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Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Protein lipidations are vital co/post-translational modifications that tether lipid tails to specific protein amino acids, allowing them to anchor to biological membranes, switch their subcellular localization, and modulate association with other proteins. Such lipidations are thus crucial for multiple biological processes including signal transduction, protein trafficking, and membrane localization and are implicated in various diseases as well. Examples of lipid-anchored proteins include the Ras family of proteins that undergo farnesylation; actin and gelsolin that are myristoylated; phospholipase D that is palmitoylated; glycosylphosphatidylinositol-anchored proteins; and others. Here, we develop parameters for cysteine-targeting farnesylation, geranylgeranylation, and palmitoylation, as well as glycine-targeting myristoylation for the latest version of the Martini 3 coarse-grained force field. The parameters are developed using the CHARMM36m all-atom force field parameters as reference. The behavior of the coarse-grained models is consistent with that of the all-atom force field for all lipidations and reproduces key dynamical and structural features of lipid-anchored peptides, such as the solvent-accessible surface area, bilayer penetration depth, and representative conformations of the anchors. The parameters are also validated in simulations of the lipid-anchored peripheral membrane proteins Rheb and Arf1, after comparison with independent all-atom simulations. The parameters, along with mapping schemes for the popular martinize2 tool, are available for download at 10.5281/zenodo.7849262 and also as supporting information.

Ras proteins are membrane-anchored GTPases that regulate key cellular signaling networks. It has been recently shown that different anionic lipid types can affect the properties of Ras in terms of dimerization/clustering on the cell membrane. To understand the effects of anionic lipids on key spatiotemporal properties of dimeric K-Ras4B, we perform all-atom molecular dynamics simulations of the dimer K-Ras4B in the presence and absence of Raf[RBD/CRD] effectors on two model anionic lipid membranes: one containing 78% mol DOPC, 20% mol DOPS, and 2% mol PIP2 and another one with enhanced concentration of anionic lipids containing 50% mol DOPC, 40% mol DOPS, and 10% mol PIP2. Analysis of our results unveils the orientational space of dimeric K-Ras4B and shows that the stability of the dimer is enhanced on the membrane containing a high concentration of anionic lipids in the absence of Raf effectors. This enhanced stability is also observed in the presence of Raf[RBD/CRD] effectors although it is not influenced by the concentration of anionic lipids in the membrane, but rather on the ability of Raf[CRD] to anchor to the membrane. We generate dominant K-Ras4B conformations by Markov state modeling and yield the population of states according to the K-Ras4B orientation on the membrane. For the membrane containing anionic lipids, we observe correlations between the diffusion of K-Ras4B and PIP2 and anchoring of anionic lipids to the Raf[CRD] domain. We conclude that the presence of effectors with the Raf[CRD] domain anchoring on the membrane as well as the membrane composition both influence the conformational stability of the K-Ras4B dimer, enabling the preservation of crucial interface interactions.

Relative binding free-energy (RBFE) calculations are experiencing resurgence in the computer-aided drug design of novel small molecules due to performance gains allowed by cutting-edge molecular mechanic force fields and computer hardware. Application of RBFE to soluble proteins is becoming a routine, while recent studies outline necessary steps to successfully apply RBFE at the orthosteric site of membrane-embedded G-protein-coupled receptors (GPCRs). In this work, we apply RBFE to a congeneric series of antagonists that bind to a lipid-exposed, extra-helical site of the P2Y1 receptor. We find promising performance of RBFE, such that it may be applied in a predictive manner on drug discovery programs targeting lipid-exposed sites. Further, by the application of the microkinetic model, binding at a lipid-exposed site can be split into (1) membrane partitioning of the drug molecule followed by (2) binding at the extra-helical site. We find that RBFE can be applied to calculate the free energy of each step, allowing the uncoupling of observed binding free energy from the influence of membrane affinity. This protocol may be used to identify binding hot spots at extra-helical sites and guide drug discovery programs toward optimizing intrinsic activity at the target.

Protein-protein complex assembly is one of the major drivers of biological response. Understanding the mechanisms of protein oligomerization/dimerization would allow one to elucidate how these complexes participate in biological activities and could ultimately lead to new approaches in designing novel therapeutic agents. However, determining the exact association pathways and structures of such complexes remains a challenge. Here, we use parallel tempering metadynamics simulations in the well-tempered ensemble to evaluate the performance of Martini 2.2P and Martini open-beta 3 (Martini 3) force fields in reproducing the structure and energetics of the dimerization process of membrane proteins and proteins in an aqueous solution in reasonable accuracy and throughput. We find that Martini 2.2P systematically overestimates the free energy of association by estimating large barriers in distinct areas, which likely leads to overaggregation when multiple monomers are present. In comparison, the less viscous Martini 3 results in a systematic underestimation of the free energy of association for proteins in solution, while it performs well in describing the association of membrane proteins. In all cases, the near-native dimer complexes are identified as minima in the free energy surface albeit not always as the lowest minima. In the case of Martini 3, we find that the spurious supramolecular protein aggregation present in Martini 2.2P multimer simulations is alleviated and thus this force field may be more suitable for the study of protein oligomerization. We propose that the use of enhanced sampling simulations with a refined coarse-grained force field and appropriately defined collective variables is a robust approach for studying the protein dimerization process, although one should be cautious of the ranking of energy minima.

The main protease (M pro ) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of M pro , a cysteine protease, have been determined, facilitating structure-based drug design. M pro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41-Cys145, M pro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nucleophile Cys145 have been debated in previous studies of SARS-CoV M pro , but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 M pro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of M pro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α-ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N δ (HD) and N ϵ (HE) protonation of His41 and His164, respectively, the α-ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 M pro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.

The Kv11.1 potassium channel, encoded by the human ether-a-go-go-related gene (hERG), plays an essential role in the cardiac action potential. hERG blockade by small molecules can induce "torsade de pointes" arrhythmias and sudden death; as such, it is an important off-target to avoid during drug discovery. Recently, a cryo-EM structure of the open channel state of hERG was reported, opening the door to in silico docking analyses and interpretation of hERG structure-activity relationships, with a view to avoiding blocking activity. Despite this, docking directly to this cryo-EM structure has been reported to yield binding modes that are unable to explain known mutagenesis data. In this work, we use molecular dynamics simulations to sample a range of channel conformations and run ensemble docking campaigns at the known hERG binding site below the selectivity filter, composed of the central cavity and the four deep hydrophobic pockets. We identify a hERG conformational state allowing discrimination of blockers vs nonblockers from docking; furthermore, the binding pocket agrees with mutagenesis data, and blocker binding modes fit the hERG blocker pharmacophore. We then use the same protocol to identify a binding pocket in the hERG channel pore for hERG activators, again agreeing with the reported mutagenesis. Our approach may be useful in drug discovery campaigns to prioritize candidate compounds based on hERG liability via virtual docking screens.

Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliable protein-ligand x-ray structures and binding affinity data has required the use of constructed datasets for the training and evaluation of CNN molecular recognition models. Here, we outline various sources of bias in one such widely-used dataset, the Directory of Useful Decoys: Enhanced (DUD-E). We have constructed and performed tests to investigate whether CNN models developed using DUD-E are properly learning the underlying physics of molecular recognition, as intended, or are instead learning biases inherent in the dataset itself. We find that superior enrichment efficiency in CNN models can be attributed to the analogue and decoy bias hidden in the DUD-E dataset rather than successful generalization of the pattern of protein-ligand interactions. Comparing additional deep learning models trained on PDBbind datasets, we found that their enrichment performances using DUD-E are not superior to the performance of the docking program AutoDock Vina. Together, these results suggest that biases that could be present in constructed datasets should be thoroughly evaluated before applying them to machine learning based methodology development., Competing Interests: The authors’ affiliation with Deep Waters, LLC (T.K., A.C.) and Novartis (C.J.D., V.H., and J.S.D.) does not alter our adherence to PLOS ONE policies on sharing data and materials.

A simple descriptor calculated from molecular dynamics simulations of the membrane partitioning event is found to correlate well with experimental measurements of passive membrane permeation from the high-throughput MDCK-LE assay using a data set of 49 drug-like molecules. This descriptor approximates the energy cost of translocation across the hydrophobic membrane core (flip-flop), which for many molecules limits permeability. Performance is found to be superior in comparison to calculated properties such as clogP, clogD, or polar surface area. Furthermore, the atomistic simulations provide a structural understanding of the partitioned drug-membrane complex, facilitating medicinal chemistry optimization of membrane permeability.

Passive membrane permeation of small molecules is essential to achieve the required absorption, distribution, metabolism, and excretion (ADME) profiles of drug candidates, in particular intestinal absorption and transport across the blood-brain barrier. Computational investigations of this process typically involve either building QSAR models or performing free energy calculations of the permeation event. Although insightful, these methods rarely bridge the gap between computation and experiment in a quantitative manner, and identifying structural insights to apply toward the design of compounds with improved permeability can be difficult. In this work, we combine molecular dynamics simulations capturing the kinetic steps of permeation at the atomistic level with a dynamic mechanistic model describing permeation at the in vitro level, finding a high level of agreement with experimental permeation measurements. Calculation of the kinetic rate constants determining each step in the permeation event allows derivation of structure-kinetic relationships of permeation. We use these relationships to probe the structural determinants of membrane permeation, finding that the desolvation/loss of hydrogen bonding required to leave the membrane partitioned position controls the membrane flip-flop rate, whereas membrane partitioning determines the rate of leaving the membrane.

Ligand binding to membrane proteins may be significantly influenced by the interaction of ligands with the membrane. In particular, the microscopic ligand concentration within the membrane surface solvation layer may exceed that in bulk solvent, resulting in overestimation of the intrinsic protein-ligand binding contribution to the apparent/measured affinity. Using published binding data for a set of small molecules with the β2 adrenergic receptor, we demonstrate that deconvolution of membrane and protein binding contributions allows for improved structure-activity relationship analysis and structure-based drug design. Molecular dynamics simulations of ligand bound membrane protein complexes were used to validate binding poses, allowing analysis of key interactions and binding site solvation to develop structure-activity relationships of β2 ligand binding. The resulting relationships are consistent with intrinsic binding affinity (corrected for membrane interaction). The successful structure-based design of ligands targeting membrane proteins may require an assessment of membrane affinity to uncouple protein binding from membrane interactions.

Blockade of the hERG potassium channel prolongs the ventricular action potential (AP) and QT interval, and triggers early after depolarizations (EADs) and torsade de pointes (TdP) arrhythmia. Opinions differ as to the causal relationship between hERG blockade and TdP, the relative weighting of other contributing factors, definitive metrics of preclinical proarrhythmicity, and the true safety margin in humans. Here, we have used in silico techniques to characterize the effects of channel gating and binding kinetics on hERG occupancy, and of blockade on the human ventricular AP. Gating effects differ for compounds that are sterically compatible with closed channels (becoming trapped in deactivated channels) versus those that are incompatible with the closed/closing state, and expelled during deactivation. Occupancies of trappable blockers build to equilibrium levels, whereas those of non-trappable blockers build and decay during each AP cycle. Occupancies of ~83% (non-trappable) versus ~63% (trappable) of open/inactive channels caused EADs in our AP simulations. Overall, we conclude that hERG occupancy at therapeutic exposure levels may be tolerated for nontrappable, but not trappable blockers capable of building to the proarrhythmic occupancy level. Furthermore, the widely used Redfern safety index may be biased toward trappable blockers, overestimating the exposure-IC50 separation in nontrappable cases.

A statistical-mechanical framework for estimation of solvation entropies and enthalpies is proposed, which is based on the analysis of water as a mixture of correlated water oxygens and water hydrogens. Entropic contributions of increasing order are cast in terms of a Mutual Information Expansion that is evaluated to pairwise interactions. In turn, the enthalpy is computed directly from a distance-based hydrogen bonding energy algorithm. The resulting expressions are employed for grid-based analyses of Molecular Dynamics simulations. In this first assessment of the methodology, we obtained global estimates of the excess entropy and enthalpy of water that are in good agreement with experiment and examined the method's ability to enable detailed elucidation of solvation thermodynamic structures, which can provide valuable knowledge toward molecular design.

Proton translocation pathways of selected variants of the green fluorescent protein (GFP) and Pseudomonas fluorescens mannitol 2-dehydrogenase (PfM2DH) were investigated via an explicit solvent molecular dynamics-based analysis protocol that allows for direct quantitative relationship between a crystal structure and its time-averaged solute-solvent structure obtained from simulation. Our study of GFP is in good agreement with previous research suggesting that the proton released from the chromophore upon photoexcitation can diffuse through an extended internal hydrogen bonding network that allows for the proton to exit to bulk or be recaptured by the anionic chromophore. Conversely for PfM2DH, we identified the most probable ionization states of key residues along the proton escape channel from the catalytic site to bulk solvent, wherein the solute and high-density solvent crystal structures of binary and ternary complexes were properly reproduced. Furthermore, we proposed a plausible mechanism for this proton translocation process that is consistent with the state-dependent structural shifts observed in our analysis. The time-averaged structures generated from our analyses facilitate validation of MD simulation results and provide a comprehensive profile of the dynamic all-occupancy solvation network within and around a flexible solute, from which detailed hydrogen-bonding networks can be inferred. In this way, potential drawbacks arising from the elucidation of these networks by examination of static crystal structures or via alternate rigid-protein solvation analysis procedures can be overcome. Complementary studies aimed at the effective use of our methodology for alternate implementations (e.g., ligand design) are currently underway.

A new class of quinoline-based kinase inhibitors has been discovered that both disrupt cyclin dependent 2 (CDK2) interaction with its cyclin A subunit and act as ATP competitive inhibitors. The key strategy for discovering this class of protein-protein disrupter compounds was to screen the monomer CDK2 in an affinity-selection/mass spectrometry-based technique and to perform secondary assays that identified compounds that bound only to the inactive CDK2 monomer and not the active CDK2/cyclin A heterodimer. Through a series of chemical modifications the affinity (Kd) of the original hit improved from 1 to 0.005μM., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The discovery of lead compound 2e was described. Its covalent binding to HCV NS5B polymerase enzyme was investigated by X-ray analysis. The results of distribution, metabolism and pharmacokinetics were reported. Compound 2e was demonstrated to be potent (replicon GT-1b EC50 = 0.003 μM), highly selective, and safe in in vitro and in vivo assays., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

LMX1B encodes a homeodomain-containing transcription factor that is essential during development. Mutations in LMX1B cause nail-patella syndrome, characterized by dysplasia of the patellae, nails, and elbows and FSGS with specific ultrastructural lesions of the glomerular basement membrane (GBM). By linkage analysis and exome sequencing, we unexpectedly identified an LMX1B mutation segregating with disease in a pedigree of five patients with autosomal dominant FSGS but without either extrarenal features or ultrastructural abnormalities of the GBM suggestive of nail-patella-like renal disease. Subsequently, we screened 73 additional unrelated families with FSGS and found mutations involving the same amino acid (R246) in 2 families. An LMX1B in silico homology model suggested that the mutated residue plays an important role in strengthening the interaction between the LMX1B homeodomain and DNA; both identified mutations would be expected to diminish such interactions. In summary, these results suggest that isolated FSGS could result from mutations in genes that are also involved in syndromic forms of FSGS. This highlights the need to include these genes in all diagnostic approaches to FSGS that involve next-generation sequencing.

Drug design efforts in the emerging 2-aminothiazole-4-carboxamide class of CHK1 inhibitors have uncovered specific combinations of key substructures within the molecule; resulting in significant improvements in cell-based activity while retaining a greater than one hundred-fold selectivity against CDK2. The X-ray crystal structure of a complex between compound 39 and the CHK1 protein detailing a 'U-shaped' topology and key interactions with the protein surface at the ATP site is also reported., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

A novel series of CHK1 inhibitors with a distinctive hinge binding mode, exemplified by 2-aryl-N-(2-(piperazin-1-yl)phenyl)thiazole-4-carboxamide, was discovered through high-throughput screening using the affinity selection-mass spectrometry (AS-MS)-based Automated Ligand Identification System (ALIS) platform. Structure-based ligand design and optimization led to significant improvements in potency to the single digit nanomolar range and hundred-fold selectivity against CDK2.

The synthesis and hit-to-lead SAR development of a pyrazolo[1,5-a]pyrimidine hit 4 is described leading to a series of potent, selective CHK1 inhibitors such as compound 17r. In the Letter, the further utility of the pyrazolo[1,5-a]pyrimidine template for the development of potent, selective kinase inhibitors is detailed., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Previous efforts by our group have established pyrazolo[1,5-a]pyrimidine as a viable core for the development of potent and selective CDK inhibitors. As part of an effort to utilize the pyrazolo[1,5-a]pyrimidine core as a template for the design and synthesis of potent and selective kinase inhibitors, we focused on a key regulator in the cell cycle progression, CHK1. Continued SAR development of the pyrazolo[1,5-a]pyrimidine core at the C5 and C6 positions, in conjunction with previously disclosed SAR at the C3 and C7 positions, led to the discovery of potent and selective CHK1 inhibitors., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

SAR exploration from an initial hit, (S)-N-(2-cyclohexenylethyl)-2-fluoro-6-(2-(1-hydroxy-3-phenylpropan-2-ylamino)-2-oxoethoxy)benzamide (1), identified using our proprietary automated ligand identification system (ALIS),(1) has led to a novel series of selective hepatitis C virus (HCV) NS5B polymerase inhibitors with improved in vitro potency as exemplified by (S)-2-fluoro-6-(2-(1-hydroxy-3-phenylpropan-2-ylamino)-2-oxoethoxy)-N-isopentyl-N-methylbenzamidecarboxamide (41) (IC(50)=0.5 microM). The crystal structure of an analogue (44) was solved and provided rationalization of the SAR of this series, which binds in a distinct manner in the palm domain of NS5B, consistent with biochemical analysis using enzyme mutant variants. These data warrant further lead optimization efforts on this novel series of non-nucleoside inhibitors targeting the HCV polymerase., (2010 Elsevier Ltd. All rights reserved.)

Current antimitotic cancer chemotherapy based on vinca alkaloids and taxanes target tubulin, a protein required not only for mitotic spindle formation but also for the overall structural integrity of terminally differentiated cells. Among many innovations targeting specific mitotic events, inhibition of motor enzymes including KSP (or Eg5) has been validated as a highly productive approach. Many reported KSP inhibitors bind to an induced allosteric site near the site of ATP hydrolysis, and some have been tested in clinical trials with varying degrees of success. This allosteric site was defined in detail by X-ray crystallography of inhibitor complexes, yet complementary information on binding thermodynamics is still lacking. Using two model ATP-uncompetitive inhibitors, monastrol and ispinesib, we report here the results of thermal denaturation and isothermal titration calorimetric studies. These binding studies were conducted with the wild-type KSP motor domain as well as two ispinesib mutants (D130V and A133D) identified to confer resistance to ispinesib treatment. The thermodynamic parameters obtained were placed in the context of the available structural information and corresponding models of the two ispinesib-resistant mutants. The resulting overall information formed a strong basis for future structure-based design of inhibitors of KSP and related motor enzymes.

Serine and threonine kinases play an important role in signal-transduction pathways. Within this kinase family, cyclin-dependent kinase (CDK)2 is an attractive target for oncology involved in cell cycle regulation. In recent years, kinase inhibition has become a major area for therapeutic involvement. As we discuss here, these efforts have resulted in a considerable increase in the number of available high-resolution structures of CDK2-inhibitor complexes. A large amount of structural-based and computational work has allowed the identification of novel chemical scaffolds and structural motifs to design potent CDK2 inhibitors. Of any kinase, CDK2 has the most structures available from the protein databank, averaging 22 new structures per year since 2002. A protein-ligand interaction fingerprint analysis of the available CDK2 protein-ligand complexes indicates that structural diversity is attainable from the structure-based design of CDK2 inhibitors. Since the first CDK2 structure was published in 1996, seven new chemical entities (NCEs) have been advanced to clinical stages. To date, only three of these NCEs have had their complexes published in the protein databank. This review summarizes the structurally informed efforts in the field of CDK2 inhibitor design.

soaPDB is a web application that allows generation and organization of saved PDB searches, and offers automatic email alerts. This tool is used from a web interface to store PDB searches and results in a backend relational database. Written using the Ruby on Rails open-source web framework, soaPDB is easy to deploy, maintain and customize. soaPDB is freely available upon request for local installation and is also available at http://soapdb.dyndns.org:3000.

CDK2 inhibitors containing the related bicyclic heterocycles pyrazolopyrimidines and imidazopyrazines were discovered through high-throughput screening. Crystal structures of inhibitors with these bicyclic cores and two more related ones show that all but one have a common binding mode featuring two hydrogen bonds (H-bonds) to the backbone of the kinase hinge region. Even though ab initio computations indicated that the imidazopyrazine core would bind more tightly to the hinge, pyrazolopyrimidines gain an advantage in potency through participation of N4 in an H-bond network involving two catalytic residues and bridging water molecules. Further insight into inhibitor/CDK2 interactions was gained from analysis of additional crystal structures. Significant gains in potency were obtained by optimizing the fit of hydrophobic substituents to the gatekeeper region of the ATP binding site. The most potent inhibitors have good selectivity.

In the preceding paper (Duca, J. S.; Madison, V. S.; Voigt, J. H. J. Chem. Inf. Model. 2008, 48, 659-668), the accuracy of docking and affinity predictions of the Gold and Glide programs were investigated using single protein conformations spanning 150 CDK2/inhibitor crystallographic complexes. High docking accuracy was observed with both methods; furthermore, Glide showed modest log(IC50)/score correlations. In this part of the study, the effect of combining docking results from multiple protein conformations in a consensus fashion was probed. This approach enhanced docking accuracy only for Glide, which was attributed to the nature of its scoring function. For log(IC50)/score correlations, particular emphasis was placed on considering only scores from correctly docked poses. Using multiple instead of single protein structures showed an improvement in the correlations. Validation sets and scrambling experiments were used to examine the statistical significance and predictivity of these correlations. Rather than actual improvements in scoring accuracy, docking to multiple protein conformations produced overfitting artifacts.

Predicting protein/ligand binding affinity is one of the most challenging computational chemistry tasks. Numerous methods have been developed to address this challenge, but they all have limitations. Failure to account for protein flexibility has been a shortcoming of many methods. In this cross-docking study the data set comprised 150 inhibitor complexes of the protein kinase CDK2. Gold and Glide performed well in terms of docking accuracy. The chance of cross-docking a ligand within a 2 A RMSD of its experimental pose was found to be 50%. Relative binding potency was not properly predicted from scoring functions, even though cross-docking of each inhibitor into each protein structure was performed and only scores of correctly docked ligands were considered. An accompanying paper (Voigt, J. H.; Elkin, C.; Madison, V. S. Duca, J. S. J. Chem. Inf. Model. 2008, 48, 669-678) covers cross-docking and docking accuracy from the perspective of using multiple protein structures.

Ab initio calculations were employed to compute pKa values and tautomer properties of a series of substituted pyrazolopyridines. The results show that the neutral 1H tautomer predominates, but upon protonation this proton migrates to give the preferred charged [2H,7H] tautomer. The basicity of the pyrazolopyridines is correlated with the electron donating capability of the 4-substituent. Ab initio free energy calculations were also used to identify determinants of binding affinity for some recently published pyrazolopyridine inhibitors of CDK2. Hydrogen-bonding affinity may be one important component of binding strength., (Copyright 2005 Wiley Periodicals, Inc)

TNF-alpha converting enzyme (TACE) is a multidomain, membrane-anchored protein that includes a Zn-dependent protease domain. It releases the soluble form of cytokine tumor necrosis factor-alpha (TNF-alpha) from its membrane-bound precursor. TACE is a metalloprotease containing a catalytic glutamic acid, Glu-406, and a Zn(2+) ion ligated to three imidazoles. The protonation states of the active site glutamic acid and inhibitors are important factors in understanding the potency of inhibitors with acidic zinc-ligating groups such as hydroxamic and carboxylic acids. Density functional methods were utilized to compute pK(a) values using a model of the catalytic site of TACE and to predict a concomitant mechanism of binding, consistent with lowering the pK(a) of the bound ligand and raising the pK(a) of the active site Glu-406. Weak acids, such as hydroxamic acids, bind in their neutral form and then transfer an acidic proton to Glu-406. Stronger acids, such as carboxylic acids, bind in their anionic form and require preprotonation of Glu-406. Similar binding events would be expected for other zinc-dependent proteases.

The 4D-QSAR paradigm has been used to develop a formalism to estimate molecular similarity measures as a function of conformation, alignment, and atom type. It is possible to estimate the molecular similarity of pairs of molecules based upon the entire ensemble of conformational states each molecule can adopt at a given temperature, normally room temperature. Molecular similarity can be measured in terms of the types of atoms composing each molecule leading to multiple measures of molecular similarity. Multiple measures of molecular similarity can also arise from using different alignment rules to perform relative molecular similarity, RMS, analysis. An alignment independent method of determining molecular similarity measures, referred to as absolute molecular similarity, AMS, analysis has been developed. Various sets and libraries of compounds, including the amino acids, are analyzed using 4D-QSAR molecular similarity analysis to demonstrate the features of the formalism. Exploration of molecular similarity as a function of chirality, identification of common embedded 3D pharmacophores in compounds, and elucidation of 3D-isosteric compounds from structurally diverse libraries are carried out in the application studies.

Two major advances have been made in the computational perception and utilization of pharmacophores in compound libraries, both real and virtual. Firstly, a hierarchical set of filtering calculations has emerged that can be used to efficiently partition a library into a trial set of pharmacophores. This sequential filtering permits large libraries to be efficiently processed, as well as compounds judged as 'hits' to be analyzed in great detail. Secondly, new and extended methods of QSAR (quantitative structure-activity relationship) analysis have evolved to translate pharmacophore information into QSAR models that, in turn, can be used as virtual high-throughput screens for activity profiling of a library.

A methodology called quantitative component analysis of mixtures (QCAM) was used to analyze an existing set of product formulation data to determine if the irritating ingredients in the mixtures could be identified. Eye irritation scores, based on a rat model, for 18 mixtures having a composite total of 37 components, were analyzed by QCAM. QCAM relates a net toxicity measure of a mixture to the toxicities of the individual components of the mixture through linear, quadratic, and pairwise cross-component concentration-dependent interactions. A correlation model is established using a particular genetic algorithm employing either multidimensional linear regression or partial least-squares regression fitting. Cornea eye irritation and average eye irritation are well-explained in terms of a linear model of, at most, three components over the set of mixtures. Moreover, extensive cornea and average eye irritations are due to only one of these three components of the mixtures. Also, one of the three significant components was predicted to decrease the extent of eye irritation, and subsequently identified as an "anti-irritant" in contact lens solutions. A reasonable linear correlation model could also be developed for conjunctiva irritation, but no significant iris irritation model could be constructed. The addition of quadratic and/or cross-component concentration terms to a linear correlation model did not statistically improve the overall resultant model. The QCAM models permit estimation of the intrinsic (self) toxicity of each of the components of a mixture, and may aid in the reduction, and ultimate elimination, of the need for animal eye irritation studies.

The nucleophilic substitution reaction of the chlorides RMe(2)CCH(2)Cl (R = Me, 4; Ph, 5a; PhCH(2), 5b) and their relative reactivities toward diphenyl phosphide ions were studied under irradiation in liquid ammonia. The relative reactivities determined were k(5a)()/k(4)() congruent with 9 and k(5b)()/k(4)() congruent with 0.85. These reactions are proposed to occur through the S(RN)1 mechanism. The higher reactivity of 5a is explained on the basis of its higher electron affinity due to the phenyl substitution and the efficient intramolecular electron transfer from this group to the C-Cl sigma bond (intra-ET catalysis). Although 5b also has a phenyl ring, its lower reactivity is ascribed to a decrease in the rate of the intra-ET by elongation of the bridge in one methylene unit. The relative reactivity of 5a versus 5b (k(5a)()/k(5b)() congruent with 6.4) is proposed to indicate the ratio of the intra-ET rates of the radical anions of both compounds. AM1 calculations performed on the system are in agreement with the experimental results.