Biophysical limits of protein-ligand binding

Reanalysis of a μ opioid receptor crystal structure reveals a covalent adduct with BU72

BACKGROUND

The first crystal structure of the active μ opioid receptor (μOR) exhibited several unexplained features. The ligand BU72 exhibited many extreme deviations from ideal geometry, along with unexplained electron density. I previously showed that inverting the benzylic configuration resolved these problems, establishing revised stereochemistry of BU72 and its analog BU74. However, another problem remains unresolved: additional unexplained electron density contacts both BU72 and a histidine residue in the N-terminus, revealing the presence of an as-yet unidentified atom.

RESULTS

These short contacts and uninterrupted density are inconsistent with non-covalent interactions. Therefore, BU72 and μOR form a covalent adduct, rather than representing two separate entities as in the original model. A subsequently proposed magnesium complex is inconsistent with multiple lines of evidence. However, oxygen fits the unexplained density well. While the structure I propose is tentative, similar adducts have been reported previously in the presence of reactive oxygen species. Moreover, known sources of reactive oxygen species were present: HEPES buffer, nickel ions, and a sequence motif that forms redox-active nickel complexes. This motif contacts the unexplained density. The adduct exhibits severe strain, and the tethered N-terminus forms contacts with adjacent residues. These forces, along with the nanobody used as a G protein substitute, would be expected to influence the receptor conformation. Consistent with this, the intracellular end of the structure differs markedly from subsequent structures of active μOR bound to Gi protein.

CONCLUSIONS

Later Gi-bound structures are likely to be more accurate templates for ligand docking and modelling of active G protein-bound μOR. The possibility of reactions like this should be considered in the choice of protein truncation sites and purification conditions, and in the interpretation of excess or unexplained density.

Design, in Silico Studies and Biological Evaluation of New Chiral Thiourea and 1,3-Thiazolidine-4,5-dione Derivatives

In this study, new chiral thiourea and 1,3-thiazolidine-4,5-dione derivatives were synthesized, it was aimed to evaluate the various biological activities and molecular docking of these compounds. Firstly, the new thioureas (1-16) were obtained by reacting 1-naphthylisothiocyanate with different chiral amines. Then, the chiral thioureas were cyclized with oxalyl chloride to obtain 1,3-thiazolidine-4,5-dione derivatives (17-32). All compounds were evaluated with several in vitro antioxidant and enzyme inhibition activities. Compound 30 was the most active compound against AChE, with a value of IC50 =8.09±0.58 μM. On the other hand, all compounds were tested in silico absorption, distribution, metabolism, and excretion (ADME) assays to better understand their bioavailability. These physicochemical properties, pharmacokinetics, and drug-likeness of all compounds were calculated using SwissADME. Furthermore, according to molecular docking analyses compound 30 exhibited significant binding affinities for all enzymes. Based on our overall observations, compound 30 could be recommended as a potential lead for the therapuetic of Alzheimer's.

The Cactus (Opuntia ficus-indica) Cladodes and Callus Extracts: A Study Combined with LC-MS Metabolic Profiling, In-Silico, and In-Vitro Analyses

Opuntia ficus-indica (OF) phytochemicals have received considerable attention because of their health benefits. However, the structure-activity relationship between saponin and flavonoid antioxidant compounds among secondary metabolites has rarely been reported. In a molecular docking study, selected compounds from both Opuntia ficus-indica callus (OFC) and OF ethanol extract were found to be involved in Toll-like receptor 4 and mitogen-activated protein kinase (MAPK) signaling pathways. High affinity was specific for MAPK, and it was proposed to inhibit the oxidative and inflammatory responses with poricoic acid H (-8.3 Kcal/mol) and rutin (-9.0 Kcal/mol). The pro-inflammatory cytokine factors at a concentration of 200 μg/mL were LPS-stimulated TNF-α (OFC 72.33 ng/mL, OF 66.78 ng/mL) and IL-1β (OFC 49.10 pg/mL, OF 34.45 pg/mL), both of which significantly decreased OF (p < 0.01, p < 0.001). Taken together, increased NO, PGE₂, and pro-inflammatory cytokines were significantly decreased in a dose-dependent manner in cells pretreated with OFC and the OF extract (p < 0.05). These findings suggest that OFC and OF have important potential as natural antioxidant, anti-inflammatory agents in health-promoting foods and medicine.

Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

Improving the generalizability of protein-ligand binding predictions with AI-Bind

Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. AI-Bind is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery.

Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools

Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.

Cholic acid inhibits amyloid fibrillation: Interplay of protonation and deprotonation

Amyloidopathies are the consequence of misfolding with subsequent aggregation affecting people worldwide. Irrespective of speedy advancement in the field of therapeutics no agent for treating amyloidopathies has been discovered and thus targeting amyloid fibrillation process via repositioning of small molecules can be fruitful. According to previous reports potential amyloid inhibitors possess unique features like, hydrophobicity, aromaticity, charge etc. Herein, we have explored the effect of Cholic acid (CA) on amyloid fibrillation irrespective of the charge (determined by Zetasizer) using four proteins Human Serum Albumin, Bovine Serum Albumin, Human Insulin and Beta-lactoglobulin (HSA, BSA, HI and BLG) employing biophysical, imaging and computational techniques. ThT results revealed that CA in both protonated and deprotonated form is potent to curb HSA, BSA, BLG aggregation ~50% and HI aggregation ~96% in a dose dependent manner (in accord with CD, ANS and Congo red assay). Interestingly, CA treated samples displayed reduced cytotoxicity (Hemolytic assay) with altered morphology (TEM) and mechanism behind inhibition may be the interaction of CA with proteins via hydrophobic interactions and hydrogen bonding (supported by molecular docking results). This study proved CA (irrespective of the pH) a potential inhibitor of amyloidosis thus can be helpful in generalizing and repurposing the related drugs/compounds for their anti-aggregation behavior as an implication towards treating amyloidopathies.

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

In Silico Pesticide Discovery for New Anti-Tobacco Mosaic Virus Agents: Reactivity, Molecular Docking, and Molecular Dynamics Simulations

Considerable data are available regarding the molecular genetics of the tobacco mosaic virus. The disease caused by the tobacco mosaic virus is still out of control due to the lack of an efficient functional antagonist chemical molecule. Extensive research was carried out to try to find effective new anti-tobacco mosaic virus agents, however no study could find an effective agent which could completely inhibit the disease caused by the virus. In recent years, molecular docking, combined with molecular dynamics, which is considered to be one of the most important methods of drug discovery and design, were used to evaluate the type of binding between the ligand and its protein enzyme. The aim of the current work was to assess the in silico anti-tobacco mosaic virus activity for a selection of 41 new and 2 reference standard compounds. These compounds were chosen to examine their reactivity and binding efficiency with the tobacco mosaic virus coat protein (PDB ID: 2OM3). A comparison was made between the activity of the selected compounds and that for ningnanmycin and ribavirin, which are common inhibitors of plant viruses. The simulation results obtained from the molecular docking and molecular dynamics showed that two compounds of the antofine analogues could bind with the tobacco mosaic virus coat protein receptor better than ningnanmycin and ribavirin.

Conserved features of the MlaD domain aid the trafficking of hydrophobic molecules

In Gram-negative bacteria, the maintenance of lipid asymmetry (Mla) system is involved in the transport of phospholipids between the inner (IM) and outer membrane. The Mla system utilizes a unique IM-associated periplasmic solute-binding protein, MlaD, which possesses a conserved domain, MlaD domain. While proteins carrying the MlaD domain are known to be primarily involved in the trafficking of hydrophobic molecules, not much is known about this domain itself. Thus, in this study, the characterization of the MlaD domain employing bioinformatics analysis is reported. The profiling of the MlaD domain of different architectures reveals the abundance of glycine and hydrophobic residues and the lack of cysteine residues. The domain possesses a conserved N-terminal region and a well-preserved glycine residue that constitutes a consensus motif across different architectures. Phylogenetic analysis shows that the MlaD domain archetypes are evolutionarily closer and marked by the conservation of a functionally crucial pore loop located at the C-terminal region. The study also establishes the critical role of the domain-associated permeases and the driving forces governing the transport of hydrophobic molecules. This sheds sufficient light on the structure-function-evolutionary relationship of MlaD domain. The hexameric interface analysis reveals that the MlaD domain itself is not a sole player in the oligomerization of the proteins. Further, an operonic and interactome map analysis reveals that the Mla and the Mce systems are dependent on the structural homologs of the nuclear transport factor 2 superfamily.

SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

In drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore, researchers hope for a high correlation between docking score and pose with key interactive residues, although scoring functions as free energy surrogates of protein–ligand complexes have failed to provide collinearity. Recently, various machine learning or deep learning methods have been proposed to overcome the drawbacks of scoring functions. Despite being highly accurate, their featurization process is complex and the meaning of the embedded features cannot directly be interpreted by human recognition without an additional feature analysis. Here, we propose SMPLIP-Score (Substructural Molecular and Protein–Ligand Interaction Pattern Score), a direct interpretable predictor of absolute binding affinity. Our simple featurization embeds the interaction fingerprint pattern on the ligand-binding site environment and molecular fragments of ligands into an input vectorized matrix for learning layers (random forest or deep neural network). Despite their less complex features than other state-of-the-art models, SMPLIP-Score achieved comparable performance, a Pearson’s correlation coefficient up to 0.80, and a root mean square error up to 1.18 in pK units with several benchmark datasets (PDBbind v.2015, Astex Diverse Set, CSAR NRC HiQ, FEP, PDBbind NMR, and CASF-2016). For this model, generality, predictive power, ranking power, and robustness were examined using direct interpretation of feature matrices for specific targets.

Exploring the structural basis and atomistic binding mechanistic of the selective antagonist blockade at D3 dopamine receptor over D2 dopamine receptor

More recently, there has been a paradigm shift toward selective drug targeting in the treatment of neurological disorders, including drug addiction, schizophrenia, and Parkinson's disease mediated by the different dopamine receptor subtypes. Antagonists with higher selectivity for D₃ dopamine receptor (D3DR) over D₂ dopamine receptor (D2DR) have been shown to attenuate drug-seeking behavior and associated side effects compared to non-subtype selective antagonists. However, high conservations among constituent residues of both proteins, particularly at the ligand-binding pockets, remain a challenge to therapeutic drug design. Recent studies have reported the discovery of two small-molecules R-VK4-40 and Y-QA31 which substantially inhibited D3DR with >180-fold selectivity over D2DR. Therefore, in this study, we seek to provide molecular and structural insights into these differential binding mechanistic using meta-analytic computational simulation methods. Findings revealed that R-VK4-40 and Y-QA31 adopted shallow binding modes and were more surface-exposed at D3DR while on the contrary, they exhibited deep hydrophobic pocket binding at D2DR. Also, two non-conserved residues; Tyr36^1.39 and Ser182^45.51 were identified in D3DR, based on their crucial roles and contributions to the selective binding of R-VK4-40 and Y-QA31. Importantly, both antagonists exhibited high affinities in complex with D3DR compared to D2DR, while van der Waals energies contributed majorly to their binding and stability. Structural analyses also revealed the distinct stabilizing effects of both compounds on D3DR secondary architecture relative to D2DR. Therefore, findings herein pinpointed the origin and mechanistic of selectivity of the compounds, which may assist in the rational design of potential small molecules of the D₂ -like dopamine family receptor subtype with improved potency and selectivity.

Computational Design of an Allosteric Antibody Switch by Deletion and Rescue of a Complex Structural Constellation

Therapeutic monoclonal antibodies have transformed medicine, especially with regards to treating cancers and disorders of the immune system. More than 50 antibody-derived drugs have already reached the clinic, the majority of which target cytokines or cell-surface receptors. Unfortunately, many of these targets have pleiotropic functions: they serve multiple different roles, and often not all of these roles are disease-related. This can be problematic because antibodies act throughout the body, and systemic neutralization of such targets can lead to safety concerns. To address this, we have developed a strategy whereby an antibody's ability to recognize its antigen is modulated by a second layer of control, relying on addition of an exogenous small molecule. In previous studies, we began to explore this idea by introducing a deactivating tryptophan-to-glycine mutation in the domain-domain interface of a single-chain variable fragment (scFv), and then restoring activity by adding back indole to fit the designed cavity. Here, we now describe a novel computational strategy for enumerating larger cavities that can be formed by simultaneously introducing multiple adjacent large-to-small mutations; we then carry out a complementary virtual screen to identify druglike compounds to match each candidate cavity. We first demonstrate the utility of this strategy in a fluorescein-binding single-chain variable fragment (scFv) and experimentally characterize a triple mutant with reduced antigen-binding (Rip-3) that can be rescued using a complementary ligand (Stitch-3). Because our design is built upon conserved residues in the antibody framework, we then show that the same mutation/ligand pair can also be used to modulate antigen-binding in an scFv build from a completely unrelated framework. This set of residues is present in many therapeutic antibodies as well, suggesting that this mutation/ligand pair may serve as a general starting point for introducing ligand-dependence into many clinically relevant antibodies.

Hydrogen-Deuterium Exchange Mass-Spectrometry of Secondary Active Transporters: From Structural Dynamics to Molecular Mechanisms

Membrane transporters allow the selective transport of otherwise poorly permeable solutes across the cell membrane and thus, play a key role in maintaining cellular homeostasis in all kingdoms of life. Importantly, these proteins also serve as important drug targets. Over the last decades, major progress in structural biology methods has elucidated important structure-function relationships in membrane transporters. However, structures obtained using methods such as X-ray crystallography and high-resolution cryogenic electron microscopy merely provide static snapshots of an intrinsically dynamic, multi-step transport process. Therefore, there is a growing need for developing new experimental approaches capable of exploiting the data obtained from the high-resolution snapshots in order to investigate the dynamic features of membrane proteins. Here, we present the basic principles of hydrogen-deuterium exchange mass-spectrometry (HDX-MS) and recent advancements in its use to study membrane transporters. In HDX-MS experiments, minute amounts of a protein sample can be used to investigate its structural dynamics under native conditions, without the need for chemical labelling and with practically no limit on the protein size. Thus, HDX-MS is instrumental for resolving the structure-dynamic landscapes of membrane proteins in their apo (ligand-free) and ligand-bound forms, shedding light on the molecular mechanism underlying the transport process and drug binding.

Identification of novel inhibitors for TNFα, TNFR1 and TNFα-TNFR1 complex using pharmacophore-based approaches

BACKGROUND

Tumor necrosis factor α (TNFα) is a multifunctional cytokine with a potent pro-inflammatory effect. It is a validated therapeutic target molecule for several disorders related to autoimmunity and inflammation. TNFα-TNF receptor-1 (TNFR1) signaling contributes to the pathological processes of these disorders. The current study is focused on finding novel small molecules that can directly bind to TNFα and/or TNFR1, preventing the interaction between TNFα or TNFR1, and regulating downstream signaling pathways.

METHODS

Cheminformatics pipeline (pharmacophore modeling, virtual screening, molecular docking and in silico ADMET analysis) was used to screen for novel TNFα and TNFR1 inhibitors in the Zinc database. The pharmacophore-based models were generated to screen for the best drug like compounds in the Zinc database.

RESULTS

The 39, 37 and 45 best hit molecules were mapped with the core pharmacophore features of TNFα, TNFR1, and the TNFα-TNFR1 complex respectively. They were further evaluated by molecular docking, protein-ligand interactions and in silico ADMET studies. The molecular docking analysis revealed the binding energies of TNFα, TNFR1 and the TNFα-TNFR1 complex, the basis of which was used to select the top five best binding energy compounds. Furthermore, in silico ADMET studies clearly revealed that all 15 compounds (ZINC09609430, ZINC49467549, ZINC13113075, ZINC39907639, ZINC25251930, ZINC02968981, ZINC09544246, ZINC58047088, ZINC72021182, ZINC08704414, ZINC05462670, ZINC35681945, ZINC23553920, ZINC05328058, and ZINC17206695) satisfied the Lipinski rule of five and had no toxicity.

CONCLUSIONS

The new selective TNFα, TNFR1 and TNFα-TNFR1 complex inhibitors can serve as anti-inflammatory agents and are promising candidates for further research.

Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations

In our recent efforts to map protein surfaces using mixed-solvent molecular dynamics (MixMD) (Ghanakota, P.; Carlson, H. A. Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems. J. Phys. Chem. B 2016, 120, 8685-8695), we were able to successfully capture active sites and allosteric sites within the top-four most occupied hotspots. In this study, we describe our approach for estimating the thermodynamic profile of the binding sites identified by MixMD. First, we establish a framework for calculating free energies from MixMD simulations, and we compare our approach to alternative methods. Second, we present a means to obtain a relative ranking of the binding sites by their configurational entropy. The theoretical maximum and minimum free energy and entropy values achievable under such a framework along with the limitations of the techniques are discussed. Using this approach, the free energy and relative entropy ranking of the top-four MixMD binding sites were computed and analyzed across our allosteric protein targets: Abl Kinase, Androgen Receptor, Pdk1 Kinase, Farnesyl Pyrophosphate Synthase, Chk1 Kinase, Glucokinase, and Protein Tyrosine Phosphatase 1B.

Chemical Equilibrium at the Tick-Host Feeding Interface:A Critical Examination of Biological Relevance in Hematophagous Behavior

Ticks secrete hundreds to thousands of proteins into the feeding site, that presumably all play important functions in the modulation of host defense mechanisms. The current review considers the assumption that tick proteins have functional relevance during feeding. The feeding site may be described as a closed system and could be treated as an ideal equilibrium system, thereby allowing modeling of tick-host interactions in an equilibrium state. In this equilibrium state, the concentration of host and tick proteins and their affinities will determine functional relevance at the tick-host interface. Using this approach, many characterized tick proteins may have functional relevant concentrations and affinities at the feeding site. Conversely, the feeding site is not an ideal closed system, but is dynamic and changing, leading to possible overestimation of tick protein concentration at the feeding site and consequently an overestimation of functional relevance. Ticks have evolved different possible strategies to deal with this dynamic environment and overcome the barrier that equilibrium kinetics poses to tick feeding. Even so, cognisance of the limitations that equilibrium binding place on deductions of functional relevance should serve as an important incentive to determine both the concentration and affinity of tick proteins proposed to be functional at the feeding site.

Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes

Electrostatic interactions between small molecules and their respective receptors are essential for molecular recognition and are also key contributors to the binding free energy. Assessing the electrostatic match of protein-ligand complexes therefore provides important insights into why ligands bind and what can be changed to improve binding. Ideally, the ligand and protein electrostatic potentials at the protein-ligand interaction interface should maximize their complementarity while minimizing desolvation penalties. In this work, we present a fast and efficient tool to calculate and visualize the electrostatic complementarity (EC) of protein-ligand complexes. We compiled benchmark sets demonstrating electrostatically driven structure-activity relationships (SAR) from literature data, including kinase, protein-protein interaction, and GPCR targets, and used these to demonstrate that the EC method can visualize, rationalize, and predict electrostatically driven ligand affinity changes and help to predict compound selectivity. The methodology presented here for the analysis of EC is a powerful and versatile tool for drug design.

A generalized quantitative antibody homeostasis model: maintenance of global antibody equilibrium by effector functions

The homeostasis of antibodies can be characterized as a balanced production, target-binding and receptor-mediated elimination regulated by an interaction network, which controls B-cell development and selection. Recently, we proposed a quantitative model to describe how the concentration and affinity of interacting partners generates a network. Here we argue that this physical, quantitative approach can be extended for the interpretation of effector functions of antibodies. We define global antibody equilibrium as the zone of molar equivalence of free antibody, free antigen and immune complex concentrations and of dissociation constant of apparent affinity: [Ab]=[Ag]=[AbAg]=K_D. This zone corresponds to the biologically relevant K_D range of reversible interactions. We show that thermodynamic and kinetic properties of antibody–antigen interactions correlate with immunological functions. The formation of stable, long-lived immune complexes correspond to a decrease of entropy and is a prerequisite for the generation of higher-order complexes. As the energy of formation of complexes increases, we observe a gradual shift from silent clearance to inflammatory reactions. These rules can also be applied to complement activation-related immune effector processes, linking the physicochemical principles of innate and adaptive humoral responses. Affinity of the receptors mediating effector functions shows a wide range of affinities, allowing the continuous sampling of antibody-bound antigen over the complete range of concentrations. The generation of multivalent, multicomponent complexes triggers effector functions by crosslinking these receptors on effector cells with increasing enzymatic degradation potential. Thus, antibody homeostasis is a thermodynamic system with complex network properties, nested into the host organism by proper immunoregulatory and effector pathways. Maintenance of global antibody equilibrium is achieved by innate qualitative signals modulating a quantitative adaptive immune system, which regulates molecular integrity of the host by tuning the degradation and recycling of molecules from silent removal to inflammatory elimination.

A systematic analysis of atomic protein-ligand interactions in the PDB

As the protein databank (PDB) recently passed the cap of 123 456 structures, it stands more than ever as an important resource not only to analyze structural features of specific biological systems, but also to study the prevalence of structural patterns observed in a large body of unrelated structures, that may reflect rules governing protein folding or molecular recognition. Here, we compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins - 6444 of which have experimental binding affinity - representing 750 873 protein-ligand atomic interactions, and analyzed the frequency, geometry and impact of each interaction type. We find that hydrophobic interactions are generally enriched in high-efficiency ligands, but polar interactions are over-represented in fragment inhibitors. While most observations extracted from the PDB will be familiar to seasoned medicinal chemists, less expected findings, such as the high number of C-H···O hydrogen bonds or the relatively frequent amide-π stacking between the backbone amide of proteins and aromatic rings of ligands, uncover underused ligand design strategies.

Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding

In this review, we address a fundamental question: What is the range of conformational energies seen in ligands in protein-ligand crystal structures? This value is important biophysically, for better understanding the protein-ligand binding process; and practically, for providing a parameter to be used in many computational drug design methods such as docking and pharmacophore searches. We synthesize a selection of previously reported conflicting results from computational studies of this issue and conclude that high ligand conformational energies really are present in some crystal structures. The main source of disagreement between different analyses appears to be due to divergent treatments of electrostatics and solvation. At the same time, however, for many ligands, a high conformational energy is in error, due to either crystal structure inaccuracies or incorrect determination of the reference state. Aside from simple chemistry mistakes, we argue that crystal structure error may mainly be because of the heuristic weighting of ligand stereochemical restraints relative to the fit of the structure to the electron density. This problem cannot be fixed with improvements to electron density fitting or with simple ligand geometry checks, though better metrics are needed for evaluating ligand and binding site chemistry in addition to geometry during structure refinement. The ultimate solution for accurately determining ligand conformational energies lies in ultrahigh-resolution crystal structures that can be refined without restraints.

Mapping a protein recognition centre with chiral photoactive ligands. An integrated approach combining photophysics, reactivity, proteomics and molecular dynamics simulation studies

A multidisciplinary strategy to obtain structural information on the intraprotein region is described here. As probe ligands, (S)- and (R)-CPFMe (the methyl esters of the chiral drug carprofen) have been selected, while bovine α1-acid glycoprotein (BAAG) has been chosen as a biological host. The procedure involves the separate irradiation of the BAAG/(S)-CPFMe and BAAG/(R)-CPFMe complexes, coupled with fluorescence spectroscopy, laser flash photolysis, proteomic analysis, docking and molecular dynamics simulations. Thus, irradiation of the BAAG/CPFMe complexes at λ = 320 nm was followed by fluorescence spectroscopy. The intensity of the emission band obtained after irradiation indicated photodehalogenation, whereas its structureless shape suggested covalent binding of the resulting radical CBZMe˙ to the biopolymer. After gel filtration chromatography, the spectra still displayed emission, in agreement with covalent attachment of CBZMe˙ to BAAG. Stereodifferentiation was observed in this process. After trypsin digestion and ESI-MS/MS, the incorporation of CBZMe was detected at Phe68. Docking and molecular dynamics simulation studies, which were carried out using a homology model of BAAG, reveal that the closer proximity of the aromatic moiety of the (S)-enantiomer to the phenyl group of Phe68 would be responsible for the experimentally observed, more effective chemical modification of the protein. The proposed tridimensional structure of BAAG covalently modified by the two enantiomers is also provided. In principle, this approach can be extended to a variety of protein/ligand complexes.

Full and Partial Agonism of a Designed Enzyme Switch

Chemical biology has long sought to build protein switches for use in molecular diagnostics, imaging, and synthetic biology. The overarching challenge for any type of engineered protein switch is the ability to respond in a selective and predictable manner that caters to the specific environments and time scales needed for the application at hand. We previously described a general method to design switchable proteins, called "chemical rescue of structure", that builds de novo allosteric control sites directly into a protein's functional domain. This approach entails first carving out a buried cavity in a protein via mutation, such that the protein's structure is disrupted and activity is lost. An exogenous ligand is subsequently added to substitute for the atoms that were removed by mutation, restoring the protein's structure and thus its activity. Here, we begin to ask what principles dictate such switches' response to different activating ligands. Using a redesigned β-glycosidase enzyme as our model system, we find that the designed effector site is quite malleable and can accommodate both larger and smaller ligands, but that optimal rescue comes only from a ligand that perfectly replaces the deleted atoms. Guided by these principles, we then altered the shape of this cavity by using different cavity-forming mutations, and predicted different ligands that would better complement these new cavities. These findings demonstrate how the protein switch's response can be tuned via small changes to the ligand with respect to the binding cavity, and ultimately enabled us to design an improved switch. We anticipate that these insights will help enable the design of future systems that tune other aspects of protein activity, whereby, like evolved protein receptors, remolding the effector site can also adjust additional outputs such as substrate selectivity and activation of downstream signaling pathways.

Chemical Inhibition of Protein Methyltransferases

Protein methyltransferases (PMTs) participate in the epigenetic control of cell fate and other signaling pathways that are deregulated in disease, and the first PMT inhibitors have entered clinical trials in oncology. This review discusses structural studies that recently uncovered the mode of action of compounds in the clinic, as well as challenges and opportunities in the development of PMT inhibitors. It examines inhibitors that compete with the highly polar cofactor but preserve cell penetrance, and allosteric modes of inhibition. Vectors of optimization at the substrate-binding site and the potential of fragment screening approaches are discussed. Finally, the review presents strategies focused on targeting non-catalytic domains of PMTs or scaffolding subunits of chromatin complexes. Overall, although targeting PMTs remains a challenge, recent successes in the field are diverse and encouraging.

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.

Discovery of a Potent and Selective Coactivator Associated Arginine Methyltransferase 1 (CARM1) Inhibitor by Virtual Screening

Protein arginine methyltransferases (PRMTs) represent an emerging target class in oncology and other disease areas. So far, the most successful strategy to identify PRMT inhibitors has been to screen large to medium-size chemical libraries. Attempts to develop PRMT inhibitors using receptor-based computational methods have met limited success. Here, using virtual screening approaches, we identify 11 CARM1 (PRMT4) inhibitors with ligand efficiencies ranging from 0.28 to 0.84. CARM1 selective hits were further validated by orthogonal methods. Two structure-based rounds of optimization produced 27 (SGC2085), a CARM1 inhibitor with an IC50 of 50 nM and more than hundred-fold selectivity over other PRMTs. These results indicate that virtual screening strategies can be successfully applied to Rossmann-fold protein methyltransferases.

Discovery of a Potent Class I Protein Arginine Methyltransferase Fragment Inhibitor

Protein methyltransferases (PMTs) are a promising target class in oncology and other disease areas. They are composed of SET domain methyltransferases and structurally unrelated Rossman-fold enzymes that include protein arginine methyltransferases (PRMTs). In the absence of a well-defined medicinal chemistry tool-kit focused on PMTs, most current inhibitors were identified by screening large and diverse libraries of leadlike molecules. So far, no successful fragment-based approach was reported against this target class. Here, by deconstructing potent PRMT inhibitors, we find that chemical moieties occupying the substrate arginine-binding site can act as efficient fragment inhibitors. Screening a fragment library against PRMT6 produced numerous hits, including a 300 nM inhibitor (ligand efficiency of 0.56) that decreased global histone 3 arginine 2 methylation in cells, and can serve as a warhead for the development of PRMT chemical probes.

Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set

Scoring functions are often applied in combination with molecular docking methods to predict ligand binding poses and ligand binding affinities or to identify active compounds through virtual screening. An objective benchmark for assessing the performance of current scoring functions is expected to provide practical guidance for the users to make smart choices among available methods. It can also elucidate the common weakness in current methods for future improvements. The primary goal of our comparative assessment of scoring functions (CASF) project is to provide a high-standard, publicly accessible benchmark of this type. Our latest study, i.e., CASF-2013, evaluated 20 popular scoring functions on an updated set of protein-ligand complexes. This data set was selected out of 8302 protein-ligand complexes recorded in the PDBbind database (version 2013) through a fairly complicated process. Sample selection was made by considering the quality of complex structures as well as binding data. Finally, qualified complexes were clustered by 90% similarity in protein sequences. Three representative complexes were chosen from each cluster to control sample redundancy. The final outcome, namely, the PDBbind core set (version 2013), consists of 195 protein-ligand complexes in 65 clusters with binding constants spanning nearly 10 orders of magnitude. In this data set, 82% of the ligand molecules are "druglike" and 78% of the protein molecules are validated or potential drug targets. Correlation between binding constants and several key properties of ligands are discussed. Methods and results of the scoring function evaluation will be described in a companion work in this issue (doi: 10.1021/ci500081m ).