatalytic pocket near the interface of the CDK2/Cyclin complex. Docking and molecular dynamics simulations were used to select the peptides, and detailed dynamical network analysis revealed that these peptides weaken the complex formation via allosteric interactions. Our experiments showed that upon binding to the noncatalytic pocket, these peptides break the CDK2/Cyclin complex partially and diminish its kinase activity in vitro. The binding affinity of these peptides measured by Surface Plasmon Resonance can reach as low as 0.5 µM.

Discovery of highly potent TNFα inhibitors using virtual screen

Tumor necrosis factor-α (TNFα) is a validated therapeutic target for various autoimmune disorders such as rheumatoid arthritis and asthma. All TNFα inhibitors currently on the market are biologics, making the development of small molecule alternatives in urgent need. However, only a few successful cases of direct TNFα antagonization in vitro have been reported. Here, we present the identification of several small molecule candidates able to effectively reduce TNFα activity in vitro and in cell assays. Virtual screen targeting TNFα dimer was performed on the SPECS database and 101 compounds were selected for experimental testing. Two compounds, 1 and 2, displayed considerable inhibitory activity. Follow-up structure-activity relationship analysis of compound 1 identified 3 molecules with low micromolar cell-level inhibitory activity. Compound 11 showed an IC50 value of 14 μM, making it among the most potent TNFα small molecule inhibitors reported. These compounds provide new scaffolds for future development of small molecule drugs against TNFα.

Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening

Target structure-based virtual screening, which employs protein-small molecule docking to identify potential ligands, has been widely used in small-molecule drug discovery. In the present study, we used a protein-protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all-to-all protein-protein docking run on a large dataset was performed. The three-dimensional rigid docking program SDOCK was used to examine protein-protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z-score, and convergency of the low-score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all-to-all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor-α (TNFα), which is a well-known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top-ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein-protein docking for the discovery of novel binding proteins for specific protein targets.