Reaching for high-hanging fruit in drug discovery at protein-protein interfaces

Using a stacked ensemble learning framework to predict modulators of protein-protein interactions

Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, cancer treatment, and other fields. In this study, we developed a stacking ensemble computational framework, SELPPI, based on a genetic algorithm and tree-based machine learning method for effectively predicting new modulators targeting protein-protein interactions. More specifically, extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), random forest (RF), cascade forest, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) were used as basic learners. Seven types of chemical descriptors were taken as the input characteristic parameters. Primary predictions were obtained with each basic learner-descriptor pair. Then, the 6 methods mentioned above were used as meta learners and trained on the primary prediction in turn. The most efficient method was utilized as the meta learner. Finally, the genetic algorithm was used to select the optimal primary prediction output as the input of the meta learner for secondary prediction to obtain the final result. We systematically evaluated our model on the pdCSM-PPI datasets. To our knowledge, our model outperformed all existing models, which demonstrates its great power.

Metadynamics simulations of ligands binding to protein surfaces: a novel tool for rational drug design

Structure-based drug design protocols may encounter difficulties to investigate poses when the biomolecular targets do not exhibit typical binding pockets. In this study, by providing two concrete examples from our labs, we suggest that the combination of metadynamics free energy methods (validated against affinity measurements), along with experimental structural information (by X-ray crystallography and NMR), can help to identify the poses of ligands on protein surfaces. The simulation workflow proposed here was implemented in a widely used code, namely GROMACS, and it could straightforwardly be applied to various drug-design campaigns targeting ligands' binding to protein surfaces.

DNA-PKcs as an upstream mediator of OCT4-induced MYC activation in small cell lung cancer

Small cell lung cancer (SCLC) is a neuroendocrine tumor noted for the rapid development of both metastases and resistance to chemotherapy. High mutation burden, ubiquitous loss of TP53 and RB1, and a mutually exclusive amplification of MYC gene family members contribute to genomic instability and make the development of new targeted agents a challenge. Previously, we reported a novel OCT4-induced MYC transcriptional activation pathway involving c-MYC, pOCT4^S111, and MAPKAPK2 in progressive neuroblastoma, also a neuroendocrine tumor. Using tumor microarray analysis of clinical samples and preclinical models, we now report a correlation in expression between these proteins in SCLC. In correlating c-MYC protein expression with genomic amplification, we determined that some SCLC cell lines exhibited high c-MYC without genomic amplification, implying amplification-independent MYC activation. We then confirmed direct interaction between OCT4 and DNA-PKcs and identified specific OCT4 and DNA-PKcs binding sites. Knock-down of both POU5F1 (encoding OCT4) and PRKDC (encoding DNA-PKcs) resulted in decreased c-MYC expression. Further, we confirmed binding of OCT4 to the promoter/enhancer region of MYC. Together, these data establish the presence of a DNA-PKcs/OCT4/c-MYC pathway in SCLCs. We then disruptively targeted this pathway and demonstrated anticancer activity in SCLC cell lines and xenografts using both DNA-PKcs inhibitors and a protein-protein interaction inhibitor of DNA-PKcs and OCT4. In conclusion, we demonstrate here that DNA-PKcs can mediate high c-MYC expression in SCLCs, and that this pathway may represent a new therapeutic target for SCLCs with high c-MYC expression.

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

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

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.

Intracellular Delivery of an Antibody-Like Molecule Capable of Inhibiting c-Myc

A modular nanotransporter (MNT) carrying the sequence of an antibody-like molecule, anti-c-Myc nanobody, was synthesized and characterized. It was demonstrated that the created MNT is able to interact with the target protein, c-Myc oncogene, with a dissociation constant of 46 ± 14 nM, internalize into target cells, change Myc-dependent expression, and exert an antiproliferative effect.

Functional advantages of building nanosystems using multiple molecular components

Over half of all the natural nanomachines in living organisms are multimeric and likely exploit the self-assembly of their components to provide functional benefits. However, the advantages and disadvantages of building nanosystems using multiple molecular components remain relatively unexplored at the thermodynamic, kinetic and functional levels. In this study we used theory and a simple DNA-based model that forms the same nanostructures with different numbers of components to advance our knowledge in this area. Despite its lower assembly rate, we found that a system built with three components may undergo a more cooperative assembly transition from less preorganized components, which facilitates the emergence of functionalities. Using simple variations of its components, we also found that trimeric nanosystems display a much higher level of programmability than their dimeric counterparts because they can assemble with various levels of cooperativity, self-inhibition and time-dependent properties. We show here how two simple strategies (for example, cutting and adding components) can be employed to efficiently programme the regulatory function of a more complex, artificially selected, RNA-cleaving catalytic nanosystem.

Structural Adaptation of the Single-Stranded DNA-Binding Protein C-Terminal to DNA Metabolizing Partners Guides Inhibitor Design

Single-stranded DNA-binding protein (SSB) is a bacterial interaction hub and an appealing target for antimicrobial therapy. Understanding the structural adaptation of the disordered SSB C-terminus (SSB-Ct) to DNA metabolizing enzymes (e.g., ExoI and RecO) is essential for designing high-affinity SSB mimetic inhibitors. Molecular dynamics simulations revealed the transient interactions of SSB-Ct with two hot spots on ExoI and RecO. The residual flexibility of the peptide-protein complexes allows adaptive molecular recognition. Scanning with non-canonical amino acids revealed that modifications at both termini of SSB-Ct could increase the affinity, supporting the two-hot-spot binding model. Combining unnatural amino acid substitutions on both segments of the peptide resulted in enthalpy-enhanced affinity, accompanied by enthalpy-entropy compensation, as determined by isothermal calorimetry. NMR data and molecular modeling confirmed the reduced flexibility of the improved affinity complexes. Our results highlight that the SSB-Ct mimetics bind to the DNA metabolizing targets through the hot spots, interacting with both of segments of the ligands.

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

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

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network

While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins makes these methods ineffective in dealing with novel proteins. The advent of AlphaFold2 presents a significant opportunity and also a challenge to predict PPIs in a straightforward way based on monomer structures while controlling bias from protein sequences. In this work, we established Structure and Graph-based Predictions of Protein Interactions (SGPPI), a structure-based DL framework for predicting PPIs, using the graph convolutional network. In particular, SGPPI focused on protein patches on the protein-protein binding interfaces and extracted the structural, geometric and evolutionary features from the residue contact map to predict PPIs. We demonstrated that our model outperforms traditional machine learning methods and state-of-the-art DL-based methods using non-representation-bias benchmark datasets. Moreover, our model trained on human dataset can be reliably transferred to predict yeast PPIs, indicating that SGPPI can capture converging structural features of protein interactions across various species. The implementation of SGPPI is available at https://github.com/emerson106/SGPPI.

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology

Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

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

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

BACKGROUND

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

AREAS COVERED

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

EXPERT OPINION

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

The development of microfluidic-based western blotting: Technical advances and future perspectives

Over the past two decades significant technical advancement in the field of western blotting has been made possible through the utilization of microfluidic technologies. In this review we provide a critical overview of these advancements, highlighting the advantages and disadvantages of each approach. Particular attention is paid to the development of now commercially available systems, including those for single cell analysis. This review also discusses more recent developments, including algorithms for automation and/or improved quantitation, the utilization of different materials/chemistries, use of projection electrophoresis, and the development of triBlots. Finally, the review includes commentary on future advances in the field based on current developments, and the potential of these systems for use as point-of-care devices in healthcare.

Discovery of small-molecule PD-1/PD-L1 antagonists through combined virtual screening and experimental validation

Inhibition of the interaction between the PD-1 protein on activated lymphocytes and the PD-L1 protein on tumors represents a novel therapeutic approach for selective activation of the innate immune response against a variety of cancers. Therefore, the present study utilized a combined virtual and experimental screening approach to screen databases of both lead-like and larger molecules for identification of novel inhibitors of PD-1/PD-L1 interaction. First, high-throughput virtual screening of ∼3.7 million lead-like molecules using a rigid-receptor docking approach against both human PD-1 and PD-L1 proteins revealed possible small-molecule tractability of PD-1, but not PD-L1, binding interface. The subsequent work, therefore, involved screening of the National Cancer Institute (NCI) compound database against the PD-1 pocket. Several NCI compounds were identified with potential to bind to the PD-1 pocket and in turn inhibit the PD-1/PD-L1 interaction. The dynamic binding behavior of these molecules was further investigated using long 100 ns molecular dynamics (MD) stimulation revealing NSC631535 to be a potentially stable binder at PD-1 interface pocket. In support of these MD data, the experimental testing of NSC631535 exhibited 50% inhibition at ∼15 μM test concentration. The observed activity of this compound is promising as despite its relatively low molecular weight (415.5 g/mol) it is still capable of inhibiting the PD-1/PD-L1 interaction having a large interface area (∼1970 Å²). In summary, our integrated computational and experimental screening led to identification of a novel PD-1 antagonist that may serve as a starting point for further optimization into more potent small-molecule PD-1/PD-L1 inhibitors for cancer immunotherapy.

Biomimetic Synthesis of Chejuenolides A-C by a Cryptic Lactone-Based Macrocyclization: Stereochemical Implications in Biosynthesis

A hypothetical Mannich macrocyclization in the biosynthesis of chejuenolides A-C served as the basis for the synthetic design herein. Using a lactone-based linear precursor constructed via a tactic sequence of aldol-Julia-aldol reactions on a gram scale, the biomimetic total synthesis and structural validation of chejuenolides A-C were successfully achieved for the first time. The β-oxo-δ-lactone unit in the macrocyclized adducts was fragile and readily converted to a series of C2/C18-diastereoisomers via a decarboxylation and protonation pathway. Stereochemical identification of the biosynthetic precursor (O3P2) confirmed structural adherence to the given macrocycles and previously clarified lankacidins. Moreover, the stereovariants of the linear precursor designed for the macrocyclization event highlighted the unparalleled impact of using this biomimetic approach to determine the stereoselectivity in the proposed enzymatic reaction by reviving the lost or unstable intermediate.

High-Throughput Screening of Stapled Helical Peptides in Drug Discovery

Therapeutic peptides have revolutionized treatment for a number of human diseases. In particular, the past two decades have witnessed rapid progress of stapled helical peptides in drug discovery. Stapled helical peptides are chemically modified and constrained in their bioactive α-helical conformation. Compared to unstabilized linear peptides, stapled helical peptides exhibit superior binding affinity and selectivity, enhanced membrane permeability, and improved metabolic stability, presenting exciting promise for targeting otherwise challenging protein-protein interfaces. In this Perspective, we summarize recent applications of high-throughput screening technologies for identification of potent stapled helical peptides with optimized binding properties. We expect to provide a broad reference to accelerate the development of stapled helical peptides as the next generation of therapeutic peptides for various human diseases.

Integrating Protein Interaction Surface Prediction with a Fragment-Based Drug Design: Automatic Design of New Leads with Fragments on Energy Surfaces

Protein-protein interactions (PPIs) have emerged in the past years as significant pharmacological targets in the development of new therapeutics due to their key roles in determining pathological pathways. Herein, we present fragments on energy surfaces, a simple and general design strategy that integrates the analysis of the dynamic and energetic signatures of proteins to unveil the substructures involved in PPIs, with docking, selection, and combination of drug-like fragments to generate new PPI inhibitor candidates. Specifically, structural representatives of the target protein are used as inputs for the blind physics-based prediction of potential protein interaction surfaces using the matrix of low coupling energy decomposition method. The predicted interaction surfaces are subdivided into overlapping windows that are used as templates to direct the docking and combination of fragments representative of moieties typically found in active drugs. This protocol is then applied and validated using structurally diverse, important PPI targets as test systems. We demonstrate that our approach facilitates the exploration of the molecular diversity space of potential ligands, with no requirement of prior information on the location and properties of interaction surfaces or on the structures of potential lead compounds. Importantly, the hit molecules that emerge from our ab initio design share high chemical similarity with experimentally tested active PPI inhibitors. We propose that the protocol we describe here represents a valuable means of generating initial leads against difficult targets for further development and refinement.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

tLyp-1: A peptide suitable to target NRP-1 receptor

Targeting vascular endothelial growth factor receptor (VEFGR) and its co-receptor neuropilin-1 (NRP-1) is an interesting vascular strategy. tLyp-1 is a tumor-homing and penetrating peptide of 7 amino acids (CGNKRTR). It is a truncated form of Lyp-1 (CGNKRTRGC), which is known to target NRP-1 receptor, with high affinity and specificity. It is mediated by endocytosis via C-end rule (CendR) internalization pathway. The aim of this study is to evaluate the importance of each amino acid in the tLyp-1 sequence through alanine-scanning (Ala-scan) technique, during which each of the amino acid in the sequence was systematically replaced by alanine to produce 7 different analogues. In silico approach through molecular docking and molecular dynamics are employed to understand the interaction between the peptide and its analogues with the NRP-1 receptor, followed by in vitro ligand binding assay study. The C-terminal Arg is crucial in the interaction of tLyp-1 with NRP-1 receptor. Substituting this residue dramatically reduces the affinity of this peptide which is clearly seen in this study. Lys-4 is also important in the interaction, which is confirmed via the in vitro study and the MM-PBSA analysis. The finding in this study supports the CendR, in which the presence of R/K-XX-R/K motif is essential in the binding of a ligand with NRP-1 receptor. This presented work will serve as a guide in the future work pertaining the development of active targeting agent towards NRP-1 receptor.

AMPA GluA2 subunit competitive inhibitors for PICK1 PDZ domain: Pharmacophore-based virtual screening, molecular docking, molecular dynamics simulation, and ADME studies

PICK1 (Protein interacting with C kinase-1) plays a key role in the regulation of intracellular trafficking of AMPA GluA2 subunit that is linked with synaptic plasticity. PICK1 is a scaffolding protein and binds numerous proteins through its PDZ domain. Research showed that synaptic plasticity is altered upon disrupting the GluA2-PDZ interactions. Inhibiting PDZ and GluA2 binding lead to beneficial effects in the cure of neurological diseases thus, targeting PDZ domain is proposed as a novel therapeutic target in such diseases. For this, various classes of synthetic peptides were tested. Though small organic molecules have been utilized to prevent these interactions, the number of such molecules is inadequate. Hence, in this study, ten molecular libraries containing large number of molecules were screened against the PDZ domain using pharmacophore-based virtual screening to find the best hits for the PDZ domain. Molecular docking and molecular dynamics simulation studies revealed that Hit_II is a potent inhibitor for the PDZ domain and confirm the allosteric nature of Hit_III. Additionally, ADME analysis suggests the drug-likeness of both Hit_II and Hit_III. This study suggests that tested hits may have potency against the PDZ domain and can be considered effective to treat neurological disorders.Communicated by Ramaswamy H. Sarma.

Computed cancer interactome explains the effects of somatic mutations in cancers

Protein-protein interactions (PPIs) are involved in almost all essential cellular processes. Perturbation of PPI networks plays critical roles in tumorigenesis, cancer progression, and metastasis. While numerous high-throughput experiments have produced a vast amount of data for PPIs, these data sets suffer from high false positive rates and exhibit a high degree of discrepancy. Coevolution of amino acid positions between protein pairs has proven to be useful in identifying interacting proteins and providing structural details of the interaction interfaces with the help of deep learning methods like AlphaFold (AF). In this study, we applied AF to investigate the cancer protein-protein interactome. We predicted 1,798 PPIs for cancer driver proteins involved in diverse cellular processes such as transcription regulation, signal transduction, DNA repair, and cell cycle. We modeled the spatial structures for the predicted binary protein complexes, 1,087 of which lacked previous 3D structure information. Our predictions offer novel structural insight into many cancer-related processes such as the MAP kinase cascade and Fanconi anemia pathway. We further investigated the cancer mutation landscape by mapping somatic missense mutations (SMMs) in cancer to the predicted PPI interfaces and performing enrichment and depletion analyses. Interfaces enriched or depleted with SMMs exhibit different preferences for functional categories. Interfaces enriched in mutations tend to function in pathways that are deregulated in cancers and they may help explain the molecular mechanisms of cancers in patients; interfaces lacking mutations appear to be essential for the survival of cancer cells and thus may be future targets for PPI modulating drugs.

Computer- and NMR-Aided Design of Small-Molecule Inhibitors of the Hub1 Protein

By binding to the spliceosomal protein Snu66, the human ubiquitin-like protein Hub1 is a modulator of the spliceosome performance and facilitates alternative splicing. Small molecules that bind to Hub1 would be of interest to study the protein-protein interaction of Hub1/Snu66, which is linked to several human pathologies, such as hypercholesterolemia, premature aging, neurodegenerative diseases, and cancer. To identify small molecule ligands for Hub1, we used the interface analysis, peptide modeling of the Hub1/Snu66 interaction and the fragment-based NMR screening. Fragment-based NMR screening has not proven sufficient to unambiguously search for fragments that bind to the Hub1 protein. This was because the Snu66 binding pocket of Hub1 is occupied by pH-sensitive residues, making it difficult to distinguish between pH-induced NMR shifts and actual binding events. The NMR analyses were therefore verified experimentally by microscale thermophoresis and by NMR pH titration experiments. Our study found two small peptides that showed binding to Hub1. These peptides are the first small-molecule ligands reported to interact with the Hub1 protein.

Templated Generation of a Bcl-xL Inhibitor by Isomer-Free SPAAC Based on Azacyclonon-5-yne

High-affinity inhibitors of large protein-protein interactions often have a high molecular weight, which compromises their cell permeability and oral bioavailability. We recently presented isomer-free, strain-promoted azide-alkyne cycloaddition (iSPAAC) as a method by which to generate large, chemically uniform bioactive molecules inside living cells from two smaller components with higher cell permeability. Here, we present the synthesis of Fmoc-protected azacyclonon-5-yne (Fmoc-ACN) as the first cyclononyne suitable for iSPAAC. ACN facilitated the structure-guided development of a single-digit micromolar triazole inhibitor of the protein-protein interaction domain of the antiapoptotic protein Bcl-x_L . Inhibitor formation in aqueous buffer at 37 °C, templated by the target protein Bcl-x_L , proceeded 2800 times faster than the reaction between Fmoc-ACN and benzyl azide under standard conditions in acetonitrile. Our data demonstrate the utility of cyclononynes for iSPAAC and their potential for achieving vastly accelerated templated reactions in aqueous environments.

DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs

The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase. The experimental dataset is mainly collected from PROTAC-DB and appropriately labeled according to the DC₅₀ and Dmax values. In the model of DeepPROTACs, the ligands as well as the ligand binding pockets are generated and represented with graphs and fed into Graph Convolutional Networks for feature extraction. While SMILES representations of linkers are fed into a Bidirectional Long Short-Term Memory layer to generate the features. Experiments show that DeepPROTACs model achieves 77.95% average prediction accuracy and 0.8470 area under receiver operating characteristic curve on the test set. DeepPROTACs is available online at a web server ( https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/ ) and at github ( https://github.com/fenglei104/DeepPROTACs ).

Genetic Code Expansion in Pseudomonas putida KT2440

Pseudomonas putida KT2440 is an emerging microbial chassis for biobased chemical production from renewable feedstocks and environmental bioremediation. However, tools for studying, engineering, and modulating protein complexes and biosynthetic enzymes in this organism are largely underdeveloped. Genetic code expansion for the incorporation of unnatural amino acids (unAAs) into proteins can advance such efforts and, furthermore, enable additional controls of biological processes of the strain. In this work, we established the orthogonality of two widely used archaeal tRNA synthetase and tRNA pairs in KT2440. Following the optimization of decoding systems, four unAAs were incorporated into proteins in response to a UAG stop codon at 34.6-78% efficiency. In addition, we demonstrated the utility of genetic code expansion through the incorporation of a photocross-linking amino acid, p-benzoyl-l-phenylalanine (pBpa), into glutathione S-transferase (GstA) and a chemosensory response regulator (CheY) for protein-protein interaction studies in KT2440. This work reported the successful genetic code expansion in KT2440 for the first time. Given the diverse structure and functions of unAAs that have been added to protein syntheses using the archaeal systems, our research lays down a solid foundation for future work to study and enhance the biological functions of KT2440.

Increased slow dynamics defines ligandability of BTB domains

Efficient determination of protein ligandability, or the propensity to bind small-molecules, would greatly facilitate drug development for novel targets. Ligandability is currently assessed using computational methods that typically consider the static structural properties of putative binding sites or by experimental fragment screening. Here, we evaluate ligandability of conserved BTB domains from the cancer-relevant proteins LRF, KAISO, and MIZ1. Using fragment screening, we discover that MIZ1 binds multiple ligands. However, no ligands are uncovered for the structurally related KAISO or LRF. To understand the principles governing ligand-binding by BTB domains, we perform comprehensive NMR-based dynamics studies and find that only the MIZ1 BTB domain exhibits backbone µs-ms time scale motions. Interestingly, residues with elevated dynamics correspond to the binding site of fragment hits and recently defined HUWE1 interaction site. Our data argue that examining protein dynamics using NMR can contribute to identification of cryptic binding sites, and may support prediction of the ligandability of novel challenging targets.

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

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

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.

Exploiting the Innate Plasticity of the Programmed Cell Death-1 (PD1) Receptor to Design Pembrolizumab H3 Loop Mimics

Checkpoint blockade of the immunoreceptor programmed cell death-1 (PD1) with its ligand-1 (PDL1) by monoclonal antibodies such as pembrolizumab provided compelling clinical results in various cancer types, yet the molecular mechanism by which this drug blocks the PD1/PDL1 interface remains unclear. To address this question, we examined the conformational motion of PD1 associated with the binding of pembrolizumab. Our results revealed that the innate plasticity of both C'D and FG loops is crucial to form a deep binding groove (371 Å3 ) across several distant epitopes of PD1. This analysis ultimately provided a rational-design to create pembrolizumab H3 loop mimics [RDYRFDMGFD] into β-hairpin scaffolds. As a result, a 20-residue long β-hairpin peptide 1 e was identified as a first-in-class potent PD1-inhibitor (EC50 of 0.29 μM; Ki of 41 nM).

Native Mass Spectrometry-Guided Screening Identifies Hit Fragments for HOP-HSP90 PPI Inhibition

Contemporary medicinal chemistry considers fragment-based drug discovery (FBDD) and inhibition of protein-protein interactions (PPI) as important means of expanding the volume of druggable chemical space. However, the ability to robustly identify valid fragments and PPI inhibitors is an enormous challenge, requiring the application of sensitive biophysical methodology. Accordingly, in this study, we exploited the speed and sensitivity of nanoelectrospray (nano-ESI) native mass spectrometry to identify a small collection of fragments which bind to the TPR2AB domain of HOP. Follow-up biophysical assessment of a small selection of binding fragments confirmed binding to the single TPR2A domain, and that this binding translated into PPI inhibitory activity between TPR2A and the HSP90 C-terminal domain. An in-silico assessment of binding fragments at the PPI interfacial region, provided valuable structural insight for future fragment elaboration strategies, including the identification of losartan as a weak, albeit dose-dependent inhibitor of the target PPI.

Translational aspect in peptide drug discovery and development: An emerging therapeutic candidate

In the last two decades, protein-protein interactions (PPIs) have been used as the main target for drug development. However, with larger or superficial binding sites, it has been extremely difficult to disrupt PPIs with small molecules. On the other hand, intracellular PPIs cannot be targeted by antibodies that cannot penetrate the cell membrane. Peptides that have a combination of conformational rigidity and flexibility can be used to target difficult binding interfaces with appropriate binding affinity and specificity. Since the introduction of insulin nearly a century ago, more than 80 peptide drugs have been approved to treat a variety of diseases. These include deadly diseases such as cancer and human immunodeficiency virus infection. It is also useful against diabetes, chronic pain, and osteoporosis. Today, more research is being done on these drugs as lessons learned from earlier approaches, which are still valid today, complement newer approaches such as peptide display libraries. At the same time, integrated genomics and peptide display libraries are new strategies that open new avenues for peptide drug discovery. The purpose of this review is to examine the problems in elucidating the peptide-protein recognition mechanism. This is important to develop peptide-based interventions that interfere with endogenous protein interactions. New approaches are being developed to improve the binding affinity and specificity of existing approaches and to develop peptide agents as potentially useful drugs. We also highlight the key challenges that must be overcome in peptide drug development to realize their potential and provide an overview of recent trends in peptide drug development. In addition, we take an in-depth look at early efforts in human hormone discovery, smart medicinal chemistry and design, natural peptide drugs, and breakthrough advances in molecular biology and peptide chemistry.

R, T. K. P, Naik MR, S. J. A novel family of small molecule HIF-1 alpha stabilizers for the treatment of diabetic wounds; an integrated in silico, in vitro, and in vivo strategy

Hypoxia-inducible factor-1 alpha (HIF-1α) is a crucial regulator of wound healing, which includes epithelialization, angiogenesis, granulation, tissue development, and wound contraction. Even though diabetic wounds are hypoxic, HIF-1α levels are decreased during healing. Diabetic wound healing necessitates the modulation of hypoxia-induced responses by VHL-HIF-1α protein-protein inhibition. Our proposed hypothesis is to increase HIF-1α levels by inhibiting VHL and HIF-1α interactions by novel small bioactive molecules, accelerating diabetic wound healing. A three features (two aromatic rings and one hydrogen bond acceptor) pharmacophore hypothesis was generated from the existing HIF-1α modulators. Virtual screening was done based on the generated pharmacophore, and a library consisting of the top 20 out of 3728 compounds was selected using ZINCPharmer. Of the top 20 molecules, the pyrazole moiety was identified as the top "HIT". Five analogues of pyrazole were designed, and Scifinder ascertained the novelty. The designed compounds were synthesized and characterized by IR, Mass, and NMR. Preliminarily, we have carried out a scratch wound assay using 3T3L1 cell lines. All the synthesized compounds showed significant wound healing activity. Further, to validate the in vitro assay, the compound CI, which showed effective in vitro results was used for in vivo study. Using the diabetes mouse model, comprising streptozotocin-induced (STZ) diabetic mice and scratch wound assay, we demonstrated that inhibiting the VHL and HIF-1α connection is a promising strategy for treating diabetic ulcers. Molecules CI and CP were found to have substantial in silico, in vitro, and in vivo outcomes.

Designing a Nitro-Induced Sutured Biomacromolecule to Engineer Electroconductive Adhesive Hydrogels

Nitro-functionality, with a large deficit of negative charge, embraces biological importance and has proven its therapeutic essence even in chemotherapy. Functionally, with its strong electron-withdrawing capability, nitro can manipulate the electron density of organic moieties and regulates cellular-biochemical reactions. However, the chemistry of nitro-functionality to introduce physiologically relevant macroscopic properties from the molecular skeleton is unknown. Therefore, herein, a neurotransmitter moiety, dopamine, was chemically modified with a nitro-group to explore its influence on synthesizing a multifunctional biomaterial for therapeutic applications. Chemically, while the nitro-group perturbed the aromatic electron density of nitrocatecholic domain, it facilitated the suturing of nitrocatechol moieties to regain its aromaticity through a radical transfer mechanism, forming a novel macromolecular structure. Incorporation of the sutured-nitrocatecholic strand (S-nCAT) in a gelatin-based hydrogel introduced an electroconductive microenvironment through the delocalization of π-electrons in S-nCAT, while maintaining its catechol-mediated adhesive property for tissue repairing/sealing. Meanwhile, the engineered hydrogel enriched with noncovalent interactions, demonstrated excellent mechano-physical properties to support tissue functions. Cytocompatibility of the bioadhesive was assessed with in vitro and in vivo studies, confirming its potential usage for biomedical applications. In conclusion, this novel chemical approach enabled designing a multifunctional biomaterial by manipulating the electronic properties of small bioactive molecules for various biomedical applications.

Evolution of Potential Antimitotic Stapled Peptides from Multiple Helical Peptide Stretches of the Tubulin Heterodimer Interface: Helix-Mimicking Stapled Peptide Tubulin Inhibitors

Protein-protein interactions play a crucial role in microtubule dynamics. Microtubules are considered as a key target for the design and development of anticancer therapeutics, where inhibition of tubulin-tubulin interactions plays a crucial role. Here, we focused on a few key helical stretches at the interface of α,β-tubulin heterodimers and developed a structural mimic of these helical peptides, which can serve as potent inhibitors of microtubule polymerization. To induce helicity, we have made stapled analogues of these sequences. Thereafter, we modified the lead sequences of the antimitotic stapled peptides with halo derivatives. It is observed that halo-substituted stapled peptides follow an interesting trend for the electronegativity of halogen atoms in interaction patterns with tubulin and a correlation in the toxicity profile. Remarkably, we found that para-fluorophenylalanine-modified stapled peptide is the most potent inhibitors, which perturbs microtubule dynamics, induces apoptotic death, and inhibits the growth of melanoma.

Structure-Activity relationship of 1-(Furan-2ylmethyl)Pyrrolidine-Based Stimulation-2 (ST2) inhibitors for treating graft versus host disease

An elevated plasma level of soluble ST2 (sST2) is a risk biomarker for graft-versus-host disease (GVHD) and death in patients receiving hematopoietic cell transplantation (HCT). sST2 functions as a trap for IL-33 and amplifies the pro-inflammatory type 1 and 17 response while suppressing the tolerogenic type 2 and regulatory T cells activation during GVHD development. We previously identified small-molecule ST2 inhibitors particularly iST2-1 that reduces plasma sST2 levels and improved survival in two animal models. Here, we reported the structure-activity relationship of the furanylmethylpyrrolidine-based ST2 inhibitors based on iST2-1. Based on the biochemical AlphaLISA assay, we improved the activity of iST2-1 by 6-fold (∼6 μM in IC50 values) in the inhibition of ST2/IL-33 and confirmed the activities of the compounds in a cellular reporter assay. To determine the inhibition of the alloreactivity in vitro, we used the mixed lymphocyte reaction assay to demonstrate that our ST2 inhibitors decreased CD4+ and CD8+ T cells proliferation and increased Treg population. The data presented in this work are critical to the development of ST2 inhibitors in future.

Broad-Spectrum Small-Molecule Inhibitors of the SARS-CoV-2 Spike-ACE2 Protein-Protein Interaction from a Chemical Space of Privileged Protein Binders

Therapeutically useful small-molecule inhibitors (SMIs) of protein−protein interactions (PPIs) initiating the cell attachment and entry of viruses could provide novel alternative antivirals that act via mechanisms similar to that of neutralizing antibodies but retain the advantages of small-molecule drugs such as oral bioavailability and low likelihood of immunogenicity. From screening our library, which is focused around the chemical space of organic dyes to provide good protein binders, we have identified several promising SMIs of the SARS-CoV-2 spike—ACE2 interaction, which is needed for the attachment and cell entry of this coronavirus behind the COVID-19 pandemic. They included organic dyes, such as Congo red, direct violet 1, and Evans blue, which seem to be promiscuous PPI inhibitors, as well as novel drug-like compounds (e.g., DRI-C23041). Here, we show that in addition to the original SARS-CoV-2 strain, these SMIs also inhibit this PPI for variants of concern including delta (B.1.617.2) and omicron (B.1.1.529) as well as HCoV-NL63 with low- or even sub-micromolar activity. They also concentration-dependently inhibited SARS-CoV-2-S expressing pseudovirus entry into hACE2-expressing cells with low micromolar activity (IC50 < 10 μM) both for the original strain and the delta variant. DRI-C23041 showed good therapeutic (selectivity) index, i.e., separation between activity and cytotoxicity (TI > 100). Specificities and activities require further optimization; nevertheless, these results provide a promising starting point toward novel broad-spectrum small-molecule antivirals that act via blocking the interaction between the spike proteins of coronaviruses and their ACE2 receptor initiating cellular entry.

Targeted Protein Degradation to Overcome Resistance in Cancer Therapies: PROTAC and N-Degron Pathway

Extensive progress in understanding the molecular mechanisms of cancer growth and proliferation has led to the remarkable development of drugs that target cancer-driving molecules. Most target molecules are proteins such as kinases and kinase-associated receptors, which have enzymatic activities needed for the signaling cascades of cells. The small molecule inhibitors for these target molecules greatly improved therapeutic efficacy and lowered the systemic toxicity in cancer therapies. However, long-term and high-dosage treatment of small inhibitors for cancer has produced other obstacles, such as resistance to inhibitors. Among recent approaches to overcoming drug resistance to cancers, targeted protein degradation (TPD) such as proteolysis-targeting chimera (PROTAC) technology adopts a distinct mechanism of action by which a target protein is destroyed through the cellular proteolytic system, such as the ubiquitin–proteasome system or autophagy. Here, we review the currently developed PROTACs as the representative TPD molecules for cancer therapy and the N-degrons of the N-degron pathways as the potential TPD ligands.

Shedding Light on the Drug-Target Prediction of the Anti-Inflammatory Peptide TnP with Bioinformatics Tools

Peptide–protein interactions are involved in various fundamental cellular functions, and their identification is crucial for designing efficacious peptide therapeutics. Drug–target interactions can be inferred by in silico prediction using bioinformatics and computational tools. We patented the TnP family of synthetic cyclic peptides, which is in the preclinical stage of developmental studies for chronic inflammatory diseases such as multiple sclerosis. In an experimental autoimmune enceph-alomyelitis model, we found that TnP controls neuroinflammation and prevents demyelination due to its capacity to cross the blood–brain barrier and to act in the central nervous system blocking the migration of inflammatory cells responsible for neuronal degeneration. Therefore, the identification of potential targets for TnP is the objective of this research. In this study, we used bioinformatics and computational approaches, as well as bioactivity databases, to evaluate TnP–target prediction for proteins that were not experimentally tested, specifically predicting the 3D structure of TnP and its biochemical characteristics, TnP–target protein binding and docking properties, and dynamics of TnP competition for the protein/receptor complex interaction, construction of a network of con-nectivity and interactions between molecules as a result of TnP blockade, and analysis of similarities with bioactive molecules. Based on our results, integrins were identified as important key proteins and considered responsible to regulate TnP-governed pharmacological effects. This comprehensive in silico study will help to understand how TnP induces its anti-inflammatory effects and will also facilitate the identification of possible side effects, as it shows its link with multiple biologically important targets in humans.

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

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

Discovery and Structure-Based Design of Macrocyclic Peptides Targeting STUB1

Recent evidence suggests that deletion of STUB1─a pivotal negative regulator of interferon-γ sensing─may potentially clear malignant cells. However, current studies rely primarily on genetic approaches, as pharmacological inhibitors of STUB1 are lacking. Identifying a tool compound will be a step toward validating the target in a broader therapeutic sense. Herein, screening more than a billion macrocyclic peptides resulted in STUB1 binders, which were further optimized by a structure-enabled in silico design. The strategy to replace the macrocyclic peptides' hydrophilic and solvent-exposed region with a hydrophobic scaffold improved cellular permeability while maintaining the binding conformation. Further substitution of the permeability-limiting terminal aspartic acid with a tetrazole bioisostere retained the binding to a certain extent while improving permeability, suggesting a path forward. Although not optimal for cellular study, the current lead provides a valuable template for further development into selective tool compounds for STUB1 to enable target validation.

Microscale Thermophoresis as a Tool to Study Protein Interactions and Their Implication in Human Diseases

The review highlights how protein–protein interactions (PPIs) have determining roles in most life processes and how interactions between protein partners are involved in various human diseases. The study of PPIs and binding interactions as well as their understanding, quantification and pharmacological regulation are crucial for therapeutic purposes. Diverse computational and analytical methods, combined with high-throughput screening (HTS), have been extensively used to characterize multiple types of PPIs, but these procedures are generally laborious, long and expensive. Rapid, robust and efficient alternative methods are proposed, including the use of Microscale Thermophoresis (MST), which has emerged as the technology of choice in drug discovery programs in recent years. This review summarizes selected case studies pertaining to the use of MST to detect therapeutically pertinent proteins and highlights the biological importance of binding interactions, implicated in various human diseases. The benefits and limitations of MST to study PPIs and to identify regulators are discussed.

Solubility-Aware Protein Binding Peptide Design Using AlphaFold

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

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Mapping functional regions of essential bacterial proteins with dominant-negative protein fragments

Massively parallel measurements of dominant-negative inhibition by protein fragments have been used to map protein interaction sites and discover peptide inhibitors. However, the underlying principles governing fragment-based inhibition have thus far remained unclear. Here, we adapted a high-throughput inhibitory fragment assay for use in Escherichia coli, applying it to a set of 10 essential proteins. This approach yielded single amino acid resolution maps of inhibitory activity, with peaks localized to functionally important interaction sites, including oligomerization interfaces and folding contacts. Leveraging these data, we performed a systematic analysis to uncover principles of fragment-based inhibition. We determined a robust negative correlation between susceptibility to inhibition and cellular protein concentration, demonstrating that inhibitory fragments likely act primarily by titrating native protein interactions. We also characterized a series of trade-offs related to fragment length, showing that shorter peptides allow higher-resolution mapping but suffer from lower inhibitory activity. We employed an unsupervised statistical analysis to show that the inhibitory activities of protein fragments are largely driven not by generic properties such as charge, hydrophobicity, and secondary structure, but by the more specific characteristics of their bespoke macromolecular interactions. Overall, this work demonstrates fundamental characteristics of inhibitory protein fragment function and provides a foundation for understanding and controlling protein interactions in vivo.

Fragment hopping protocol for the design of small-molecule protein-protein interaction inhibitors

Fragment-based ligand discovery (FBLD) is one of the most successful approaches to designing small-molecule protein-protein interaction (PPI) inhibitors. The incorporation of computational tools to FBLD allows the exploration of chemical space in a time- and cost-efficient manner. Herein, a computational protocol for the development of small-molecule PPI inhibitors using fragment hopping, a fragment-based de novo design approach, is described and a case study is presented to illustrate the efficiency of this protocol. Fragment hopping facilitates the design of PPI inhibitors from scratch solely based on key binding features in the PPI complex structure. This approach is an open system that enables the inclusion of different state-of-the-art programs and softwares to improve its performances.

Designing of disruptor molecules to restrain the protein-protein interaction network of VANG1/SCRIB/NOS1AP using fragment-based drug discovery techniques

Governing protein-protein interaction networks are the cynosure of cell signaling and oncogenic networks. Multifarious processes when aligned with one another can result in a dysregulated output which can result in cancer progression. In the current research, one such network of proteins comprising VANG1/SCRIB/NOS1AP, which is responsible for cell migration, is targeted. The proteins are modeled using in-silico approaches, and the interaction is visualized utilizing protein-protein docking. Designing drugs for the convoluted protein network can serve as a challenging task that can be overcome by fragment-based drug designing, a recent game-changer in the computational drug discovery strategy for protein interaction networks. The model is exposed to the extraction of hotspots, also known as the restrained regions for small molecular hits. The hotspot regions are subjected to a library of generated fragments, which are then recombined and rejoined to develop small molecular disruptors of the macromolecular assemblage. Rapid screening methods using pharmacokinetic tools and 2D interaction studies resulted in four molecules that could serve the purpose of a disruptor. The final validation is executed by long-range simulations of 100 ns and exploring the stability of the complex using several parameters leading to the emergence of two novel molecules VNS003 and VNS005 that could be used as the disruptors of the protein assembly VANG1/SCRIB/NOS1AP. Also, the molecules were explored as single protein targets approbated via molecular docking and 100 ns molecular dynamics simulation. This concluded VNS003 as the most suitable inhibitor module capable of acting as a disruptor of a macromolecular assembly as well as acting on individual protein chains, thus leading to the primary hindrance in the formation of the protein interaction complex.