Features of protein-protein interactions that translate into potent inhibitors: topology, surface area and affinity

Co-training based prediction of multi-label protein-protein interactions

Prediction of protein-protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-Label prediction of Protein-Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain.

Binding Mechanism between Platelet Glycoprotein and Cyclic Peptide Elucidated by McMD-Based Dynamic Docking

The cyclic peptide OS1 (amino acid sequence: CTERMALHNLC), which has a disulfide bond between both termini cysteine residues, inhibits complex formation between the platelet glycoprotein Ibα (GPIbα) and the von Willebrand factor (vWF) by forming a complex with GPIbα. To study the binding mechanism between GPIbα and OS1 and, therefore, the inhibition mechanism of the protein-protein GPIbα-vWF complex, we have applied our multicanonical molecular dynamics (McMD)-based dynamic docking protocol starting from the unbound state of the peptide. Our simulations have reproduced the experimental complex structure, although the top-ranking structure was an intermediary one, where the peptide was bound in the same location as in the experimental structure; however, the β-switch of GPIbα attained a different conformation. Our analysis showed that subsequent refolding of the β-switch results in a more stable binding configuration, although the transition to the native configuration appears to take some time, during which OS1 could dissociate. Our results show that conformational changes in the β-switch are crucial for successful binding of OS1. Furthermore, we identified several allosteric binding sites of GPIbα that might also interfere with vWF binding, and optimization of the peptide to target these allosteric sites might lead to a more effective inhibitor, as these are not dependent on the β-switch conformation.

A Rationally Designed Synthetic Antiviral Peptide Binder Targeting the Receptor-Binding Domain of SARS-CoV-2

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel coronavirus, is the causative agent responsible for the spread of the COVID19 pandemic across the globe. The global impact of the COVID19 pandemic, the successful approval of vaccines for controlling the pandemic, and the further resurgence of COVID19 necessitate the exploration and validation of alternative therapeutic avenues targeting SARS-CoV-2. The initial entry and further invasion by SARS-CoV-2 require strong protein-protein interactions (PPIs) between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptors expressed on the cell surfaces of various tissues. In principle, disruption of the PPIs between the RBD of SARS-CoV-2 and the ACE2 receptor by designer peptides with optimized pharmacology appears to be an ideal choice for potentially preventing viral entry with minimal immunogenicity. In this context, the current study describes a short, synthetic designer peptide (codenamed SR16, ≤18 aa, molecular weight ≤2.5 kDa), which has a few noncoded amino acids, demonstrates a helical conformation in solution, and also engages the RBD of SARS-CoV-2 through a high-affinity interaction, as judged from a battery of biophysical studies. Further, the designer peptide demonstrates resistance to trypsin degradation, appears to be nontoxic to mammalian cells, and also does not induce hemolysis in freshly isolated human erythrocytes. In summary, SR16 appears to be an ideal peptide binder targeting the RBD of SARS-CoV-2, which has the potential for further optimization and development as an antiviral agent targeting SARS-CoV-2.

Database-Driven Identification of Structurally Similar Protein-Protein Interfaces

Analyzing the similarity of protein interfaces in protein-protein interactions gives new insights into protein function and assists in discovering new drugs. Usually, tools that assess the similarity focus on the interactions between two protein interfaces, while sometimes we only have one predicted interface. Herein, we present PiMine, a database-driven protein interface similarity search. It compares interface residues of one or two interacting chains by calculating and searching tetrahedral geometric patterns of α-carbon atoms and calculating physicochemical and shape-based similarity. On a dedicated, tailor-made dataset, we show that PiMine outperforms commonly used comparison tools in terms of early enrichment when considering interfaces of sequentially and structurally unrelated proteins. In an application example, we demonstrate its usability for protein interaction partner prediction by comparing predicted interfaces to known protein-protein interfaces.

Recent Progress in Peptide-Based Molecular Probes for Disease Bioimaging

Recent strides in molecular pathology have unveiled distinctive alterations at the molecular level throughout the onset and progression of diseases. Enhancing the in vivo visualization of these biomarkers is crucial for advancing disease classification, staging, and treatment strategies. Peptide-based molecular probes (PMPs) have emerged as versatile tools due to their exceptional ability to discern these molecular changes with unparalleled specificity and precision. In this Perspective, we first summarize the methodologies for crafting innovative functional peptides, emphasizing recent advancements in both peptide library technologies and computer-assisted peptide design approaches. Furthermore, we offer an overview of the latest advances in PMPs within the realm of biological imaging, showcasing their varied applications in diagnostic and therapeutic modalities. We also briefly address current challenges and potential future directions in this dynamic field.

Rational design of antibody-like peptides for targeting the human complement fragment protein C5a

Human complement fragment 5a (C5a) is one of the most potent glycoproteins generated downstream of C3a and C4a during late-stage activation of the complement signaling cascade. C5a recruits receptors like C5aR1 and C5aR2 and is established to play a critical role in complement-mediated inflammation. Thus, excessive C5a in the plasma due to aberrant activation of the complement contributes to the pathophysiology of several chronic inflammatory diseases. Therefore, restricting the excessive interaction of C5a with its receptors by neutralizing C5a has been one of the most effective therapeutic strategies for the management of inflammatory diseases. Indeed, antibodies targeting C5 (Eculizumab), the precursor of C5a, and C5a (Vilobelimab) have already been approved by the FDA. Still, small designer peptides that work like antibodies and can target and stop C5a from interacting with its receptors seem to be a possible therapeutic alternative to antibodies because they are smaller, cheaper to make, more specific to their target, and can get through membrane barriers. As a proof-of-principle, the current study describes the computational design and evaluation of a pair of peptides that are able to form stable high-affinity complexes with the epitope regions of C5a that are important for the recruitment of C5aR1 and C5aR2. The computational data further supports the potential of designer peptides for mimicking the function of antibodies targeting C5a. However, further experimental studies will be required to establish the structure-function relationship of the designer peptides and also to establish the hypothesis of antibody-like peptides targeting C5a.

Antiviral peptides inhibiting the main protease of SARS-CoV-2 investigated by computational screening and in vitro protease assay

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in viral replication and transcription and received great attention as a vital target for drug/peptide development. Therapeutic agents such as small-molecule drugs or peptides that interact with the Cys-His present in the catalytic site of Mpro are an efficient way to inhibit the protease. Although several emergency-approved vaccines showed good efficacy and drastically dropped the infection rate, evolving variants are still infecting and killing millions of people globally. While a small-molecule drug (Paxlovid) received emergency approval, small-molecule drugs have low target specificity and higher toxicity. Besides small-molecule drugs, peptide therapeutics are thus gaining increasing popularity as they are easy to synthesize and highly selective and have limited side effects. In this study, we investigated the therapeutic value of 67 peptides targeting Mpro using molecular docking. Subsequently, molecular dynamics (MD) simulations were implemented on eight protein-peptide complexes to obtain molecular-level information on the interaction between these peptides and the Mpro active site, which revealed that temporin L, indolicidin, and lymphocytic choriomeningitis virus (LCMV) GP1 are the best candidates in terms of stability, interaction, and structural compactness. These peptides were synthesized using the solid-phase peptide synthesis protocol, purified by reversed-phase high-performance liquid chromatography (RP-HPLC), and authenticated by mass spectrometry (MS). The in vitro fluorometric Mpro activity assay was used to validate the computational results, where temporin L and indolicidin were observed to be very active against SARS-CoV-2 Mpro with IC50 values of 38.80 and 87.23 μM, respectively. A liquid chromatography-MS (LC-MS) assay was developed, and the IC50 value of temporin L was measured at 23.8 μM. The solution-state nuclear magnetic resonance (NMR) structure of temporin L was determined in the absence of sodium dodecyl sulfate (SDS) micelles and was compared to previous temporin structures. This combined investigation provides critical insights and assists us to further develop peptide inhibitors of SARS-CoV-2 Mpro through structural guided investigation.

Peptide Therapeutics: Unveiling the Potential against Cancer-A Journey through 1989

The United States Food and Drug Administration (FDA) has approved a plethora of peptide-based drugs as effective drugs in cancer therapy. Peptides possess high specificity, permeability, target engagement, and a tolerable safety profile. They exhibit selective binding with cell surface receptors and proteins, functioning as agonists or antagonists. They also serve as imaging agents for diagnostic applications or can serve a dual-purpose as both diagnostic and therapeutic (theragnostic) agents. Therefore, they have been exploited in various forms, including linkers, peptide conjugates, and payloads. In this review, the FDA-approved prostate-specific membrane antigen (PSMA) peptide antagonists, peptide receptor radionuclide therapy (PRRT), somatostatin analogs, antibody-drug conjugates (ADCs), gonadotropin-releasing hormone (GnRH) analogs, and other peptide-based anticancer drugs are analyzed in terms of their chemical structures and properties, therapeutic targets and mechanisms of action, development journey, administration routes, and side effects.

Electrophile Scanning Reveals Reactivity Hotspots for the Design of Covalent Peptide Binders

Protein-protein interactions (PPIs) are intriguing targets in drug discovery and development. Peptides are well suited to target PPIs, which typically present with large surface areas lacking distinct features and deep binding pockets. To improve binding interactions with these topologies and advance the development of PPI-focused therapeutics, potential ligands can be equipped with electrophilic groups to enable binding through covalent mechanisms of action. We report a strategy termed electrophile scanning to identify reactivity hotspots in a known peptide ligand and demonstrate its application in a model PPI. Cysteine mutants of a known ligand are used to install protein-reactive modifiers via a palladium oxidative addition complex (Pd-OAC). Reactivity hotspots are revealed by cross-linking reactions with the target protein under physiological conditions. In a model PPI with the 9-mer peptide antigen VL9 and major histocompatibility complex (MHC) class I protein HLA-E, we identify two reactivity hotspots that afford up to 87% conversion to the protein-peptide conjugate within 4 h. The reactions are specific to the target protein in vitro and dependent on the peptide sequence. Moreover, the cross-linked peptide successfully inhibits molecular recognition of HLA-E by CD94-NKG2A possibly due to structural changes enacted at the PPI interface. The results illustrate the potential application of electrophile scanning as a tool for rapid discovery and development of covalent peptide binders.

Comparative Analysis of Cyclization Techniques in Stapled Peptides: Structural Insights into Protein-Protein Interactions in a SARS-CoV-2 Spike RBD/hACE2 Model System

Medicinal chemistry is constantly searching for new approaches to develop more effective and targeted therapeutic molecules. The design of peptidomimetics is a promising emerging strategy that is aimed at developing peptides that mimic or modulate the biological activity of proteins. Among these, stapled peptides stand out for their unique ability to stabilize highly frequent helical motifs, but they have failed to be systematically reported. Here, we exploit chemically diverse helix-inducing i, i + 4 constraints-lactam, hydrocarbon, triazole, double triazole and thioether-on two distinct short sequences derived from the N-terminal peptidase domain of hACE2 upon structural characterization and in silico alanine scan. Our overall objective was to provide a sequence-independent comparison of α-helix-inducing staples using circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy. We identified a 9-mer lactam stapled peptide derived from the hACE2 sequence (His34-Gln42) capable of reaching its maximal helicity of 55% with antiviral activity in bioreporter- and pseudovirus-based inhibition assays. To the best of our knowledge, this study is the first comprehensive investigation comparing several cyclization methods with the goal of generating stapled peptides and correlating their secondary structures with PPI inhibitions using a highly topical model system (i.e., the interaction of SARS-CoV-2 Spike RBD with hACE2).

Revealing the interaction between peptide drugs and permeation enhancers in the presence of intestinal bile salts

Permeability enhancer-based formulations offer a promising approach to enhance the oral bioavailability of peptides. We used all-atom molecular dynamics simulations to investigate the interaction between two permeability enhancers (sodium caprate, and SNAC), and four different peptides (octreotide, hexarelin, degarelix, and insulin), in the presence of taurocholate, an intestinal bile salt. The permeability enhancers exhibited distinct effects on peptide release based on their properties, promoting hydrophobic peptide release while inhibiting water-soluble peptide release. Lowering peptide concentrations in the simulations reduced peptide-peptide interactions but increased their interactions with the enhancers and taurocholates. Introducing peptides randomly with enhancer and taurocholate molecules yielded dynamic molecular aggregation, and reduced peptide-peptide interactions and hydrogen bond formation compared to peptide-only systems. The simulations provided insights into molecular-level interactions, highlighting the specific contacts between peptide residues responsible for aggregation, and the interactions between peptide residues and permeability enhancers/taurocholates that are crucial within the mixed colloids. Therefore, our results can provide insights into how modifications of these critical contacts can be made to alter drug release profiles from peptide-only or mixed peptide-PE-taurocholate aggregates. To further probe the molecular nature of permeability enhancers and peptide interactions, we also analyzed insulin secondary structures using Fourier transform infrared spectroscopy. The presence of SNAC led to an increase in β-sheet formation in insulin. In contrast, both in the absence and presence of caprate, α-helices, and random structures dominated. These molecular-level insights can guide the design of improved permeability enhancer-based dosage forms, allowing for precise control of peptide release profiles near the intended absorption site.

Drug discovery by targeting the protein-protein interactions involved in autophagy

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

Controlling the Chameleonic Behavior and Membrane Permeability of Cyclosporine Derivatives via Backbone and Side Chain Modifications

Some macrocycles exhibit enhanced membrane permeability through conformational switching in different environmental polarities, a trait known as chameleonic behavior. In this study, we demonstrate specific backbone and side chain modifications that can control chameleonic behavior and passive membrane permeability using a cyclosporin O (CsO) scaffold. To quantify chameleonic behavior, we used a ratio of the population of the closed conformation obtained in polar solvent and nonpolar solvent for each CsO derivative. We found that β-hydroxylation at position 1 (1 and 3) can encode chameleonicity and improve permeability. However, the conformational stabilization induced by adding an additional transannular H-bond (2 and 5) leads to a much slower rate of membrane permeation. Our CsO scaffold provides a platform for the systematic study of the relationship among conformation, membrane permeability, solubility, and protein binding. This knowledge contributes to the discovery of potent beyond the rule of five (bRo5) macrocycles capable of targeting undruggable targets.

Peptide-Based Therapeutics in Cancer Therapy

A monster called cancer is still one of the most challenging human problems and one of the leading causes of death in the world. Different types of treatment methods are used for cancer therapy; however, there are challenges such as high cost and harmful side effects in using these methods. Recent years have witnessed a surge in the development of therapeutic peptides for a wide range of diseases, notably cancer. Peptides are preferred over antibiotics, radiation therapy, and chemotherapy in the treatment of cancer due to a number of aspects, including flexibility, easy modification, low immunogenicity, and inexpensive cost of production. The use of therapeutic peptides in cancer treatment is a novel and intriguing strategy. These peptides provide excellent prospects for targeted drug delivery because of their high selectivity, specificity, small dimensions, good biocompatibility, and simplicity of modification. Target specificity and minimal toxicity are benefits of therapeutic peptides. Additionally, peptides can be used to design antigens or adjuvants for vaccine development. Here, types of therapeutic peptides for cancer therapy will be discussed, such as peptide-based cancer vaccines and tumor-targeting peptides (TTP) and cell-penetrating peptides (CPP).

Modulation of a conformational ensemble by a small molecule that inhibits key protein-protein interactions involved in cell adhesion

Small molecules that regulate protein-protein interactions can be valuable drugs; however, the development of such small molecules is challenging as the molecule must interfere with an interaction that often involves a large surface area. Herein, we propose that modulating the conformational ensemble of the proteins participating in a given interaction, rather than blocking the interaction by directly binding to the interface, is a relevant strategy for interfering with a protein-protein interaction. In this study, we applied this concept to P-cadherin, a cell surface protein forming homodimers that are essential for cell-cell adhesion in various biological contexts. We first determined the crystal structure of P-cadherin with a small molecule inhibitor whose inhibitory mechanism was unknown. Molecular dynamics simulations suggest that the inhibition of cell adhesion by this small molecule results from modulation of the conformational ensemble of P-cadherin. Our study demonstrates the potential of small molecules altering the conformation ensemble of a protein as inhibitors of biological relevant protein-protein interactions.

Computational peptide engineering approach for selection of the new C05 antibody-driven peptide with potency to blocking influenza a virus attachment; from in silico to in vivo

Antiviral drugs are currently used to prevent or treat viral infections like influenza A Virus (IAV). Nonetheless, annual genetic mutations of influenza viruses make them resistant to efficient treatment by current medications. Antiviral peptides have recently attracted researchers' attention and can potentially supplant the current medications. This study aimed to design peptides against IAV propagation. For this purpose, P2 and P3 peptides were computationally designed based on the HCDR3 region of the C05 antibody (a monoclonal antibody that neutralizes influenza HA protein and inhibits the virus attachment). The synthesized peptides were tested against the influenza A virus (A/Puerto Rico/8/34 (H1N1)) in vitro, and the most efficient peptide was selected for in vivo experiments. It was shown that the designed peptide shows much more prophylactic and therapeutic effects against the virus. These findings demonstrated that the designed peptide can control the virus infection without any cytotoxicity effect. Antiviral peptide design is acknowledged as a critical tactic to manage viral infections by preventing viral binding to the host cells.Communicated by Ramaswamy H. Sarma.

Effects of Homogeneous and Heterogeneous Crowding on Translational Diffusion of Rigid Bovine Serum Albumin and Disordered Alfa-Casein

Intracellular environment includes proteins, sugars, and nucleic acids interacting in restricted media. In the cytoplasm, the excluded volume effect takes up to 40% of the volume available for occupation by macromolecules. In this work, we tested several approaches modeling crowded solutions for protein diffusion. We experimentally showed how the protein diffusion deviates from conventional Brownian motion in artificial conditions modeling the alteration of medium viscosity and rigid spatial obstacles. The studied tracer proteins were globular bovine serum albumin and intrinsically disordered α-casein. Using the pulsed field gradient NMR, we investigated the translational diffusion of protein probes of different structures in homogeneous (glycerol) and heterogeneous (PEG 300/PEG 6000/PEG 40,000) solutions as a function of crowder concentration. Our results showed fundamentally different effects of homogeneous and heterogeneous crowded environments on protein self-diffusion. In addition, the applied "tracer on lattice" model showed that smaller crowding obstacles (PEG 300 and PEG 6000) create a dense net of restrictions noticeably hindering diffusing protein probes, whereas the large-sized PEG 40,000 creates a "less restricted" environment for the diffusive motion of protein molecules.

Cryo-EM structures of actin binding proteins as tool for drug discovery

Cellular actin dynamic is controlled by a plethora of actin binding proteins (ABPs), including actin nucleating, bundling, cross-linking, capping, and severing proteins. In this review, regulation of actin dynamics by ABPs will be introduced, and the role of the F-actin severing protein cofilin-1 and the F-actin bundling protein L-plastin in actin dynamics discussed in more detail. Since up-regulation of these proteins in different kinds of cancers is associated with malignant progression of cancer cells, we suggest the cryogenic electron microscopy (Cryo-EM) structure of F- actin with the respective ABP as template for in silico drug design to specifically disrupt the interaction of these ABPs with F-actin.

Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles

Cyclic peptides have emerged as a promising class of therapeutics. However, their de novo design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. The ability to characterize cyclic peptide structural ensembles would greatly aid their rational design. In a previous pioneering study, our group demonstrated that using molecular dynamics results to train machine learning models can efficiently predict structural ensembles of cyclic pentapeptides. Using this method, which was termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), linear regression models were able to predict the structural ensembles for an independent test set with R² = 0.94 between the predicted populations for specific structures and the observed populations in molecular dynamics simulations for cyclic pentapeptides. An underlying assumption in these StrEAMM models is that cyclic peptide structural preferences are predominantly influenced by neighboring interactions, namely, interactions between (1,2) and (1,3) residues. Here we demonstrate that for larger cyclic peptides such as cyclic hexapeptides, linear regression models including only (1,2) and (1,3) interactions fail to produce satisfactory predictions (R² = 0.47); further inclusion of (1,4) interactions leads to moderate improvements (R² = 0.75). We show that when using convolutional neural networks and graph neural networks to incorporate complex nonlinear interaction patterns, we can achieve R² = 0.97 and R² = 0.91 for cyclic pentapeptides and hexapeptides, respectively.

What Makes a Good Protein-Protein Interaction Stabilizer: Analysis and Application of the Dual-Binding Mechanism

Protein-protein interactions (PPIs) are essential for biological processes including immune reactions and diseases. Inhibition of PPIs by drug-like compounds is a common basis for therapeutic approaches. In many cases the flat interface of PP complexes prevents discovery of specific compound binding to cavities on one partner and PPI inhibition. However, frequently new pockets are formed at the PP interface that allow accommodation of stabilizers which is often as desirable as inhibition but a much less explored alternative strategy. Herein, we employ molecular dynamics simulations and pocket detection to investigate 18 known stabilizers and associated PP complexes. For most cases, we find that a dual-binding mechanism, a similar stabilizer interaction strength with each protein partner, is an important prerequisite for effective stabilization. A few stabilizers follow an allosteric mechanism by stabilizing the protein bound structure and/or increase the PPI indirectly. On 226 protein-protein complexes, we find in >75% of the cases interface cavities suitable for binding of drug-like compounds. We propose a computational compound identification workflow that exploits new PP interface cavities and optimizes the dual-binding mechanism and apply it to 5 PP complexes. Our study demonstrates a great potential for in silico PPI stabilizers discovery with a wide range of therapeutic applications.

Proteomic discovery of chemical probes that perturb protein complexes in human cells

Most human proteins lack chemical probes, and several large-scale and generalizable small-molecule binding assays have been introduced to address this problem. How compounds discovered in such "binding-first" assays affect protein function, nonetheless, often remains unclear. Here, we describe a "function-first" proteomic strategy that uses size exclusion chromatography (SEC) to assess the global impact of electrophilic compounds on protein complexes in human cells. Integrating the SEC data with cysteine-directed activity-based protein profiling identifies changes in protein-protein interactions that are caused by site-specific liganding events, including the stereoselective engagement of cysteines in PSME1 and SF3B1 that disrupt the PA28 proteasome regulatory complex and stabilize a dynamic state of the spliceosome, respectively. Our findings thus show how multidimensional proteomic analysis of focused libraries of electrophilic compounds can expedite the discovery of chemical probes with site-specific functional effects on protein complexes in human cells.

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity. Although noncanonical residues can always be used, employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening. Towards this goal, the dataset comprising all possible di-, tri-, and tetra-peptide combinations of the canonical residues has been previously reported. However, with increasing computational power, the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues. Here, we provide both the complete and prefiltered libraries of all di-, tri-, tetra-, and penta-peptide sequences from 20 canonical amino acids and their homodetic (N-to-C-terminal) cyclic homologues. The FASTA, simplified molecular-input line-entry system (SMILES), and structure-data file (SDF)-three dimension (3D) libraries can be readily used for screening against protein targets. We also provide a simple method and tool for conducting identity-based filtering. Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns. As a case study, the developed library was screened against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease to identify potential small peptide inhibitors.

De novo design of protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Selected Approaches to Disrupting Protein-Protein Interactions within the MAPK/RAS Pathway

Within the MAPK/RAS pathway, there exists a plethora of protein-protein interactions (PPIs). For many years, scientists have focused efforts on drugging KRAS and its effectors in hopes to provide much needed therapies for patients with KRAS-mutant driven cancers. In this review, we focus on recent strategies to inhibit RAS-signaling via disrupting PPIs associated with SOS1, RAF, PDEδ, Grb2, and RAS.

Pre-clinical Formulation Development of an in situ Meglumine Salt of AZD5991: A Novel Macrocyclic Mcl-1 Inhibitor

PURPOSE

AZD5991 is a potent and selective macrocyclic inhibitor of Mcl-1 in clinical development. Developing an intravenous solution formulation for AZD5991 proved to be challenging primarily due to the poor intrinsic solubility of AZD5991. In this article are described studies performed to select a suitable crystalline form and to assess physicochemical properties of AZD5991 to aid in the design of a solution formulation for preclinical studies.

METHODS

It is preferable that the preclinical formulation has a line of sight for clinical formulation. For AZD5991, a concentration of at least 20 mg/ml was required for toxicology studies. Toward this goal, extensive pre-formulation characterization of AZD5991 including solid form analysis, pH-solubility profiling and solubility determination in cosolvents and other solubilizing media were carried out.

RESULTS & DISCUSSION

Crystalline Form A, which is more stable in aqueous solution and possesses acceptable thermal stability, was selected for preclinical and clinical development of AZD5991. Extensive solubility evaluation revealed an interesting pH-solubility profile that significantly enhances solubilization at pH > 8.5 to allow solution concentrations of at least 30 mg/ml by in situ meglumine salt formation.

CONCLUSION

Developing pre-clinical formulations to support in vivo studies requires a good understanding of the physicochemical properties of the drug candidates. Candidates with challenging pharmaceutic properties like the novel macrocycle molecule AZD5991, demand extensive characterization in its polymorph landscape, solubility profile and suitability evaluation of the excipients. Meglumine, a pH-adjusting and solubilizing agent, was found to be the best choice for formulating AZD5991 into an intravenous product to support preclinical studies.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Demystifying the Druggability of the MYC Family of Oncogenes

The MYC family of oncogenes (MYC, MYCN, and MYCL) encodes a basic helix-loop-helix leucine zipper (bHLHLZ) transcriptional regulator that is responsible for moving the cell through the restriction point. Through the HLHZIP domain, MYC heterodimerizes with the bHLHLZ protein MAX, which enables this MYC-MAX complex to bind to E-box regulatory DNA elements thereby controlling transcription of a large group of genes and their proteins. Translationally, MYC is one of the foremost oncogenic targets, and deregulation of expression of the MYC family gene/proteins occurs in over half of all human tumors and is recognized as a hallmark of cancer initiation and maintenance. Additionally, unexpected roles for this oncoprotein have been found in cancers that nominally have a non-MYC etiology. Although MYC is rarely mutated, its gain of function in cancer results from overexpression or from amplification. Moreover, MYC is a pleiotropic transcription factor possessing broad pathogenic prominence making it a coveted cancer target. A widely held notion within the biomedical research community is that the reliable modulation of MYC represents a tremendous therapeutic opportunity given its role in directly potentiating oncogenesis. However, the MYC-MAX heterodimer interaction contains a large surface area with a lack of well-defined binding sites creating the perception that targeting of MYC-MAX is forbidding. Here, we discuss the biochemistry behind MYC and MYC-MAX as it relates to cancer progression associated with these transcription factors. We also discuss the notion that MYC should no longer be regarded as undruggable, providing examples that a therapeutic window is achievable despite global MYC inhibition.

Bacterially Secretable Single-Chain Tandem Macrocyclic Peptides for High Affinity and Inhibitory Activity

The inhibition of protein-protein interactions (PPIs) is an effective approach for therapy. Owing to their large binding surface areas to target proteins, macrocyclic peptides are suitable molecules for PPI inhibition. In this study, we developed single-chain tandem macrocyclic peptides (STaMPtides) that inhibits the vascular endothelial growth factor (VEGF) receptor 2 (VEGFR2). They were artificially designed to comprise two different VEGFR2-binding macrocyclic peptides linked in tandem by peptide linkers and secreted by Corynebacterium glutamicum. Most potent VEGFR2-inhibitory STaMPtides with length-optimized linkers exhibited >1000 times stronger inhibitory activity than their parental monomeric peptides, possibly due to the avidity effect of heterodimerization. Our approach of using STaMPtides for PPI inhibition may be used to inhibit other extracellular factors, such as growth factors and cytokines.

Label-Free Analysis of Binding and Inhibition of SARS-Cov-19 Spike Proteins to ACE2 Receptor with ACE2-Derived Peptides by Surface Plasmon Resonance

SARS-CoV-2 has been shown to enter and infect human cells via interactions between spike protein (S glycoprotein) and angiotensin-converting enzyme 2 (ACE2). As such, it may be possible to suppress the infection of the virus via the blocking of this binding interaction through the use of specific peptides that can mimic the human ACE 2 peptidase domain (PD) α 1-helix. Herein, we report the use of competitive assays along with surface plasmon resonance (SPR) to investigate the effect of peptide sequence and length on spike protein inhibition. The characterization of these binding interactions helps us understand the mechanisms behind peptide-based viral blockage and develop SPR methodologies to quickly screen disease inhibitors. This work not only helps further our understanding of the important biological interactions involved in viral inhibition but will also aid in future studies that focus on the development of therapeutics and drug options. Two peptides of different sequence lengths, [30-42] and [22-44], based on the α 1-helix of ACE2 PD were selected for this fundamental investigation. In addition to characterizing their inhibitory behavior, we also identified the critical amino acid residues of the RBD/ACE2-derived peptides by combining experimental results and molecular docking modeling. While both investigated peptides were found to effectively block the RBD residues known to bind to ACE2 PD, our investigation showed that the shorter peptide was able to reach a maximal inhibition at lower concentrations. These inhibition results matched with molecular docking models and indicated that peptide length and composition are key in the development of an effective peptide for inhibiting biophysical interactions. The work presented here emphasizes the importance of inhibition screening and modeling, as longer peptides are not always more effective.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

Atomic-level thermodynamics analysis of the binding free energy of SARS-CoV-2 neutralizing antibodies

Understanding how protein-protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect-dehydration penalty-must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody-protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

Potential antiviral peptides against the nucleoprotein of SARS-CoV-2

Nucleoprotein is a conserved structural protein of SARS-CoV-2, which is involved in several functions, including replication, packaging, and transcription. In this research, 21 antiviral peptides that are known to have inhibitory function against nucleoprotein in several other viruses, were screened computationally against the nucleoprotein of SARS-CoV-2. The complexes of five best performing peptides (AVP1142, AVP1145, AVP1148, AVP1150, AVP1155) with nucleoprotein were selected for subsequent screening via 5 ns molecular dynamics (MD) simulation. Two peptides, namely AVP1145 and AVP1155, came out as promising candidates and hence were selected for 200 ns MD simulation for further validation, incorporating a DMPC-based membrane environment. In the long MD simulation, both AVP1155 and AVP1145 utilized multiple residues-mainly aromatic, acidic, and nonpolar residues-as interacting points to remain in contact with the nucleoprotein and formed predominantly hydrogen bonds along with hydrophobic and electrostatic interactions. However, AVP1155 proved to be superior to AVP1145 when its complex with nucleoprotein was analyzed in terms of root-mean-square deviation, root-mean-square fluctuation, radius of gyration, solvent accessible surface area and free energy landscape. In a nutshell, the findings of this research may guide future studies in the development of selective peptide inhibitors of SARS-CoV-2 nucleoprotein.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-022-02514-4.

KEAP1-NRF2 protein-protein interaction inhibitors: Design, pharmacological properties and therapeutic potential

The transcription factor nuclear factor erythroid 2-related factor 2 (NRF2) is considered the master regulator of the phase II antioxidant response. It controls a plethora of cytoprotective genes related to oxidative stress, inflammation, and protein homeostasis, among other processes. Activation of these pathways has been described in numerous pathologies including cancer, cardiovascular, respiratory, renal, digestive, metabolic, autoimmune, and neurodegenerative diseases. Considering the increasing interest of discovering novel NRF2 activators due to its clinical application, initial efforts were devoted to the development of electrophilic drugs able to induce NRF2 nuclear accumulation by targeting its natural repressor protein Kelch-like ECH-associated protein 1 (KEAP1) through covalent modifications on cysteine residues. However, off-target effects of these drugs prompted the development of an innovative strategy, the search of KEAP1-NRF2 protein-protein interaction (PPI) inhibitors. These innovative activators are proposed to target NRF2 in a more selective way, leading to potentially improved drugs with the application for a variety of diseases that are currently under investigation. In this review, we summarize known KEAP1-NRF2 PPI inhibitors to date and the bases of their design highlighting the most important features of their respective interactions. We also discuss the preclinical pharmacological properties described for the most promising compounds.

Plant and fungi derived analgesic natural products targeting voltage-gated sodium and calcium channels

Voltage-gated sodium and calcium channels (VGSCs and VGCCs) play an important role in the modulation of physiologically relevant processes in excitable cells that range from action potential generation to neurotransmission. Once their expression and/or function is altered in disease, specific pharmacological approaches become necessary to mitigate the negative consequences of such dysregulation. Several classes of small molecules have been developed with demonstrated effectiveness on VGSCs and VGCCs; however, off-target effects have also been described, limiting their use and spurring efforts to find more specific and safer molecules to target these channels. There are a great number of plants and herbal preparations that have been empirically used for the treatment of diseases in which VGSCs and VGCCs are involved. Some of these natural products have progressed to clinical trials, while others are under investigation for their action mechanisms on signaling pathways, including channels. In this review, we synthesize information from ~30 compounds derived from natural sources like plants and fungi and delineate their effects on VGSCs and VGCCs in human disease, particularly pain. [Figure: see text].

Construction of a synthetic methodology-based library and its application in identifying a GIT/PIX protein-protein interaction inhibitor

In recent years, the flourishing of synthetic methodology studies has provided concise access to numerous molecules with new chemical space. These compounds form a large library with unique scaffolds, but their application in hit discovery is not systematically evaluated. In this work, we establish a synthetic methodology-based compound library (SMBL), integrated with compounds obtained from our synthetic researches, as well as their virtual derivatives in significantly larger scale. We screen the library and identify small-molecule inhibitors to interrupt the protein-protein interaction (PPI) of GIT1/β-Pix complex, an unrevealed target involved in gastric cancer metastasis. The inhibitor 14-5-18 with a spiro[bicyclo[2.2.1]heptane-2,3'-indolin]-2'-one scaffold, considerably retards gastric cancer metastasis in vitro and in vivo. Since the PPI targets are considered undruggable as they are hard to target, the successful application illustrates the structural specificity of SMBL, demonstrating its potential to be utilized as compound source for more challenging targets.

Functional mapping of the 14-3-3 hub protein as a guide to design 14-3-3 molecular glues

Molecular glues represent an evolution in drug discovery, however, targeted stabilization of protein complexes remains challenging, owing to a paucity of drug design rules. The functional mapping of hotspots has been critical to protein-protein interaction (PPI) inhibitor research, however, the orthogonal approach to stabilize PPIs has not exploited this information. Utilizing the hub protein 14-3-3 as a case study we demonstrate that functional mapping of hotspots provides a triage map for 14-3-3 molecular glue development. Truncation and mutation studies allowed deconvoluting the energetic contributions of sidechain and backbone interactions of a 14-3-3-binding non-natural peptide. Three central 14-3-3 hotspots were identified and their thermodynamic characteristics profiled. In addition to the phospho-binding pocket; (i) Asn226, (ii) Lys122 and (iii) the hydrophobic patch formed by Leu218, Ile219 and Leu222 were critical for protein complex formation. Exploiting this hotspot information allowed a peptide-based molecular glue that elicits high cooperativity (α = 36) and selectively stabilizes the 14-3-3/ChREBP PPI to be uniquely developed.

Hybrid in vitro/in silico analysis of low-affinity protein-protein interactions that regulate signal transduction by Sema6D

Semaphorins constitute a large family of secreted and membrane-bound proteins that signal through cell-surface receptors, plexins. Semaphorins generally use low-affinity protein-protein interactions to bind with their specific plexin(s) and regulate distinct cellular processes such as neurogenesis, immune response, and organogenesis. Sema6D is a membrane-bound semaphorin that interacts with class A plexins. Sema6D exhibited differential binding affinities to class A plexins in prior cell-based assays, but the molecular mechanism underlying this selectivity is not well understood. Therefore, we performed hybrid in vitro/in silico analysis to examine the binding mode of Sema6D to class A plexins and to identify residues that give rise to the differential affinities and thus contribute to the selectivity within the same class of semaphorins. Our biophysical binding analysis indeed confirmed that Sema6D has a higher affinity for Plexin-A1 than for other class A plexins, consistent with the binding selectivity observed in the previous cell-based assays. Unexpectedly, our present crystallographic analysis of the Sema6D-Plexin-A1 complex showed that the pattern of polar interactions is not interaction-specific because it matches the pattern in the prior structure of the Sema6A-Plexin-A2 complex. Thus, we performed in silico alanine scanning analysis and discovered hotspot residues that selectively stabilized the Sema6D-Plexin-A1 pair via Van der Waals interactions. We then validated the contribution of these hotspot residues to the variation in binding affinity with biophysical binding analysis and molecular dynamics simulations on the mutants. Ultimately, our present results suggest that shape complementarity in the binding interfaces is a determinant for binding selectivity.

Hotspot Identification and Drug Design of Protein-Protein Interaction Modulators Using the Fragment Molecular Orbital Method

Protein-protein interactions (PPIs) are essential for the function of many proteins. Aberrant PPIs have the potential to lead to disease, making PPIs promising targets for drug discovery. There are over 64,000 PPIs in the human interactome reference database; however, to date, very few PPI modulators have been approved for clinical use. Further development of PPI-specific therapeutics is highly dependent on the availability of structural data and the existence of reliable computational tools to explore the interface between two interacting proteins. The fragment molecular orbital (FMO) quantum mechanics method offers comprehensive and computationally inexpensive means of identifying the strength (in kcal/mol) and the chemical nature (electrostatic or hydrophobic) of the molecular interactions taking place at the protein-protein interface. We have integrated FMO and PPI exploration (FMO-PPI) to identify the residues that are critical for protein-protein binding (hotspots). To validate this approach, we have applied FMO-PPI to a dataset of protein-protein complexes representing several different protein subfamilies and obtained FMO-PPI results that are in agreement with published mutagenesis data. We observed that critical PPIs can be divided into three major categories: interactions between residues of two proteins (intermolecular), interactions between residues within the same protein (intramolecular), and interactions between residues of two proteins that are mediated by water molecules (water bridges). We extended our findings by demonstrating how this information obtained by FMO-PPI can be utilized to support the structure-based drug design of PPI modulators (SBDD-PPI).

Application of nanopore sensors for biomolecular interactions and drug discovery

Biomolecular interactions, including protein-protein, protein-nucleic acid, and protein/nucleic acid-ligand interactions, play crucial roles in various cellular signaling and biological processes, and offer attractive therapeutic targets in numerous human diseases. Currently, drug discovery is limited by the low efficiency and high cost of conventional ensemble-averaging-based techniques for biomolecular interaction analysis and high-throughput drug screening. Nanopores are an emerging technology for single-molecule sensing of biomolecules. Owing to the robust advantages of single-molecule sensing, nanopore sensors have contributed tremendously to nucleic acid sequencing and disease diagnostics. In this minireview, we summarize the recent developments and outlooks in single-molecule sensing of various biomolecular interactions for drug discovery applications using biological and solid-state nanopore sensors.

Adaptor-Specific Antibody Fragment Inhibitors for the Intracellular Modulation of p97 (VCP) Protein-Protein Interactions

Protein-protein interactions (PPIs) form complex networks to drive cellular signaling and cellular functions. Precise modulation of a target PPI helps explain the role of the PPI in cellular events and possesses therapeutic potential. For example, valosin-containing protein (VCP/p97) is a hub protein that interacts with more than 30 adaptor proteins involved in various cellular functions. However, the role of each p97 PPI during the relevant cellular event is underexplored. The development of small-molecule PPI modulators remains challenging due to a lack of grooves and pockets in the relatively large PPI interface and the fact that a common binding groove in p97 binds to multiple adaptors. Here, we report an antibody fragment-based modulator for the PPI between p97 and its adaptor protein NSFL1C (p47). We engineered these antibody modulators by phage display against the p97-interacting domain of p47 and minimizing binding to other p97 adaptors. The selected antibody fragment modulators specifically disrupt the intracellular p97/p47 interaction. The potential of this antibody platform to develop PPI inhibitors in therapeutic applications was demonstrated through the inhibition of Golgi reassembly, which requires the p97/p47 interaction. This study presents a unique approach to modulate specific intracellular PPIs using engineered antibody fragments, demonstrating a method to dissect the function of a PPI within a convoluted PPI network.

Solubility-Aware Protein Binding Peptide Design Using AlphaFold

New protein–protein interactions (PPIs) are identified, but PPIs have different physicochemical properties compared with conventional targets, making it difficult to use small molecules. Peptides offer a new modality to target PPIs, but designing appropriate peptide sequences by computation is challenging. Recently, AlphaFold and RoseTTAFold have made it possible to predict protein structures from amino acid sequences with ultra-high accuracy, enabling de novo protein design. We designed peptides likely to have PPI as the target protein using the “binder hallucination” protocol of AfDesign, a de novo protein design method using AlphaFold. However, the solubility of the peptides tended to be low. Therefore, we designed a solubility loss function using solubility indices for amino acids and developed a solubility-aware AfDesign binder hallucination protocol. The peptide solubility in sequences designed using the new protocol increased with the weight of the solubility loss function; moreover, they captured the characteristics of the solubility indices. Moreover, the new protocol sequences tended to have higher affinity than random or single residue substitution sequences when evaluated by docking binding affinity. Our approach shows that it is possible to design peptide sequences that can bind to the interface of PPI while controlling solubility.

Small Molecule Agents Targeting PD-1 Checkpoint Pathway for Cancer Immunotherapy: Mechanisms of Action and Other Considerations for Their Advanced Development

Pioneering success of antibodies targeting immune checkpoints such as programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) has changed the outlook of cancer therapy. Although these antibodies show impressive durable clinical activity, low response rates and immune-related adverse events are becoming increasingly evident in antibody-based approaches. For further strides in cancer immunotherapy, novel treatment strategies including combination therapies and alternate therapeutic modalities are highly warranted. Towards this discovery and development of small molecule, checkpoint inhibitors are actively being pursued, and the efforts have culminated in the ongoing clinical testing of orally bioavailable checkpoint inhibitors. This review focuses on the small molecule agents targeting PD-1 checkpoint pathway for cancer immunotherapy and highlights various chemotypes/scaffolds and their characterization including binding and functionality along with reported mechanism of action. The learnings from the ongoing small molecule clinical trials and crucial points to be considered for their clinical development are also discussed.

Computational design of stapled peptide inhibitor against SARS‐CoV ‐2 receptor binding domain

Since its first detection in 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) has been the cause of millions of deaths worldwide. Despite the development and administration of different vaccines, the situation is still worrisome as the virus is constantly mutating to produce newer variants some of which are highly infectious. This raises an urgent requirement to understand the infection mechanism and thereby design therapeutic‐based treatment for COVID‐19. The gateway of the virus to the host cell is mediated by the binding of the receptor binding domain (RBD) of the virus spike protein to the angiotensin‐converting enzyme 2 (ACE2) of the human cell. Therefore, the RBD of SARS‐CoV‐2 can be used as a target to design therapeutics. The α1 helix of ACE2, which forms direct contact with the RBD surface, has been used as a template in the current study to design stapled peptide therapeutics. Using computer simulation, the mechanism and thermodynamics of the binding of six stapled peptides with RBD have been estimated. Among these, the one with two lactam stapling agents has shown binding affinity, sufficient to overcome RBD‐ACE2 binding. Analyses of the mechanistic detail reveal that a reorganization of amino acids at the RBD‐ACE2 interface produces favorable enthalpy of binding whereas conformational restriction of the free peptide reduces the loss in entropy to result higher binding affinity. The understanding of the relation of the nature of the stapling agent with their binding affinity opens up the avenue to explore stapled peptides as therapeutic against SARS‐CoV‐2.

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.

Therapeutic peptides: current applications and future directions

Peptide drug development has made great progress in the last decade thanks to new production, modification, and analytic technologies. Peptides have been produced and modified using both chemical and biological methods, together with novel design and delivery strategies, which have helped to overcome the inherent drawbacks of peptides and have allowed the continued advancement of this field. A wide variety of natural and modified peptides have been obtained and studied, covering multiple therapeutic areas. This review summarizes the efforts and achievements in peptide drug discovery, production, and modification, and their current applications. We also discuss the value and challenges associated with future developments in therapeutic peptides.

Challenges in Discovering Drugs That Target the Protein-Protein Interactions of Disordered Proteins

Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.

Probing GPCR Dimerization Using Peptides

G protein-coupled receptors (GPCRs) are the largest class of membrane proteins and the most common and extensively studied pharmacological target. Numerous studies over the last decade have confirmed that GPCRs do not only exist and function in their monomeric form but in fact, have the ability to form dimers or higher order oligomers with other GPCRs, as well as other classes of receptors. GPCR oligomers have become increasingly attractive to investigate as they have the ability to modulate the pharmacological responses of the receptors which in turn, could have important functional roles in diseases, such as cancer and several neurological & neuropsychiatric disorders. Despite the growing evidence in the field of GPCR oligomerisation, the lack of structural information, as well as targeting the 'undruggable' protein-protein interactions (PPIs) involved in these complexes, has presented difficulties. Outside the field of GPCRs, targeting PPIs has been widely studied, with a variety of techniques being investigated; from small-molecule inhibitors to disrupting peptides. In this review, we will demonstrate several physiologically relevant GPCR dimers and discuss an array of strategies and techniques that can be employed when targeting these complexes, as well as provide ideas for future development.