Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Insilico molecular modelling to identify PDK-1 targeting agents based on its protein-protein docking interaction

PDK1, an attractive cancer target that downstreams 23 other kinases towards cell growth, survival and metabolism has gaining attention due to allosteric effect of ligands bound to it. Generally, the drug design strategy using pharmacophores is either a single protein structure or ensemble or ligand-based. Apart from these methods, yet another new approach of protein-protein docking with state of art computational tool like Schrodinger Suite to generate pharmacophores based on the interacting partners of the protein is proposed in this work. The structure-based pharmacophoric features were picked up from docking the ten interacting partners of PDK1 and screened against the Enamine libraries containing protein-protein interacting compound collection, advanced, protein mimetic and allosteric compounds. High throughput virtual screening against the PIF pocket of PDK1 yields an indole scaffold. The identified indole derivative is proposed to be a strong activator that binds in the protein-protein interaction site of PDK1 which was further confirmed by molecular metadynamics simulations, free energy surface analysis and MM-GBSA calculations. Thus, the pharmacophores generated by the interacting proteins for PPI can facilitate the virtual screening in structure-based drug discovery of similar therapeutic targets.Communicated by Ramaswamy H. Sarma.

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

Structure-activity relationships of middle-size cyclic peptides, KRAS inhibitors derived from an mRNA display

Cyclic peptides are attracting attention as therapeutic agents due to their potential for oral absorption and easy access to tough intracellular targets. LUNA18, a clinical KRAS inhibitor, was transformed-without scaffold hopping-from the initial hit by using an mRNA display library that met our criteria for drug-likeness. In drug discovery using mRNA display libraries, hit compounds always possess a site linked to an mRNA tag. Here, we describe our examination of the Structure-Activity Relationship (SAR) using X-ray structures for chemical optimization near the site linked to the mRNA tag, equivalent to the C-terminus. Structural modifications near the C-terminus demonstrated a relatively wide range of tolerance for side chains. Furthermore, we show that a single atom modification is enough to change the pharmacokinetic (PK) profile. Since there are four positions where side chain modification is permissible in terms of activity, it is possible to flexibly adjust the pharmacokinetic profile by structurally optimizing the side chain. The side chain transformation findings demonstrated here may be generally applicable to hits obtained from mRNA display libraries.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Quantum Chemistry-Based Protein-Protein Docking without Empirical Parameters

This study developed a novel protein-protein docking approach based on quantum chemistry. To judge the appropriateness of complex structures, we introduced two criterion values, EV1 and EV2, computed using the fragment molecular orbital method without any empirical parameters. These criterion values enable us to search complex structures in which patterns of the electrostatic potential of the two proteins are optimally aligned at their interface. The performance of our method was validated using 53 complexes in a benchmark set provided for protein-protein docking. When employing bound state structures, docking success rates reached 64% for EV1 and 76% for EV2. On the other hand, when employing unbound state structures, docking success rates reached 13% for EV1 and 17% for EV2.

Strategies for converting turn-motif and cyclic peptides to small molecules for targeting protein-protein interactions

The development of small molecules that interact with protein-protein interactions is an ongoing challenge. Peptides offer a starting point in the drug discovery process for targeting protein-interactions due to their larger, more flexible structure and the structurally diverse properties that allow for a greater interaction with the protein. The techniques for rapidly identifying potent cyclic peptides and turn-motif peptides are highly effective, but this potential has not yet transferred to approved drug candidates. By applying the properties of the peptide-protein interaction the development of small molecules for drug discovery has the potential to be more efficient. In this review, we discuss the methods that allow for the unique binding properties of peptides to proteins, and the methods deployed to transfer these qualities to potent small molecules.

Arginine (315) is required for the PLIN2-CGI-58 interface and plays a functional role in regulating nascent LDs formation in bovine adipocytes

Perilipin-2 (PLIN2) can anchor to lipid droplets (LDs) and play a crucial role in regulating nascent LDs formation. Bimolecular fluorescence complementation (BiFC) and flow cytometry were examined to verify the PLIN2-CGI-58 interaction efficiency in bovine adipocytes. GST-Pulldown assay was used to detect the key site arginine315 function in PLIN2-CGI-58 interaction. Experiments were also examined to research these mutations function of PLIN2 in LDs formation during adipocytes differentiation, LDs were measured after staining by BODIPY, lipogenesis-related genes were also detected. Results showed that Leucine (L371A, L311A) and glycine (G369A, G376A) mutations reduced interaction efficiencies. Serine (S367A) mutations enhanced the interaction efficiency. Arginine (R315A) mutations resulted in loss of fluorescence in the cytoplasm and disrupted the interaction with CGI-58, as verified by pulldown assay. R315W mutations resulted in a significant increase in the number of LDs compared with wild-type (WT) PLIN2 or the R315A mutations. Lipogenesis-related genes were either up- or downregulated when mutated PLIN2 interacted with CGI-58. Arginine315 in PLIN2 is required for the PLIN2-CGI-58 interface and could regulate nascent LD formation and lipogenesis. This study is the first to study amino acids on the PLIN2 interface during interaction with CGI-58 in bovine and highlight the role played by PLIN2 in the regulation of bovine adipocyte lipogenesis.

Structure-based drug design and molecular dynamics studies of an allosteric modulator targeting the protein-protein interaction site of PDK1

CONTEXT

Protein-protein interaction interfaces play a major role in cell signaling pathways. There is always a great interest in developing protein-protein interaction (PPI) inhibitors of kinases, as they are challenging due to their hydrophobicity, flat surface, specificity, potency, etc. 3 Phosphoinositide-dependent kinase-1 (PDK1), which is involved in the PI3K/PDK1/AKT pathway, is a cancer target that has gained insight for the past two decades. PDK1 possesses a protein interaction fragment (PIF) pocket, which is a well-known PPI that targets allosteric modulators. This work focusses on energy-based pharmacophore model development which on virtual screening could yield novel scaffolds towards the drug designing objective for the kind of PDK1 modulators. A novel pyrazolo pyridine molecule was identified as an allosteric modulator that binds to the PPI site. The metadynamics simulations with free energy profiles further revealed the conformational allosteric changes stimulated on the protein structure upon ligand binding. The cytotoxic activity (IC50-20 μM) of the identified compound against five different cancer cell lines and cell cycle analysis supported the anticancer activity of the identified compound.

METHODS

All the computational works were carried out by the most commonly used Schrodinger Suite software. The pharmacophore was validated by Receiver Operation Characteristics (ROC) and screening against allosteric Enamine database library. The Optimized Potential Liquid Simulations (OPLS-2005) was used to minimize the structures for molecular docking calculations, and inbuilt scoring method of ranking the compounds based on docking score and Glide energy was used. Molecular dynamics simulations were conducted by Desmond implemented in Maestro. The hit compound was purchased from Enamine and tested against different cancer cell lines by MTT assay, apoptosis by western blotting technique, and by flow cytometry analysis.

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins

Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations.

Design and Synthesis of Novel Helix Mimetics Based on the Covalent H-Bond Replacement and Amide Surrogate

A novel hydrogen bond surrogate-based (HBS) α-helix mimetic was designed by the combination of covalent H-bond replacement and the use of an ether linkage to substitute an amide bond within a short peptide sequence. The new helix template could be placed in position other than the N-terminus of a short peptide, and the CD studies demonstrate that the template adopts stable conformations in aqueous buffer at exceptionally high temperatures.

Quantification and Neutralization of the Interfacial Electrostatic Potential and Visualization of the Dispersion Interaction in Visualization of the Interfacial Electrostatic Complementarity

Visualization of the interfacial electrostatic complementarity (VIINEC) is a quantum chemistry-based method to examine protein-protein interactions (PPI). In VIINEC, the electrostatic complementarity between proteins at the interface is visually and quantitatively evaluated using the partial electrostatic potential (pESP), which is defined based on the fragment molecular orbital method. In this work, new quantification and neutralization methods of the pESP were proposed together with a method to visualize the dispersion interaction. The reliability and efficiency of these methods were evaluated using 17 models of the complex. It was found that the quantification of the electrostatic complementarity with the pESP using the new neutralization method has a high correlation with the interaction energy, supporting the reliability of VIINEC. As an illustrative example, the PPI between a major histocompatibility complex class I molecule and a T-cell receptor was examined, which demonstrated the value of VIINEC in chemical and biological research.

Antioxidant and antithrombotic properties of fruit, leaf, and seed extracts of the Halhalı olive (Olea europaea L.) native to the Hatay region in Turkey

The olive (Olea europaea L.) is one of the most important plants grown in many Mediterranean countries that has a high economic value. Olives, which are specific to each region, have different bioactive components. In this study, we investigated the phenolic/flavonoid contents, as well as antioxidant, antimicrobial, and antithrombotic activities of the fruit, leaf, and seed extracts obtained from the Halhalı olive grown in Arsuz district of Hatay, Turkey. Antioxidant activities of the phenolic compounds found in the olive fruit, seed, and leaf extracts were determined by employing established in vitro systems. Total phenolics were determined as gallic acid equivalents, while total flavonoids were determined as quercetin equivalents. Also, we evaluated a possible interaction between oleuropein and aggregation-related glycoproteins of the platelet surface via docking studies. The extracts showed effective antioxidant activity. The seed extract had the highest phenolic content of 317.24 μg GAE, while the fruit extract had the highest flavonoid content of 4.43 μg. The highest potential for metal chelating activity was found in the leaf extract, with an IC50 value of 13.33 mg/mL. Also, the leaf extract showed higher levels of antioxidant, antithrombotic, and antimicrobial activity, compared to the fruit and seed extracts. The docking scores of oleuropein against the target molecules GPVI, α2β1, and GPIbα were calculated as –3.798, –4.315, and –6.464 kcal/mol, respectively. The olive fruit, leaf, and seed extracts used as experimental material in our study have remarkable antioxidant, antimicrobial, and antithrombotic potential.

A Modular Synthesis of Teraryl-Based α-Helix Mimetics, Part 4: Core Fragments with Two Halide Leaving Groups Featuring Side Chains of Proteinogenic Amino Acids

Teraryl-based α-helix mimetics have proven to be useful compounds for the inhibition of protein-protein interactions (PPI). We have developed a modular and flexible approach for the synthesis of teraryl-based α-helix mimetics using a benzene core unit featuring two halide leaving groups of differentiated reactivity in the Pd-catalyzed cross-coupling used for teraryl assembly. The use of para-bromo iodoarene core fragments resolved the issue of hydrolysis during cross-coupling that was observed when using triflate as a leaving group. We report a complete set of para-bromoiodoarene core fragments decorated with side chains of all proteinogenic amino acids relevant for PPI (Ala, Arg, Asn, Asp, Cys, Gln, Glu, His, Ile, Leu, Lys, Met, Phe, Ser, Thr, Trp, Tyr and Val). In order to be compatible with general cross-coupling conditions, some of the nucleophilic side chains had to be provided in a protected form to serve as stable building blocks.

Systematic Tracing of Susceptible Animals to SARS-CoV-2 by a Bioinformatics Framework

Since the outbreak of SARS-CoV-2 in 2019, the Chinese horseshoe bats were considered as a potential original host of SARS-CoV-2. In addition, cats, tigers, lions, mints, and ferrets were naturally or experimentally infected with SARS-CoV-2. For the surveillance and control of this highly infectious disease, it is critical to trace susceptible animals and predict the consequence of potential mutations at the binding region of viral spike protein and host ACE2 protein. This study proposed a novel bioinformatics framework to systematically trace susceptible animals to SARS-CoV-2 and predict the binding affinity between susceptible animals’ mutated/un-mutated ACE2 receptors. As a result, we identified a few animals posing a potential risk of infection with SARS-CoV-2 using the docking analysis of ACE2 protein and viral spike protein. The binding affinity of some of these species is weaker than that of humans but more potent than that of Chinese horseshoe bats. We also found that a few point mutations in human ACE2 protein or viral spike protein could significantly enhance their binding affinity, posing an enormous potential threat to public health. The ancestors of the Omicron may evolve rapidly through the accumulation of mutations in infecting the host and jumped into human beings. These findings indicate that if the epidemic expands, there may be a human-animal-human transmission route, which will increase the difficulty of disease prevention and control.

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.

Interaction Analysis on the SARS-CoV-2 Spike Protein Receptor Binding Domain Using Visualization of the Interfacial Electrostatic Complementarity

Visualization of the interfacial electrostatic complementarity (VIINEC) is a recently developed method for analyzing protein-protein interactions using electrostatic potential (ESP) calculated via the ab initio fragment molecular orbital method. In this Letter, the molecular interactions of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein with human angiotensin-converting enzyme 2 (ACE2) and B38 neutralizing antibody were examined as an illustrative application of VIINEC. The results of VIINEC revealed that the E484 of RBD has a role in making a local electrostatic complementary with ACE2 at the protein-protein interface, while it causes a considerable repulsive electrostatic interaction. Furthermore, the calculated ESP map at the interface of the RBD/B38 complex was significantly different from that of the RBD/ACE2 complex, which is discussed herein in association with the mechanism of the specificity of the antibody binding to the target protein.

Molecular Docking and Virtual Screening of an Influenza Virus Inhibitor That Disrupts Protein-Protein Interactions

Influenza is an acute respiratory infection caused by the influenza virus, but few drugs are available for its treatment. Consequently, researchers have been engaged in efforts to discover new antiviral mechanisms that can lay the foundation for novel anti-influenza drugs. The viral RNA-dependent RNA polymerase (RdRp) is an enzyme that plays an indispensable role in the viral infection process, which is directly linked to the survival of the virus. Methods of inhibiting PB1-PB2 (basic polymerase 1-basic polymerase 2) interactions, which are a key part of RdRp enzyme activity, are integral in the design of novel antiviral drugs, a specific PB1-PB2 interactions inhibitor has not been reported. We have screened Enamine's database and conducted a parallel screening of multiple docking schemes, followed by simulations of molecular dynamics to determine the structure of a stable ligand-PB1 complex. We also calculated the free energy of binding between the screened compounds and PB1 protein. Ultimately, we screened and identified a potential PB1-PB2 inhibitor using the ADMET prediction model.

Exploring the Binding Interaction of Raf Kinase Inhibitory Protein With the N-Terminal of C-Raf Through Molecular Docking and Molecular Dynamics Simulation

Protein-protein interactions are indispensable physiological processes regulating several biological functions. Despite the availability of structural information on protein-protein complexes, deciphering their complex topology remains an outstanding challenge. Raf kinase inhibitory protein (RKIP) has gained substantial attention as a favorable molecular target for numerous pathologies including cancer and Alzheimer’s disease. RKIP interferes with the RAF/MEK/ERK signaling cascade by endogenously binding with C-Raf (Raf-1 kinase) and preventing its activation. In the current investigation, the binding of RKIP with C-Raf was explored by knowledge-based protein-protein docking web-servers including HADDOCK and ZDOCK and a consensus binding mode of C-Raf/RKIP structural complex was obtained. Molecular dynamics (MD) simulations were further performed in an explicit solvent to sample the conformations for when RKIP binds to C-Raf. Some of the conserved interface residues were mutated to alanine, phenylalanine and leucine and the impact of mutations was estimated by additional MD simulations and MM/PBSA analysis for the wild-type (WT) and constructed mutant complexes. Substantial decrease in binding free energy was observed for the mutant complexes as compared to the binding free energy of WT C-Raf/RKIP structural complex. Furthermore, a considerable increase in average backbone root mean square deviation and fluctuation was perceived for the mutant complexes. Moreover, per-residue energy contribution analysis of the equilibrated simulation trajectory by HawkDock and ANCHOR web-servers was conducted to characterize the key residues for the complex formation. One residue each from C-Raf (Arg398) and RKIP (Lys80) were identified as the druggable “hot spots” constituting the core of the binding interface and corroborated by additional long-time scale (300 ns) MD simulation of Arg398Ala mutant complex. A notable conformational change in Arg398Ala mutant occurred near the mutation site as compared to the equilibrated C-Raf/RKIP native state conformation and an essential hydrogen bonding interaction was lost. The thirteen binding sites assimilated from the overall analysis were mapped onto the complex as surface and divided into active and allosteric binding sites, depending on their location at the interface. The acquired information on the predicted 3D structural complex and the detected sites aid as promising targets in designing novel inhibitors to block the C-Raf/RKIP interaction.

SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2

Small molecule modulators of protein–protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase.

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

It has been noticed that the efficiency of drug development has been decreasing in the past few decades. To overcome the situation, protein-protein interactions (PPIs) have been identified as new drug targets as early as 2000. PPIs are more abundant in human cells than single proteins and play numerous important roles in cellular processes including diseases. However, PPIs have very different physicochemical features from the conventional drug targets, which make targeting PPIs challenging. Therefore, as of now, only a small number of PPI inhibitors have been approved or progressed to a stage of clinical trial. In this article, we first overview previous works that analyzed differences between PPIs with PPI targeting ligands and conventional drugs with their binding pockets. Then, we constructed an up-to-date list of PPI targeting drugs that have been approved or are currently under clinical trial and have bound drug-target structures available. Using the dataset, we analyzed the PPIs and their ligands using several scores of druggability. Druggability scores showed that PPI sites and their drugs targeting PPIs are less druggable than conventional binding pockets and drugs, which also indicates that PPI drugs do not follow the conventional rules for drug design, such as Lipinski's rule of five. Our analyses suggest that developing a new rule would be beneficial for guiding PPI-drug discovery.

Structural basis of polyethylene glycol recognition by antibody

Background

Polyethylene glycol (PEG) is widely used in industry and medicine. Anti-PEG antibodies have been developed for characterizing PEGylated drugs and other applications. However, the underlying mechanism for specific PEG binding has not been elucidated.

Methods

The Fab of two cognate anti-PEG antibodies 3.3 and 2B5 were each crystallized in complex with PEG, and their structures were determined by X-ray diffraction. The PEG-Fab interactions in these two crystals were analyzed and compared with those in a PEG-containing crystal of an unrelated anti-hemagglutinin 32D6-Fab. The PEG-binding stoichiometry was examined by using analytical ultracentrifuge (AUC).

Results

A common PEG-binding mode to 3.3 and 2B5 is seen with an S-shaped core PEG fragment bound to two dyad-related Fab molecules. A nearby satellite binding site may accommodate parts of a longer PEG molecule. The core PEG fragment mainly interacts with the heavy-chain residues D31, W33, L102, Y103 and Y104, making extensive contacts with the aromatic side chains. At the center of each half-circle of the S-shaped PEG, a water molecule makes alternating hydrogen bonds to the ether oxygen atoms, in a similar configuration to that of a crown ether-bound lysine. Each satellite fragment is clamped between two arginine residues, R52 from the heavy chain and R29 from the light chain, and also interacts with several aromatic side chains. In contrast, the non-specifically bound PEG fragments in the 32D6-Fab crystal are located in the elbow region or at lattice contacts. The AUC data suggest that 3.3-Fab exists as a monomer in PEG-free solution but forms a dimer in the presence of PEG-550-MME, which is about the size of the S-shaped core PEG fragment.

Conclusions

The differing amino acids in 3.3 and 2B5 are not involved in PEG binding but engaged in dimer formation. In particular, the light-chain residue K53 of 2B5-Fab makes significant contacts with the other Fab in a dimer, whereas the corresponding N53 of 3.3-Fab does not. This difference in the protein-protein interaction between two Fab molecules in a dimer may explain the temperature dependence of 2B5 in PEG binding, as well as its inhibition by crown ether.

Chimeric Protein-Protein Interface Inhibitors Allow Efficient Inhibition of Type III Secretion Machinery and Pseudomonas aeruginosa Virulence

Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen naturally resistant to many common antibiotics and acquires new resistance traits at an alarming pace. Targeting the bacterial virulence factors by an antivirulence strategy, therefore, represents a promising alternative approach besides antibiotic therapy. The Type III secretion system (T3SS) of P. aeruginosa is one of its main virulence factors. It consists of more than 20 proteins building a complex syringe-like machinery enabling the injection of toxin into host cells. Previous works showed that disrupting interactions between components of this machinery efficiently lowers the bacterial virulence. Using automated target-based screening of commercial and in-house libraries of small molecules, we identified compounds inhibiting the protein-protein interaction between PscE and PscG, the two cognate chaperones of the needle subunit PscF of P. aeruginosa T3SS. Two hits were selected and assembled using Split/Mix/Click chemistry to build larger hybrid analogues. Their efficacy and toxicity were evaluated using phenotypic analysis including automated microscopy and image analysis. Two nontoxic hybrid leads specifically inhibited the T3SS and reduced the ex vivo cytotoxicity of bacteria and their virulence in Galleria mellonella.

Quantum Molecular Dynamics, Topological, Group Theoretical and Graph Theoretical Studies of Protein-Protein Interactions

BACKGROUND

Protein-protein interactions (PPIs) are becoming increasingly important as PPIs form the basis of multiple aggregation-related diseases such as cancer, Creutzfeldt-Jakob, and Alzheimer's diseases. This mini-review presents hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies facilitate the discovery of some PPI inhibitors of therapeutic importance.

OBJECTIVE

The objective of this review is to present hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies enable the discovery of some PPI inhibitors of therapeutic importance.

METHODS

This article presents a detailed survey of hybrid quantum dynamics that combines classical and quantum MD for PPIs. The article also surveys various developments pertinent to topological, graph theoretical, group theoretical and docking studies of PPIs and highlight how the methods facilitate the discovery of some PPI inhibitors of therapeutic importance.

RESULTS

It is shown that it is important to include higher-level quantum chemical computations for accurate computations of free energies and electrostatics of PPIs and Drugs with PPIs, and thus techniques that combine classical MD tools with quantum MD are preferred choices. Topological, graph theoretical and group theoretical techniques are shown to be important in studying large network of PPIs comprised of over 100,000 proteins where quantum chemical and other techniques are not feasible. Hence, multiple techniques are needed for PPIs.

CONCLUSION

Drug discovery and our understanding of complex PPIs require multifaceted techniques that involve several disciplines such as quantum chemistry, topology, graph theory, knot theory and group theory, thus demonstrating a compelling need for a multi-disciplinary approach to the problem.

Investigating cyclic peptides inhibiting CD2-CD58 interactions through molecular dynamics and molecular docking methods

The CD2-CD58 protein-protein interaction is known to favor the recognition of antigen presenting cells by T cells. The structural, energetics, and dynamical properties of three known cyclic CD58 ligands, named P6, P7, and RTD-c, are studied through molecular dynamics (MD) simulations and molecular docking calculations. The ligands are built so as to mimic the C and F β-strands of protein CD2, connected via turn inducers. The MD analyses focus on the location of the ligands with respect to the experimental binding site and on the direct and water-mediated hydrogen bonds (H bonds) they form with CD58. Ligand P6, with a sequence close to the experimental β-strands of CD2, presents characteristics that explain its higher experimental affinity, e.g., the lower mobility and flexibility at the CD58 surface, and the larger number and occurrence frequency of ligand-CD58 H bonds. For the two other ligands, the structural modifications lead to changes in the binding pattern with CD58 and its dynamics. In parallel, a large set of molecular docking calculations, carried out with various search spaces and docking algorithms, are compared to provide a consensus view of the preferred ligand binding modes. The analysis of the ligand side chain locations yields results that are consistent with the CD2-CD58 crystal structure and suggests various binding modes of the experimentally identified hot spot of the ligands, i.e., Tyr86. P6 is shown to form a number of contacts that are also present in the experimental CD2-CD58 structure.

Protein structure and computational drug discovery

The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.

Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex

Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS-TLR4-MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein-protein interaction (PPI) in TLR4-MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4-MD-2) and dimerization (MD-2-TLR4*) protein-protein interaction interfaces in TLR4-MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4-MD-2 protein-protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4-MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.

Computer-Aided Drug Design in Epigenetics

Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

Homo- and Heterodimerization of Proteins in Cell Signaling: Inhibition and Drug Design

Protein dimerization controls many physiological processes in the body. Proteins form homo-, hetero-, or oligomerization in the cellular environment to regulate the cellular processes. Any deregulation of these processes may result in a disease state. Protein-protein interactions (PPIs) can be inhibited by antibodies, small molecules, or peptides, and inhibition of PPI has therapeutic value. PPI drug discovery research has steadily increased in the last decade, and a few PPI inhibitors have already reached the pharmaceutical market. Several PPI inhibitors are in clinical trials. With advancements in structural and molecular biology methods, several methods are now available to study protein homo- and heterodimerization and their inhibition by drug-like molecules. Recently developed methods to study PPI such as proximity ligation assay and enzyme-fragment complementation assay that detect the PPI in the cellular environment are described with examples. At present, the methods used to design PPI inhibitors can be classified into three major groups: (1) structure-based drug design, (2) high-throughput screening, and (3) fragment-based drug design. In this chapter, we have described some of the experimental methods to study PPIs and their inhibition. Examples of homo- and heterodimers of proteins, their structural and functional aspects, and some of the inhibitors that have clinical importance are discussed. The design of PPI inhibitors of epidermal growth factor receptor heterodimers and CD2-CD58 is discussed in detail.

The cytochrome c–cyclo[6]aramide complex as a supramolecular catalyst in methanol

A hydrogen-bonded aromatic amide macrocycle forms a host–guest complex with cytochrome c, which acts as a supramolecular catalyst for the oxidation of benzhydrol even at low temperatures.

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

Modulation of interaction of mutant TP53 and wild type BRCA1 by alkaloids: a computational approach towards targeting protein-protein interaction as a futuristic therapeutic intervention strategy for breast cancer impediment

Protein-protein interactions (PPI) are a new emerging class of novel therapeutic targets. In order to probe these interactions, computational tools provide a convenient and quick method towards the development of therapeutics. Keeping this in view the present study was initiated to analyse interaction of tumour suppressor protein p53 (TP53) and breast cancer associated protein (BRCA1) as promising target against breast cancer. Using computational approaches such as protein-protein docking, hot spot analyses, molecular docking and molecular dynamics simulation (MDS), stepwise analyses of the interactions of the wild type and mutant TP53 with that of wild type BRCA1 and their modulation by alkaloids were done. Protein-protein docking method was used to generate both wild type and mutant complexes of TP53-BRCA1. Subsequently, the complexes were docked using sixteen different alkaloids, fulfilling ADMET and Lipinski's rule of five criteria, and were compared with that of a well-known inhibitor of PPI, namely nutlin. The alkaloid dicentrine was found to be the best docked alkaloid among all the docked alklaloids as well as that of nutlin. Furthermore, MDS analyses of both wild type and mutant complexes with the best docked alkaloid i.e. dicentrine, revealed higher stability of mutant complex than that of the wild one, in terms of average RMSD, RMSF and binding free energy, corroborating the results of docking. Results suggested more pronounced interaction of BRCA1 with mutant TP53 leading to increased expression of mutated TP53 thus showing a dominant negative gain of function and hampering wild type TP53 function leading to tumour progression.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Protein-Protein Interactions (PPIs) as an Alternative to Targeting the ATP Binding Site of Kinase

Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.

Recent Advancements in Docking Methodologies

Nowadays molecular docking has become an important methodology in CADD (Computer-Aided Drug Design)-assisted drug discovery process. It is an important computational tool widely used to predict binding mode, binding affinity and binding free energy of a protein-ligand complex. The important factors responsible for accurate results in docking studies are correct binding site prediction, use of suitable small-molecule databases, consistent docking pose, high dock score with good MD (Molecular Dynamics), clarity whether the compound is an inhibitor or agonist, etc. However, still there are several limitations which make it difficult to obtain accurate results from docking studies. In this chapter, the main focus is on recent advancements in various aspects of molecular docking such as ligand sampling, protein flexibility, scoring functions, fragment docking, post-processing, docking into homology models and protein-protein docking.

Molecular docking as a popular tool in drug design, an in silico travel

New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism-or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein-protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Molecular Dynamics Simulations and Structural Analysis of Giardia duodenalis 14-3-3 Protein-Protein Interactions

Giardiasis is a gastrointestinal diarrheal illness caused by the protozoan parasite Giardia duodenalis, which affects annually over 200 million people worldwide. The limited antigiardial drug arsenal and the emergence of clinical cases refractory to standard treatments dictate the need for new chemotherapeutics. The 14-3-3 family of regulatory proteins, extensively involved in protein-protein interactions (PPIs) with pSer/pThr clients, represents a highly promising target. Despite homology with human counterparts, the single 14-3-3 of G. duodenalis (g14-3-3) is characterized by a constitutive phosphorylation in a region critical for target binding, thus affecting the function and the conformation of g14-3-3/clients interaction. However, to approach the design of specific small molecule modulators of g14-3-3 PPIs, structural elucidations are required. Here, we present a detailed computational and crystallographic study exploring the implications of g14-3-3 phosphorylation on protein structure and target binding. Self-Guided Langevin Dynamics and classical molecular dynamics simulations show that phosphorylation affects locally and globally g14-3-3 conformation, inducing a structural rearrangement more suitable for target binding. Profitable features for g14-3-3/clients interaction were highlighted using a hydrophobicity-based descriptor to characterize g14-3-3 client peptides. Finally, the X-ray structure of g14-3-3 in complex with a mode-1 prototype phosphopeptide was solved and combined with structure-based simulations to identify molecular features relevant for clients binding to g14-3-3. The data presented herein provide a further and structural understanding of g14-3-3 features and set the basis for drug design studies.

Pharmacological Targeting of AMP-Activated Protein Kinase and Opportunities for Computer-Aided Drug Design

As a central regulator of metabolism, the AMP-activated protein kinase (AMPK) is an established therapeutic target for metabolic diseases. Beyond the metabolic area, the number of medical fields that involve AMPK grows continuously, expanding the potential applications for AMPK modulators. Even though indirect AMPK activators are used in the clinics for their beneficial metabolic outcome, the few described direct agonists all failed to reach the market to date, which leaves options open for novel targeting methods. As AMPK is not actually a single molecule and has different roles depending on its isoform composition, the opportunity for isoform-specific targeting has notably come forward, but the currently available modulators fall short of expectations. In this review, we argue that with the amount of available structural and ligand data, computer-based drug design offers a number of opportunities to undertake novel and isoform-specific targeting of AMPK.