Current Challenges and Opportunities in Designing Protein-Protein Interaction Targeted Drugs

Determining the Electrostatic Contributions of GTPase-GEF Complexes on Interfacial Drug Binding Specificity: A Case Study of a Protein-Drug-Protein Complex

Understanding the factors that contribute to specificity of protein-protein interactions allows for design of orthosteric small molecules. Within this environment, a small molecule requires both structural and electrostatic complementarity. While the structural contribution to protein-drug-protein specificity is well characterized, electrostatic contributions require more study. To this end, we used a series of protein complexes involving Arf1 bound to guanine nucleotide exchange factors (GEFs) that are sensitive or resistant to the small molecule brefeldin A (BFA). By comparing BFA-sensitive Arf1-Gea1p and Arf1-ARNO with different combinations of four BFA sensitizing ARNO mutations (ARNOwt, ARNO1M, ARNO3M, and ARNO4M), we describe how electrostatic environments at each interface guide BFA binding specificity. We labeled Arf1 with cyanocysteine at several interfacial sites and measured by nitrile adsorption frequencies to map changes in electric field at each interface using the linear Stark equation. Temperature dependence of nitrile vibrational spectra was used to investigate differences in hydrogen bonding environments. These comparisons showed that interfacial electric field at the surface of Arf1 varied substantially depending on the GEF. The greatest differences were seen between Arf1-ARNOwt and Arf1-ARNO4M, suggesting a greater change in electric field is required for BFA binding to Arf1-ARNO. Additionally, rigidity of the interface of the Arf1-ARNO complex correlated strongly with BFA sensitivity, indicating that flexible interfaces are sensitive to disruption upon orthosteric small molecule binding. These findings demonstrate a qualitatively consistent electrostatic environment for Arf1 binding and more subtle differences preventing BFA specificity. We discuss how these results will guide improved design of other small molecules that can target protein-protein interfaces.

AI Promoted Virtual Screening, Structure-Based Hit Optimization, and Synthesis of Novel COVID-19 S-RBD Domain Inhibitors

Coronavirus disease 2019 (COVID-19) is caused by a new, highly pathogenic severe-acute-respiratory syndrome coronavirus 2 (SARS-CoV-2) that infects human cells through its transmembrane spike (S) glycoprotein. The receptor-binding domain (RBD) of the S protein interacts with the angiotensin-converting enzyme II (ACE2) receptor of the host cells. Therefore, pharmacological targeting of this interaction might prevent infection or spread of the virus. Here, we performed a virtual screening to identify small molecules that block S-ACE2 interaction. Large compound libraries were filtered for drug-like properties, promiscuity and protein-protein interaction-targeting ability based on their ADME-Tox descriptors and also to exclude pan-assay interfering compounds. A properly designed AI-based virtual screening pipeline was applied to the remaining compounds, comprising approximately 10% of the starting data sets, searching for molecules that could bind to the RBD of the S protein. All molecules were sorted according to their screening score, grouped based on their structure and postfiltered for possible interaction patterns with the ACE2 receptor, yielding 31 hits. These hit molecules were further tested for their inhibitory effect on Spike RBD/ACE2 (19-615) interaction. Six compounds inhibited the S-ACE2 interaction in a dose-dependent manner while two of them also prevented infection of human cells from a pseudotyped virus whose entry is mediated by the S protein of SARS-CoV-2. Of the two compounds, the benzimidazole derivative CKP-22 protected Vero E6 cells from infection with SARS-CoV-2, as well. Subsequent, hit-to-lead optimization of CKP-22 was effected through the synthesis of 29 new derivatives of which compound CKP-25 suppressed the Spike RBD/ACE2 (19-615) interaction, reduced the cytopathic effect of SARS-CoV-2 in Vero E6 cells (IC50 = 3.5 μM) and reduced the viral load in cell culture supernatants. Early in vitro ADME-Tox studies showed that CKP-25 does not possess biodegradation or liver metabolism issues, while isozyme-specific CYP450 experiments revealed that CKP-25 was a weak inhibitor of the CYP450 system. Moreover, CKP-25 does not elicit mutagenic effect on Escherichia coli WP2 uvrA strain. Thus, CKP-25 is considered a lead compound against COVID-19 infection.

Revisiting Pyrimidine-Embedded Molecular Frameworks to Probe the Unexplored Chemical Space for Protein-Protein Interactions

ConspectusProtein-protein interactions (PPIs) are essential in numerous biological processes and diseases, making them attractive yet challenging drug targets. While many advances have been made in traditional drug discovery, targeting PPIs has been difficult due to a lack of specialized chemical libraries designed to modulate these interactions. Current libraries mainly focus on conventional target proteins like enzymes or receptors as substrate analogs rather than small-molecule modulators targeting PPIs. These traditional drug targets behave differently from PPIs. Conventional druggable targets have relatively small surfaces and binding pockets that have allowed them to be targeted with current libraries, but PPIs behave differently than these traditional drug targets. As a result, there is an urgent need for an innovative approach to expand the druggable space.To address this, we developed a privileged substructure-based diversity-oriented synthesis (pDOS) strategy, aimed at creating maximal skeletal diversity to explore broader biochemical space. Pyrimidine serves as the privileged substructure in our approach, which employs several strategies: (i) silver-catalyzed or iodine-mediated tandem cyclizations to generate pyrimidine-embedded polyheterocycles; (ii) diverse pairing strategies to produce pyrimidodiazepine-containing polyheterocyclic skeletons with enhanced scaffold saturation; (iii) skeletal transformation to develop pyrimidine-fused medium-sized azacycles via chemoselective cleavages or migrations of N-N or C-N bond; (iv) design of small-molecule peptidomimetics that systematically mimic three pivotal protein secondary structures using pyrimidodiazepine-based scaffolds; and (v) identification of pyrimidodiazepine-based small-molecules that allosterically inhibits the interaction between human ACE2 and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein to block viral entry into host cells.Through these approaches, we generated 39 distinct pyrimidine-embedded frameworks, demonstrating significant molecular diversity validated by chemoinformatic analyses such as Tanimoto similarity and principal moment of inertia (PMI) analysis. This molecular diversity extends pyrimidine structures beyond traditional linear or bicyclic forms, creating polyheterocycles with enhanced 3D structural diversity. These novel frameworks overcome the limitation of simpler privileged scaffolds, offering promising tools for modulating PPIs.Our pDOS approach highlights how privileged structure-embedded polyheterocycles, particularly those based on pyrimidine, can effectively target previously undruggable PPIs. This strategy provides a new direction for drug discovery, allowing for the development of small molecules that operate beyond traditional drug-like rules. In addition to expanding the chemical space for PPI modulation, our pDOS strategy enables the creation of scaffolds that are particularly suited for targeting complex and dynamic protein interfaces. This innovation could significantly impact therapeutic development, offering solutions for previously intractable drug targets. By expanding the scope of pyrimidine-based scaffolds, we have opened up new possibilities for targeting PPIs and advancing chemical biology.This perspective demonstrates the potential outlines of our pDOS strategy in creating structurally diverse frameworks, offering a platform for the discovery of PPI modulators and facilitating the exploration of untapped biochemical spaces in drug development, potentially transforming the way we approach these complex biological interactions.

Mixed Stereochemistry Macrocycle Acts as a Helix-Stabilizing Peptide N-Cap

Interactions between proteins and α-helical peptides have been the focus of drug discovery campaigns. However, the large interfaces formed between multiple turns of an α-helix and a binding protein represent a significant challenge to inhibitor discovery. Modified peptides featuring helix-stabilizing macrocycles have shown promise as inhibitors of these interactions. Here, we tested the ability of N-terminal to side-chain thioether-cyclized peptides to inhibit the α-helix binding protein Mcl-1, by screening a trillion-scale library. The enriched peptides were lariats featuring a small, four-amino-acid N-terminal macrocycle followed by a short linear sequence that resembled the natural α-helical Mcl-1 ligands. These "Heliats" (helical lariats) bound Mcl-1 with tens of nM affinity, and inhibited the interaction between Mcl-1 and a natural peptide ligand. Macrocyclization was found to stabilize α-helical structures and significantly contribute to affinity and potency. Yet, the 2nd and 3rd positions within the macrocycle were permissible to sequence variation, so that a minimal macrocyclic motif, of an N-acetylated d-phenylalanine at the 1st position thioether connected to a cysteine at the 4th, could be grafted into a range of peptides and stabilize helical conformations. We found that d-stereochemistry is more helix-stabilizing than l- at the 1st position in the motif, as the d-amino acid can utilize polyproline II torsional angles that allow for more optimal intrachain hydrogen bonding. This mixed stereochemistry macrocyclic N-cap is synthetically accessible, requiring only minor modifications to standard solid-phase peptide synthesis, and its compatibility with peptide screening can provide ready access to helix-focused peptide libraries for de novo inhibitor discovery.

De Novo Rational Design of Peptide-Based Protein-Protein Inhibitors (Pep-PPIs) Approach by Mapping the Interaction Motifs of the PP Interface and Physicochemical Filtration: A Case on p25-Cdk5-Mediated Neurodegenerative Diseases

Protein-protein interactions, or PPIs, are a part of every biological activity and have been linked to a number of diseases, including cancer, infectious diseases, and neurological disorders. As such, targeting PPIs is considered a strategic and vital approach in the development of new medications. Nonetheless, the wide and flat contact interface makes it difficult to find small-molecule PP inhibitors. An alternative strategy would be to use the PPI interaction motifs as building blocks for the design of peptide-based inhibitors. Herein, we designed 12-mer peptide inhibitors to target p25-inducing-cyclin-dependent kinase (Cdk5) hyperregulation, a PPI that has been shown to perpetuate neuroinflammation, which is one of the major causal implications of neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and frontotemporal dementia. We generated a library of 5 062 500 peptide combination sequences (PCS) derived from the interaction motif of Cdk5/p25 PP interface. The 20 amino acids were differentiated into six groups, namely, hydrophobic (aliphatic), aromatic, basic, acidic, unique, and polar uncharged, on the basis of their physiochemical properties. To preserve the interaction motif necessary for ideal binding, de novo modeling of all possible peptide sequence substitutions was considered. A set of filters, backed by the Support Vector Machine (SVM) algorithm, was then used to create a shortlisted custom peptide library that met specific bioavailability, toxicity, and therapeutic relevance, leading to a refined library of 15 PCS. A greedy algorithm and coarse-grained force field were used to predict peptide structure and folding before subsequent modeling studies. Molecular docking was performed to estimate the relative binding affinities, and out of the top hits, Pep15 was subjected to molecular dynamics simulations and binding free-energy calculations in comparison to a known peptide inhibitor with experimental data (template peptide). Interestingly, the identified peptide through our protocol, Pep15, was found to show a significantly higher binding affinity than the reference template peptide (-48.10 ± 0.23 kcal/mol and -17.53 ± 0.27 kcal/mol, respectively). In comparison to the template peptide, Pep15 was found to possess a more compact and buried surface area, tighter binding landscape, and reduced conformational variability, leading to enhanced structural and kinetic stability of the Cdk5/p25 complex. Notably, both peptide inhibitors were found to have a minimal impact on the architectural integrity of the Cdk5/p25 secondary structure. Herein, we propose Pep15 as a novel and potentially disruptive peptide drug for Cdk5/p25-mediated neurodegenerative phenotypes that require further clinical investigation. The systematic protocol and findings of this report would serve as a valuable tool in the identification of critical PPI interface reactive residues, designing of analogs, and identification of more potent peptide-based PPI inhibitors.

Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.

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.

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.

Electrostatic Impact of Brefeldin A on Thiocyanate Probes Surrounding the Interface of Arf1-BFA-ARNO4M, a Protein-Drug-Protein Complex

Protein-protein interactions regulate many cellular processes, making them ideal drug candidates. Design of such drugs, however, is hindered by a lack of understanding of the factors that contribute to the interaction specificity. Specific protein-protein complexes possess both structural and electrostatic complementarity, and while structural complementarity of protein complexes has been extensively investigated, fundamental understanding of the complicated networks of electrostatic interactions at these interfaces is lacking, thus hindering the rational design of orthosterically binding small molecules. To better understand the electrostatic interactions at protein interfaces and how a small molecule could contribute to and fit within that environment, we used a model protein-drug-protein system, Arf1-BFA-ARNO4M, to investigate how small molecule brefeldin A (BFA) perturbs the Arf1-ARNO4M interface. By using nitrile probe labeled Arf1 sites and measuring vibrational Stark effects as well as temperature dependent infrared shifts, we measured changes in the electric field and hydrogen bonding at this interface upon BFA binding. At all five probe locations of Arf1, we found that the vibrational shifts resulting from BFA binding corroborate trends found in Poisson-Boltzmann calculations of surface potentials of Arf1-ARNO4M and Arf1-BFA-ARNO4M, where BFA contributes negative electrostatic potential to the protein interface. The data also corroborate previous hypotheses about the mechanism of interfacial binding and confirm that alternating patches of hydrophobic and polar interactions lead to BFA binding specificity. These findings demonstrate the impact of BFA on this protein-protein interface and have implications for the design of other interfacial drug candidates.

Recent Deep Learning Applications to Structure-Based Drug Design

Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning-based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.

Discovery of Ligands for TRIM58, a Novel Tissue-Selective E3 Ligase

Redirecting E3 ligases to neo-substrates, leading to their proteasomal disassembly, known as targeted protein degradation (TPD), has emerged as a promising alternative to traditional, occupancy-driven pharmacology. Although the field has expanded tremendously over the past years, the choice of E3 ligases remains limited, with an almost exclusive focus on CRBN and VHL. Here, we report the discovery of novel ligands to the PRY-SPRY domain of TRIM58, a RING ligase that is specifically expressed in erythroid precursor cells. A DSF screen, followed by validation using additional biophysical methods, led to the identification of TRIM58 ligand TRIM-473. A basic SAR around the chemotype was established by utilizing a competitive binding assay employing a short FP peptide probe derived from an endogenous TRIM58 substrate. The X-ray co-crystal structure of TRIM58 in complex with TRIM-473 gave insights into the binding mode and potential exit vectors for bifunctional degrader design.

TMPRSS2 and SARS-CoV-2 SPIKE interaction assay for uHTS

SARS-CoV-2, the coronavirus that causes the disease COVID-19, has claimed millions of lives over the past 2 years. This demands rapid development of effective therapeutic agents that target various phases of the viral replication cycle. The interaction between host transmembrane serine protease 2 (TMPRSS2) and viral SPIKE protein is an important initial step in SARS-CoV-2 infection, offering an opportunity for therapeutic development of viral entry inhibitors. Here, we report the development of a time-resolved fluorescence/Förster resonance energy transfer (TR-FRET) assay for monitoring the TMPRSS2-SPIKE interaction in lysate from cells co-expressing these proteins. The assay was configured in a 384-well-plate format for high-throughput screening with robust assay performance. To enable large-scale compound screening, we further miniaturized the assay into 1536-well ultrahigh-throughput screening (uHTS) format. A pilot screen demonstrated the utilization of the assay for uHTS. Our optimized TR-FRET uHTS assay provides an enabling platform for expanded screening campaigns to discover new classes of small-molecule inhibitors that target the SPIKE and TMPRSS2 protein-protein interaction.

Design of rigid protein-protein interaction inhibitors enables targeting of undruggable Mcl-1

The structure-based design of small-molecule inhibitors targeting protein-protein interactions (PPIs) remains a huge challenge as the drug must bind typically wide and shallow protein sites. A PPI target of high interest for hematological cancer therapy is myeloid cell leukemia 1 (Mcl-1), a prosurvival guardian protein from the Bcl-2 family. Despite being previously considered undruggable, seven small-molecule Mcl-1 inhibitors have recently entered clinical trials. Here, we report the crystal structure of the clinical-stage inhibitor AMG-176 bound to Mcl-1 and analyze its interaction along with clinical inhibitors AZD5991 and S64315. Our X-ray data reveal high plasticity of Mcl-1 and a remarkable ligand-induced pocket deepening. Nuclear Magnetic Resonance (NMR)-based free ligand conformer analysis demonstrates that such unprecedented induced fit is uniquely achieved by designing highly rigid inhibitors, preorganized in their bioactive conformation. By elucidating key chemistry design principles, this work provides a roadmap for targeting the largely untapped PPI class more successfully.

Identification of small-molecule protein-protein interaction inhibitors for NKG2D

NKG2D (natural-killer group 2, member D) is a homodimeric transmembrane receptor that plays an important role in NK, γδ+, and CD8+ T cell-mediated immune responses to environmental stressors such as viral or bacterial infections and oxidative stress. However, aberrant NKG2D signaling has also been associated with chronic inflammatory and autoimmune diseases, and as such NKG2D is thought to be an attractive target for immune intervention. Here, we describe a comprehensive small-molecule hit identification strategy and two distinct series of protein-protein interaction inhibitors of NKG2D. Although the hits are chemically distinct, they share a unique allosteric mechanism of disrupting ligand binding by accessing a cryptic pocket and causing the two monomers of the NKG2D dimer to open apart and twist relative to one another. Leveraging a suite of biochemical and cell-based assays coupled with structure-based drug design, we established tractable structure-activity relationships with one of the chemical series and successfully improved both the potency and physicochemical properties. Together, we demonstrate that it is possible, albeit challenging, to disrupt the interaction between NKG2D and multiple protein ligands with a single molecule through allosteric modulation of the NKG2D receptor dimer/ligand interface.

Going beyond Binary: Rapid Identification of Protein-Protein Interaction Modulators Using a Multifragment Kinetic Target-Guided Synthesis Approach

Kinetic target-guided synthesis (KTGS) is a powerful screening approach that enables identification of small molecule modulators for biomolecules. While many KTGS variants have emerged, a majority of the examples suffer from limited throughput and a poor signal/noise ratio, hampering reliable hit detection. Herein, we present our optimized multifragment KTGS screening strategy that tackles these limitations. This approach utilizes selected reaction monitoring liquid chromatography tandem mass spectrometry for hit detection, enabling the incubation of 190 fragment combinations per screening well. Consequentially, our fragment library was expanded from 81 possible combinations to 1710, representing the largest KTGS screening library assembled to date. The expanded library was screened against Mcl-1, leading to the discovery of 24 inhibitors. This work unveils the true potential of KTGS with respect to the rapid and reliable identification of hits, further highlighting its utility as a complement to the existing repertoire of screening methods used in drug discovery.

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

Virtual screening and molecular dynamics simulations provide insight into repurposing drugs against SARS-CoV-2 variants Spike protein/ACE2 interface

After over two years of living with Covid-19 and hundreds of million cases worldwide there is still an unmet need to find proper treatments for the novel coronavirus, due also to the rapid mutation of its genome. In this context, a drug repositioning study has been performed, using in silico tools targeting Delta Spike protein/ACE2 interface. To this aim, it has been virtually screened a library composed by 4388 approved drugs through a deep learning-based QSAR model to identify protein-protein interactions modulators for molecular docking against Spike receptor binding domain (RBD). Binding energies of predicted complexes were calculated by Molecular Mechanics/Generalized Born Surface Area from docking and molecular dynamics simulations. Four out of the top twenty ranking compounds showed stable binding modes on Delta Spike RBD and were evaluated also for their effectiveness against Omicron. Among them an antihistaminic drug, fexofenadine, revealed very low binding energy, stable complex, and interesting interactions with Delta Spike RBD. Several antihistaminic drugs were found to exhibit direct antiviral activity against SARS-CoV-2 in vitro, and their mechanisms of action is still debated. This study not only highlights the potential of our computational methodology for a rapid screening of variant-specific drugs, but also represents a further tool for investigating properties and mechanisms of selected drugs.

3JC48-3 (methyl 4'-methyl-5-(7-nitrobenzo[c][1,2,5]oxadiazol-4-yl)-[1,1'-biphenyl]-3-carboxylate): a novel MYC/MAX dimerization inhibitor reduces prostate cancer growth

The proto-oncogene cellular myelocytomatosis (c-Myc) is a transcription factor that is upregulated in several human cancers. Therapeutic targeting of c-Myc remains a challenge because of a disordered protein tertiary structure. The basic helical structure and zipper protein of c-Myc forms an obligate heterodimer with its partner MYC-associated factor X (MAX) to function as a transcription factor. An attractive strategy is to inhibit MYC/MAX dimerization to decrease c-Myc transcriptional function. Several methods have been described to inhibit MYC/MAX dimerization including small molecular inhibitors and proteomimetics. We studied the effect of a second-generation small molecular inhibitor 3JC48-3 on prostate cancer growth and viability. In our experimental studies, we found 3JC48-3 decreases prostate cancer cells' growth and viability in a dose-dependent fashion in vitro. We confirmed inhibition of MYC/MAX dimerization by 3JC48-3 using immunoprecipitation experiments. We have previously shown that the MYC/MAX heterodimer is a transcriptional repressor of a novel kinase protein kinase D1 (PrKD1). Treatment with 3JC48-3 upregulated PrKD1 expression and phosphorylation of known PrKD1 substrates: the threonine 120 (Thr-120) residue in beta-catenin and the serine 216 (Ser-216) in Cell Division Cycle 25 (CDC25C). The mining of gene expression in human metastatic prostate cancer samples demonstrated an inverse correlation between PrKD1 and c-Myc expression. Normal mice and mice with patient-derived prostate cancer xenografts (PDX) tolerated intraperitoneal injections of 3JC48-3 up to 100 mg/kg body weight without dose-limiting toxicity. Preliminary results in these PDX mouse models suggest that 3JC48-3 may be effective in decreasing the rate of tumor growth. In conclusion, our study demonstrates that 3JC48-3 is a potent MYC/MAX heterodimerization inhibitor that decreases prostate cancer growth and viability associated with upregulation of PrKD1 expression and kinase activity.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life's Mechanism

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5-10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

First-in-Class Allosteric Inhibitors of DNMT3A Disrupt Protein-Protein Interactions and Induce Acute Myeloid Leukemia Cell Differentiation

We previously identified two structurally related pyrazolone (compound 1) and pyridazine (compound 2) allosteric inhibitors of DNMT3A through screening of a small chemical library. Here, we show that these compounds bind and disrupt protein-protein interactions (PPIs) at the DNMT3A tetramer interface. This disruption is observed with distinct partner proteins and occurs even when the complexes are acting on DNA, which better reflects the cellular context. Compound 2 induces differentiation of distinct myeloid leukemia cell lines including cells with mutated DNMT3A R882. To date, small molecules targeting DNMT3A are limited to competitive inhibitors of AdoMet or DNA and display extreme toxicity. Our work is the first to identify small molecules with a mechanism of inhibition involving the disruption of PPIs with DNMT3A. Ongoing optimization of compounds 1 and 2 provides a promising basis to induce myeloid differentiation and treatment of diseases that display aberrant PPIs with DNMT3A, such as acute myeloid leukemia.

Application of In Silico Filtering and Isothermal Titration Calorimetry for the Discovery of Small Molecule Inhibitors of MDM2

The initial discovery phase of protein modulators, which consists of filtering molecular libraries and in vitro direct binding validation, is central in drug discovery. Thus, virtual screening of large molecular libraries, together with the evaluation of binding affinity by isothermal calorimetry, generates an efficient experimental setup. Herein, we applied virtual screening for discovering small molecule inhibitors of MDM2, a major negative regulator of the tumor suppressor p53, and thus a promising therapeutic target. A library of 20 million small molecules was screened against an averaged model derived from multiple structural conformations of MDM2 based on published structures. Selected molecules originating from the computational filtering were tested in vitro for their direct binding to MDM2 via isothermal titration calorimetry. Three new molecules, representing distinct chemical scaffolds, showed binding to MDM2. These were further evaluated by exploring structure-similar chemical analogues. Two scaffolds were further evaluated by de novo synthesis of molecules derived from the initial molecules that bound MDM2, one with a central oxoazetidine acetamide and one with benzene sulfonamide. Several molecules derived from these scaffolds increased wild-type p53 activity in MCF7 cancer cells. These set a basis for further chemical optimization and the development of new chemical entities as anticancer drugs.

In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex

Malaria causes about half a million deaths per year, mainly in children below 5 years of age. Cytoadherence of Plasmodium falciparum infected erythrocytes in brain and placenta has been linked to severe malaria and malarial related deaths. Cytoadherence is mediated by binding of human receptor gC1qR to the DBLβ12 domain of a P. falciparum erythrocyte membrane protein family 1 (PfEMP1) protein. In the present work, molecular dynamic simulation was extensively studied for the gC1qR-DBLβ12 complex. The stabilized protein complex was used to study the protein–protein interface interactions and mapping of interactive amino acid residues as hotspot were performed. Prediction of inhibitors were performed by using virtual protein–protein inhibitor database Timbal screening of about 15,000 compounds. In silico mutagenesis studies, binding profile and protein ligand interaction fingerprinting were used to strengthen the screening of the potential inhibitors of gC1qR-DBLβ12 interface. Six compounds were selected and were further subjected to the MAIP analysis and ADMET studies. From these six compounds, the compounds 3, 5, and 6 were found to outperform on all screening criteria from the rest selected compounds. These compounds may provide novel drugs to treat and manage severe falciparum malaria. Additionally. the identified hotspots can be used in future for designing novel interventions for disruption of interface interactions, such as through peptides or vaccines. Futher in vitro and in vivo studies are required for the confirmation of these compounds as potential inhibitors of gC1qR-DBLβ12 interaction.

Structure-based assessment and druggability classification of protein-protein interaction sites

The featureless interface formed by protein–protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.

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.

Interfacial Peptides as Affinity Modulating Agents of Protein-Protein Interactions

The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, "hot spots", as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ-TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP.

Analysis of physicochemical properties of protein-protein interaction modulators suggests stronger alignment with the "rule of five"

Despite the important roles played by protein-protein interactions (PPIs) in disease, they have been long considered as 'undruggable'. However, recent advances have suggested that PPIs are druggable but may not follow conventional rules of 'drug ability'. Here we explore which physicochemical parameters are essential for a PPI modulator to be a clinical drug by analysing the physicochemical properties of small-molecule PPI modulators in the market, in clinical trials, and published. Our analysis reveals that those compounds currently on the market have a larger range of values for most of the physicochemical parameters, whereas those in clinical trials fit much more stringently to standard drug-like parameters. This observation was particularly true for molecular weight, clog P and topological polar surface area, where aside from a few outliers, most of the compounds in clinical trials fit within standard drug-like parameters. This implies that the newer PPI modulators are more drug-like than those currently on the market, suggesting that designing new PPI-specific screening libraries should remain within standard drug-like parameters in order to obtain a clinical candidate. Taken together, our analysis has important implications for designing future drug discovery campaigns aimed at targeting PPIs.

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the "Rule-of-Four" (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.

A Genetically Encoded Bioluminescence Intracellular Nanosensor for Androgen Receptor Activation Monitoring in 3D Cell Models

In recent years, there has been an increasing demand for predictive and sensitive in vitro tools for drug discovery. Split complementation assays have the potential to enlarge the arsenal of in vitro tools for compound screening, with most of them relying on well-established reporter gene assays. In particular, ligand-induced complementation of split luciferases is emerging as a suitable approach for monitoring protein–protein interactions. We hereby report an intracellular nanosensor for the screening of compounds with androgenic activity based on a split NanoLuc reporter. We also confirm the suitability of using 3D spheroids of Human Embryonic Kidney (HEK-293) cells for upgrading the 2D cell-based assay. A limit of detection of 4 pM and a half maximal effective concentration (EC₅₀) of 1.7 ± 0.3 nM were obtained for testosterone with HEK293 spheroids. This genetically encoded nanosensor also represents a new tool for real time imaging of the activation state of the androgen receptor, thus being suitable for analysing molecules with androgenic activity, including new drugs or endocrine disrupting molecules.