Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics

Modeling Chromatography Binding through Molecular Dynamics Simulations with Resin Fragments

Accurate atomistic modeling of the interactions of a chromatography resin with a solute can inform the selection of purification conditions for a product, an important problem in the biotech and pharmaceutical industries. We present a molecular dynamics simulation-based approach for the qualitative prediction of interaction sites (specificity) and retention times (affinity) of a protein for a given chromatography resin. We mimicked the resin with an unrestrained ligand composed of the resin headgroup coupled with successively larger fragments of the agarose backbone. The interactions of the ligand with the protein are simulated in an explicit solvent using the Replica Exchange Molecular Dynamics enhanced sampling approach in conjunction with Hydrogen Mass Repartitioning (REMD-HMR). We computed the ligand interaction surface from the simulation trajectories and correlated the features of the interaction surface with experimentally determined retention times. The simulation and analysis protocol were first applied to a series of ubiquitin mutants for which retention times on Capto MMC resin are available. The ubiquitin simulations helped identify the optimal ligand that was used in subsequent simulations on six proteins for which Capto MMC elution times are available. For each of the six proteins, we computed the interaction surface and characterized it in terms of a range of simulation-averaged residue-level physicochemical descriptors. Modeling of the salt concentrations required for elution with respect to the descriptors resulted in a linear fit in terms of aromaphilicity and Kyte-Doolittle hydrophobicity that was robust to outliers, showed high correlation, and correctly ranked the protein elution order. The physics-based model building approach described here does not require a large experimental data set and can be readily applied to different resins and diverse biomolecules.

Exploring Aromatic Cage Flexibility Using Cosolvent Molecular Dynamics Simulations─An In-Silico Case Study of Tudor Domains

Cosolvent molecular dynamics (MD) simulations have proven to be powerful in silico tools to predict hotspots for binding regions on protein surfaces. In the current study, the method was adapted and applied to two Tudor domain-containing proteins, namely Spindlin1 (SPIN1) and survival motor neuron protein (SMN). Tudor domains are characterized by so-called aromatic cages that recognize methylated lysine residues of protein targets. In the study, the conformational transitions from closed to open aromatic cage conformations were investigated by performing MD simulations with cosolvents using six different probe molecules. It is shown that a trajectory clustering approach in combination with volume and atomic distance tracking allows a reasonable discrimination between open and closed aromatic cage conformations and the docking of inhibitors yields very good reproducibility with crystal structures. Cosolvent MDs are suitable to capture the flexibility of aromatic cages and thus represent a promising tool for the optimization of inhibitors.

A druggable conformational switch in the c-MYC transactivation domain

The c-MYC oncogene is activated in over 70% of all human cancers. The intrinsic disorder of the c-MYC transcription factor facilitates molecular interactions that regulate numerous biological pathways, but severely limits efforts to target its function for cancer therapy. Here, we use a reductionist strategy to characterize the dynamic and structural heterogeneity of the c-MYC protein. Using probe-based Molecular Dynamics (MD) simulations and machine learning, we identify a conformational switch in the c-MYC amino-terminal transactivation domain (termed coreMYC) that cycles between a closed, inactive, and an open, active conformation. Using the polyphenol epigallocatechin gallate (EGCG) to modulate the conformational landscape of coreMYC, we show through biophysical and cellular assays that the induction of a closed conformation impedes its interactions with the transformation/transcription domain-associated protein (TRRAP) and the TATA-box binding protein (TBP) which are essential for the transcriptional and oncogenic activities of c-MYC. Together, these findings provide insights into structure-activity relationships of c-MYC, which open avenues towards the development of shape-shifting compounds to target c-MYC as well as other disordered transcription factors for cancer treatment.

Preventing lipophilic aggregation in cosolvent molecular dynamics simulations with hydrophobic probes using Plumed Automatic Restraining Tool (PART)

Cosolvent molecular dynamics (MD) simulations are molecular dynamics simulations used to identify preferable locations of small organic fragments on a protein target. Most cosolvent molecular dynamics workflows make use of only water-soluble fragments, as hydrophobic fragments would cause lipophilic aggregation. To date the two approaches that allow usage of hydrophobic cosolvent molecules are to use a low (0.2 M) concentration of hydrophobic probes, with the disadvantage of a lower sampling speed, or to use force field modifications, with the disadvantage of a difficult and inflexible setup procedure. Here we present a third alternative, that does not suffer from low sampling speed nor from cumbersome preparation procedures. We have built an easy-to-use open source command line tool PART (Plumed Automatic Restraining Tool) to generate a PLUMED file handling all intermolecular restraints to prevent lipophilic aggregation. We have compared restrained and unrestrained cosolvent MD simulations, showing that restraints are necessary to prevent lipophilic aggregation at hydrophobic probe concentrations of 0.5 M. Furthermore, we benchmarked PART generated restraints on a test set of four proteins (Factor-Xa, HIV protease, P38 MAP kinase and RNase A), showing that cosolvent MD with PART generated restraints qualitatively reproduces binding features of cocrystallised ligands.

Identifying Potential Ligand Binding Sites on Glycogen Synthase Kinase 3 Using Atomistic Cosolvent Simulations

Glycogen synthase kinase 3 β (GSK3β) is a serine/threonine kinase that phosphorylates several protein substrates in crucial cell signaling pathways. Owing to its therapeutic importance, there is a need to develop GSK3β inhibitors with high specificity and potency. One approach is to find small molecules that can allosterically bind to the GSK3β protein surface. We have employed fully atomistic mixed-solvent molecular dynamics (MixMD) simulations to identify three plausible allosteric sites on GSK3β that can facilitate the search for allosteric inhibitors. Our MixMD simulations narrow down the allosteric sites to precise regions on the GSK3β surface, thereby improving upon the previous predictions of the locations of these sites.

Probing the Protein-Excipient Interaction in the Orally Delivered Protein by Solid-State Hydrogen-Deuterium Exchange Mass Spectrometry and Molecular Dynamics

The oral administration of protein therapeutics in solid dosage form is gaining popularity due to its benefits, such as improved medication adherence, convenience, and ease of use for patients compared to traditional parental delivery. However, formulating oral biologics presents challenges related to pH barriers, enzymatic breakdown, and poor bioavailability. Therefore, understanding the interaction between excipients and protein therapeutics in the solid state is crucial for formulation development. In this Letter, we present a case study focused on investigating the role of excipients in protein aggregation during the production of a solid dosage form of a single variable domain on a heavy chain (VHH) protein. We employed solid-state hydrogen-deuterium exchange coupled with mass spectrometry (ssHDX-MS) at both intact protein and peptide levels to assess differences in protein-excipient interactions between two formulations. ssHDX-MS analysis revealed that one formulation effectively prevents protein aggregation during compaction by blocking β-sheets across the VHH protein, thereby preventing β-sheet-β-sheet interactions. Spatial aggregation propensity (SAP) mapping and cosolvent simulation from molecular dynamics (MD) simulation further validated the protein-excipient interaction sites identified through ssHDX-MS. Additionally, the MD simulation demonstrated that the interaction between the VHH protein and excipients involves hydrophilic interactions and/or hydrogen bonding. This novel approach holds significant potential for understanding protein-excipient interactions in the solid state and can guide the formulation and process development of orally delivered protein dosage forms, ultimately enhancing their efficacy and stability.

AAp-MSMD: Amino Acid Preference Mapping on Protein-Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics

Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.

In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana

Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.

An Exploration of Small Molecules That Bind Human Single-Stranded DNA Binding Protein 1

Human single-stranded DNA binding protein 1 (hSSB1) is critical to preserving genome stability, interacting with single-stranded DNA (ssDNA) through an oligonucleotide/oligosaccharide binding-fold. The depletion of hSSB1 in cell-line models leads to aberrant DNA repair and increased sensitivity to irradiation. hSSB1 is over-expressed in several types of cancers, suggesting that hSSB1 could be a novel therapeutic target in malignant disease. hSSB1 binding studies have focused on DNA; however, despite the availability of 3D structures, small molecules targeting hSSB1 have not been explored. Quinoline derivatives targeting hSSB1 were designed through a virtual fragment-based screening process, synthesizing them using AlphaLISA and EMSA to determine their affinity for hSSB1. In parallel, we further screened a structurally diverse compound library against hSSB1 using the same biochemical assays. Three compounds with nanomolar affinity for hSSB1 were identified, exhibiting cytotoxicity in an osteosarcoma cell line. To our knowledge, this is the first study to identify small molecules that modulate hSSB1 activity. Molecular dynamics simulations indicated that three of the compounds that were tested bound to the ssDNA-binding site of hSSB1, providing a framework for the further elucidation of inhibition mechanisms. These data suggest that small molecules can disrupt the interaction between hSSB1 and ssDNA, and may also affect the ability of cells to repair DNA damage. This test study of small molecules holds the potential to provide insights into fundamental biochemical questions regarding the OB-fold.

Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening

The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED's ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both in silico tests with the Directory of Useful Decoys-Enhanced dataset and wet-lab tests on Covid-19 3CLpro-inhibitors. ExptGMS improved the active compound enrichment in top-ranked molecules by approximately 20%. Furthermore, ExptGMS identified four active inhibitors of 3CLpro, with the most effective showing an IC50 value of 1.9 µM. We also developed an online database containing experimental ED grids for over 17,000 proteins to facilitate the use of ExptGMS for academic users.

Interaction of an anticancer benzopyrane derivative with DNA: Biophysical, biochemical, and molecular modeling studies

BACKGROUND

SIMR1281 is a potent anticancer lead candidate with multi- target activity against several proteins; however, its mechanism of action at the molecular level is not fully understood. Revealing the mechanism and the origin of multitarget activity is important for the rational identification and optimization of multitarget drugs.

METHODS

We have used a variety of biophysical (circular dichroism, isothermal titration calorimetry, viscosity, and UV DNA melting), biochemical (topoisomerase I & II assays) and computational (molecular docking and MD simulations) methods to study the interaction of SIMR1281 with duplex DNA structures.

RESULTS

The biophysical results revealed that SIMR1281 binds to dsDNA via an intercalation-binding mode with an average binding constant of 3.1 × 10⁶ M^-1. This binding mode was confirmed by the topoisomerases' inhibition assays and molecular modeling simulations, which showed the intercalation of the benzopyrane moiety between DNA base pairs, while the remaining moieties (thiazole and phenyl rings) sit in the minor groove and interact with the flanking base pairs adjacent to the intercalation site.

CONCLUSIONS

The DNA binding characteristics of SIMR1281, which can disrupt/inhibit DNA function as confirmed by the topoisomerases' inhibition assays, indicate that the observed multi-target activity might originate from ligand intervention at nucleic acids level rather than due to direct interactions with multiple biological targets at the protein level.

GENERAL SIGNIFICANCE

The findings of this study could be helpful to guide future optimization of benzopyrane-based ligands for therapeutic purposes.

Fragment screening using biolayer interferometry reveals ligands targeting the SHP-motif binding site of the AAA+ ATPase p97

Biosensor techniques have become increasingly important for fragment-based drug discovery during the last years. The AAA+ ATPase p97 is an essential protein with key roles in protein homeostasis and a possible target for cancer chemotherapy. Currently available p97 inhibitors address its ATPase activity and globally impair p97-mediated processes. In contrast, inhibition of cofactor binding to the N-domain by a protein-protein-interaction inhibitor would enable the selective targeting of specific p97 functions. Here, we describe a biolayer interferometry-based fragment screen targeting the N-domain of p97 and demonstrate that a region known as SHP-motif binding site can be targeted with small molecules. Guided by molecular dynamics simulations, the binding sites of selected screening hits were postulated and experimentally validated using protein- and ligand-based NMR techniques, as well as X-ray crystallography, ultimately resulting in the first structure of a small molecule in complex with the N-domain of p97. The identified fragments provide insights into how this region could be targeted and present first chemical starting points for the development of a protein-protein interaction inhibitor preventing the binding of selected cofactors to p97.

A pocket-based 3D molecule generative model fueled by experimental electron density

We report for the first time the use of experimental electron density (ED) as training data for the generation of drug-like three-dimensional molecules based on the structure of a target protein pocket. Similar to a structural biologist building molecules based on their ED, our model functions with two main components: a generative adversarial network (GAN) to generate the ligand ED in the input pocket and an ED interpretation module for molecule generation. The model was tested on three targets: a kinase (hematopoietic progenitor kinase 1), protease (SARS-CoV-2 main protease), and nuclear receptor (vitamin D receptor), and evaluated with a reference dataset composed of over 8000 compounds that have their activities reported in the literature. The evaluation considered the chemical validity, chemical space distribution-based diversity, and similarity with reference active compounds concerning the molecular structure and pocket-binding mode. Our model can generate molecules with similar structures to classical active compounds and novel compounds sharing similar binding modes with active compounds, making it a promising tool for library generation supporting high-throughput virtual screening. The ligand ED generated can also be used to support fragment-based drug design. Our model is available as an online service to academic users via https://edmg.stonewise.cn/#/create .

Cosolvent Sites-Based Discovery of Mycobacterium Tuberculosis Protein Kinase G Inhibitors

Computer-aided drug discovery methods play a major role in the development of therapeutically important small molecules, but their performance needs to be improved. Molecular dynamics simulations in mixed solvents are useful in understanding protein–ligand recognition and improving molecular docking predictions. In this work, we used ethanol as a cosolvent to find relevant interactions for ligands toward protein kinase G, an essential protein of Mycobacterium tuberculosis (Mtb). We validated the hot spots by screening a database of fragment-like compounds and another one of known kinase inhibitors. Next, we performed a pharmacophore-guided docking simulation and found three low micromolar inhibitors, including one with a novel chemical scaffold that we expanded to four derivative compounds. Binding affinities were characterized by intrinsic fluorescence quenching assays, isothermal titration calorimetry, and the analysis of melting curves. The predicted binding mode was confirmed by X-ray crystallography. Finally, the compounds significantly inhibited the viability of Mtb in infected THP-1 macrophages.

Cosolvent Simulations with Fragment-Bound Proteins Identify Hot Spots to Direct Lead Growth

In drug design, chemical groups are sequentially added to improve a weak-binding fragment into a tight-binding lead molecule. Often, the direction to make these additions is unclear, and there are numerous chemical modifications to choose. Lead development can be guided by crystal structures of the fragment-bound protein, but this alone is unable to capture structural changes like closing or opening of the binding site and any side-chain movements. Accounting for adaptation of the site requires a dynamic approach. Here, we use molecular dynamics calculations of small organic solvents with protein-fragment pairs to reveal the nearest "hot spots". These close hot spots show the direction to make appropriate additions and suggest types of chemical modifications that could improve binding affinity. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that is well established for finding binding "hot spots" in active sites and allosteric sites of proteins. We simulated 20 fragment-bound and apo forms of key pharmaceutical targets to map out hot spots for potential lead space. Furthermore, we analyzed whether the presence of a fragment facilitates the probes' binding in the lead space, a type of binding cooperativity. To the best of our knowledge, this is the first use of cosolvent MD conducted with bound inhibitors in the simulation. Our work provides a general framework to extract molecular features of binding sites to choose chemical groups for growing lead molecules. Of the 20 systems, 17 systems were well mapped by MixMD. For the three not-mapped systems, two had lead growth out into solution away from the protein, and the third had very small modifications which indicated no nearby hot spots. Therefore, our lack of mapping in three systems was appropriate given the experimental data (true-negative cases). The simulations are run for very short time scales, making this method tractable for use in the pharmaceutical industry.

Ammonia stimulates SCAP/Insig dissociation and SREBP-1 activation to promote lipogenesis and tumour growth

Tumorigenesis is associated with elevated glucose and glutamine consumption, but how cancer cells can sense their levels to activate lipid synthesis is unknown. Here, we reveal that ammonia, released from glutamine, promotes lipogenesis via activation of sterol regulatory element-binding proteins (SREBPs), endoplasmic reticulum-bound transcription factors that play a central role in lipid metabolism. Ammonia activates the dissociation of glucose-regulated, N-glycosylated SREBP-cleavage-activating protein (SCAP) from insulin-inducible gene protein (Insig), an endoplasmic reticulum-retention protein, leading to SREBP translocation and lipogenic gene expression. Notably, 25-hydroxycholesterol blocks ammonia to access its binding site on SCAP. Mutating aspartate D428 to alanine prevents ammonia binding to SCAP, abolishes SREBP-1 activation and suppresses tumour growth. Our study characterizes the unknown role, opposite to sterols, of ammonia as a key activator that stimulates SCAP-Insig dissociation and SREBP-1 activation to promote tumour growth and demonstrates that SCAP is a critical sensor of glutamine, glucose and sterol levels to precisely control lipid synthesis.

Extracting Atomic Contributions to Binding Free Energy Using Molecular Dynamics Simulations with Mixed Solvents (MDmix)

BACKGROUND

Mixed solvents MD (MDmix) simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery. One of the assumptions behind the methodology is the transferability of free energy values from the simulated cosolvent molecules to larger drug-like molecules. However, the binding free energy maps (ΔGbind) calculated for the different moieties of the cosolvent molecules (e.g. a hydroxyl map for the ethanol) are largely influenced by the rest of the solvent molecule and do not reflect the intrinsic affinity of the moiety in question. As such, they are hardly transferable to different molecules.

METHOD

To achieve transferable energies, we present here a method for decomposing the molecular binding free energy into accurate atomic contributions.

RESULT

We demonstrate with two qualitative visual examples how the corrected energy maps better match known binding hotspots and how they can reveal hidden hotspots with actual drug design potential.

CONCLUSION

Atomic decomposition of binding free energies derived from MDmix simulations provides transferable and quantitative binding free energy maps.

Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites

The identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.

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.

Divide and Conquer. Pocket-Opening Mixed-Solvent Simulations in the Perspective of Docking Virtual Screening Applications for Drug Discovery

The existence of a druggable binding pocket is a prerequisite for computational drug-target interaction studies including virtual screening. Retrospective studies have shown that extended sampling methods like Markov State Modeling and mixed-solvent simulations can identify cryptic pockets relevant for drug discovery. Here, we apply a combination of mixed-solvent molecular dynamics (MD) and time-structure independent component analysis (TICA) to four retrospective case studies: NPC2, the CECR2 bromodomain, TEM-1, and MCL-1. We compare previous experimental and computational findings to our results. It is shown that the successful identification of cryptic pockets depends on the system and the cosolvent probes. We used alternative TICA internal features such as the unbiased backbone coordinates or backbone dihedrals versus biased interatomic distances. We found that in the case of NPC2, TEM-1, and MCL-1, the use of unbiased features is able to identify cryptic pockets, although in the case of the CECR2 bromodomain, more specific features are required to properly capture a pocket opening. In the perspective of virtual screening applications, it is shown how docking studies with the parent ligands depend critically on the conformational state of the targets.

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment

Cytochrome P450 enzymes (CYPs) are the largest group of enzymes involved in human drug metabolism. Ligand tunnels connect their active site buried at the core of the membrane-anchored protein to the surrounding solvent environment. Recently, evidence of a superficial allosteric site, here denoted as hotspot 1 (H1), involved in the regulation of ligand access in a soluble prokaryotic CYP emerged. Here, we applied multi-scale computational modeling techniques to study the conservation and functionality of this allosteric site in the nine most relevant mammalian CYPs responsible for approximately 70% of drug metabolism. In total, we systematically analyzed over 44 μs of trajectories from conventional MD, cosolvent MD, and metadynamics simulations. Our bioinformatic analysis and simulations with organic probe molecules revealed the site to be well conserved in the CYP2 family with the exception of CYP2E1. In the presence of a ligand bound to the H1 site, we could observe an enlargement of a ligand tunnel in several members of the CYP2 family. Further, we could detect the facilitation of ligand translocation by H1 interactions with statistical significance in CYP2C8 and CYP2D6, even though all other enzymes except for CYP2C19, CYP2E1, and CYP3A4 presented a similar trend. As the detailed comprehension of ligand access and egress phenomena remains one of the most relevant challenges in the field, this work contributes to its elucidation and ultimately helps in estimating the selectivity of metabolic transformations using computational techniques.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

Probe Confined Dynamic Mapping for G Protein-Coupled Receptor Allosteric Site Prediction

Targeting G protein-coupled receptors (GPCRs) through allosteric sites offers advantages over orthosteric sites in identifying drugs with increased selectivity and potentially reduced side effects. In this study, we developed a probe confined dynamic mapping protocol that allows the prediction of allosteric sites at both the GPCR extracellular and intracellular sides, as well as at the receptor-lipid interface. The applied harmonic wall potential enhanced sampling of probe molecules in a selected area of a GPCR while preventing membrane distortion in molecular dynamics simulations. The specific probes derived from GPCR allosteric ligand structures performed better in allosteric site mapping compared to commonly used cosolvents. The M₂ muscarinic, β₂ adrenergic, and P₂Y₁ purinergic receptors were selected for the protocol's retrospective validation. The protocol was next validated prospectively to locate the binding site of [5-fluoro-4-(hydroxymethyl)-2-methoxyphenyl]-(4-fluoro-1H-indol-1-yl)methanone at the D₂ dopamine receptor, and subsequent mutagenesis confirmed the prediction. The protocol provides fast and efficient prediction of key amino acid residues surrounding allosteric sites in membrane proteins and facilitates the structure-based design of allosteric modulators.

Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations

The lack of conformational sampling in virtual screening projects can lead to inefficient results because many of the potential drugs may not be able to bind to the target protein during the static docking simulations. Here, we performed ensemble docking for around 2000 United States Food and Drug Administration (FDA)-approved drugs with the RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a target. The representative protein structures were generated by clustering classical molecular dynamics trajectories, which were evolved using three solvent scenarios, namely, pure water, benzene/water and phenol/water mixtures. The introduction of dynamic effects in the theoretical model showed improvement in docking results in terms of the number of strong binders and binding sites in the protein. Some of the discovered pockets were found only for the cosolvent simulations, where the nonpolar probes induced local conformational changes in the protein that lead to the opening of transient pockets. In addition, the selection of the ligands based on a combination of the binding free energy and binding free energy gap between the best two poses for each ligand provided more suitable binders than the selection of ligands based solely on one of the criteria. The application of cosolvent molecular dynamics to enhance the sampling of the configurational space is expected to improve the efficacy of virtual screening campaigns of future drug discovery projects.

The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design

The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.

Development of a Novel Pharmacophore Model Guided by the Ensemble of Waters and Small Molecule Fragments Bound to SARS-CoV-2 Main Protease

Recent fragment‐based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS‐CoV‐2 M^pro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to M^pro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo‐ or small molecule fragment‐bound structures of M^pro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS‐CoV‐2 M^pro. It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC‐AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.

New perspectives in cancer drug development: computational advances with an eye to design

Computational chemistry has come of age in drug discovery. Indeed, most pharmaceutical development programs rely on computer-based data and results at some point. Herein, we discuss recent applications of advanced simulation techniques to difficult challenges in drug discovery. These entail the characterization of allosteric mechanisms and the identification of allosteric sites or cryptic pockets determined by protein motions, which are not immediately evident in the experimental structure of the target; the study of ligand binding mechanisms and their kinetic profiles; and the evaluation of drug-target affinities. We analyze different approaches to tackle challenging and emerging biological targets. Finally, we discuss the possible perspectives of future application of computation in drug discovery.

Computer-Aided Rational Engineering of Signal Sensitivity of Quorum Sensing Protein LuxR in a Whole-Cell Biosensor

LuxR, a bacterial quorum sensing-related transcription factor that responds to the signaling molecule 3-oxo-hexanoyl-homoserine lactone (3OC6-HSL). In this study, we employed molecular dynamics simulation and the Molecular Mechanics Generalized Born Surface Area (MM-GB/SA) method to rationally identify residues in Vibrio fischeri LuxR that are important for its interaction with 3OC6-HSL. Isoleucine-46 was selected for engineering as the key residue for interaction with 3OC6-HSL-LuxR-I46F would have the strongest binding energy to 3OC6-HSL and LuxR-I46R the weakest binding energy. Stable wild-type (WT) LuxR, I46F and I46R variants were produced in Escherichia coli (E. coli) in the absence of 3OC6-HSL by fusion with maltose-binding protein (MBP). Dissociation constants for 3OC6-HSL from MBP-fusions of WT-, I46F- and I46R-LuxR determined by surface plasmon resonance confirmed the binding affinity. We designed and constructed a novel whole-cell biosensor on the basis of LuxR-I46F in E. coli host cells with a reporting module that expressed green fluorescent protein. The biosensor had high sensitivity in response to the signaling molecule 3OC6-HSL produced by the target bacterial pathogen Yersinia pestis. Our work demonstrates a practical, generalizable framework for the rational design and adjustment of LuxR-family proteins for use in bioengineering and bioelectronics applications.

Computational insights into the known inhibitors of human soluble epoxide hydrolase

Human soluble epoxide hydrolase (hsEH) is involved in the hydrolysis of epoxyeicosatrienoic acids (EETs), which have potent anti-inflammatory properties. Given that EET conversion generates nonbioactive molecules, inhibition of this enzyme would be beneficial. Past decades of work on hsEH inhibitors resulted in numerous potential compounds, of which a hundred hsEH-ligand complexes were crystallized and deposited in the Protein Data Bank (PDB). We analyzed all deposited hsEH-ligand complexes to gain insight into the binding of inhibitors and to provide feedback on the future drug design processes. We also reviewed computationally driven strategies that were used to propose novel hsEH inhibitors.

Analysis of the Binding Sites on BAX and the Mechanism of BAX Activators through Extensive Molecular Dynamics Simulations

The BAX protein is a pro-apoptotic member of the Bcl-2 family, which triggers apoptosis by causing permeabilization of the mitochondrial outer membrane. However, the activation mechanism of BAX is far from being understood. Although a few small-molecule BAX activators have been reported in the literature, their crystal structures in complex with BAX have not been resolved. So far, their binding modes were modeled at most by simple molecular docking efforts. Lack of an in-depth understanding of the activation mechanism of BAX hinders the development of more effective BAX activators. In this work, we employed cosolvent molecular dynamics simulation to detect the potential binding sites on the surface of BAX and performed a long-time molecular dynamics simulation (50 μs in total) to derive the possible binding modes of three BAX activators (i.e., BAM7, BTC-8, and BTSA1) reported in the literature. Our results indicate that the trigger, S184, and vMIA sites are the three major binding sites on the full-length BAX structure. Moreover, the canonical hydrophobic groove is clearly detected on the α9-truncated BAX structure, which is consistent with the outcomes of relevant experimental studies. Interestingly, it is observed that solvent probes bind to the trigger bottom pocket more stably than the PPI trigger site. Each activator was subjected to unbiased molecular dynamics simulations started at the three major binding sites in five parallel jobs. Our MD results indicate that all three activators tend to stay at the trigger site with favorable MM-GB/SA binding energies. BAM7 and BTSA1 can enter the trigger bottom pocket and thereby enhance the movement of the α1-α2 loop, which may be a key factor at the early stage of BAX activation. Our molecular modeling results may provide useful guidance for designing smart biological experiments to further explore BAX activation and directing structure-based efforts toward discovering more effective BAX activators.

Evidence of a putative glycosaminoglycan binding site on the glycosylated SARS-CoV-2 spike protein N-terminal domain

SARS-CoV-2 has rapidly spread throughout the world's population since its initial discovery in 2019. The virus infects cells via a glycosylated spike protein located on its surface. The protein primarily binds to the angiotensin-converting enzyme-2 (ACE2) receptor, using glycosaminoglycans (GAGs) as co-receptors. Here, we performed bioinformatics and molecular dynamics simulations of the spike protein to investigate the existence of additional GAG binding sites on the receptor-binding domain (RBD), separate from previously reported heparin-binding sites. A putative GAG binding site in the N-terminal domain (NTD) of the protein was identified, encompassing residues 245-246. We hypothesized that GAGs of a sufficient length might bridge the gap between this site and the PRRARS furin cleavage site, including the mutation S247R. Docking studies using GlycoTorch Vina and subsequent MD simulations of the spike trimer in the presence of dodecasaccharides of the GAGs heparin and heparan sulfate supported this possibility. The heparan sulfate chain bridged the gap, binding the furin cleavage site and S247R. In contrast, the heparin chain bound the furin cleavage site and surrounding glycosylation structures, but not S247R. These findings identify a site in the spike protein that favors heparan sulfate binding that may be particularly pertinent for a better understanding of the recent UK and South African strains. This will also assist in future targeted therapy programs that could include repurposing clinical heparan sulfate mimetics.

Elaboration of Non-naturally Occurring Helical Tripeptides as p53-MDM2/MDMX Interaction Inhibitors

Protein-protein interactions (PPIs) are often mediated by helical, strand and/or coil secondary structures at the interface regions. We previously showed that non-naturally occurring, stable helical trimers of bicyclic β-amino acids (Abh) with all-trans amide bonds can block the p53-MDM2/MDMX α-helix-helix interaction, which plays a role in regulating p53 function. Here, we conducted docking and molecular dynamics calculations to guide the structural optimization of our reported compounds, focusing on modifications of the C-terminal/N-terminal residues. We confirmed that the modified peptides directly bind to MDM2 by means of thermal shift assay, isothermal titration calorimetry, and enzyme-linked immunosorbent assay (ELISA) experiments. Biological activity assay in human osteosarcoma cell line SJSA-1, which has wild-type p53 and amplification of the Mdm2 gene, indicated that these peptides are membrane-permeable p53-MDM2/MDMX interaction antagonists that can rescue p53 function in the cells.

Fragment dissolved molecular dynamics: a systematic and efficient method to locate binding sites

Diverse computational methods to support fragment-based drug discovery (FBDD) are available in the literature. Despite their demonstrated efficacy in supporting FBDD campaigns, they exhibit some drawbacks such as protein denaturation or ligand aggregation that have not yet been clearly overcome in the framework of biomolecular simulations. In the present work, we discuss a systematic semi-automatic novel computational procedure, designed to surpass these difficulties. The method, named fragment dissolved Molecular Dynamics (fdMD), utilizes simulation boxes of solvated small fragments, adding a repulsive Lennard-Jones potential term to avoid aggregation, which can be easily used to solvate the targets of interest. This method has the advantage of solvating the target with a low number of ligands, thus preventing the denaturation of the target, while simultaneously generating a database of ligand-solvated boxes that can be used in further studies. A number of scripts are made available to analyze the results and obtain the descriptors proposed as a means to trustfully discard spurious binding sites. To test our method, four test cases of different complexity have been solvated with ligand boxes and four molecular dynamics runs of 200 ns length have been run for each system, which have been extended up to 1 μs when needed. The reported results point out that the selected number of replicas are enough to identify the correct binding sites irrespective of the initial structure, even in the case of proteins having several close binding sites for the same ligand. We also propose a set of descriptors to analyze the results, among which the average MMGBSA and the average KDEEP energies have emerged as the most robust ones.

The Interplay of Ligand Properties and Core Size Dictates the Hydrophobicity of Monolayer-Protected Gold Nanoparticles

The hydrophobicity of monolayer-protected gold nanoparticles is a crucial design parameter that influences self-assembly, preferential binding to proteins and membranes, and other nano-bio interactions. Predicting the effects of monolayer components on nanoparticle hydrophobicity is challenging due to the nonadditive, cooperative perturbations to interfacial water structure that dictate hydrophobicity at the nanoscale. In this work, we quantify nanoparticle hydrophobicity by using atomistic molecular dynamics simulations to calculate local hydration free energies at the nanoparticle-water interface. The simulations reveal that the hydrophobicity of large gold nanoparticles is determined primarily by ligand end group chemistry, as expected. However, for small gold nanoparticles, long alkanethiol ligands interact to form anisotropic bundles that lead to substantial spatial variations in hydrophobicity even for homogeneous monolayer compositions. We further show that nanoparticle hydrophobicity is modulated by changing the ligand structure, ligand chemistry, and gold core size, emphasizing that single-ligand properties alone are insufficient to characterize hydrophobicity. Finally, we illustrate that hydration free energy measurements correlate with the preferential binding of propane as a representative hydrophobic probe molecule. Together, these results show that both physical and chemical properties influence the hydrophobicity of small nanoparticles and must be considered together when predicting gold nanoparticle interactions with biomolecules.

Computational Selectivity Assessment of Protease Inhibitors against SARS-CoV-2

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a serious global health threat. Since no specific therapeutics are available, researchers around the world screened compounds to inhibit various molecular targets of SARS-CoV-2 including its main protease (Mpro) essential for viral replication. Due to the high urgency of these discovery efforts, off-target binding, which is one of the major reasons for drug-induced toxicity and safety-related drug attrition, was neglected. Here, we used molecular docking, toxicity profiling, and multiple molecular dynamics (MD) protocols to assess the selectivity of 33 reported non-covalent inhibitors of SARS-CoV-2 Mpro against eight proteases and 16 anti-targets. The panel of proteases included SARS-CoV Mpro, cathepsin G, caspase-3, ubiquitin carboxy-terminal hydrolase L1 (UCHL1), thrombin, factor Xa, chymase, and prostasin. Several of the assessed compounds presented considerable off-target binding towards the panel of proteases, as well as the selected anti-targets. Our results further suggest a high risk of off-target binding to chymase and cathepsin G. Thus, in future discovery projects, experimental selectivity assessment should be directed toward these proteases. A systematic selectivity assessment of SARS-CoV-2 Mpro inhibitors, as we report it, was not previously conducted.

Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics

Protein dynamics play an important role in small molecule binding and can pose a significant challenge in the identification of potential binding sites. Cryptic binding sites have been defined as sites which require significant rearrangement of the protein structure to become physically accessible to a ligand. Mixed-solvent MD (MixMD) is a computational protocol which maps the surface of the protein using molecular dynamics (MD) of the unbound protein solvated in a 5% box of probe molecules with explicit water. This method has successfully identified known active and allosteric sites which did not require reorganization. In this study, we apply the MixMD protocol to identify known cryptic sites of 12 proteins characterized by a wide range of conformational changes. Of these 12 proteins, three require reorganization of side chains, five require loop movements, and four require movement of more significant structures such as whole helices. In five cases, we find that standard MixMD simulations are able to map the cryptic binding sites with at least one probe type. In two cases (guanylate kinase and TIE-2), accelerated MD, which increases sampling of torsional angles, was necessary to achieve mapping of portions of the cryptic binding site missed by standard MixMD. For more complex systems where movement of a helix or domain is necessary, MixMD was unable to map the binding site even with accelerated dynamics, possibly due to the limited timescale (100 ns for individual simulations). In general, similar conformational dynamics are observed in water-only simulations and those with probe molecules. This could imply that the probes are not driving opening events but rather take advantage of mapping sites that spontaneously open as part of their inherent conformational behavior. Finally, we show that docking to an ensemble of conformations from the standard MixMD simulations performs better than docking the apo crystal structure in nine cases and even better than half of the bound crystal structures. Poorer performance was seen in docking to ensembles of conformations from the accelerated MixMD simulations.

Small Glycols Discover Cryptic Pockets on Proteins for Fragment-Based Approaches

Cryptic pockets are visible in ligand-bound protein structures but are occluded in unbound structures. Utilizing these pockets in fragment-based drug-design provides an attractive option for proteins not tractable by classical binding sites. However, owing to their hidden nature, they are difficult to identify. Here, we show that small glycols find cryptic pockets on a diverse set of proteins. Initial crystallography experiments serendipitously revealed the ability of ethylene glycol, a small glycol, to identify a cryptic pocket on the W6A mutant of the RBSX protein (RBSX-W6A). Explicit-solvent molecular dynamics (MD) simulations of RBSX-W6A with the exposed state of the cryptic pocket (ethylene glycol removed) revealed closure of the pocket reiterating that the exposed state of cryptic pockets in general are unstable in the absence of ligands. Also, no change in the pocket was observed for simulations of RBSX-W6A with the occluded state of the cryptic pocket, suggesting that water molecules are not able to open the cryptic pocket. "Cryptic-pocket finding" potential of small glycols was then supported and generalized through additional crystallography experiments, explicit-cosolvent MD simulations, and protein data set construction and analysis. The cryptic pocket on RBSX-W6A was found again upon repeating the crystallography experiments with another small glycol, propylene glycol. Use of ethylene glycol as a probe molecule in cosolvent MD simulations led to the enhanced sampling of the exposed state of experimentally observed cryptic sites on a test set of two proteins (Niemann-Pick C2, Interleukin-2). Further, analyses of protein structures with validated cryptic sites showed that ethylene glycol molecules bind to sites on proteins (Bcl-xL, G-actin, myosin II, and glutamate receptor 2), which become apparent upon binding of biologically relevant ligands. Our study thus suggests potential application of the small glycols in experimental and computational fragment-based approaches to identify cryptic pockets in apparently undruggable and/or difficult targets, making these proteins amenable to drug-design strategies.

Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-Based Drug Design

Cryptic sites are binding pockets that are transiently formed in an apo form or that are induced by ligand binding. The investigation of cryptic sites is crucial for drug discovery, since these sites are ubiquitous in disease-related human proteins, and targeting them expands the number of drug targets greatly. However, although many computational studies have attempted to identify cryptic sites, the detection remains challenging. Here, we aimed to characterize and detect cryptic sites in terms of structural fluctuations in an apo form, investigating proteins each of which possesses a cryptic site. From their X-ray structures, we saw that aromatic residues tended to be found in cryptic sites. To examine structural fluctuations of the apo forms, we performed molecular dynamics (MD) simulations, producing probability distributions of the solvent-accessible surface area per aromatic residue. To detect aromatic residues in cryptic sites, we have proposed a "cryptic-site index" based on the distribution, demonstrating the performance via several measures, such as recall and specificity. Besides, we found that high-ranking aromatic residues were likely to probe concaves in a cryptic site. This implies that such fluctuations provide a profile of scaffolds of compounds with the potential to bind to a particular cryptic site.

Comprehensive 3D-RISM analysis of the hydration of small molecule binding sites in ligand-free protein structures

Hydration is a critical factor in the ligand binding process. Herein, to examine the hydration states of ligand binding sites, the three‐dimensional distribution function for the water oxygen site, g_O(r), is computed for 3,706 ligand‐free protein structures based on the corresponding small molecule–protein complexes using the 3D‐RISM theory. For crystallographic waters (CWs) close to the ligand, g_O(r) reveals that several CWs are stabilized by interaction networks formed between the ligand, CW, and protein. Based on the g_O(r) for the crystallographic binding pose of the ligand, hydrogen bond interactions are dominant in the highly hydrated regions while weak interactions such as CH‐O are dominant in the moderately hydrated regions. The polar heteroatoms of the ligand occupy the highly hydrated and moderately hydrated regions in the crystallographic (correct) and wrongly docked (incorrect) poses, respectively. Thus, the g_O(r) of polar heteroatoms may be used to distinguish the correct binding poses.

How to make an undruggable enzyme druggable: lessons from ras proteins

Significant advances have been made toward discovering allosteric inhibitors for challenging drug targets such as the Ras family of membrane-associated signaling proteins. Malfunction of Ras proteins due to somatic mutations is associated with up to a quarter of all human cancers. Computational techniques have played critical roles in identifying and characterizing allosteric ligand-binding sites on these proteins, and to screen ligand libraries against those sites. These efforts, combined with a wide range of biophysical, structural, biochemical and cell biological experiments, are beginning to yield promising inhibitors to treat malignancies associated with mutated Ras proteins. In this chapter, we discuss some of these developments and how the lessons learned from Ras might be applied to similar other challenging drug targets.

The AutoDock suite at 30

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.

A Benzene-Mapping Approach for Uncovering Cryptic Pockets in Membrane-Bound Proteins

Molecular dynamics (MD) simulations in combination with small organic probes present in the solvent have previously been used as a method to reveal cryptic pockets that may not have been identified in experimental structures. We report such a method implemented within the CHARMM force field using the GROMACS simulation package to effectively explore cryptic pockets on the surfaces of membrane-embedded proteins using benzene as a probe molecule. This method, for which we have made implementation files freely available, relies on modified nonbonded parameters in addition to repulsive potentials between membrane lipids and benzene molecules. The method was tested on part of the outer shell of the dengue virus (DENV), for which research into a safe and effective neutralizing antibody or drug molecule is still ongoing. In particular, the envelope (E) protein, associated with the membrane (M) protein, is a lipid membrane-embedded complex which forms a dimer in the mature viral envelope. Solvent mapping was performed for the full, membrane-embedded EM protein complex and compared with similar calculations performed for the isolated, soluble E protein ectodomain dimer in the solvent. Ectodomain-only simulations with benzene exhibited unfolding effects not observed in the more physiologically relevant membrane-associated systems. A cryptic pocket which has been experimentally shown to bind n-octyl-β-d-glucoside detergent was consistently revealed in all benzene-containing simulations. The addition of benzene also enhanced the flexibility and hydrophobic exposure of cryptic pockets at a key, functional interface in the E protein and revealed a novel, potentially druggable pocket that may be targeted to prevent conformational changes associated with viral entry into the cell.

Modeling Epac1 interactions with the allosteric inhibitor AM-001 by co-solvent molecular dynamics

The exchange proteins activated by cAMP (EPAC) are implicated in a large variety of physiological processes and they are considered as promising targets for a wide range of therapeutic applications. Several recent reports provided evidence for the therapeutic effectiveness of the inhibiting EPAC1 activity cardiac diseases. In that context, we recently characterized a selective EPAC1 antagonist named AM-001. This compound was featured by a non-competitive mechanism of action but the localization of its allosteric site to EPAC1 structure has yet to be investigated. Therefore, we performed cosolvent molecular dynamics with the aim to identify a suitable allosteric binding site. Then, the docking and molecular dynamics were used to determine the binding of the AM-001 to the regions highlighted by cosolvent molecular dynamics for EPAC1. These analyses led us to the identification of a suitable allosteric AM-001 binding pocket at EPAC1. As a model validation, we also evaluated the binding poses of the available AM-001 analogues, with a different biological potency. Finally, the complex EPAC1 with AM-001 bound at the putative allosteric site was further refined by molecular dynamics. The principal component analysis led us to identify the protein motion that resulted in an inactive like conformation upon the allosteric inhibitor binding.

Chemical-Shift Perturbations Reflect Bile Acid Binding to Norovirus Coat Protein: Recognition Comes in Different Flavors

Bile acids have been reported as important cofactors promoting human and murine norovirus (NoV) infections in cell culture. The underlying mechanisms are not resolved. Through the use of chemical shift perturbation (CSP) NMR experiments, we identified a low-affinity bile acid binding site of a human GII.4 NoV strain. Long-timescale MD simulations reveal the formation of a ligand-accessible binding pocket of flexible shape, allowing the formation of stable viral coat protein-bile acid complexes in agreement with experimental CSP data. CSP NMR experiments also show that this mode of bile acid binding has a minor influence on the binding of histo-blood group antigens and vice versa. STD NMR experiments probing the binding of bile acids to virus-like particles of seven different strains suggest that low-affinity bile acid binding is a common feature of human NoV and should therefore be important for understanding the role of bile acids as cofactors in NoV infection.

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.

Site-Resolved and Quantitative Characterization of Very Weak Protein-Ligand Interactions

Very weak interactions between small organic molecules and proteins have long been predicted and are expected to have dissociation constants of hundreds of millimolar and above. Unfortunately, quantitative evaluation of binding in a high-resolution structural context for this affinity regime is particularly difficult and often impossible using existing experimental approaches. Here, we show that nanoscale encapsulation of single protein molecules within the water core of reverse micelles enables the detection and evaluation of weak binding interactions at atomic resolution using solution NMR spectroscopy. This strategy is used to survey the interactions of a set of small molecules with the cytokine interleukin-1β (IL-1β). The interaction of IL-1β with these molecules is found to vary from more diffuse and weak binding modes to more specific and with a relatively higher affinity. The interactions detected here cover a large portion of the protein surface and have dissociation constants mostly in the low molar range. These results illustrate the ability of a protein to interact productively with a variety of small molecule functional groups and point to a broader potential to target even relatively featureless protein surfaces for applications in chemical biology and drug discovery.