Computational protocol for predicting the binding affinities of zinc containing metalloprotein-ligand complexes

Molecular Docking and Dynamics of Phytochemicals From Chinese Herbs With SARS-CoV-2 RdRp

The novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is causing coronavirus disease 2019 (COVID-19) pandemic. Ancient Chinese herbal formulas are effective for diseases caused by viral infection, and their effects on COVID-19 are currently being examined. To directly evaluate the role of Chinese herbs in inhibiting replication of SARS-CoV-2, we investigated how the phytochemicals from Chinese herbs interact with the viral RNA-dependent RNA polymerase (RdRP). Total 1025 compounds were screened, and then 181compounds were selected for molecular docking analysis. Four phytochemicals licorice glycoside E, diisooctyl phthalate, (-)-medicocarpin, and glycyroside showed good binding affinity with RdRp. The best complex licorice glycoside E/RdRp forms 3 hydrogen bonds, 4 hydrophobic interactions, 1 pair of Pi-cation/stacking, and 4 salt bridges. Furthermore, docking complexes licorice glycoside E/RdRp and diisooctyl phthalate/RdRp were optimized by molecular dynamics simulation to obtain the stable conformation. These studies indicate that they are promising as antivirals against SARS-CoV-2.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Allosterically Coupled Multisite Binding of Testosterone to Human Serum Albumin

Human serum albumin (HSA) acts as a carrier for testosterone, other sex hormones, fatty acids, and drugs. However, the dynamics of testosterone's binding to HSA and the structure of its binding sites remain incompletely understood. Here, we characterize the dynamics of testosterone's binding to HSA and the stoichiometry and structural location of the binding sites using 2-dimensional nuclear magnetic resonance (2D NMR), fluorescence spectroscopy, 4,4'-dianilino-1,1'-binaphthyl-5,5'-disulfonic acid dipotassium salt partitioning, and equilibrium dialysis, complemented by molecular modeling. 2D NMR studies showed that testosterone competitively displaced 18-[13C]-oleic acid from at least 3 known fatty acid binding sites on HSA that also bind many drugs. Binding isotherms of testosterone's binding to HSA generated using fluorescence spectroscopy and equilibrium dialysis were nonlinear and the apparent dissociation constant varied with different concentrations of testosterone and HSA. The binding isotherms neither conformed to a linear binding model with 1:1 stoichiometry nor to 2 independent binding sites; the binding isotherms were most consistent with 2 or more allosterically coupled binding sites. Molecular dynamics studies revealed that testosterone's binding to fatty acid binding site 3 on HSA was associated with conformational changes at site 6, indicating that residues in in these 2 distinct binding sites are allosterically coupled. There are multiple, allosterically coupled binding sites for testosterone on HSA. Testosterone shares these binding sites on HSA with free fatty acids, which could displace testosterone from HSA under various physiological states or disease conditions, affecting its bioavailability.

Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method

Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-metallo and metallo PL complexes. Bappl+ outperforms other state-of-the-art scoring functions, achieving a high Pearson correlation coefficient of up to ~ 0.76 with low standard deviations. The biggest contributors to the increased performance are the use of a machine-learning model and the enlarged training dataset. We have also evaluated the performance of Bappl+ on target-specific proteins, which highlighted the limitations of our function and provides a way for further improvements. We believe that Bappl+ methodology could prove valuable in ranking candidate molecules against a target metallo or non-metallo protein by reliably predicting their binding affinities, thus helping in the drug discovery process.

AMBER Force Field Parameters for Cobalt-Containing Biological Systems: A Systematic Derivation Study

In the present work, the parameterization of a set of cobalt-containing systems has been performed to create a comprehensive library for bonding parameters of biological Co-containing systems. A standard process for the extraction and validation of parameters was employed, which could be used to create force field parameters for the other metal-containing systems. All protein data banks were searched to extract common chemical groups in bonding with cobalt, and finally, 16 structures were designed to represent the binding model of the chemical moieties with cobalt. The Hessian matrix of each structure was computed at the B3LYP/6-311++G(2d,2p) level of theory and the Seminario method was employed to compute cobalt bond stretching and angle bending parameters. Validation of the derived parameters was performed using structural minimization and molecular dynamics (MD) simulations of four models. Further validation was performed using an extensive MD simulation on carbonic anhydrase II as a common cobalt-containing metalloprotein. The results demonstrated that among models, the bonded model in combination with the RESP charges can produce the most reliable and accurate structural conformations for the metal site of cobalt-containing systems.

Identification of a Novel Multifunctional Ligand for Simultaneous Inhibition of Amyloid-Beta (Aβ42) and Chelation of Zinc Metal Ion

Zinc binding to β-amyloid structure could promote amyloid-β aggregation, as well as reactive oxygen species (ROS) production, as suggested in many experimental and theoretical studies. Therefore, the introduction of multifunctional drugs capable of chelating zinc metal ion and inhibiting Aβ aggregation is a promising strategy in the development of AD treatment. The present study has evaluated the efficacy of a new bifunctional peptide drug using molecular docking and molecular dynamics (MD) simulations. This drug comprises two different domains, an inhibitor domain, obtained from the C-terminal hydrophobic region of Aβ, and a Zn²⁺ chelating domain, derived from rapeseed meal, merge with a linker. The multifunctionality of the ligand was evaluated using a comprehensive set of MD simulations spanning up to 3.2 μs including Aβ relaxation, ligand-Zn²⁺ bilateral interaction, and, more importantly, ligand-Zn²⁺-Aβ₄₂ trilateral interactions. Analysis of the results strongly indicated that the bifunctional ligand can chelate zinc metal ion and avoid Aβ aggregation simultaneously. The present study illustrated that the proposed ligand has considerable hydrophobic interactions and hydrogen bonding with monomeric Aβ in the presence of zinc metal ion. Therefore, in light of these considerable interactions and contacts, the α-helical structure of Aβ has been enhanced, while the β-sheet formation is prevented and the α-helix native structure is protected. Furthermore, the analysis of interactions between Aβ and ligand-zinc complex revealed that the zinc metal ion is coordinated to Met13, the ending residue of the ligand and merely one residue in Aβ. The results have proven the previous experimental and theoretical findings in the literature about Aβ interactions with zinc metal ion and also Aβ interactions with the first domain of the proposed ligand. Moreover, the current research has evaluated the chelation using MD simulation and linear interaction energy (LIE) methods, and the result has been satisfactorily verified with previous experimental and theoretical (DFT) studies.

Stimulation of heat shock protein 90 chaperone function through binding of a novobiocin analog KU-32

Heat shock protein 90 (Hsp90) is a eukaryotic chaperone responsible for the folding and functional activation of numerous client proteins, many of which are oncoproteins. Thus, Hsp90 inhibition has been intensely pursued, resulting in the development of many potential Hsp90 inhibitors, not all of which are well-characterized. Hsp90 inhibitors not only abrogate its chaperone functions, but also could help us gain insight into the structure-function relationship of this chaperone. Here, using biochemical and cell-based assays along with isothermal titration calorimetry, we investigate KU-32, a derivative of the Hsp90 inhibitor novobiocin (NB), for its ability to modulate Hsp90 chaperone function. Although NB and KU-32 differ only slightly in structure, we found that upon binding, they induce completely opposite conformational changes in Hsp90. We observed that NB and KU-32 both bind to the C-terminal domain of Hsp90, but surprisingly, KU-32 stimulated the chaperone functions of Hsp90 via allosteric modulation of its N-terminal domain, responsible for the chaperone's ATPase activity. In vitro and in silico studies indicated that upon KU-32 binding, Hsp90 undergoes global structural changes leading to the formation of a "partially closed" intermediate that selectively binds ATP and increases ATPase activity. We also report that KU-32 promotes HeLa cell survival and enhances the refolding of an Hsp90 substrate inside the cell. This discovery explains the effectiveness of KU-32 analogs in the management of neuropathies and may facilitate the design of molecules that promote cell survival by enhancing Hsp90 chaperone function and reducing the load of misfolded proteins in cells.

Computational Approaches to Matrix Metalloprotease Drug Design

Matrix metalloproteinases (MMPs) are a family of zinc-containing enzymes required for homeostasis. These enzymes are an important class of drug targets as their over expression is associated with many disease states. Most of the inhibitors reported against this class of proteins have failed in clinical trials due to lack of specificity. In order to assist in drug design endeavors for MMP targets, a computationally tractable pathway is presented, comprising, (1) docking of small molecule inhibitors against the target MMPs, (2) derivation of quantum mechanical charges on the zinc ion in the active site and the amino acids coordinating with zinc including the inhibitor molecule, (3) molecular dynamics simulations on the docked ligand-MMP complexes, and (4) evaluation of binding affinities of the ligand-MMP complexes via an accurate scoring function for zinc containing metalloprotein-ligand complexes. The above pathway was applied to study the interaction of the inhibitor Batimastat with MMPs, which resulted in a high correlation between the predicted and experimental binding free energies, suggesting the potential applicability of the pathway.

CSM-lig: a web server for assessing and comparing protein-small molecule affinities

Determining the affinity of a ligand for a given protein is a crucial component of drug development and understanding their biological effects. Predicting binding affinities is a challenging and difficult task, and despite being regarded as poorly predictive, scoring functions play an important role in the analysis of molecular docking results. Here, we present CSM-Lig (http://structure.bioc.cam.ac.uk/csm_lig), a web server tailored to predict the binding affinity of a protein-small molecule complex, encompassing both protein and small-molecule complementarity in terms of shape and chemistry via graph-based structural signatures. CSM-Lig was trained and evaluated on different releases of the PDBbind databases, achieving a correlation of up to 0.86 on 10-fold cross validation and 0.80 in blind tests, performing as well as or better than other widely used methods. The web server allows users to rapidly and automatically predict binding affinities of collections of structures and assess the interactions made. We believe CSM-lig would be an invaluable tool for helping assess docking poses, the effects of multiple mutations, including insertions, deletions and alternative splicing events, in protein-small molecule affinity, unraveling important aspects that drive protein–compound recognition.

Predicting the binding modes and sites of metabolism of xenobiotics

Metabolism studies are an essential integral part of ADMET profiling of drug candidates to evaluate their safety and efficacy. Cytochrome P-450 (CYP) metabolizes a wide variety of xenobiotics/drugs. The binding modes of these compounds with CYP and their intrinsic reactivities decide the metabolic products. We report here a novel computational protocol, which comprises docking of ligands to heme-containing CYPs and prediction of binding energies through a newly developed scoring function, followed by analyses of the docked structures and molecular orbitals of the ligand molecules, for predicting the sites of metabolism (SOM) of ligands. The calculated binding free energies of 121 heme-containing protein-ligand docked complexes yielded a correlation coefficient of 0.84 against experiment. Molecular orbital analyses of the resultant top three unique poses of the docked complexes provided a success rate of 87% in identifying the experimentally known sites of metabolism of the xenobiotics. The SOM prediction methodology is freely accessible at .

Understanding the binding of inhibitors of matrix metalloproteinases by molecular docking, quantum mechanical calculations, molecular dynamics simulations, and a MMGBSA/MMBappl study

Matrix metalloproteinases (MMPs) consist of a class of proteins required for normal tissue function. Their over expression is associated with many disease states and hence the interest in MMPs as drug targets. Almost all MMP inhibitors have been reported to fail in clinical trials due to lack of specificity. Zinc in the binding site of metalloproteinases performs essential biological functions and contributes to the binding affinity of inhibitors. The multiple possibilities for coordination geometry and the consequent charge on the zinc atom indicate that parameters developed are not directly transferable across different families of zinc metalloproteinases with different zinc coordination geometries, active sites and ligand architectures which makes it difficult to evaluate metal-ligand interactions. In order to assist in drug design endeavors for MMP targets, a computationally tractable pathway is presented, comprising docking of small molecule inhibitors against the target MMPs, derivation of quantum mechanical charges on the zinc ion in the active site and the amino acids coordinating with zinc including the inhibitor molecule, molecular dynamics simulations on the docked ligand-MMP complexes and evaluation of binding affinities of the ligand-MMP complexes via an accurate scoring function for zinc containing metalloprotein-ligand complexes. The above pathway was applied to study the interaction of inhibitor Batimastat with MMPs, which resulted in a high correlation between the predicted binding free energies and experiment, suggesting the potential applicability of the pathway. We then proceeded to formulate a few design principles which identify the key protein residues for generating molecules with high affinity and specificity against each of the MMPs.

Bi and tri-substituted phenyl rings containing bisbenzimidazoles bind differentially with DNA duplexes: a biophysical and molecular simulation study

Recently synthesis of programmable DNA ligands which can regulate transcription factors have increased the interest of researchers on the functional ability of DNA interacting compounds. A series of DNA interacting compounds are being designed which can differentiate between GC and AT rich DNA. In this study, we have studied the specificity of a few novel bisbenzimidazoles having different bi/tri-substituted phenyl rings, with DNA duplexes using spectroscopic methods. This study entails an integrative approach where we combine biophysical methods and molecular dynamics simulation studies to establish suitable scaffolds to target A/T DNA. We have designed a few analogues of Hoechst 33342 viz.; dimethoxy (DMA), trimethoxy (TMA), dichloro (DCA) and difluoro (DFA) functionalities and performed molecular docking of newly designed analogues with biologically relevant AT and GC rich DNA sequences. The docking studies, along with molecular dynamics (MD) simulations of d(ATATATATATATATAT)2, d(GA4T4C)2, d(GT4A4C)2 and GC rich sequence: d(GCGCGCGCGCGCGCGC)2 complexed with DMA, TMA and DFA, showed that these molecules have higher binding affinity towards AT rich DNA. None of these compounds exhibited an affinity to GC rich DNA rather we observed that these compounds destabilize GC rich DNA. The binding was characterized by strong stabilization of the polynucleotides against thermal strand separation in thermal melting experiments. New insights into the molecules binding to DNA have emerged from these studies. All the DNA binding ligands stabilized d(GA4T4C)2 and d(GT4A4C)2 more out of the five oligomers used for the study, suggesting that these ligands bind 'A4T4' and 'T4A4' strongly as compared to 'ATAT' base pairs.

Novel codon insert in HIV type 1 clade B reverse transcriptase associated with low-level viremia during antiretroviral therapy

We investigated the pol genotype in two phylogenetically and epidemiologically linked partners, who were both experiencing persistent low-level viremia during antiretroviral therapy. In one partner we identified a new residue insertion between codon 248 and 249 of the HIV-1 RNA reverse transcriptase (RT) coding region (HXB2 numbering). We then investigated the potential impact of identified mutations in RT and antiretroviral binding affinity using a novel computational approach.

One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery

A major goal in computational chemistry has been to discover the set of rules that can accurately predict the binding affinity of any protein-drug complex, using only a single snapshot of its three-dimensional structure. Despite the continual development of structure-based models, predictive accuracy remains low, and the fundamental factors that inhibit the inference of all-encompassing rules have yet to be fully explored. Using statistical learning theory and information theory, here we prove that even the very best generalized structure-based model is inherently limited in its accuracy, and protein-specific models are always likely to be better. Our results refute the prevailing assumption that large data sets and advanced machine learning techniques will yield accurate, universally applicable models. We anticipate that the results will aid the development of more robust virtual screening strategies and scoring function error estimations.

A New Quantum Calibrated Force Field for Zinc-Protein Complex

A quantum calibrated polarizable-charge transfer force field (QPCT) has been proposed to accurately describe the interaction dynamics of zinc-protein complexes. The parameters of the QPCT force field were calibrated by quantum chemistry calculation and capture the polarization and charge transfer effect. QPCTs are validated by molecular dynamic simulation of the hydration shell of the zinc ion, five proteins containing the most common zinc-binding sites (ZnCys2His2, ZnCys3His1, ZnCys4, Zn2Cys6), as well as protein-ligand binding energy in zinc protein MMP3. The calculated results show excellent agreement with the experimental measurement and with results from QM/MM simulation, demonstrating that QPCT is accurate enough to maintain the correct structural integrity of the zinc binding pocket and provide accurate interaction dynamics of the zinc-residue complex. The current approach can also be extended to the study of interaction dynamics of other metal-containing proteins by recalibrating the corresponding parameters to the specific complexes.

Genomes to hits in silico - a country path today, a highway tomorrow: a case study of chikungunya

These are exciting times for bioinformaticians, computational biologists and drug designers with the genome and proteome sequences and related structural databases growing at an accelerated pace. The post-genomic era has triggered high expectations for a rapid and successful treatment of diseases. However, in this biological information rich and functional knowledge poor scenario, the challenges are indeed grand, no less than the assembly of the genome of the whole organism. These include functional annotation of genes, identification of druggable targets, prediction of three-dimensional structures of protein targets from their amino acid sequences, arriving at lead compounds for these targets followed by a transition from bench to bedside. We propose here a "Genome to Hits In Silico" strategy (called Dhanvantari) and illustrate it on Chikungunya virus (CHIKV). "Genome to hits" is a novel pathway incorporating a series of steps such as gene prediction, protein tertiary structure determination, active site identification, hit molecule generation, docking and scoring of hits to arrive at lead compounds. The current state of the art for each of the steps in the pathway is high-lighted and the feasibility of creating an automated genome to hits assembly line is discussed.

Sanjeevini: a freely accessible web-server for target directed lead molecule discovery

Background

Computational methods utilizing the structural and functional information help to understand specific molecular recognition events between the target biomolecule and candidate hits and make it possible to design improved lead molecules for the target.

Results

Sanjeevini represents a massive on-going scientific endeavor to provide to the user, a freely accessible state of the art software suite for protein and DNA targeted lead molecule discovery. It builds in several features, including automated detection of active sites, scanning against a million compound library for identifying hit molecules, all atom based docking and scoring and various other utilities to design molecules with desired affinity and specificity against biomolecular targets. Each of the modules is thoroughly validated on a large dataset of protein/DNA drug targets.

Conclusions

The article presents Sanjeevini, a freely accessible user friendly web-server, to aid in drug discovery. It is implemented on a tera flop cluster and made accessible via a web-interface at http://www.scfbio-iitd.res.in/sanjeevini/sanjeevini.jsp. A brief description of various modules, their scientific basis, validation, and how to use the server to develop in silico suggestions of lead molecules is provided.

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .

Structure and dynamics of a dizinc metalloprotein: effect of charge transfer and polarization

Structures and dynamics of a recently designed dizinc metalloprotein (DFsc) (J. Mol. Biol. 2003, 334, 1101) are studied by molecular dynamics simulation using a dynamically adapted polarized force field derived from fragment quantum calculation for protein in solvent. To properly describe the effect of charge transfer and polarization in the present approach, quantum chemistry calculation of the zinc-binding group is periodically performed (on-the-fly) to update the atomic charges of the zinc-binding group during the MD simulation. Comparison of the present result with those obtained from simulations under standard AMBER force field reveals that charge transfer and polarization are critical to maintaining the correct asymmetric metal coordination in the DFsc. Detailed analysis of the result also shows that dynamic fluctuation of the zinc-binding group facilitates solvent interaction with the zinc ions. In particular, the dynamic fluctuation of the zinc-zinc distance is shown to be an important feature of the catalytic function of the di-ion zinc-binding group. Our study demonstrates that the dynamically adapted polarization approach is computationally practical and can be used to study other metalloprotein systems.

QM/MM X-ray refinement of zinc metalloenzymes

Zinc metalloenzymes play an important role in biology. However, due to the limitation of molecular force field energy restraints used in X-ray refinement at medium or low resolutions, the precise geometry of the zinc coordination environment can be difficult to distinguish from ambiguous electron density maps. Due to the difficulties involved in defining accurate force fields for metal ions, the QM/MM (quantum-mechanical/molecular-mechanical) method provides an attractive and more general alternative for the study and refinement of metalloprotein active sites. Herein we present three examples that indicate that QM/MM based refinement yields a superior description of the crystal structure based on R and R(free) values and on the inspection of the zinc coordination environment. It is concluded that QM/MM refinement is an useful general tool for the improvement of the metal coordination sphere in metalloenzyme active sites.

Energies, Geometries, and Charge Distributions of Zn Molecules, Clusters, and Biocenters from Coupled Cluster, Density Functional, and Neglect of Diatomic Differential Overlap Models

We present benchmark databases of Zn-ligand bond distances, bond angles, dipole moments, and bond dissociation energies for Zn-containing small molecules and Zn coordination compounds with H, CH3, C2H5, NH3, O, OH, H2O, F, Cl, S, and SCH3 ligands. The test set also includes clusters with Zn-Zn bonds. In addition, we calculated dipole moments and binding energies for Zn centers in coordination environments taken from zinc metalloenzyme X-ray structures, representing both structural and catalytic zinc centers. The benchmark values are based on relativistic-core coupled cluster calculations. These benchmark calculations are used to test the predictions of four density functionals, namely B3LYP and the more recently developed M05-2X, M06, and M06-2X levels of theory, and six semiempirical methods, including neglect of diatomic differential overlap (NDDO) calculations incorporating the new PM3 parameter set for Zn called ZnB, developed by Brothers and co-workers, and the recent PM6 parametrization of Stewart. We found that the best DFT method to reproduce dipole moments and dissociation energies of our Zn compound database is M05-2X, which is consistent with a previous study employing a much smaller and less diverse database and a much larger set of density functionals. Here we show that M05-2X geometries and single-point coupled cluster calculations with M05-2X geometries can also be used as benchmarks for larger compounds, where coupled cluster optimization is impractical, and in particular we use this strategy to extend the geometry, binding energy, and dipole moment databases to additional molecules, and we extend the tests involving crystal-site coordination compounds to two additional proteins. We find that the most predictive NDDO methods for our training set are PM3 and MNDO/d. Notably, we also find large errors in B3LYP for the coordination compounds based on experimental X-ray geometries.

Interaction model based on local protein substructures generalizes to the entire structural enzyme-ligand space

Chemogenomics is a new strategy in in silico drug discovery, where the ultimate goal is to understand molecular recognition for all molecules interacting with all proteins in the proteome. To study such cross interactions, methods that can generalize over proteins that vary greatly in sequence, structure, and function are needed. We present a general quantitative approach to protein-ligand binding affinity prediction that spans the entire structural enzyme-ligand space. The model was trained on a data set composed of all available enzymes cocrystallized with druglike ligands, taken from four publicly available interaction databases, for which a crystal structure is available. Each enzyme was characterized by a set of local descriptors of protein structure that describe the binding site of the cocrystallized ligand. The ligands in the training set were described by traditional QSAR descriptors. To evaluate the model, a comprehensive test set consisting of enzyme structures and ligands was manually curated. The test set contained enzyme-ligand complexes for which no crystal structures were available, and thus the binding modes were unknown. The test set enzymes were therefore characterized by matching their entire structures to the local descriptor library constructed from the training set. Both the training and the test set contained enzyme-ligand complexes from all major enzyme classes, and the enzymes spanned a large range of sequences and folds. The experimental binding affinities (p K i) ranged from 0.5 to 11.9 (0.7-11.0 in the test set). The induced model predicted the binding affinities of the external test set enzyme-ligand complexes with an r (2) of 0.53 and an RMSEP of 1.5. This demonstrates that the use of local descriptors makes it possible to create rough predictive models that can generalize over a wide range of protein targets.

Atomic constraints between the voltage sensor and the pore domain in a voltage-gated K+ channel of known structure

In voltage-gated K⁺ channels (Kv), membrane depolarization promotes a structural reorganization of each of the four voltage sensor domains surrounding the conducting pore, inducing its opening. Although the crystal structure of Kv1.2 provided the first atomic resolution view of a eukaryotic Kv channel, several components of the voltage sensors remain poorly resolved. In particular, the position and orientation of the charged arginine side chains in the S4 transmembrane segments remain controversial. Here we investigate the proximity of S4 and the pore domain in functional Kv1.2 channels in a native membrane environment using electrophysiological analysis of intersubunit histidine metallic bridges formed between the first arginine of S4 (R294) and residues A351 or D352 of the pore domain. We show that histidine pairs are able to bind Zn²⁺ or Cd²⁺ with high affinity, demonstrating their close physical proximity. The results of molecular dynamics simulations, consistent with electrophysiological data, indicate that the position of the S4 helix in the functional open-activated state could be shifted by ∼7–8 Å and rotated counterclockwise by 37° along its main axis relative to its position observed in the Kv1.2 x-ray structure. A structural model is provided for this conformation. The results further highlight the dynamic and flexible nature of the voltage sensor.

Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems

Drug resistance is a significant obstacle in the effective treatment of diseases with rapidly mutating targets, such as AIDS, malaria, and certain forms of cancer. Such targets are remarkably efficient at exploring the space of functional mutants and at evolving to evade drug binding while still maintaining their biological role. To overcome this challenge, drug regimens must be active against potential target variants. Such a goal may be accomplished by one drug molecule that recognizes multiple variants or by a drug "cocktail"--a small collection of drug molecules that collectively binds all desired variants. Ideally, one wants the smallest cocktail possible due to the potential for increased toxicity with each additional drug. Therefore, the task of designing a regimen for multiple target variants can be framed as an optimization problem--find the smallest collection of molecules that together "covers" the relevant target variants. In this work, we formulate and apply this optimization framework to theoretical model target ensembles. These results are analyzed to develop an understanding of how the physical properties of a target ensemble relate to the properties of the optimal cocktail. We focus on electrostatic variation within target ensembles, as it is one important mechanism by which drug resistance is achieved. Using integer programming, we systematically designed optimal cocktails to cover model target ensembles. We found that certain drug molecules covered much larger regions of target space than others, a phenomenon explained by theory grounded in continuum electrostatics. Molecules within optimal cocktails were often dissimilar, such that each drug was responsible for binding variants with a certain electrostatic property in common. On average, the number of molecules in the optimal cocktails correlated with the number of variants, the differences in the variants' electrostatic properties at the binding interface, and the level of binding affinity required. We also treated cases in which a subset of target variants was to be avoided, modeling the common challenge of closely related host molecules that may be implicated in drug toxicity. Such decoys generally increased the size of the required cocktail and more often resulted in infeasible optimizations. Taken together, this work provides practical optimization methods for the design of drug cocktails and a theoretical, physics-based framework through which useful insights can be achieved.

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method.