Druggable protein-protein interactions--from hot spots to hot segments

A strategy to boost xanthine oxidase and angiotensin converting enzyme inhibitory activities of peptides via molecular docking and module substitution

Molecular docking and activity evaluation screened the dipeptide module GP with low xanthine oxidase (XOD) inhibitory activity and modules KE and KN with high activity, and identified them as low- and high-contribution modules, respectively. We hypothesized the substitution of low-contribution modules in peptides with high contributions would boost their XOD inhibitory activity. In the XOD inhibitory peptide GPAGPR, substitution of GP with both KE and KN led to enhanced affinity between the peptides and XOD. They also increased XOD inhibitory activity (26.4% and 10.3%) and decreased cellular uric acid concentrations (28.0% and 10.4%). RNA sequencing indicated that these improvements were attributable to the inhibition of uric acid biosynthesis. In addition, module substitution increased the angiotensin-converting enzyme inhibitory activity of GILRP and GAAGGAF by 84.8% and 76.5%. This study revealed that module substitution is a feasible strategy to boost peptide activity, and provided information for the optimization of hydrolysate preparation conditions.

Validation of aminodeoxychorismate synthase and anthranilate synthase as novel targets for bispecific antibiotics inhibiting conserved protein-protein interactions

Multi-resistant bacteria are a rapidly emerging threat to modern medicine. It is thus essential to identify and validate novel antibacterial targets that promise high robustness against resistance-mediating mutations. This can be achieved by simultaneously targeting several conserved function-determining protein-protein interactions in enzyme complexes from prokaryotic primary metabolism. Here, we selected two evolutionary related glutamine amidotransferase complexes, aminodeoxychorismate synthase and anthranilate synthase, that are required for the biosynthesis of folate and tryptophan in most prokaryotic organisms. Both enzymes rely on the interplay of a glutaminase and a synthase subunit that is conferred by a highly conserved subunit interface. Consequently, inhibiting subunit association in both enzymes by one competing bispecific inhibitor has the potential to suppress bacterial proliferation. We comprehensively verified two conserved interface hot-spot residues as potential inhibitor-binding sites in vitro by demonstrating their crucial role in subunit association and enzymatic activity. For in vivo target validation, we generated genomically modified Escherichia coli strains in which subunit association was disrupted by modifying these central interface residues. The growth of such strains was drastically retarded on liquid and solid minimal medium due to a lack of folate and tryptophan. Remarkably, the bacteriostatic effect was observed even in the presence of heat-inactivated human plasma, demonstrating that accessible host metabolite concentrations do not compensate for the lack of folate and tryptophan within the tested bacterial cells. We conclude that a potential inhibitor targeting both enzyme complexes will be effective against a broad spectrum of pathogens and offer increased resilience against antibiotic resistance.

IMPORTANCE

Antibiotics are indispensable for the treatment of bacterial infections in human and veterinary medicine and are thus a major pillar of modern medicine. However, the exposure of bacteria to antibiotics generates an unintentional selective pressure on bacterial assemblies that over time promotes the development or acquisition of resistance mechanisms, allowing pathogens to escape the treatment. In that manner, humanity is in an ever-lasting race with pathogens to come up with new treatment options before resistances emerge. In general, antibiotics with novel modes of action require more complex pathogen adaptations as compared to chemical derivates of existing entities, thus delaying the emergence of resistance. In this contribution, we use modified Escherichia coli strains to validate two novel targets required for folate and tryptophan biosynthesis that can potentially be targeted by one and the same bispecific protein-protein interaction inhibitor and promise increased robustness against bacterial resistances.

Leveraging machine learning models for peptide-protein interaction prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Molecular Modeling of Self-Assembling Peptides

Peptide epitopes mediate as many as 40% of protein-protein interactions and fulfill signaling, inhibition, and activation roles within the cell. Beyond protein recognition, some peptides can self- or coassemble into stable hydrogels, making them a readily available source of biomaterials. While these 3D assemblies are routinely characterized at the fiber level, there are missing atomistic details about the assembly scaffold. Such atomistic detail can be useful in the rational design of more stable scaffold structures and with improved accessibility to functional motifs. Computational approaches can in principle reduce the experimental cost of such an endeavor by predicting the assembly scaffold and identifying novel sequences that adopt said structure. Yet, inaccuracies in physical models and inefficient sampling have limited atomistic studies to short (two or three amino acid) peptides. Given recent developments in machine learning and advances in sampling strategies, we revisit the suitability of physical models for this task. We use the MELD (Modeling Employing Limited Data) approach to drive self-assembly in combination with generic data in cases where conventional MD is unsuccessful. Finally, despite recent developments in machine learning algorithms for protein structure and sequence predictions, we find the algorithms are not yet suited for studying the assembly of short peptides.

Intracellular delivery of Parkin-RING0-based fragments corrects Parkin-induced mitochondrial dysfunction through interaction with SLP-2

BACKGROUND

Loss-of-function mutations in the PRKN gene, encoding Parkin, are the most common cause of autosomal recessive Parkinson's disease (PD). We have previously identified mitoch ondrial Stomatin-like protein 2 (SLP-2), which functions in the assembly of respiratory chain proteins, as a Parkin-binding protein. Selective knockdown of either Parkin or SLP-2 led to reduced mitochondrial and neuronal function in neuronal cells and Drosophila, where a double knockdown led to a further worsening of Parkin-deficiency phenotypes. Here, we investigated the minimal Parkin region involved in the Parkin-SLP-2 interaction and explored the ability of Parkin-fragments and peptides from this minimal region to restore mitochondrial function.

METHODS

In fibroblasts, human induced pluripotent stem cell (hiPSC)-derived neurons, and neuroblastoma cells the interaction between Parkin and SLP-2 was investigated, and the Parkin domain responsible for the binding to SLP-2 was mapped. High resolution respirometry, immunofluorescence analysis and live imaging were used to analyze mitochondrial function.

RESULTS

Using a proximity ligation assay, we quantitatively assessed the Parkin-SLP-2 interaction in skin fibroblasts and hiPSC-derived neurons. When PD-associated PRKN mutations were present, we detected a significantly reduced interaction between the two proteins. We found a preferential binding of SLP-2 to the N-terminal part of Parkin, with a highest affinity for the RING0 domain. Computational modeling based on the crystal structure of Parkin protein predicted several potential binding sites for SLP-2 within the Parkin RING0 domain. Amongst these, three binding sites were observed to overlap with natural PD-causing missense mutations, which we demonstrated interfere substantially with the binding of Parkin to SLP-2. Finally, delivery of the isolated Parkin RING0 domain and a Parkin mini-peptide, conjugated to cell-permeant and mitochondrial transporters, rescued compromised mitochondrial function in Parkin-deficient neuroblastoma cells and hiPSC-derived neurons with endogenous, disease causing PRKN mutations.

CONCLUSIONS

These findings place further emphasis on the importance of the protein-protein interaction between Parkin and SLP-2 for the maintenance of optimal mitochondrial function. The possibility of restoring an abolished binding to SLP-2 by delivering the Parkin RING0 domain or the Parkin mini-peptide involved in this specific protein-protein interaction into cells might represent a novel organelle-specific therapeutic approach for correcting mitochondrial dysfunction in Parkin-linked PD.

Molecular Dynamics Simulations in Protein-Protein Docking

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Distinct or Overlapping Areas of Mitochondrial Thioredoxin 2 May Be Used for Its Covalent and Strong Non-Covalent Interactions with Protein Ligands

In silico approaches were employed to examine the characteristics of interactions between human mitochondrial thioredoxin 2 (HsTrx2) and its 38 previously identified mitochondrial protein ligands. All interactions appeared driven mainly by electrostatic forces. The statistically significant residues of HsTrx2 for interactions were characterized as "contact hot spots". Since these were identical/adjacent to putative thermodynamic hot spots, an energy network approach identified their neighbors to highlight possible contact interfaces. Three distinct areas for binding emerged: (i) one around the active site for covalent interactions, (ii) another antipodal to the active site for strong non-covalent interactions, and (iii) a third area involved in both kinds of interactions. The contact interfaces of HsTrx2 were projected as respective interfaces for Escherichia coli Trx1 (EcoTrx1), 2, and HsTrx1. Comparison of the interfaces and contact hot spots of HsTrx2 to the contact residues of EcoTx1 and HsTrx1 from existing crystal complexes with protein ligands supported the hypothesis, except for a part of the cleft/groove adjacent to Trp30 preceding the active site. The outcomes of this study raise the possibility for the rational design of selective inhibitors for the interactions of HsTrx2 with specific protein ligands without affecting the entirety of the functions of the Trx system.

An Update on Protein Kinases as Therapeutic Targets-Part II: Peptides as Allosteric Protein Kinase C Modulators Targeting Protein-Protein Interactions

Human protein kinases are highly-sought-after drug targets, historically harnessed for treating cancer, cardiovascular disease, and an increasing number of autoimmune and inflammatory conditions. Most current treatments involve small molecule protein kinase inhibitors that interact orthosterically with the protein kinase ATP-binding pocket. As a result, these compounds are often poorly selective and highly toxic. Part I of this series reviews the role of PKC isoforms in various human diseases, featuring cancer and cardiovascular disease, as well as translational examples of PKC modulation applied to human health and disease. In the present Part II, we discuss alternative allosteric binding mechanisms for targeting PKC, as well as novel drug platforms, such as modified peptides. A major goal is to design protein kinase modulators with enhanced selectivity and improved pharmacological properties. To this end, we use molecular docking analysis to predict the mechanisms of action for inhibitor-kinase interactions that can facilitate the development of next-generation PKC modulators.

Streamlined Protein-Protein Interface Loop Mimicry

Cyclic peptides comprising endocyclic organic fragments, "cyclo-organopeptides", can be probes for perturbing protein-protein interactions (PPIs). Finding loop mimics is difficult because of high conformational variability amongst targets. Backbone Matching (BM), introduced here, helps solve this problem in the illustrative cases by facilitating efficient evaluation of virtual cyclo-organopeptide core-structure libraries. Thus, 86 rigid organic fragments were selected to build a library of 602 cyclo-organopeptides comprising Ala and organic parts: "cyclo-{-(Ala)n -organo-}". The central hypothesis is "hit" library members have accessible low energy conformers corresponding to backbone structures of target protein loops, while library members which cannot attain this conformation are probably unworthy of further evaluation. BM thereby prioritizes candidate loop mimics, so that less than 10 cyclo-organopeptides are needed to be prepared to find leads for two illustrative PPIs: iNOS ⋅ SPSB2, and uPA ⋅ uPAR.

Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning

Protein-peptide interactions play a key role in biological processes. Understanding the interactions that occur within a receptor-peptide complex can help in discovering and altering their biological functions. Various computational methods for modeling the structures of receptor-peptide complexes have been developed. Recently, accurate structure prediction enabled by deep learning methods has significantly advanced the field of structural biology. AlphaFold (AF) is among the top-performing structure prediction methods and has highly accurate structure modeling performance on single-chain targets. Shortly after the release of AlphaFold, AlphaFold-Multimer (AFM) was developed in a similar fashion as AF for prediction of protein complex structures. AFM has achieved competitive performance in modeling protein-peptide interactions compared to previous computational methods; however, still further improvement is needed. Here, we present DistPepFold, which improves protein-peptide complex docking using an AFM-based architecture through a privileged knowledge distillation approach. DistPepFold leverages a teacher model that uses native interaction information during training and transfers its knowledge to a student model through a teacher-student distillation process. We evaluated DistPepFold's docking performance on two protein-peptide complex datasets and showed that DistPepFold outperforms AFM. Furthermore, we demonstrate that the student model was able to learn from the teacher model to make structural improvements based on AFM predictions.

Conformationally constrained cyclic grafted peptidomimetics targeting protein-protein interactions

Sunflower trypsin inhibitor-1 (SFTI-1) structure is used for designing grafted peptides as a possible therapeutic agent. The grafted peptide exhibits multiple conformations in solution due to the presence of proline in the structure of the peptide. To lock the grafted peptide into a major conformation in solution, a dibenzofuran moiety (DBF) was incorporated in the peptide backbone structure, replacing the Pro-Pro sequence. NMR studies indicated a major conformation of the grafted peptide in solution. Detailed structural studies suggested that SFTI-DBF adopts a twisted beta-strand structure in solution. The surface plasmon resonance analysis showed that SFTI-DBF binds to CD58 protein. A model for the protein-SFTI-DBF complex was proposed based on docking studies. These studies suggested that SFTI-1 grafted peptide can be used to design stable peptides for therapeutic purposes by grafting organic functional groups and amino acids. However, when a similar strategy was used with another grafted peptide, the resulting peptide did not produce a single major conformation, and its biological activity was lost. Thus, conformational constraints depend on the sequence of amino acids used for SFTI-1 grafting.

Maleimide constrained BAD BH3 domain peptides as BCL-xL Inhibitors: A Versatile Approach to Rapidly Identify Sites Compatible with Peptide Constraining

Development of protein-protein interaction (PPI) inhibitors remains a major challenge. A significant number of PPIs are mediated by helical recognition epitopes; although peptides derived from such epitopes are attractive templates for inhibitor design, they may not readily adopt a bioactive conformation, are susceptible to proteolysis and rarely elicit optimal cell uptake properties. Constraining peptides has therefore emerged as a useful method to mitigate against these liabilities in the development of PPI inhibitors. Building on our recently reported method for constraining peptides by reaction of dibromomaleimide derivatives with two cysteines positioned in an i and i + 4 relationship, in this study, we showcase the power of the method for rapid identification of ideal constraining positions using a maleimide-staple scan based on a 19-mer sequence derived from the BAD BH3 domain. We found that the maleimide constraint had little or a detrimental impact on helicity and potency in most sequences, but successfully identified i, i + 4 positions where the maleimide constraint was tolerated. Analyses using modelling and molecular dynamics (MD) simulations revealed that the inactive constrained peptides likely lose interactions with the protein as a result of introducing the constraint.

Efficient Inhibition of Bacterial Biofilm Through Interference of Protein-Protein Interaction of Master Regulator Proteins: a Proof of Concept Study with SinR- SinI Complex of Bacillus subtilis

Biofilm-associated microbial growth is a major cause of environmental, industrial, and public health concern. Therefore, there is a pressing need to discover and develop efficient antibiofilm strategies. Regulatory proteins vital for biofilm formation might be ideal targets for developing novel antibiofilm therapeutics. Their activities often depend on protein-protein interactions. Therefore, such targets present unique opportunities and challenges to drug discovery. In Bacillus subtilis, a model organism for studying biofilms, SinR acts as the master regulator of the biofilm formation cascade. Under favourable growth conditions, it represses the epsA-O and tapA-sipW-tasA operons, which encode for essential structural components of biofilms. Under unfavourable growth conditions, SinI, an agonist protein, inactivates SinR by forming a heterotrimeric complex. This results in derepression of epsA-O and tapA-sipW-tasA operons and leads to the phenotypic switch from planktonic to biofilm-associated form. We hypothesized that inhibiting SinR-SinI interaction might warrant repression of epsA-O and tapA-sipW-tasA operons and inhibit biofilm formation. To evaluate this hypothesis, we carried out a drug repurposing study for identifying potential inhibitors of SinI. Cefoperazone and itraconazole were identified as potential inhibitors with virtual screening. The stability of their interaction with SinI was assessed in extended MD performed over 100 ns. Both cefoperazone and itraconazole showed stable interaction. In in vitro studies, cefoperazone hindered the interaction of purified recombinant SinI and SinR. In the whole cell-based biofilm inhibition assays also cefoperazone was found to efficiently inhibited biofilm formation. These results provide proof of concept for targeting protein-protein interaction of master regulators as potential target for discovery and development of antibiofilm therapeutics. We propose that similar drug repurposing studies targeting key regulators of biofilm formation cascade could be an efficient approach for discovering novel anti-biofilm therapeutics against priority pathogens.

Bicyclic Schellman Loop Mimics (BSMs): Rigid Synthetic C-Caps for Enforcing Peptide Helicity

Macrocyclic peptides are the prevalent way to mimic interface helices for disruption of protein interactions, but current strategies to do this via synthetic C-cap mimics are underdeveloped and suboptimal. Bioinformatic studies described here were undertaken to better understand Schellman loops, the most common C-caps in proteins, to design superior synthetic mimics. An algorithm (Schellman Loop Finder) was developed, and data mining with this led to the discovery that these secondary structures are often stabilized by combinations of three hydrophobic side chains, most frequently from Leu, to form hydrophobic triangles. That insight facilitated design of synthetic mimics, bicyclic Schellman loop mimics (BSMs), where the hydrophobic triumvirate was replaced by 1,3,5-trimethylbenzene. We demonstrate that BSMs can be made quickly and efficiently, and are more rigid and helix-inducing than the best current C-cap mimics, which are rare and all monocycles.

Rational design of Harakiri (HRK)-derived constrained peptides as BCL-xL inhibitors

Using the HRK BH3 domain, sequence hybridization and in silico methods we show dibromomaleimide staple scanning can be used to inform the design of BCL-x_L selective peptidomimetic ligands. These HRK-inspired reagents may serve as starting points for the discovery of therapeutics to target BCL-x_L-overexpressed cancers.

Potential Self-Peptide Inhibitors of the SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease (Mpro) plays an essential role in viral replication, cleaving viral polyproteins into functional proteins. This makes Mpro an important drug target. Mpro consists of an N-terminal catalytic domain and a C-terminal α-helical domain (MproC). Previous studies have shown that peptides derived from a given protein sequence (self-peptides) can affect the folding and, in turn, the function of that protein. Since the SARS-CoV-1 MproC is known to stabilize its Mpro and regulate its function, we hypothesized that SARS-CoV-2 MproC-derived self-peptides may modulate the folding and the function of SARS-CoV-2 Mpro. To test this, we studied the folding of MproC in the presence of various self-peptides using coarse-grained structure-based models and molecular dynamics simulations. In these simulations of MproC and one self-peptide, we found that two self-peptides, the α1-helix and the loop between α4 and α5 (loop4), could replace the equivalent native sequences in the MproC structure. Replacement of either sequence in full-length Mpro should, in principle, be able to perturb Mpro function albeit through different mechanisms. Some general principles for the rational design of self-peptide inhibitors emerge: The simulations show that prefolded self-peptides are more likely to replace native sequences than those which do not possess structure. Additionally, the α1-helix self-peptide is kinetically stable and once inserted rarely exchanges with the native α1-helix, while the loop4 self-peptide is easily replaced by the native loop4, making it less useful for modulating function. In summary, a prefolded α1-derived peptide should be able to inhibit SARS-CoV-2 Mpro function.

Pattern enrichment analysis for phage selection of stapled peptide ligands

Phage display is the most widely used technique to discover de novo peptides that bind to target proteins. However, it is associated with some challenges such as compositional bias. In this study, to overcome these difficulties, we devised a 'pattern enrichment analysis.' In this method, two samples (one obtained by affinity selection, the other simply amplified without selection) are prepared, and the two sequence datasets read on next-generation sequencer are compared to find the three-residue pattern most enriched in the selected sample. This allows us to compare two sequence datasets with high coverage and facilitates the identification of peptide sequences and the key residues for binding. We also demonstrated that this approach in the combination with structured peptide libraries allowed spatial mapping of the enriched sequence patterns. Here, we prepared a phage library displaying chemically stapled helical peptides with the X1C2X3X4X5X6X7X8C9X10 sequence, where X is any amino acid. To validate our method, we performed screening against the HDM2 protein. The results showed that the hydrophobic residues (Phe, Tyr, Trp and Leu) that are key to interactions with HDM2 were clearly identified by the pattern enrichment analysis. We also performed selection targeting the SARS-CoV-2 spike RBD in the same manner. The results showed that similar patterns were enriched among the hit peptides that inhibited the protein-protein interaction.

Multivalent binding kinetics resolved by fluorescence proximity sensing

Multivalent protein interactors are an attractive modality for probing protein function and exploring novel pharmaceutical strategies. The throughput and precision of state-of-the-art methodologies and workflows for the effective development of multivalent binders is currently limited by surface immobilization, fluorescent labelling and sample consumption. Using the gephyrin protein, the master regulator of the inhibitory synapse, as benchmark, we exemplify the application of Fluorescence proximity sensing (FPS) for the systematic kinetic and thermodynamic optimization of multivalent peptide architectures. High throughput synthesis of +100 peptides with varying combinatorial dimeric, tetrameric, and octameric architectures combined with direct FPS measurements resolved on-rates, off-rates, and dissociation constants with high accuracy and low sample consumption compared to three complementary technologies. The dataset and its machine learning-based analysis deciphered the relationship of specific architectural features and binding kinetics and thereby identified binders with unprecedented protein inhibition capacity; thus, highlighting the value of FPS for the rational engineering of multivalent inhibitors.

Fluorescence proximity sensing enables high throughput determination of binding affinities and kinetics of peptide inhibitors with varying valency and multivalent architecture.

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions

Protein-protein interactions (PPIs) and protein-metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody-epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein-protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed "metabolic entanglement", was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.

Tools and techniques for rational designing of antimicrobial peptides for aquaculture

Fisheries and aquaculture industries remain essential sources of food and nutrition for millions of people worldwide. Indiscriminate use of antibiotics has led to the emergence of antimicrobial-resistant bacteria and posed a severe threat to public health. Researchers have opined that antimicrobial peptides (AMPs) can be the best possible alternative to curb the rising tide of antimicrobial resistance in aquaculture. AMPs may also help to achieve the objectives of one health approach. The natural AMPs are associated with several shortcomings, like less in vivo stability, toxicity to host cell, high cost of production and low potency in a biological system. In this review, we have provided a comprehensive outline about the strategies for designing synthetic mimics of natural AMPs with high potency. Moreover, the freely available AMP databases and the information about the molecular docking tools are enlisted. We also provided in silico template for rationally designing the AMPs from fish piscidins or other peptides. The rationally designed piscidin (rP1 and rp2) may be used to tackle microbial infections in aquaculture. Further, the protocol can be used to develop the truncated mimics of natural AMPs having more potency and protease stability.

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.

Accelerating therapeutic protein design

Protein structures provide for defined microenvironments that can support complex pharmacological functions, otherwise unachievable by small molecules. The advent of therapeutic proteins has thus greatly broadened the range of manageable disorders. Leveraging the knowledge and recent advances in de novo protein design methods has the prospect of revolutionizing how protein drugs are discovered and developed. This review lays out the main challenges facing therapeutic proteins discovery and development, and how present and future advancements of protein design can accelerate the protein drug pipelines.

A Selective Inhibitor of Cardiac Troponin I Phosphorylation by Delta Protein Kinase C (δPKC) as a Treatment for Ischemia-Reperfusion Injury

Myocardial infarction is the leading cause of cardiovascular mortality, with myocardial injury occurring during ischemia and subsequent reperfusion (IR). We previously showed that the inhibition of protein kinase C delta (δPKC) with a pan-inhibitor (δV1-1) mitigates myocardial injury and improves mitochondrial function in animal models of IR, and in humans with acute myocardial infarction, when treated at the time of opening of the occluded blood vessel, at reperfusion. Cardiac troponin I (cTnI), a key sarcomeric protein in cardiomyocyte contraction, is phosphorylated by δPKC during reperfusion. Here, we describe a rationally-designed, selective, high-affinity, eight amino acid peptide that inhibits cTnI's interaction with, and phosphorylation by, δPKC (ψTnI), and prevents tissue injury in a Langendorff model of myocardial infarction, ex vivo. Unexpectedly, we also found that this treatment attenuates IR-induced mitochondrial dysfunction. These data suggest that δPKC phosphorylation of cTnI is critical in IR injury, and that a cTnI/δPKC interaction inhibitor should be considered as a therapeutic target to reduce cardiac injury after myocardial infarction.

PPI-HotspotDB: Database of Protein-Protein Interaction Hot Spots

Single-point mutations of certain residues (so-called hot spots) impair/disrupt protein-protein interactions (PPIs), leading to pathogenesis and drug resistance. Conventionally, a PPI-hot spot is identified when its replacement decreased the binding free energy significantly, generally by ≥2 kcal/mol. The relatively few mutations with such a significant binding free energy drop limited the number of distinct PPI-hot spots. By defining PPI-hot spots based on mutations that have been manually curated in UniProtKB to significantly impair/disrupt PPIs in addition to binding free energy changes, we have greatly expanded the number of distinct PPI-hot spots by an order of magnitude. These experimentally determined PPI-hot spots along with available structures have been collected in a database called PPI-Hotspot^DB. We have applied the PPI-Hotspot^DB to create a nonredundant benchmark, PPI-Hotspot+PDB^BM, for assessing methods to predict PPI-hot spots using the free structure as input. PPI-Hotspot^DB will benefit the design of mutagenesis experiments and development of PPI-hot spot prediction methods. The database and benchmark are freely available at https://ppihotspot.limlab.dnsalias.org.

Peptides and peptidomimetics as therapeutic agents for Covid-19

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Covid-19 pandemic has caused high morbidity and mortality rates worldwide. Virus entry into cells can be blocked using several strategies, including inhibition of protein-protein interactions (PPIs) between the viral spike glycoprotein and cellular receptors, as well as blocking of spike protein conformational changes that are required for cleavage/activation and fusogenicity. The spike-mediated viral attachment and entry into cells via fusion of the viral envelope with cellular membranes involve PPIs mediated by short peptide fragments exhibiting particular secondary structures. Thus, peptides that can inhibit these PPIs may be used as potential antiviral agents preventing virus entry and spread. This review is focused on peptides and peptidomimetics as PPI modulators and protease inhibitors against SARS-CoV-2.

Understanding p300-transcription factor interactions using sequence variation and hybridization

The hypoxic response is central to cell function and plays a significant role in the growth and survival of solid tumours. HIF-1 regulates the hypoxic response by activating over 100...

Pharmacologically Targeting the Fibroblast Growth Factor 14 Interaction Site on the Voltage-Gated Na+ Channel 1.6 Enables Isoform-Selective Modulation

Voltage-gated Na⁺ (Na_v) channels are the primary molecular determinant of the action potential. Among the nine isoforms of the Na_v channel α subunit that have been described (Na_v1.1-Na_v1.9), Na_v1.1, Na_v1.2, and Na_v1.6 are the primary isoforms expressed in the central nervous system (CNS). Crucially, these three CNS Na_v channel isoforms display differential expression across neuronal cell types and diverge with respect to their subcellular distributions. Considering these differences in terms of their localization, the CNS Na_v channel isoforms could represent promising targets for the development of targeted neuromodulators. However, current therapeutics that target Na_v channels lack selectivity, which results in deleterious side effects due to modulation of off-target Na_v channel isoforms. Among the structural components of the Na_v channel α subunit that could be pharmacologically targeted to achieve isoform selectivity, the C-terminal domains (CTD) of Na_v channels represent promising candidates on account of displaying appreciable amino acid sequence divergence that enables functionally unique protein–protein interactions (PPIs) with Na_v channel auxiliary proteins. In medium spiny neurons (MSNs) of the nucleus accumbens (NAc), a critical brain region of the mesocorticolimbic circuit, the PPI between the CTD of the Na_v1.6 channel and its auxiliary protein fibroblast growth factor 14 (FGF14) is central to the generation of electrical outputs, underscoring its potential value as a site for targeted neuromodulation. Focusing on this PPI, we previously developed a peptidomimetic derived from residues of FGF14 that have an interaction site on the CTD of the Na_v1.6 channel. In this work, we show that whereas the compound displays dose-dependent effects on the activity of Na_v1.6 channels in heterologous cells, the compound does not affect Na_v1.1 or Na_v1.2 channels at comparable concentrations. In addition, we show that the compound correspondingly modulates the action potential discharge and the transient Na+ of MSNs of the NAc. Overall, these results demonstrate that pharmacologically targeting the FGF14 interaction site on the CTD of the Na_v1.6 channel is a strategy to achieve isoform-selective modulation, and, more broadly, that sites on the CTDs of Na_v channels interacted with by auxiliary proteins could represent candidates for the development of targeted therapeutics.

Divide-and-link peptide docking: a fragment-based peptide docking protocol

Protein-peptide interactions are crucial for various important cellular regulations, and are also a basis for understanding protein-protein interactions, protein folding and peptide drug design. Due to the limited structural data obtained using experimental methods, it is necessary to predict protein-peptide interaction modes using computational methods. In the present work, we designed a fragment-based docking protocol, Divide-and-Link Peptide Docking (DLPepDock), to predict protein-peptide binding modes. This protocol contains the following steps: dividing the peptide into fragments and separately docking the fragments using a third-party small molecular docking tool, linking the docked fragmental poses to form the whole peptide conformations via fragmental coordinate transformation using our in-house program, removing unreasonable poses according to several geometrical filters, extracting representative conformations after clustering for further minimization using the steepest descent and conjugation gradient methods based on a full-atom molecular force field and finally scoring using the MM/PBSA binding energy calculation implemented in Amber. When tested on the LEADS-PEP benchmark data set of 26 diverse complexes with peptides of 6-12 residues, FlexPepDock ab initio and AutoDock CrankPep achieved superior results. DLPepDock performed better than the other 15 docking protocols implemented in nine docking programs (HPepDock, DockThor, rDock, Glide, LeDock, AutoDock, AutoDock Vina, Surflex, and GOLD). The Linux scripts to call the third-party tools and run all the calculations.

Towards optimizing peptide-based inhibitors of protein-protein interactions: predictive saturation variation scanning (PreSaVS)

A simple-to-implement and experimentally validated computational workflow for sequence modification of peptide inhibitors of protein–protein interactions (PPIs) is described.

An experimentally validated approach for in silico modification of peptide based protein–protein interaction inhibitors is described.

Differential Modulation of the Voltage-Gated Na+ Channel 1.6 by Peptides Derived From Fibroblast Growth Factor 14

The voltage-gated Na⁺ (Nav) channel is a primary molecular determinant of the initiation and propagation of the action potential. Despite the central role of the pore-forming α subunit in conferring this functionality, protein:protein interactions (PPI) between the α subunit and auxiliary proteins are necessary for the full physiological activity of Nav channels. In the central nervous system (CNS), one such PPI occurs between the C-terminal domain of the Nav1.6 channel and fibroblast growth factor 14 (FGF14). Given the primacy of this PPI in regulating the excitability of neurons in clinically relevant brain regions, peptides targeting the FGF14:Nav1.6 PPI interface could be of pre-clinical value. In this work, we pharmacologically evaluated peptides derived from FGF14 that correspond to residues that are at FGF14's PPI interface with the CTD of Nav1.6. These peptides, Pro-Leu-Glu-Val (PLEV) and Glu-Tyr-Tyr-Val (EYYV), which correspond to residues of the β12 sheet and β8-β9 loop of FGF14, respectively, were shown to inhibit FGF14:Nav1.6 complex assembly. In functional studies using whole-cell patch-clamp electrophysiology, PLEV and EYYV were shown to confer differential modulation of Nav1.6-mediated currents through mechanisms dependent upon the presence of FGF14. Crucially, these FGF14-dependent effects of PLEV and EYYV on Nav1.6-mediated currents were further shown to be dependent on the N-terminal domain of FGF14. Overall, these data suggest that the PLEV and EYYV peptides represent scaffolds to interrogate the Nav1.6 channel macromolecular complex in an effort to develop targeted pharmacological modulators.

Strategies for targeting undruggable targets

INTRODUCTION

Undruggable targets refer to clinically meaningful therapeutic targets that are 'difficult to drug' or 'yet to be drugged' via traditional approaches. Featuring characteristics of lacking defined ligand-binding pockets, non-catalytic protein-protein interaction functional modes and less-investigated 3D structures, these undruggable targets have been targeted with novel therapeutic entities developed with the progress of unconventional drug discovery approaches, such as targeted degradation molecules and display technologies.

AREA COVERED

This review first presents the concept of 'undruggable' exemplified by RAS and other targets. Next, detailed strategies are illustrated in two aspects: innovation of therapeutic entities and development of unconventional drug discovery technologies. Finally, case studies covering typical undruggable targets (Bcl-2, p53, and RAS) are depicted to further demonstrate the feasibility of the strategies and entities above.

EXPERT OPINION

Targeting the undruggable expands the scope of therapeutically reachable targets. Consequently, it represents the drug discovery frontier. Biomedical studies are capable of dissecting disease mechanisms, thus broadening the list of undruggable targets. Encouraged by the recent approval of the KRAS inhibitor Sotorasib, we believe that merging multiple discovery approaches and exploiting various novel therapeutic entities would pave the way for dealing with more 'undruggable' targets in the future.

Development of Allosteric Modulators of Voltage-Gated Na+ Channels: A Novel Approach for an Old Target

Given their primacy in governing the action potential (AP) of excitable cells, voltage-gated Na+ (Nav) channels are important pharmacological targets of therapeutics for a diverse array of clinical indications. Despite historically being a traditional drug target, therapeutics targeting Nav channels lack isoform selectivity, giving rise to off-target side effects. To develop isoform-selective modulators of Nav channels with improved target-specificity, the identification and pharmacological targeting of allosteric sites that display structural divergence among Nav channel isoforms represents an attractive approach. Despite the high homology among Nav channel α subunit isoforms (Nav1.1-Nav1.9), there is considerable amino acid sequence divergence among their constituent C-terminal domains (CTD), which enables structurally and functionally specific protein: protein interactions (PPI) with auxiliary proteins. Although pharmacological targeting of such PPI interfaces between the CTDs of Nav channels and auxiliary proteins represents an innovate approach for developing isoform-selective modulators of Nav channels, appreciable modulation of PPIs using small molecules has conventionally been difficult to achieve. After briefly discussing the challenges of modulating PPIs using small molecules, this current frontier review that follows subsequently expounds on approaches for circumventing such difficulties in the context of developing small molecule modulators of PPIs between transmembrane ion channels and their auxiliary proteins. In addition to broadly discussing such approaches, the implementation of such approaches is specifically discussed in the context of developing small molecule modulators between the CTD of Nav channels and auxiliary proteins. Developing allosteric modulators of ion channels by targeting their PPI interfaces with auxiliary proteins represents an innovative and promising strategy in ion channel drug discovery that could expand the "druggable genome" and usher in first-in-class PPI-targeting therapeutics for a multitude of channelopathies.

Divide et impera: An In Silico Screening Targeting HCMV ppUL44 Processivity Factor Homodimerization Identifies Small Molecules Inhibiting Viral Replication

Human cytomegalovirus (HCMV) is a leading cause of severe diseases in immunocompromised individuals, including AIDS patients and transplant recipients, and in congenitally infected newborns. The utility of available drugs is limited by poor bioavailability, toxicity, and emergence of resistant strains. Therefore, it is crucial to identify new targets for therapeutic intervention. Among the latter, viral protein–protein interactions are becoming increasingly attractive. Since dimerization of HCMV DNA polymerase processivity factor ppUL44 plays an essential role in the viral life cycle, being required for oriLyt-dependent DNA replication, it can be considered a potential therapeutic target. We therefore performed an in silico screening and selected 18 small molecules (SMs) potentially interfering with ppUL44 homodimerization. Antiviral assays using recombinant HCMV TB4-UL83-YFP in the presence of the selected SMs led to the identification of four active compounds. The most active one, B3, also efficiently inhibited HCMV AD169 strain in plaque reduction assays and impaired replication of an AD169-GFP reporter virus and its ganciclovir-resistant counterpart to a similar extent. As assessed by Western blotting experiments, B3 specifically reduced viral gene expression starting from 48 h post infection, consistent with the inhibition of viral DNA synthesis measured by qPCR starting from 72 h post infection. Therefore, our data suggest that inhibition of ppUL44 dimerization could represent a new class of HCMV inhibitors, complementary to those targeting the DNA polymerase catalytic subunit or the viral terminase complex.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

An Approach for the Real-Time Quantification of Cytosolic Protein-Protein Interactions in Living Cells

In recent years, cell-based assays have been frequently used in molecular interaction analysis. Cell-based assays complement traditional biochemical and biophysical methods, as they allow for molecular interaction analysis, mode of action studies, and even drug screening processes to be performed under physiologically relevant conditions. In most cellular assays, biomolecules are usually labeled to achieve specificity. In order to overcome some of the drawbacks associated with label-based assays, we have recently introduced "cell-based molography" as a biosensor for the analysis of specific molecular interactions involving native membrane receptors in living cells. Here, we expand this assay to cytosolic protein-protein interactions. First, we created a biomimetic membrane receptor by tethering one cytosolic interaction partner to the plasma membrane. The artificial construct is then coherently arranged into a two-dimensional pattern within the cytosol of living cells. Thanks to the molographic sensor, the specific interactions between the coherently arranged protein and its endogenous interaction partners become visible in real time without the use of a fluorescent label. This method turns out to be an important extension of cell-based molography because it expands the range of interactions that can be analyzed by molography to those in the cytosol of living cells.

Recent advances in β-catenin/BCL9 protein-protein interaction inhibitors

Wnt/β-catenin signaling is crucial both in normal embryonic development and throughout the life of an organism. Moreover, aberrant Wnt signaling has been associated with various diseases, especially cancer and fibrosis. Recent research suggests that direct targeting of the β-catenin/BCL9 protein-protein interaction (PPI) is a promising strategy to block the Wnt pathway. Progress in understanding the cocrystalline complex and mechanism of action of the β-catenin/BCL9 interaction facilitates the discovery process of its inhibitors, but only a few inhibitors have been reported. In this review, the discovery and development of β-catenin/BCL9 PPI inhibitors in the areas of drug design, structure-activity relationships and biological and biochemical properties are summarized. In addition, perspectives for the future development of β-catenin/BCL9 PPI inhibitors are explored.

Predicting cryptic ligand binding sites based on normal modes guided conformational sampling

To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by normal modes solved from the coarse-grained elastic models followed by atomistic backbone refinement and side-chain repacking. Despite the observations of complex and diverse conformational changes associated with ligand binding, we found that simply sampling along each of the lowest 30 modes is near optimal for adequately restructuring cryptic sites so they can be detected by existing pocket finding programs like fpocket and concavity. We further trained machine-learning protocols to optimize the combination of the sampling-enhanced pocket scores with other dynamic and conservation scores, which only slightly improved the performance. As assessed based on a training set of 84 known cryptic sites and a test set of 14 proteins, our method achieved high accuracy of prediction (with area under the receiver operating characteristic curve >0.8) comparable to the CryptoSite server. Compared with CryptoSite and other methods based on extensive molecular dynamics simulation, our method is much faster (1-2 hours for an average-size protein) and simpler (using only pocket scores), so it is suitable for high-throughput processing of large datasets of protein structures at the genome scale.

Fragment-linking peptide design yields a high-affinity ligand for microtubule-based transport

Synthetic peptides are attractive candidates to manipulate protein-protein interactions inside the cell as they mimic natural interactions to compete for binding. However, protein-peptide interactions are often dynamic and weak. A challenge is to design peptides that make improved interactions with the target. Here, we devise a fragment-linking strategy-"mash-up" design-to deliver a high-affinity ligand, KinTag, for the kinesin-1 motor. Using structural insights from natural micromolar-affinity cargo-adaptor ligands, we have identified and combined key binding features in a single, high-affinity ligand. An X-ray crystal structure demonstrates interactions as designed and reveals only a modest increase in interface area. Moreover, when genetically encoded, KinTag promotes transport of lysosomes with higher efficiency than natural sequences, revealing a direct link between motor-adaptor binding affinity and organelle transport. Together, these data demonstrate a fragment-linking strategy for peptide design and its application in a synthetic motor ligand to direct cellular cargo transport.

Dimerization of PHGDH via the catalytic unit is essential for its enzymatic function

Human D-3-phosphoglycerate dehydrogenase (PHGDH), a key enzyme in de novo serine biosynthesis, is amplified in various cancers and serves as a potential target for anticancer drug development. To facilitate this process, more information is needed on the basic biochemistry of this enzyme. For example, PHGDH was found to form tetramers in solution and the structure of its catalytic unit (sPHGDH) was solved as a dimer. However, how the oligomeric states affect PHGDH enzyme activity remains elusive. We studied the dependence of PHGDH enzymatic activity on its oligomeric states. We found that sPHGDH forms a mixture of monomers and dimers in solution with a dimer dissociation constant of ∼0.58 μM, with the enzyme activity depending on the dimer content. We computationally identified hotspot residues at the sPHGDH dimer interface. Single-point mutants at these sites disrupt dimer formation and abolish enzyme activity. Molecular dynamics simulations showed that dimer formation facilitates substrate binding and maintains the correct conformation required for enzyme catalysis. We further showed that the full-length PHGDH exists as a dynamic mixture of monomers, dimers, and tetramers in solution with enzyme concentration-dependent activity. Mutations that can completely disrupt the sPHGDH dimer show different abilities to interrupt the full-length PHGDH tetramer. Among them, E108A and I121A can also disrupt the oligomeric structures of the full-length PHGDH and abolish its enzyme activity. Our study indicates that disrupting the oligomeric structure of PHGDH serves as a novel strategy for PHGDH drug design and the hotspot residues identified can guide the design process.

GATA-targeted compounds modulate cardiac subtype cell differentiation in dual reporter stem cell line

Background

Pharmacological modulation of cell fate decisions and developmental gene regulatory networks holds promise for the treatment of heart failure. Compounds that target tissue-specific transcription factors could overcome non-specific effects of small molecules and lead to the regeneration of heart muscle following myocardial infarction. Due to cellular heterogeneity in the heart, the activation of gene programs representing specific atrial and ventricular cardiomyocyte subtypes would be highly desirable. Chemical compounds that modulate atrial and ventricular cell fate could be used to improve subtype-specific differentiation of endogenous or exogenously delivered progenitor cells in order to promote cardiac regeneration.

Methods

Transcription factor GATA4-targeted compounds that have previously shown in vivo efficacy in cardiac injury models were tested for stage-specific activation of atrial and ventricular reporter genes in differentiating pluripotent stem cells using a dual reporter assay. Chemically induced gene expression changes were characterized by qRT-PCR, global run-on sequencing (GRO-seq) and immunoblotting, and the network of cooperative proteins of GATA4 and NKX2-5 were further explored by the examination of the GATA4 and NKX2-5 interactome by BioID. Reporter gene assays were conducted to examine combinatorial effects of GATA-targeted compounds and bromodomain and extraterminal domain (BET) inhibition on chamber-specific gene expression.

Results

GATA4-targeted compounds 3i-1000 and 3i-1103 were identified as differential modulators of atrial and ventricular gene expression. More detailed structure-function analysis revealed a distinct subclass of GATA4/NKX2-5 inhibitory compounds with an acetyl lysine-like domain that contributed to ventricular cells (%Myl2-eGFP+). Additionally, BioID analysis indicated broad interaction between GATA4 and BET family of proteins, such as BRD4. This indicated the involvement of epigenetic modulators in the regulation of GATA-dependent transcription. In this line, reporter gene assays with combinatorial treatment of 3i-1000 and the BET bromodomain inhibitor (+)-JQ1 demonstrated the cooperative role of GATA4 and BRD4 in the modulation of chamber-specific cardiac gene expression.

Conclusions

Collectively, these results indicate the potential for therapeutic alteration of cell fate decisions and pathological gene regulatory networks by GATA4-targeted compounds modulating chamber-specific transcriptional programs in multipotent cardiac progenitor cells and cardiomyocytes. The compound scaffolds described within this study could be used to develop regenerative strategies for myocardial regeneration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-021-02259-z.

Query-guided protein-protein interaction inhibitor discovery

Protein-protein interactions (PPIs) are central to biological mechanisms, and can serve as compelling targets for drug discovery. Yet, the discovery of small molecule inhibitors of PPIs remains challenging given the large and typically shallow topography of the interacting protein surfaces. Here, we describe a general approach to the discovery of orthosteric PPI inhibitors that mimic specific secondary protein structures. Initially, hot residues at protein-protein interfaces are identified in silico or from experimental data, and incorporated into secondary structure-based queries. Virtual libraries of small molecules are then shape-matched against the queries, and promising ligands docked to target proteins. The approach is exemplified experimentally using two unrelated PPIs that are mediated by an α-helix (p53/hDM2) and a β-strand (GKAP/SHANK1-PDZ). In each case, selective PPI inhibitors are discovered with low μM activity as determined by a combination of fluorescence anisotropy and 1H-15N HSQC experiments. In addition, hit expansion yields a series of PPI inhibitors with defined structure-activity relationships. It is envisaged that the generality of the approach will enable discovery of inhibitors of a wide range of unrelated secondary structure-mediated PPIs.

Characterization of amino acid residues of T-cell receptors interacting with HLA-A*02-restricted antigen peptides

Background

The present study aimed to explore residues’ properties interacting with HLA-A*02-restricted peptides on T-cell receptors (TCRs) and their effects on bond types of interaction and binding free energy.

Methods

We searched the crystal structures of HLA-A*02-restricted peptide-TCR complexes from the Protein Data Bank (PDB) database and subsequently collected relevant parameters. We then employed Schrodinger to analyze the bond types of interaction and Gromacs 2019 to evaluate the TCR-antigen peptide complex’s molecular dynamics simulation. Finally, we compared the changes of bond types of interaction and binding free energy before and after residue substitution to ensure consistency of the conditions before and after residue substitution.

Results

The main sites on the antigen peptides that formed the intermolecular interaction [hydrogen bond (HB) and pi stack] with TCRs were P4, P8, P2, and P6. The hydrophobicity of the amino acids inside or outside the disulfide bond of TCRs may be related to the intermolecular interaction and binding free energy between TCRs and peptides. Residues located outside the disulfide bond of TCR α or β chains and forming pi stack force played favorable roles in the complex intermolecular interaction and binding free energy. The residues of the TCR α or β chains that interacted with peptides were replaced by alanine (Ala) or glycine (Gly), and their intermolecular binding free energy of the complex had been improved. However, it had nothing to do with the formation of HB.

Conclusions

The findings of this study suggest that the hydrophobic nature of the amino acids inside or outside the disulfide bonds on the TCR may be associated with the intermolecular interaction and binding between the TCR and polypeptide. The residues located outside the TCR α or β single-chain disulfide bond and forming the pi-stack force showed a beneficial effect on the intermolecular interaction and binding of the complex. In addition, the part of the residues on the TCR α or β single chain that produced bond types of interaction with the polypeptide after being replaced by Ala or Gly, the intermolecular binding free energy of the complex was increased, regardless of whether HB was formed.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

In silico derived small molecules targeting the finger-finger interaction between the histone lysine methyltransferase NSD1 and Nizp1 repressor

PHD fingers are small chromatin binding domains, that alone or in tandem work as versatile interaction platforms for diversified activities, ranging from the decoding of the modification status of histone tails to the specific recognition of non-histone proteins. They play a crucial role in their host protein as mutations thereof cause several human malignancies. Thus, PHD fingers are starting to be considered as valuable pharmacological targets. While inhibitors or chemical probes of the histone binding activity of PHD fingers are emerging, their druggability as non-histone interaction platform is still unexplored. In the current study, using a computational and experimental pipeline, we provide proof of concept that the tandem PHD finger of Nuclear receptor-binding SET (Su(var)3–9, Enhancer of zeste, Trithorax) domain protein 1 (PHD_VC5HCH_NSD1) is ligandable. Combining virtual screening of a small subset of the ZINC database (Zinc Drug Database, ZDD, 2924 molecules) to NMR binding assays and ITC measurements, we have identified Mitoxantrone dihydrochloride, Quinacrine dihydrochloride and Chloroquine diphosphate as the first molecules able to bind to PHD_VC5HCH_NSD1 and to reduce its documented interaction with the Zinc finger domain (C2HR_Nizp1) of the transcriptional repressor Nizp1 (NSD1-interacting Zn-finger protein). These results pave the way for the design of small molecules with improved effectiveness in inhibiting this finger-finger interaction.

Learning peptide recognition rules for a low-specificity protein

Many proteins interact with short linear regions of target proteins. For some proteins, however, it is difficult to identify a well-defined sequence motif that defines its target peptides. To overcome this difficulty, we used supervised machine learning to train a model that treats each peptide as a collection of easily-calculated biochemical features rather than as an amino acid sequence. As a test case, we dissected the peptide-recognition rules for human S100A5 (hA5), a low-specificity calcium binding protein. We trained a Random Forest model against a recently released, high-throughput phage display dataset collected for hA5. The model identifies hydrophobicity and shape complementarity, rather than polar contacts, as the primary determinants of peptide binding specificity in hA5. We tested this hypothesis by solving a crystal structure of hA5 and through computational docking studies of diverse peptides onto hA5. These structural studies revealed that peptides exhibit multiple binding modes at the hA5 peptide interface-all of which have few polar contacts with hA5. Finally, we used our trained model to predict new, plausible binding targets in the human proteome. This revealed a fragment of the protein α-1-syntrophin that binds to hA5. Our work helps better understand the biochemistry and biology of hA5, as well as demonstrating how high-throughput experiments coupled with machine learning of biochemical features can reveal the determinants of binding specificity in low-specificity proteins.

BAlaS: fast, interactive and accessible computational alanine-scanning using BudeAlaScan

MOTIVATION

In experimental protein engineering, alanine-scanning mutagenesis involves the replacement of selected residues with alanine to determine the energetic contribution of each side chain to forming an interaction. For example, it is often used to study protein-protein interactions. However, such experiments can be time-consuming and costly, which has led to the development of programmes for performing computational alanine-scanning mutagenesis (CASM) to guide experiments. While programmes are available for this, there is a need for a real-time web application that is accessible to non-expert users.

RESULTS

Here, we present BAlaS, an interactive web application for performing CASM via BudeAlaScan and visualizing its results. BAlaS is interactive and intuitive to use. Results are displayed directly in the browser for the structure being interrogated enabling their rapid inspection. BAlaS has broad applications in areas, such as drug discovery and protein-interface design.

AVAILABILITY AND IMPLEMENTATION

BAlaS works on all modern browsers and is available through the following website: https://balas.app. The project is open source, distributed using an MIT license and is available on GitHub (https://github.com/wells-wood-research/balas).

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.