Hydrophobic Binding Hot Spots of Bcl-xL Protein-Protein Interfaces by Cosolvent Molecular Dynamics Simulation

Picornavirus VP3 protein induces autophagy through the TP53-BAD-BAX axis to promote viral replication

Macroautophagy/autophagy and apoptosis are pivotal interconnected host cell responses to viral infection, including picornaviruses. Here, the VP3 proteins of picornaviruses were determined to trigger autophagy, with the autophagic flux being triggered by the TP53-BAD-BAX axis. Using foot-and-mouth disease virus (FMDV) as a model system, we unraveled a novel mechanism of how picornavirus hijacks autophagy to bolster viral replication and enhance pathogenesis. FMDV infection induced both autophagy and apoptosis in vivo and in vitro. FMDV VP3 protein facilitated the phosphorylation and translocation of TP53 from the nucleus into the mitochondria, resulting in BAD-mediated apoptosis and BECN1-mediated autophagy. The amino acid Gly129 in VP3 is essential for its interaction with TP53, and crucial for induction of autophagy and apoptosis. VP3-induced autophagy and apoptosis are both essential for FMDV replication, while, autophagy plays a more important role in VP3-mediated pathogenesis. Mutation of Gly129 to Ala129 in VP3 abrogated the autophagic regulatory function of VP3, which significantly decreased the viral replication and pathogenesis of FMDV. This suggested that VP3-induced autophagy benefits viral replication and pathogenesis. Importantly, this Gly is conserved and showed a common function in various picornaviruses. This study provides insight for developing broad-spectrum antivirals and genetic engineering attenuated vaccines against picornaviruses.Abbreviations: 3-MA, 3-methyladenine; ATG, autophagy related; BAD, BCL2 associated agonist of cell death; BAK1, BCL2 antagonist/killer 1; BAX, BCL2 associated X, apoptosis regulator; BBC3/PUMA, BCL2 binding component 3; BCL2, BCL2 apoptosis regulator; BID, BH3 interacting domain death agonist; BIP-V5, BAX inhibitor peptide V5; CFLAR/FLIP, CASP8 and FADD like apoptosis regulator; CPE, cytopathic effects; CQ, chloroquine; CV, coxsackievirus; DAPK, death associated protein kinase; DRAM, DNA damage regulated autophagy modulator; EV71, enterovirus 71; FMDV, foot-and-mouth disease virus; HAV, hepatitis A virus; KD, knockdown; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; MOI, multiplicity of infection; MTOR, mechanistic target of rapamycin kinase; PML, promyelocytic leukemia; PV, poliovirus; SVA, Seneca Valley virus; TCID50, 50% tissue culture infectious doses; TOR, target of rapamycin. TP53/p53, tumor protein p53; WCL, whole-cell lysate.

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Computational Cosolvent Mapping Analysis Leads to Identify Salicylic Acid Analogs as Weak Inhibitors of ST2 and IL33 Binding

Cytokine signaling initiated by the binding of the cytokine receptors to cytokines plays important roles in immune regulation and diseases. Structurally, cytokine receptors interact with cytokines via an extensive, rugged interface that represents a challenge in inhibitor development. Our computational analysis has previously indicated that butyric acid, mimicking acidic residues, preferentially binds to sites in ST2 (Stimulation-2) that interact with acidic residues of IL33, the endogenous cytokine for ST2. To investigate if a charged group in small molecules facilitates ligand binding to ST2, we developed a biochemical homogeneous time resolved fluorescence assay to determine the inhibition of ST2/IL33 binding by five molecules containing an aromatic ring and a charged group. Three molecules, including niacin, salicylic acid, and benzamidine, exhibit inhibition activities at millimolar concentrations. We further employed the computational cosolvent mapping analysis to identify a shared mode of interaction between niacin, salicylic acid, and ST2. The mode of interaction was further confirmed by four analogous compounds that exhibited similar or improved activities. Our study provided the evidence of inhibition of ST2 and IL33 binding by salicylic acid and analogs. The results suggest that biological activity of salicylic acid may be partly mediated through modulating extracellular cytokine receptors and cytokine interaction.

Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design

Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.

In vitro and in silico anti-leukemic activity of 2-amino-6-nitro-4-(4-oxo-2-thioxothiazolidin-5-yl)-4H-chromene-3-carbonitrile (ANC) through inhibition of anti-apoptotic Bcl-2 proteins

An array of 4H-chromene derivatives have been reported for anticancer properties but their selectivity and mode of anticancer activity are unexplored. In this context, we have investigated a biologically active synthetically designed 4H-Chromene carbonitrile derivative, 2-amino-6-nitro-4-(4-oxo-2-thioxothiazolidin-5-yl)-4H-chromene-3-carbonitrile (ANC) that is strongly and selectively inhibited Bcl-2 over expressing human leukemic (HL-60 and K562) cells for its interaction and elucidated the mode of action. The interaction of ANC was investigated against the antiapoptotic proteins such as Bcl-2, Bax, Bcl-xL and Bcl-w that were overexpressed in leukemic cells using in silico and fluorescent spectroscopic studies. Fluorescent spectroscopic based interaction studies showed that the derivative had strong interaction with Bcl-xL followed by Bcl-2/Bax and least interaction with Bcl-w. Based on the results, the ANC had strong interactions with antiapoptotic Bcl-2 and Bax proteins than the Bcl-xL and Bcl-w proteins. The in vitro biological validation of ANC treated leukemic cells showed downregulation of Bcl-xL than Bcl-2 but least effect on Bcl-w proteins. Furthermore, the ANC had possible four isomers as RR, RS, SR and SS isomers. Among them, RS isomer of ANC had shown more active that correlated with biological interactions and gene expression studies of ACN with oncoproteins. These results confirmed the induction of apoptosis by RS-ACN isomer through inhibition of antiapoptotic machineries of leukemic cells confirming the antiapoptotic Bcl-2 inhibitory activities.Communicated by Ramaswamy H. Sarma.

Discovery of A-1331852, a First-in-Class, Potent, and Orally-Bioavailable BCL-XL Inhibitor

Herein we describe the discovery of A-1331852, a first-in-class orally active BCL-X_L inhibitor that selectively and potently induces apoptosis in BCL-X_L-dependent tumor cells. This molecule was generated by re-engineering our previously reported BCL-X_L inhibitor A-1155463 using structure-based drug design. Key design elements included rigidification of the A-1155463 pharmacophore and introduction of sp³-rich moieties capable of generating highly productive interactions within the key P4 pocket of BCL-X_L. A-1331852 has since been used as a critical tool molecule for further exploring BCL-2 family protein biology, while also representing an attractive entry into a drug discovery program.

Atranorin, an antimicrobial metabolite from lichen Parmotrema rampoddense exhibited in vitro anti-breast cancer activity through interaction with Akt activity

Atranorin (ATR), lichenized secondary metabolite and depside molecule with several biological potentials such as antimicrobial, anticancer, anti-inflammatory, antinociceptive, wound healing and photoprotective activities. Cytotoxic reports of ATR are documented in several cancer cells and in vivo models but its molecular interaction studies are poorly understood. Therefore, in this present investigation, we have used the in silico studies with biological validation of the molecular targets for the anti-breast cancer mechanism of ATR. The molecular docking studies with the breast cancer oncoproteins such as Bcl-2, Bax, Akt, Bcl-w and Bcl-xL revealed the highest interaction was observed with the Akt followed by Bax, Bcl-xL and Bcl-2 & least with the Bcl-w proteins. The cytotoxicity studies showed ATR selectively inhibited MDA MB-231 and MCF-7 breast cancer cells in differential and dose-dependent manner with the IC₅₀ concentration of 5.36 ± 0.85 μM and 7.55 ± 1.2 μM respectively. Further mechanistic investigations revealed that ATR significantly inhibited ROS production and significantly down-regulated the anti apoptotic Akt than Bcl-2, Bcl-xL and Bcl-w proteins with a significant increase in the Bax level and caspases-3 activity in the breast cancer cells when comparison with Akt inhibitor, ipatasertib. In vitro biological activities well correlated with the molecular interaction data suggesting that atranorin had higher interaction with Akt than Bax and Bcl-2 but weak interaction with Bcl-w and Bcl-xL. In this present study, the first time we report the interactions of atranorin with molecular targets for anti-breast cancer potential. Hence, ATR represents the nature-inspired molecule for pharmacophore moiety for design in targeted therapy.Communicated by Ramaswamy H. Sarma.

Allosteric Binding Sites On Nuclear Receptors: Focus On Drug Efficacy and Selectivity

Nuclear receptors (NRs) are highly relevant drug targets in major indications such as oncologic, metabolic, reproductive, and immunologic diseases. However, currently, marketed drugs designed towards the orthosteric binding site of NRs often suffer from resistance mechanisms and poor selectivity. The identification of two superficial allosteric sites, activation function-2 (AF-2) and binding function-3 (BF-3), as novel drug targets sparked the development of inhibitors, while selectivity concerns due to a high conservation degree remained. To determine important pharmacophores and hydration sites among AF-2 and BF-3 of eight hormonal NRs, we systematically analyzed over 10 μ s of molecular dynamics simulations including simulations in explicit water and solvent mixtures. In addition, a library of over 300 allosteric inhibitors was evaluated by molecular docking. Based on our results, we suggest the BF-3 site to offer a higher potential for drug selectivity as opposed to the AF-2 site that is more conserved among the selected receptors. Detected similarities among the AF-2 sites of various NRs urge for a broader selectivity assessment in future studies. In combination with the Supplementary Material, this work provides a foundation to improve both selectivity and potency of allosteric inhibitors in a rational manner and increase the therapeutic applicability of this promising compound class.

Simulations of mutant p53 DNA binding domains reveal a novel druggable pocket

The DNA binding domain (DBD) of the tumor suppressor p53 is the site of several oncogenic mutations. A subset of these mutations lowers the unfolding temperature of the DBD. Unfolding leads to the exposure of a hydrophobic β-strand and nucleates aggregation which results in pathologies through loss of function and dominant negative/gain of function effects. Inspired by the hypothesis that structural changes that are associated with events initiating unfolding in DBD are likely to present opportunities for inhibition, we investigate the dynamics of the wild type (WT) and some aggregating mutants through extensive all atom explicit solvent MD simulations. Simulations reveal differential conformational sampling between the WT and the mutants of a turn region (S6-S7) that is contiguous to a known aggregation-prone region (APR). The conformational properties of the S6-S7 turn appear to be modulated by a network of interacting residues. We speculate that changes that take place in this network as a result of the mutational stress result in the events that destabilize the DBD and initiate unfolding. These perturbations also result in the emergence of a novel pocket that appears to have druggable characteristics. FDA approved drugs are computationally screened against this pocket.

From proteomics to discovery of first-in-class ST2 inhibitors active in vivo

Soluble cytokine receptors function as decoy receptors to attenuate cytokine-mediated signaling and modulate downstream cellular responses. Dysregulated overproduction of soluble receptors can be pathological, such as soluble ST2 (sST2), a prognostic biomarker in cardiovascular diseases, ulcerative colitis, and graft-versus-host disease (GVHD). Although intervention using an ST2 antibody improves survival in murine GVHD models, sST2 is a challenging target for drug development because it binds to IL-33 via an extensive interaction interface. Here, we report the discovery of small-molecule ST2 inhibitors through a combination of high-throughput screening and computational analysis. After in vitro and in vivo toxicity assessment, 3 compounds were selected for evaluation in 2 experimental GVHD models. We show that the most effective compound, iST2-1, reduces plasma sST2 levels, alleviates disease symptoms, improves survival, and maintains graft-versus-leukemia activity. Our data suggest that iST2-1 warrants further optimization to develop treatment for inflammatory diseases mediated by sST2.

Molecular Dynamics Simulation and Prediction of Druggable Binding Sites

Binding site identification and druggability evaluation are two essential steps in structure-based drug design. A druggable binding site tends to have high binding affinity to drug-like molecules. Predicting such sites can have a significant impact on a drug design campaign. This chapter focuses on summarizing the different methods that are used to predict druggable binding sites. The chapter also discusses the importance of including protein flexibility in the search process and the use of molecular dynamics simulations to address this aspect. Case studies from the literature are also summarized and discussed. We hope that this chapter would provide an overview on the different methods employed in binding site identification evaluation.

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.

Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-XL: A molecular dynamics simulation approach

B-cell lymphoma 2 (Bcl-2) family proteins are potential drug targets in cancer and have a relatively flat and flexible binding site. ABT-199 is one of the most promising selective Bcl-2 inhibitors, and A-1155463 selectively inhibits Bcl-XL. Although the amino acid sequences of the binding sites of these two inhibitors are similar, the inhibitors selectively bind the target protein. In order to determine the origin of the selectivity of these inhibitors, we conducted molecular dynamics simulations using protein-inhibitor modeling. We confirmed that ASP103 of Bcl-2 is a key residue and that hydrogen bonding between ASP103 and ABT-199 confers the Bcl-2 selectivity of this inhibitor. For Bcl-XL selectivity, the secondary structure of α-helix 3 is a key factor. PHE105, SER106, and LEU108 in the loose α-helix 3 interact with A-1155463 to confer Bcl-XL selectivity. These findings provide important insights into the molecular mechanisms of selective inhibitors of Bcl-2 family proteins.

A potent small-molecule inhibitor of the DCN1-UBC12 interaction that selectively blocks cullin 3 neddylation

The Cullin-RING E3 ubiquitin ligases (CRLs) regulate homeostasis of ~20% of cellular proteins and their activation require neddylation of their cullin subunit. Cullin neddylation is modulated by a scaffolding DCN protein through interactions with both the cullin protein and an E2 enzyme such as UBC12. Here we report the development of DI-591 as a high-affinity, cell-permeable small-molecule inhibitor of the DCN1–UBC12 interaction. DI-591 binds to purified recombinant human DCN1 and DCN2 proteins with K_i values of 10–12 nM, and disrupts the DCN1–UBC12 interaction in cells. Treatment with DI-591 selectively converts cellular cullin 3 into an un-neddylated inactive form with no or minimum effect on other cullin members. Our data firmly establish a previously unrecognized specific role of the DCN1–UBC12 interaction for cellular neddylation of cullin 3. DI-591 is an excellent probe compound to investigate the role of the cullin 3 CRL ligase in biological processes and human diseases.

Cullins are central components of the ubiquitin-proteosome system and are activated via a neddylation process mediated by the DCN1–UBC12 complex. Here, the authors develop a small molecule inhibitor of the DCN1–UBC12 interaction that specifically blocks cullin 3 neddylation and can be used to probe the cellular function of cullin 3.

Targeting YAP/TAZ-TEAD protein-protein interactions using fragment-based and computational modeling approaches

The Hippo signaling pathway, which is implicated in the regulation of organ size, has emerged as a potential target for the development of cancer therapeutics. YAP, TAZ (transcription co-activators) and TEAD (transcription factor) are the downstream transcriptional machinery and effectors of the pathway. Formation of the YAP/TAZ-TEAD complex leads to transcription of growth-promoting genes. Conversely, disrupting the interactions of the complex decreases cell proliferation. Herein, we screened a 1000-member fragment library using Thermal Shift Assay and identified a hit fragment. We confirmed its binding at the YAP/TAZ-TEAD interface by X-ray crystallography, and showed that it occupies the same hydrophobic pocket as a conserved phenylalanine of YAP/TAZ. This hit fragment serves as a scaffold for the development of compounds that have the potential to disrupt YAP/TAZ-TEAD interactions. Structure-activity relationship studies and computational modeling were also carried out to identify more potent compounds that may bind at this validated druggable binding site.

The long unstructured region of Bcl-xl modulates its structural dynamics

Bcl-xl protein has a long unstructured loop attached to its structured region which joins two helices. The necessity to have this unstructured segment in Bcl-xl is not yet well understood. To what extent the unstructured segment can influence the dynamics of the structured region of protein, with potential to influence the function, has been investigated in this work. Molecular dynamics simulation and principal component analysis show how the loop affects the internal motions of the protein, particularly its ligand binding pocket. Generally an unstructured region in the structure would promote flexibility resulting entropic stability but in contrary, here it narrows down the conformational space of the structured region of protein that could be hypothesized to impact the functional precision. Effects of the loop propagate to the binding pocket through structural rearrangements of polar side chains. The immediate suspicion of possible impact of phosphorylation to modulate the function of the protein is proven to be a fact, as the phosphorylated S49 and S62 located on the large unstructured region are seen to perturb the electrostatic network of the structure; an observation that validates and clarifies the role of loop as a modulator through biophysical and biochemical mechanisms. Proteins 2017; 85:1567-1579. © 2017 Wiley Periodicals, Inc.

Characterization of Promiscuous Binding of Phosphor Ligands to Breast-Cancer-Gene 1 (BRCA1) C-Terminal (BRCT): Molecular Dynamics, Free Energy, Entropy and Inhibitor Design

Inhibition of the protein-protein interaction (PPI) mediated by breast-cancer-gene 1 C-terminal (BRCT) is an attractive strategy to sensitize breast and ovarian cancers to chemotherapeutic agents that induce DNA damage. Such inhibitors could also be used for studies to understand the role of this PPI in DNA damage response. However, design of BRCT inhibitors is challenging because of the inherent flexibility associated with this domain. Several studies identified short phosphopeptides as tight BRCT binders. Here we investigated the thermodynamic properties of 18 phosphopeptides or peptide with phosphate mimic and three compounds with phosphate groups binding to BRCT to understand promiscuous molecular recognition and guide inhibitor design. We performed molecular dynamics (MD) simulations to investigate the interactions between inhibitors and BRCT and their dynamic behavior in the free and bound states. MD simulations revealed the key role of loops in altering the shape and size of the binding site to fit various ligands. The mining minima (M2) method was used for calculating binding free energy to explore the driving forces and the fine balance between configuration entropy loss and enthalpy gain. We designed a rigidified ligand, which showed unfavorable experimental binding affinity due to weakened enthalpy. This was because it lacked the ability to rearrange itself upon binding. Investigation of another phosphate group containing compound, C1, suggested that the entropy loss can be reduced by preventing significant narrowing of the energy well and introducing multiple new compound conformations in the bound states. From our computations, we designed an analog of C1 that introduced new intermolecular interactions to strengthen attractions while maintaining small entropic penalty. This study shows that flexible compounds do not always encounter larger entropy penalty, compared with other more rigid binders, and highlights a new strategy for inhibitor design.

Promiscuous proteins are commonly observed in biological systems, such as modular domains that recognize phosphopeptides during signal transduction. The use of phosphopeptides and compounds with phosphate groups as inhibitors to protein–protein interactions have attracted increasing interest for years. By using atomistic molecular dynamics simulations, we are able to perform detailed analyses of the dihedral space to explore protein fluctuation upon ligand binding to better understand promiscuous molecular recognition. Free energy calculation can further provide insights into the mechanism of binding, including both enthalpic and entropic contributions for molecular recognition, which assist in inhibitor design. Our calculation results show that pre-rigidifying a ligand is not always advantageous, suggesting the challenge in retaining optimized intermolecular interactions in pre-rigidified ligand. Instead, certain flexible ligands with multiple binding conformations can reduce entropic penalty, and therefore improves binding affinity. According to our computations, we can introduce new intermolecular interactions to flexible ligand to strengthen attractions while maintaining small entropic penalty by retaining its plasticity in the bound conformation. The study might cast light on a new general strategy for designing inhibitors targeting promiscuous modular domains and protein–protein interactions.

Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics

Identifying binding hotspots on protein surfaces is of prime interest in structure-based drug discovery, either to assess the tractability of pursuing a protein target or to drive improved potency of lead compounds. Computational approaches to detect such regions have traditionally relied on energy minimization of probe molecules onto static protein conformations in the absence of the natural aqueous environment. Advances in high performance computing now allow us to assess hotspots using molecular dynamics (MD) simulations. MD simulations integrate protein flexibility and the complicated role of water, thereby providing a more realistic assessment of the complex kinetics and thermodynamics at play. In this review, we describe the evolution of various cosolvent-based MD techniques and highlight a myriad of potential applications for such technologies in computational drug development.

Computational functional group mapping for drug discovery

Computational functional group mapping (cFGM) is emerging as a high-impact complement to existing widely used experimental and computational structure-based drug discovery methods. cFGM provides comprehensive atomic-resolution 3D maps of the affinity of functional groups that can constitute drug-like molecules for a given target, typically a protein. These 3D maps can be intuitively and interactively visualized by medicinal chemists to rapidly design synthetically accessible ligands. Given that the maps can inform selection of functional groups for affinity, specificity, and pharmacokinetic properties, they are of utility for both the optimization of existing drug candidates and creating novel ones. Here, I review recent advances in cFGM with emphasis on the unique information content in the approach that offers the potential of broadly facilitating structure-based ligand design.

Hydrophobicity-induced prestaining for protein detection in polyacrylamide gel electrophoresis

An aggregation-induced emission fluorescent surfactant has been used to prestain protein by means of strong hydrophobic interaction between fluorescent surfactants and proteins.

Computational solvent mapping in structure-based drug design

Over the past two decades, solvent mapping has emerged as a useful tool for identifying hot spots within binding sites on proteins for drug-like molecules and suggesting properties of potential binders. While the experimental technique requires solving multiple crystal structures of a protein in different solvents, computational solvent mapping allows for fast analysis of a protein for potential binding sites and their druggability. Recent advances in genomics, systems biology and interactomics provide a multitude of potential targets for drug development and solvent mapping can provide useful information to help prioritize targets for drug discovery projects. Here, we review various approaches to computational solvent mapping, highlight some key advances and provide our opinion on future directions in the field.

Modulating protein-protein interactions: the potential of peptides

Protein-protein interactions (PPIs) have emerged as important and challenging targets in chemical biology and medicinal chemistry. The main difficulty encountered in the discovery of small molecule modulators derives from the large contact surfaces involved in PPIs when compared with those that participate in protein-small molecule interactions. Because of their intrinsic features, peptides can explore larger surfaces and therefore represent a useful alternative to modulate PPIs. The use of peptides as therapeutics has been held back by their instability in vivo and poor cell internalization. However, more than 200 peptide drugs and homologous compounds (proteins or antibodies) containing peptide bonds are (or have been) on the market, and many alternatives are now available to tackle these limitations. This review will focus on the latest progress in the field, spanning from "lead" identification methods to binding evaluation techniques, through an update of the most successful examples described in the literature.

State-of-the-art strategies for targeting protein-protein interactions by small-molecule inhibitors

Targeting protein-protein interactions (PPIs) has emerged as a viable approach in modern drug discovery. However, the identification of small molecules enabling us to effectively interrupt their interactions presents significant challenges. In the recent past, significant advances have been made in the development of new biological and chemical strategies to facilitate the discovery process of small-molecule PPI inhibitors. This review aims to highlight the state-of-the-art technologies and the achievements made recently in this field. The "hot spots" of PPIs have been proved to be critical for small molecules to bind. Three strategies including screening, designing, and synthetic approaches have been explored for discovering PPI inhibitors by targeting the "hot spots". Although the classic high throughput screening approach can be used, fragment screening, fragment-based drug design and newly improved virtual screening are demonstrated to be more effective in the discovery of PPI inhibitors. In addition to screening approaches, design strategies including anchor-based and small molecule mimetics of secondary structures involved in PPIs have become powerful tools as well. Finally, constructing new chemically spaced libraries with high diversity and complexity is becoming an important area of interest for PPI inhibitors. The successful cases from the recent five year studies are used to illustrate how these approaches are implemented to uncover and optimize small molecule PPI inhibitors and notably some of them have become promising therapeutics.

The Application of Ligand-Mapping Molecular Dynamics Simulations to the Rational Design of Peptidic Modulators of Protein-Protein Interactions

A computational ligand-mapping approach to detect protein surface pockets that interact with hydrophobic moieties is presented. In this method, we incorporated benzene molecules into explicit solvent molecular dynamics simulations of various protein targets. The benzene molecules successfully identified the binding locations of hydrophobic hot-spot residues and all-hydrocarbon cross-links from known peptidic ligands. They also unveiled cryptic binding sites that are occluded by side chains and the protein backbone. Our results demonstrate that ligand-mapping molecular dynamics simulations hold immense promise to guide the rational design of peptidic modulators of protein-protein interactions, including that of stapled peptides, which show promise as an exciting new class of cell-penetrating therapeutic molecules.

Enrichment of druggable conformations from apo protein structures using cosolvent-accelerated molecular dynamics

Here we describe the development of an improved workflow for utilizing experimental and simulated protein conformations in the structure-based design of inhibitors for anti-apoptotic Bcl-2 family proteins. Traditional structure-based approaches on similar targets are often constrained by the sparsity of available structures and difficulties in finding lead compounds that dock against flat, flexible protein-protein interaction surfaces. By employing computational docking of known small molecule inhibitors, we have demonstrated that structural ensembles derived from either accelerated MD (aMD) or MD in the presence of an organic cosolvent generally give better scores than those assessed from analogous conventional MD. Furthermore, conformations obtained from combined cosolvent aMD simulations started with the apo-Bcl-xL structure yielded better average and minimum docking scores for known binders than an ensemble of 72 experimental apo- and ligand-bound Bcl-xL structures. A detailed analysis of the simulated conformations indicates that the aMD effectively enhanced conformational sampling of the flexible helices flanking the main Bcl-xL binding groove, permitting the cosolvent acting as small ligands to penetrate more deeply into the binding pocket and shape ligand-bound conformations not evident in conventional simulations. We believe this approach could be useful for identifying inhibitors against other protein-protein interaction systems involving highly flexible binding sites, particularly for targets with less accumulated structural data.

Identification of potential small molecule allosteric modulator sites on IL-1R1 ectodomain using accelerated conformational sampling method

The interleukin-1 receptor (IL-1R) is the founding member of the interleukin 1 receptor family which activates innate immune response by its binding to cytokines. Reports showed dysregulation of cytokine production leads to aberrant immune cells activation which contributes to auto-inflammatory disorders and diseases. Current therapeutic strategies focus on utilizing antibodies or chimeric cytokine biologics. The large protein-protein interaction interface between cytokine receptor and cytokine poses a challenge in identifying binding sites for small molecule inhibitor development. Based on the significant conformational change of IL-1R type 1 (IL-1R1) ectodomain upon binding to different ligands observed in crystal structures, we hypothesized that transient small molecule binding sites may exist when IL-1R1 undergoes conformational transition and thus suitable for inhibitor development. Here, we employed accelerated molecular dynamics (MD) simulation to efficiently sample conformational space of IL-1R1 ectodomain. Representative IL-1R1 ectodomain conformations determined from the hierarchy cluster analysis were analyzed by the SiteMap program which leads to identify small molecule binding sites at the protein-protein interaction interface and allosteric modulator locations. The cosolvent mapping analysis using phenol as the probe molecule further confirms the allosteric modulator site as a binding hotspot. Eight highest ranked fragment molecules identified from in silico screening at the modulator site were evaluated by MD simulations. Four of them restricted the IL-1R1 dynamical motion to inactive conformational space. The strategy from this study, subject to in vitro experimental validation, can be useful to identify small molecule compounds targeting the allosteric modulator sites of IL-1R and prevent IL-1R from binding to cytokine by trapping IL-1R in inactive conformations.

Site Identification by Ligand Competitive Saturation (SILCS) simulations for fragment-based drug design

Fragment-based drug design (FBDD) involves screening low molecular weight molecules ("fragments") that correspond to functional groups found in larger drug-like molecules to determine their binding to target proteins or nucleic acids. Based on the principle of thermodynamic additivity, two fragments that bind nonoverlapping nearby sites on the target can be combined to yield a new molecule whose binding free energy is the sum of those of the fragments. Experimental FBDD approaches, like NMR and X-ray crystallography, have proven very useful but can be expensive in terms of time, materials, and labor. Accordingly, a variety of computational FBDD approaches have been developed that provide different levels of detail and accuracy.The Site Identification by Ligand Competitive Saturation (SILCS) method of computational FBDD uses all-atom explicit-solvent molecular dynamics (MD) simulations to identify fragment binding. The target is "soaked" in an aqueous solution with multiple fragments having different identities. The resulting computational competition assay reveals what small molecule types are most likely to bind which regions of the target. From SILCS simulations, 3D probability maps of fragment binding called "FragMaps" can be produced. Based on the probabilities relative to bulk, SILCS FragMaps can be used to determine "Grid Free Energies (GFEs)," which provide per-atom contributions to fragment binding affinities. For essentially no additional computational overhead relative to the production of the FragMaps, GFEs can be used to compute Ligand Grid Free Energies (LGFEs) for arbitrarily complex molecules, and these LGFEs can be used to rank-order the molecules in accordance with binding affinities.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Inhibitors of protein-protein interactions: new methodologies to tackle this challenge

Several advances in the fields of crystallography, molecular modeling, biophysical assays and chemistry are converging to making protein-protein interaction targets more amenable to drug design. These include steps towards improving crystallization of protein-protein complexes, identifying the clusters of residues that constitute putative small molecule binding 'hot spots', generating new methods for detecting the binding of small molecules to target proteins, and generating custom libraries via diversity oriented synthesis to enable the identification of natural-product-like hits.

Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach

The site identification by ligand competitive saturation (SILCS) method identifies the location and approximate affinities of small molecular fragments on a target macromolecular surface by performing molecular dynamics (MD) simulations of the target in an aqueous solution of small molecules representative of different chemical functional groups. In this study, we introduce a set of small molecules to map potential interactions made by neutral hydrogen bond donors and acceptors and charged donor and acceptor fragments in addition to nonpolar fragments. The affinity pattern is obtained in the form of discretized probability or, equivalently, free energy maps, called FragMaps, which can be visualized with the target surface. We performed SILCS simulations for four proteins for which structural and thermodynamic data is available for multiple diverse ligands. Good overlap is shown between high affinity regions identified by the FragMaps and the crystallographic positions of ligand functional groups with similar chemical functionality, thus demonstrating the validity of the qualitative information obtained from the simulations. To test the ability of FragMaps in providing quantitative predictions, we calculate the previously introduced ligand grid free energy (LGFE) metric and observe its correspondence with experimentally measured binding affinity. LGFE is computed for different conformational ensembles and improvement in prediction is shown with increasing ligand conformational sampling. Ensemble generation includes a Monte Carlo sampling approach that uses the GFE FragMaps directly as the energy function. The results show that some but not all experimental trends are predicted and warrant improvements in the scoring methodology. In addition, the potential utility of atom-based free energy contributions to the LGFE scores and the use of multiple ligands in SILCS to identify displaceable water molecules during ligand design are discussed.

Dynamics of Bcl-xL in water and membrane: molecular simulations

The Bcl2 family of proteins is capable of switching the apoptotic machinery by directly controlling the release of apoptotic factors from the mitochondrial outer membrane. They have 'pro' and 'anti'-apoptotic subgroups of proteins which antagonize each other's function; however a detailed atomistic understanding of their mechanisms based on the dynamical events, particularly in the membrane, is lacking. Using molecular dynamics simulations totaling 1.6µs we outline the major differences between the conformational dynamics in water and in membrane. Using implicit models of solvent and membrane, the simulated results reveal a picture that is in agreement with the 'hit-and run' concept which states that BH3-only peptides displace the tail (which acts as a pseudo substrate of the protein itself) from its binding pocket; this helps the membrane association of the protein after which the BH3 peptide becomes free. From simulations, Bcl-xL appears to be auto-inhibited by its C-terminal tail that embeds into and covers the hydrophobic binding pocket. However the tail is unable to energetically compete with BH3-peptides in water. In contrast, in the membrane, neither the tail nor the BH3-peptides are stable in the binding pocket and appear to be easily dissociated off as the pocket expands in response to the hydrophobic environment. This renders the binding pocket large and open, thus receptive to interactions with other protein partners. Principal components of the motions are dramatically different in the aqueous and in the membrane environments and provide clues regarding the conformational transitions that Bcl-xL undergoes in the membrane, in agreement with the biochemical data.

Behavior of solvent-exposed hydrophobic groove in the anti-apoptotic Bcl-XL protein: clues for its ability to bind diverse BH3 ligands from MD simulations

Bcl-XL is a member of Bcl-2 family of proteins involved in the regulation of intrinsic pathway of apoptosis. Its overexpression in many human cancers makes it an important target for anti-cancer drugs. Bcl-XL interacts with the BH3 domain of several pro-apoptotic Bcl-2 partners. This helical bundle protein has a pronounced hydrophobic groove which acts as a binding region for the BH3 domains. Eight independent molecular dynamics simulations of the apo/holo forms of Bcl-XL were carried out to investigate the behavior of solvent-exposed hydrophobic groove. The simulations used either a twin-range cut-off or particle mesh Ewald (PME) scheme to treat long-range interactions. Destabilization of the BH3 domain-containing helix H2 was observed in all four twin-range cut-off simulations. Most of the other major helices remained stable. The unwinding of H2 can be related to the ability of Bcl-XL to bind diverse BH3 ligands. The loss of helical character can also be linked to the formation of homo- or hetero-dimers in Bcl-2 proteins. Several experimental studies have suggested that exposure of BH3 domain is a crucial event before they form dimers. Thus unwinding of H2 seems to be functionally very important. The four PME simulations, however, revealed a stable helix H2. It is possible that the H2 unfolding might occur in PME simulations at longer time scales. Hydrophobic residues in the hydrophobic groove are involved in stable interactions among themselves. The solvent accessible surface areas of bulky hydrophobic residues in the groove are significantly buried by the loop LB connecting the helix H2 and subsequent helix. These observations help to understand how the hydrophobic patch in Bcl-XL remains stable in the solvent-exposed state. We suggest that both the destabilization of helix H2 and the conformational heterogeneity of loop LB are important factors for binding of diverse ligands in the hydrophobic groove of Bcl-XL.

Structure-based design of high-affinity macrocyclic peptidomimetics to block the menin-mixed lineage leukemia 1 (MLL1) protein-protein interaction

Menin is an essential oncogenic cofactor for mixed lineage leukemia 1 (MLL1)-mediated leukemogenesis through its direct interaction with MLL1. Targeting the menin-MLL1 protein-protein interaction represents a promising strategy to block MLL1-mediated leukemogenesis. Employing a structure-based approach and starting from a linear MLL1 octapeptide, we have designed a class of potent macrocyclic peptidomimetic inhibitors of the menin-MLL1 interaction. The most potent macrocyclic peptidomimetic (MCP-1), 34, binds to menin with a K(i) value of 4.7 nM and is >600 times more potent than the corresponding acyclic peptide. Compound 34 is also less peptide-like and has a lower molecular weight than the initial MLL1 peptide. Therefore, compound 34 serves as a promising lead structure for the design of potent and cell-permeable inhibitors of the menin-MLL1 interaction.

Analysis of Flexibility and Hotspots in Bcl-xL and Mcl-1 Proteins for the Design of Selective Small-Molecule Inhibitors

Although Bcl-xL and Mcl-1, two antideath Bcl-2 members, have similar, flexible binding sites, they can achieve high binding selectivity to endogenous binding partners and synthetic small-molecule inhibitors. Here, we employed molecular dynamic (MD) simulations and hotspot analysis to investigate the conformational flexibility of these proteins and their binding hotspots at the binding sites. Backbone flexibility analyses indicate that the highest degree of flexibility in Mcl-1 is the α4 helical segment as opposed to the α3 helix in Bcl-xL among four helical segments in their binding sites. Furthermore, common and unique binding hotspots at both proteins were identified using small-molecule probes. These analyses can aid the design of potent and specific small-molecule inhibitors for these proteins.

Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations

The in-silico Site Identification by Ligand Competitive Saturation (SILCS) approach identifies the binding sites of representative chemical entities on the entire protein surface, information that can be applied for computational fragment-based drug design. In this study, we report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single step free energy perturbation (SSFEP) calculations to identify site-specific favorable chemical modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the experimental trends in relative hydration free energies of benzene analogues and for two datasets of experimental relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins is first analyzed to identify favorable benzene binding sites following which an ensemble of benzene-protein conformations for that site is obtained. The SSFEP protocol applied to that ensemble results in good reproduction of experimental free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and analysis of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.