Screening a peptidyl database for potential ligands to proteins with side-chain flexibility

Application of molecular docking in elaborating molecular mechanisms and interactions of supramolecular cyclodextrin

The cyclodextrin (CD)-based supramolecular nanomedicines have attracted growing interest because of their superior characteristics, including desirable biocompatibility, low toxicity, unique molecular structure and easy functionalization. The smart structures of CD impart host-guest interaction for meeting the multifunctional needs of disease therapy. However, it faces challenges in formulation design and inclusion mechanism clarification of the functional supramolecular assemblies owing to the complicated structures and mechanisms. Fortunately, molecular docking helps the researchers to comprehend the interaction between the drug and the target molecule for achieving high-through screening from the database. In this review, we summarized the category and characteristics of molecular docking along with the properties and applications of CD. Significantly, we highlighted the application of molecular docking in elaborating molecular mechanisms and simulating complex structures at molecular levels. The issues and development of CD and molecular docking were also presented to provide beneficial reference and new insights for supramolecular nano-systems.

Needleless electrospun phytochemicals encapsulated nanofibre based 3-ply biodegradable mask for combating COVID-19 pandemic

The emergence of COVID-19 pandemic has severely affected human health and world economies. According to WHO guidelines, continuous use of face mask is mandatory for personal protection for restricting the spread of bacteria and virus. Here, we report a 3-ply cotton-PLA-cotton layered biodegradable face-mask containing encapsulated phytochemicals in the inner-filtration layer. The nano-fibrous PLA filtration layer was fabricated using needleless electrospinning of PLA & phytochemical-based herbal-extracts. This 3-layred face mask exhibits enhanced air permeability with a differential pressure of 35.78 Pa/cm² and superior bacterial filtration efficiency of 97.9% compared to conventional face masks. Close-packed mesh structure of the nano-fibrous mat results in effective adsorption of particulate matter, aerosol particles, and bacterial targets deep inside the filtration layer. The outer hydrophobic layer of mask exhibited effective blood splash resistance up to a distance of 30 cm, ensuring its utilization for medical practices. Computational analysis of constituent phytochemicals using the LibDock algorithm predicted inhibitory activity of chemicals against the protein structured bacterial sites. The computational analysis projected superior performance of phytochemicals considering the presence of stearic acid, oleic acid, linoleic acid, and Arachidic acid exhibiting structural complementarity to inhibit targeted bacterial interface. Natural cotton fibers and PLA bio-polymer demonstrated promising biodegradable characteristics in the presence of in-house cow-dung based biodegradation slurry. Addition of jaggery to the slurry elevated the biodegradation performance, resulting in increment of change of weight from 07% to 12%. The improved performance was attributed to the increased sucrose content in biodegradation slurry, elevating the bacterial growth in the slurry. An innovative face mask has shown promising results for utilization in day-to-day life and medical frontline workers, considering the post-pandemic environmental impacts.

An efficient chemical screening method for structure-based inhibitors to nucleic acid enzymes targeting the DNA repair-replication interface and SARS CoV-2

We present a Chemistry and Structure Screen Integrated Efficiently (CASSIE) approach (named for Greek prophet Cassandra) to design inhibitors for cancer biology and pathogenesis. CASSIE provides an effective path to target master keys to control the repair-replication interface for cancer cells and SARS CoV-2 pathogenesis as exemplified here by specific targeting of Poly(ADP-ribose) glycohydrolase (PARG) and ADP-ribose glycohydrolase ARH3 macrodomains plus SARS CoV-2 nonstructural protein 3 (Nsp3) Macrodomain 1 (Mac1) and Nsp15 nuclease. As opposed to the classical massive effort employing libraries with large numbers of compounds against single proteins, we make inhibitor design for multiple targets efficient. Our compact, chemically diverse, 5000 compound Goldilocks (GL) library has an intermediate number of compounds sized between fragments and drugs with predicted favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles. Amalgamating our core GL library with an approved drug (AD) library, we employ a combined GLAD library virtual screen, enabling an effective and efficient design cycle of ranked computer docking, top hit biophysical and cell validations, and defined bound structures using human proteins or their avatars. As new drug design is increasingly pathway directed as well as molecular and mechanism based, our CASSIE approach facilitates testing multiple related targets by efficiently turning a set of interacting drug discovery problems into a tractable medicinal chemistry engineering problem of optimizing affinity and ADME properties based upon early co-crystal structures. Optimization efforts are made efficient by a computationally-focused iterative chemistry and structure screen. Thus, we herein describe and apply CASSIE to define prototypic, specific inhibitors for PARG vs distinct inhibitors for the related macrodomains of ARH3 and SARS CoV-2 Nsp3 plus the SARS CoV-2 Nsp15 RNA nuclease.

Targeting the C-Terminal Domain Small Phosphatase 1

The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.

Approaches to the Structure-Based Design of Antivirulence Drugs: Therapeutics for the Post-Antibiotic Era

The alarming rise of multidrug-resistant bacterial strains, coupled with decades of stagnation in the field of antibiotic development, necessitates exploration of new therapeutic approaches to treat bacterial infections. Targeting bacterial virulence is an attractive alternative to traditional antibiotics in that this approach disarms pathogens that cause human diseases, without placing immediate selective pressure on the target bacterium or harming commensal species. The growing number of validated virulence protein targets for which structural information has been obtained, along with advances in computational power and screening algorithms, make the rational design of antivirulence drugs a promising avenue to explore. Here, we review the principles of structure-based drug design and the exciting opportunities this technique presents for antivirulence drug discovery.

Structural Control of Nonnative Ligand Binding in Engineered Mutants of Phosphoenolpyruvate Carboxykinase

Protein engineering to alter recognition underlying ligand binding and activity has enormous potential. Here, ligand binding for Escherichia coli phosphoenolpyruvate carboxykinase (PEPCK), which converts oxaloacetate into CO₂ and phosphoenolpyruvate as the first committed step in gluconeogenesis, was engineered to accommodate alternative ligands as an exemplary system with structural information. From our identification of bicarbonate binding in the PEPCK active site at the supposed CO₂ binding site, we probed binding of nonnative ligands with three oxygen atoms arranged to resemble the bicarbonate geometry. Crystal structures of PEPCK and point mutants with bound nonnative ligands thiosulfate and methanesulfonate along with strained ATP and reoriented oxaloacetate intermediates and unexpected bicarbonate were determined and analyzed. The mutations successfully altered the bound ligand position and orientation and its specificity: mutated PEPCKs bound either thiosulfate or methanesulfonate but never both. Computational calculations predicted a methanesulfonate binding mutant and revealed that release of the active site ordered solvent exerts a strong influence on ligand binding. Besides nonnative ligand binding, one mutant altered the Mn²⁺ coordination sphere: instead of the canonical octahedral ligand arrangement, the mutant in question had an only five-coordinate arrangement. From this work, critical features of ligand binding, position, and metal ion cofactor geometry required for all downstream events can be engineered with small numbers of mutations to provide insights into fundamental underpinnings of protein-ligand recognition. Through structural and computational knowledge, the combination of designed and random mutations aids in the robust design of predetermined changes to ligand binding and activity to engineer protein function.

Robust Production, Crystallization, Structure Determination, and Analysis of [Fe-S] Proteins: Uncovering Control of Electron Shuttling and Gating in the Respiratory Metabolism of Molybdopterin Guanine Dinucleotide Enzymes

[Fe-S] clusters are essential cofactors in all domains of life. They play many biological roles due to their unique abilities for electron transfer and conformational control. Yet, producing and analyzing Fe-S proteins can be difficult and even misleading if not done anaerobically. Due to unique redox properties of [Fe-S] clusters and their oxygen sensitivity, they pose multiple challenges and can lose enzymatic activity or cause their component proteins to be structurally disordered due to [Fe-S] cluster oxidation and loss in air. Here we highlight tested protocols and strategies enabling efficient and stable [Fe-S] protein production, purification, crystallization, X-ray diffraction data collection, and structure determination. From multiple high-resolution anaerobic crystal structures, we furthermore analyze exemplary data defining [Fe-S] clusters, substrate entry, and product exit for the functional oxidation states of type II molybdo-bis(molybdopterin guanine dinucleotide) (Mo-bisMGD) enzymes. Notably, these enzymes perform electron shuttling between quinone pools and specific substrates to catalyze respiratory metabolism. The identified structure-activity relationships for this enzyme class have broad implications germane to perchlorate environments on Earth and Mars extending to an alternative mechanism underlying metabolic origins for the evolution of the oxygen atmosphere. Integrated structural analyses of type II Mo-bisMGD enzymes unveil novel distinctive shared molecular mechanisms for dynamic control of substrate entry and product release gated by hydrophobic residues. Collective findings support a prototypic model for type II Mo-bisMGD enzymes including insights for a fundamental molecular mechanistic understanding of selectivity and regulation by a conformationally gated channel with general implications for [Fe-S] cluster respiratory enzymes.

Evaluation of the novel algorithm of flexible ligand docking with moveable target-protein atoms

We present the novel docking algorithm based on the Tensor Train decomposition and the TT-Cross global optimization. The algorithm is applied to the docking problem with flexible ligand and moveable protein atoms. The energy of the protein-ligand complex is calculated in the frame of the MMFF94 force field in vacuum. The grid of precalculated energy potentials of probe ligand atoms in the field of the target protein atoms is not used. The energy of the protein-ligand complex for any given configuration is computed directly with the MMFF94 force field without any fitting parameters. The conformation space of the system coordinates is formed by translations and rotations of the ligand as a whole, by the ligand torsions and also by Cartesian coordinates of the selected target protein atoms. Mobility of protein and ligand atoms is taken into account in the docking process simultaneously and equally. The algorithm is realized in the novel parallel docking SOL-P program and results of its performance for a set of 30 protein-ligand complexes are presented. Dependence of the docking positioning accuracy is investigated as a function of parameters of the docking algorithm and the number of protein moveable atoms. It is shown that mobility of the protein atoms improves docking positioning accuracy. The SOL-P program is able to perform docking of a flexible ligand into the active site of the target protein with several dozens of protein moveable atoms: the native crystallized ligand pose is correctly found as the global energy minimum in the search space with 157 dimensions using 4700 CPU ∗ h at the Lomonosov supercomputer.

Software for molecular docking: a review

Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.

AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms

MOTIVATION

The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms.

RESULTS

We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects.

AVAILABILITY AND IMPLEMENTATION

http://adfr.scripps.edu/AutoDockFR/autosite.html CONTACT: sanner@scripps.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility

Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present AutoDockFR-AutoDock for Flexible Receptors (ADFR), a new docking engine based on the AutoDock4 scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate ADFR using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4. We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 -a receptor diversity set containing 17 pairs of apo-holo structures; and 2) CDK2 -a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains, ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2. ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added.

Receptor-based virtual screening protocol for drug discovery

Computational aided drug design (CADD) is presently a key component in the process of drug discovery and development as it offers great promise to drastically reduce cost and time requirements. In the pharmaceutical arena, virtual screening is normally regarded as the top CADD tool to screen large libraries of chemical structures and reduce them to a key set of likely drug candidates regarding a specific protein target. This chapter provides a comprehensive overview of the receptor-based virtual screening process and of its importance in the present drug discovery and development paradigm. Following a focused contextualization on the subject, the main stages of a virtual screening campaign, including its strengths and limitations, are the subject of particular attention in this review. In all of these stages special consideration will be given to practical issues that are normally the Achilles heel of the virtual screening process.

Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase

The therapeutic challenges in the treatment of tuberculosis demand multidisciplinary approaches for the identification of potential drug targets as well as fast and accurate techniques to screen huge chemical libraries. Mycobacterial cyclopropane synthase (CmaA1) has been shown to be essential for the survival of the bacteria due to its critical role in the synthesis of mycolic acids. The present study proposes pharmacophore models based on the structure of CmaA1 taking into account its various states in the cyclopropanation process, and their dynamic nature as assessed using molecular dynamics (MD) simulations. The qualities of these pharmacophore models were validated by mapping 23 molecules that have been previously reported to exhibit inhibitory activities on CmaA1. Additionally, 1398 compounds that have been shown to be inactive for tuberculosis were collected from the ChEMBL database and were screened against the models for validation. The models were further validated by comparing the results from pharmacophore mapping with the results obtained from docking these molecules with the respective protein structures. The best models are suggested by validating all the models based on their screening abilities and by comparing with docking results. The models generated from the MD trajectories were found to perform better than the one generated based on the crystal structure demonstrating the importance of incorporating receptor flexibility in drug design.

Computational prediction and in vitro analysis of potential physiological ligands of the bile acid binding site in cytochrome c oxidase

A conserved bile acid site has been crystallographically defined in the membrane domain of mammalian and Rhodobacter sphaeroides cytochrome c oxidase (RsCcO). Diverse amphipathic ligands were shown previously to bind to this site and affect the electron transfer equilibrium between heme a and a3 cofactors by blocking the K proton uptake path. Current studies identify physiologically relevant ligands for the bile acid site using a novel three-pronged computational approach: ROCS comparison of ligand shape and electrostatics, SimSite3D comparison of ligand binding site features, and SLIDE screening of potential ligands by docking. Identified candidate ligands include steroids, nicotinamides, flavins, nucleotides, retinoic acid, and thyroid hormones, which are predicted to make key protein contacts with the residues involved in bile acid binding. In vitro oxygen consumption and ligand competition assays on RsCcO wildtype and its Glu101Ala mutant support regulatory activity and specificity of some of these ligands. An ATP analog and GDP inhibit RsCcO under low substrate conditions, while fusidic acid, cholesteryl hemisuccinate, retinoic acid, and T3 thyroid hormone are more potent inhibitors under both high and low substrate conditions. The sigmoidal kinetics of RsCcO inhibition in the presence of certain nucleotides is reminiscent of previously reported ATP inhibition of mammalian CcO, suggesting regulation involving the conserved core subunits of both mammalian and bacterial oxidases. Ligand binding to the bile acid site is noncompetitive with respect to cytochrome c and appears to arrest CcO in a semioxidized state with some resemblance to the "resting" state of the enzyme.

A conserved amphipathic ligand binding region influences k-path-dependent activity of cytochrome C oxidase

A conserved, crystallographically defined bile acid binding site was originally identified in the membrane domain of mammalian and bacterial cytochrome c oxidase (CcO). Current studies show other amphipathic molecules including detergents, fatty acids, steroids, and porphyrins bind to this site and affect the already 50% inhibited activity of the E101A mutant of Rhodobacter sphaeroides CcO as well as altering the activity of wild-type and bovine enzymes. Dodecyl maltoside, Triton X100, C12E8, lysophophatidylcholine, and CHOBIMALT detergents further inhibit RsCcO E101A, with lesser inhibition observed in wild-type. The detergent inhibition is overcome in the presence of micromolar concentrations of steroids and porphyrin analogues including deoxycholate, cholesteryl hemisuccinate, bilirubin, and protoporphyrin IX. In addition to alleviating detergent inhibition, amphipathic carboxylates including arachidonic, docosahexanoic, and phytanic acids stimulate the activity of E101A to wild-type levels by providing the missing carboxyl group. Computational modeling of dodecyl maltoside, bilirubin, and protoporphyrin IX into the conserved steroid site shows energetically favorable binding modes for these ligands and suggests that a groove at the interface of subunit I and II, including the entrance to the K-path and helix VIII of subunit I, mediates the observed competitive ligand interactions involving two overlapping sites. Spectral analysis indicates that ligand binding to this region affects CcO activity by altering the K-path-dependent electron transfer equilibrium between heme a and heme a(3). The high affinity and specificity of a number of compounds for this region, and its conservation and impact on CcO activity, support its physiological significance.

Structure-based functional design of drugs: from target to lead compound

Proteomic and genomic discoveries have identified vast numbers of new drug targets for investigation. In the quest to discover drugs that modulate the function of these targets, identification of small-molecule drug leads is one of the earliest steps. Structure-based drug design has emerged as a valuable, inexpensive, and rapid computational resource that identifies lead compounds that are complementary to the structure of the target. Leads identified through this process are biologically evaluated and "hit compounds" with affinity and activity are further optimized. This chapter introduces the process of structure-based drug design, including preparation of the ligand database, preparation of the target structure, docking and scoring, and evaluation.

Characterizing flexible and intrinsically unstructured biological macromolecules by SAS using the Porod-Debye law

Unstructured proteins, RNA or DNA components provide functionally important flexibility that is key to many macromolecular assemblies throughout cell biology. As objective, quantitative experimental measures of flexibility and disorder in solution are limited, small angle scattering (SAS), and in particular small angle X-ray scattering (SAXS), provides a critical technology to assess macromolecular flexibility as well as shape and assembly. Here, we consider the Porod-Debye law as a powerful tool for detecting biopolymer flexibility in SAS experiments. We show that the Porod-Debye region fundamentally describes the nature of the scattering intensity decay by capturing the information needed for distinguishing between folded and flexible particles. Particularly for comparative SAS experiments, application of the law, as described here, can distinguish between discrete conformational changes and localized flexibility relevant to molecular recognition and interaction networks. This approach aids insightful analyses of fully and partly flexible macromolecules that is more robust and conclusive than traditional Kratky analyses. Furthermore, we demonstrate for prototypic SAXS data that the ability to calculate particle density by the Porod-Debye criteria, as shown here, provides an objective quality assurance parameter that may prove of general use for SAXS modeling and validation.

Structure-based discovery of antibacterial drugs

The modern era of antibacterial chemotherapy began in the 1930s, and the next four decades saw the discovery of almost all the major classes of antibacterial agents that are currently in use. However, bacterial resistance to many of these drugs is becoming an increasing problem. As such, the discovery of drugs with novel modes of action will be vital to meet the threats created by the emergence of resistance. Success in discovering inhibitors using high-throughput screening of chemical libraries is rare. In this Review we explore the exciting opportunities for antibacterial-drug discovery arising from structure-based drug design.

Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors

Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99% of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1% of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone.

Molecular docking screens using comparative models of proteins

Two orders of magnitude more protein sequences can be modeled by comparative modeling than have been determined by X-ray crystallography and NMR spectroscopy. Investigators have nevertheless been cautious about using comparative models for ligand discovery because of concerns about model errors. We suggest how to exploit comparative models for molecular screens, based on docking against a wide range of crystallographic structures and comparative models with known ligands. To account for the variation in the ligand-binding pocket as it binds different ligands, we calculate "consensus" enrichment by ranking each library compound by its best docking score against all available comparative models and/or modeling templates. For the majority of the targets, the consensus enrichment for multiple models was better than or comparable to that of the holo and apo X-ray structures. Even for single models, the models are significantly more enriching than the template structure if the template is paralogous and shares more than 25% sequence identity with the target.

Computational multiscale modeling in protein--ligand docking

In biological systems, the binding of small molecule ligands to proteins is a crucial process for almost every aspect of biochemistry and molecular biology. Enzymes are proteins that function by catalyzing specific biochemical reactions that convert reactants into products. Complex organisms are typically composed of cells in which thousands of enzymes participate in complex and interconnected biochemical pathways. Some enzymes serve as sequential steps in specific pathways (such as energy metabolism), while others function to regulate entire pathways and cellular functions [1]. Small molecule ligands can be designed to bind to a specific enzyme and inhibit the biochemical reaction. Inhibiting the activity of key enzymes may result in the entire biochemical pathways being turned on or off [2], [3]. Many small molecule drugs marketed today function in this generic way as enzyme inhibitors. If research identifies a specific enzyme as being crucial to the progress of disease, then this enzyme may be targeted with an inhibitor, which may slow down or reverse the progress of disease. In this way, enzymes are targeted from specific pathogens (e.g., virus, bacteria, fungi) for infectious diseases [4], [5], and human enzymes are targeted for noninfectious diseases such as cardiovascular disease, cancer, diabetes, and neurodegenerative diseases [6].

p38alpha MAP kinase C-terminal domain binding pocket characterized by crystallographic and computational analyses

The mitogen-activated protein (MAP) kinase protein family has a critical role in cellular signaling events, with MAP kinase p38alpha acting in inflammatory processes and being an important drug discovery target. MAP kinase drug design efforts have focused on small-molecule inhibitors of the ATP catalytic site, which exhibit dose-limiting adverse effects. Therefore, characterizing other potential sites that bind substrates, inhibitors, or allosteric effectors is of great interest. Here, we present the crystal structure of human p38alpha MAP kinase, which has a lead compound bound both in the active site and in the lipid-binding site of the C-terminal cap. This C-terminal cap is formed from an extension to the kinase fold, unique to the MAP kinase and cyclin-dependent kinase families and glycogen synthase kinase 3. Binding of this lead, 4-[3-(4-fluorophenyl)-1H-pyrazol-4-yl]pyridine, to wild-type p38alpha induces movement of the C-terminal cap region, creating a hydrophobic pocket centered around residue Trp197. Computational analysis of this C-terminal domain pocket indicates notable flexibility for potentially binding different-shaped compounds, including lipids, oxidized arachidonic acid species such as leukotrienes, and small-molecule effectors. Furthermore, our structural results defining the open p38alpha C-lobe pocket provide a detailed framework for the design of novel small molecules with affinities comparable to active-site binders: to bind and potentially modulate the shape and activity of p38alpha in predetermined ways. Moreover, these results and analyses of p38alpha suggest strategies for designing specific binding compounds applicable to other MAP kinases, as well as the cyclin-dependent kinase family and glycogen synthase kinase 3beta that also utilize the C-terminal insert in their interactions.

Assessing the role of polarization in docking

We describe a strategy for including ligand and protein polarization in docking that is based on the conversion of induced dipoles to induced charges. Induced charges have a distinct advantage in that they are readily implemented into a number of different computer programs, including many docking programs and hybrid QM/MM programs; induced charges are also more readily interpreted. In this study, the ligand was treated quantum mechanically to avoid parametrization issues and was polarized by the target protein, which was treated as a set of point charges. The induced dipole at a given target atom, due to polarization by the ligand and neighboring residues, was reformulated as induced charges at the given atom and its bonded neighbors, and these were allowed to repolarize the ligand in an iterative manner. The final set of polarized charges was evaluated in docking using AutoDock 4.0 on 12 protein-ligand systems against the default empirical Gasteiger charges, and against nonpolarized and partially polarized potential-derived charges. One advantage of AutoDock is that the best rmsd structure can be identified not only from the lowest energy pose but also from the largest cluster of poses. Inclusion of polarization does not always lead to the lowest energy pose having a lower rmsd, because docking is designed by necessity to be rapid rather than accurate. However, whenever an improvement in methodology, corresponding to a more thorough treatment of polarization, resulted in an increased cluster size, then there was also a corresponding decrease in the rmsd. The options for implementing polarization within a purely classical docking framework are discussed.

Identification of BRAF inhibitors through in silico screening

The BRAF protein kinase, a molecule in the RAS-RAF-MEK-ERK signaling pathway, is mutated to harbor elevated kinase activity in approximately 7% of human cancers, which makes it an important therapeutic target for inhibition. Several BRAF protein-kinase inhibitors have been developed through high-throughout screening in vitro; however, many of these compounds suffer from a lack of suitable kinase specificity and other chemotherapeutic properties. In silico screening has evolved as a powerful complimentary approach to protein-kinase inhibitor identification. Here we describe an in silico screen for BRAF inhibitors that leads to the identification of a series of purine-2,6-dione analogues with IC50 values in the single-digit micromolar range and with significant selectivity for BRAF over other representative protein kinases. The binding modes of these inhibitors to BRAF are analyzed through molecular docking to derive structure-activity relationships and to assist in the future development of more potent and more specific BRAF inhibitors.

X-ray solution scattering (SAXS) combined with crystallography and computation: defining accurate macromolecular structures, conformations and assemblies in solution

Crystallography supplies unparalleled detail on structural information critical for mechanistic analyses; however, it is restricted to describing low energy conformations of macromolecules within crystal lattices. Small angle X-ray scattering (SAXS) offers complementary information about macromolecular folding, unfolding, aggregation, extended conformations, flexibly linked domains, shape, conformation, and assembly state in solution, albeit at the lower resolution range of about 50 A to 10 A resolution, but without the size limitations inherent in NMR and electron microscopy studies. Together these techniques can allow multi-scale modeling to create complete and accurate images of macromolecules for modeling allosteric mechanisms, supramolecular complexes, and dynamic molecular machines acting in diverse processes ranging from eukaryotic DNA replication, recombination and repair to microbial membrane secretion and assembly systems. This review addresses both theoretical and practical concepts, concerns and considerations for using these techniques in conjunction with computational methods to productively combine solution scattering data with high-resolution structures. Detailed aspects of SAXS experimental results are considered with a focus on data interpretation tools suitable to model protein and nucleic acid macromolecular structures, including membrane protein, RNA, DNA, and protein-nucleic acid complexes. The methods discussed provide the basis to examine molecular interactions in solution and to study macromolecular flexibility and conformational changes that have become increasingly relevant for accurate understanding, simulation, and prediction of mechanisms in structural cell biology and nanotechnology.

Comprehensive mechanistic analysis of hits from high-throughput and docking screens against beta-lactamase

High-throughput screening (HTS) is widely used in drug discovery. Especially for screens of unbiased libraries, false positives can dominate "hit lists"; their origins are much debated. Here we determine the mechanism of every active hit from a screen of 70,563 unbiased molecules against beta-lactamase using quantitative HTS (qHTS). Of the 1,274 initial inhibitors, 95% were detergent-sensitive and were classified as aggregators. Among the 70 remaining were 25 potent, covalent-acting beta-lactams. Mass spectra, counter-screens, and crystallography identified 12 as promiscuous covalent inhibitors. The remaining 33 were either aggregators or irreproducible. No specific reversible inhibitors were found. We turned to molecular docking to prioritize molecules from the same library for testing at higher concentrations. Of 16 tested, 2 were modest inhibitors. Subsequent X-ray structures corresponded to the docking prediction. Analog synthesis improved affinity to 8 microM. These results suggest that it may be the physical behavior of organic molecules, not their reactivity, that accounts for most screening artifacts. Structure-based methods may prioritize weak-but-novel chemotypes in unbiased library screens.

Improving database enrichment through ensemble docking

While it may seem intuitive that using an ensemble of multiple conformations of a receptor in structure-based virtual screening experiments would necessarily yield improved enrichment of actives relative to using just a single receptor, it turns out that at least in the p38 MAP kinase model system studied here, a very large majority of all possible ensembles do not yield improved enrichment of actives. However, there are combinations of receptor structures that do lead to improved enrichment results. We present here a method to select the ensembles that produce the best enrichments that does not rely on knowledge of active compounds or sophisticated analyses of the 3D receptor structures. In the system studied here, the small fraction of ensembles of up to 3 receptors that do yield good enrichments of actives were identified by selecting ensembles that have the best mean GlideScore for the top 1% of the docked ligands in a database screen of actives and drug-like "decoy" ligands. Ensembles of two receptors identified using this mean GlideScore metric generally outperform single receptors, while ensembles of three receptors identified using this metric consistently give optimal enrichment factors in which, for example, 40% of the known actives outrank all the other ligands in the database.

Flexible computational docking studies of new aminoglycosides targeting RNA 16S bacterial ribosome site

Ribonucleic acids (RNAs) have only recently been viewed as a target for small-molecules drug discovery. Aminoglycoside compounds are antibiotics which bind the ribosomal A site (16S fragment) and cause misreading of the bacterial genetic code and inhibit translocation. In this work, a complete molecular modeling study is done for 16 newly derived aminoglycoside compounds where diverse nucleoside fragments are linked. Docking calculations are applied to 16S RNA target and a weak linear correlation, between experimental and calculated data, is obtained. However, one particularity of RNA is its high flexibility. To mimic this behavior, all docking calculations are followed by small molecular dynamic simulations. This last computational step improves significantly the correlation with experimental data and allowed us to establish structure-activity relationships. The overall results showed that the consideration of the RNA dynamic behavior is of great interest.

TrixX: structure-based molecule indexing for large-scale virtual screening in sublinear time

Structure-based virtual screening today is basically organized as a sequential process where the molecules of a screening library are evaluated for instance with respect to their fit with a biological target. In this paper, we present a novel structure-based screening paradigm avoiding sequential searching and therefore enabling sublinear runtime behavior. We implemented the novel paradigm in the virtual screening tool TrixX and successfully applied it in screening experiments on four targets from relevant therapeutic areas. With the screening paradigm implemented in TrixX, we propose some important extensions and modifications to traditional virtual screening approaches: Instead of processing all compounds in the screening library sequentially, TrixX first analyzes the geometric and physicochemical binding site characteristics and then draws compounds with matching features from a compound catalog. The catalog organizes the compounds by their physicochemical and geometric features making use of relational database technology with indexed tables in order to support efficient queries for compounds with specific features. A key element of the compound catalog is a highly selective geometric descriptor that carries information on the type of functional groups of the compound, their Euclidian distance, the preferred interaction direction of each functional group, and the location of steric bulk around the triangle. In a re-docking experiment with 200 protein-ligand complexes, we could show that TrixX is able to correctly predict the location of ligand functional groups in co-crystallized complexes. In a retrospective virtual screening experiment for four different targets, the enrichment factors of TrixX are comparable to the enrichment factors of FlexX and FlexX-Scan. With computing times clearly below one second per compound, TrixX counts among the fastest virtual screening tools currently available and is nearly two orders of magnitude faster than standard FlexX.

Protein-ligand docking: current status and future challenges

Understanding the ruling principles whereby protein receptors recognize, interact, and associate with molecular substrates and inhibitors is of paramount importance in drug discovery efforts. Protein-ligand docking aims to predict and rank the structure(s) arising from the association between a given ligand and a target protein of known 3D structure. Despite the breathtaking advances in the field over the last decades and the widespread application of docking methods, several downsides still exist. In particular, protein flexibility-a critical aspect for a thorough understanding of the principles that guide ligand binding in proteins-is a major hurdle in current protein-ligand docking efforts that needs to be more efficiently accounted for. In this review the key concepts of protein-ligand docking methods are outlined, with major emphasis being given to the general strengths and weaknesses that presently characterize this methodology. Despite the size of the field, the principal types of search algorithms and scoring functions are reviewed and the most popular docking tools are briefly depicted. Recent advances that aim to address some of the traditional limitations associated with molecular docking are also described. A selection of hand-picked examples is used to illustrate these features.

Effective handling of induced-fit motion in flexible docking

For structure-based drug design, where various ligand structures need to be docked to a target protein structure, a docking method that can handle conformational flexibility of not only the ligand, but also the protein, is indispensable. We have developed a simple and effective approach for dealing with the local induced-fit motion of the target protein, and implemented it in our docking tool, ADAM. Our approach efficiently combines the following two strategies: a vdW-offset grid in which the protein cavity is enlarged uniformly, and structure optimization allowing the motion of ligand and protein atoms. To examine the effectiveness of our approach, we performed docking validation studies, including redocking in 18 test cases and foreign-docking, in which various ligands from foreign crystal structures of complexes are docked into a target protein structure, in 22 cases (on five target proteins). With the original ADAM, the correct docking modes (RMSD < 2.0 A) were not present among the top 20 models in one case of redocking and four cases of foreign-docking. When the handling of induced-fit motion was implemented, the correct solutions were acquired in all 40 test cases. In foreign-docking on thymidine kinase, the correct docking modes were obtained as the top-ranked solutions for all 10 test ligands by our combinatorial approach, and this appears to be the best result ever reported with any docking tool. The results of docking validation have thus confirmed the effectiveness of our approach, which can provide reliable docking models even in the case of foreign-docking, where conformational change of the target protein cannot be ignored. We expect that this approach will contribute substantially to actual drug design, including virtual screening.

Discovering New Classes of Brugia malayi Asparaginyl-tRNA Synthetase Inhibitors and Relating Specificity to Conformational Change

SLIDE software, which models the flexibility of protein and ligand side chains while docking, was used to screen several large databases to identify inhibitors of Brugia malayi asparaginyl-tRNA synthetase (AsnRS), a target for anti-parasitic drug design. Seven classes of compounds identified by SLIDE were confirmed as micromolar inhibitors of the enzyme. Analogs of one of these classes of inhibitors, the long side-chain variolins, cannot bind to the adenosyl pocket of the closed conformation of AsnRS due to steric clashes, though the short side-chain variolins identified by SLIDE apparently bind isosterically with adenosine. We hypothesized that an open conformation of the motif 2 loop also permits the long side-chain variolins to bind in the adenosine pocket and that their selectivity for Brugia relative to human AsnRS can be explained by differences in the sequence and conformation of this loop. Loop flexibility sampling using Rigidity Optimized Conformational Kinetics (ROCK) confirms this possibility, while scoring of the relative affinities of the different ligands by SLIDE correlates well with the compounds' ranks in inhibition assays. Combining ROCK and SLIDE provides a promising approach for exploiting conformational flexibility in structure-based screening and design of species selective inhibitors.

Combining docking and molecular dynamic simulations in drug design

A rational approach is needed to maximize the chances of finding new drugs, and to exploit the opportunities of potential new drug targets emerging from genomic and proteomic initiatives, and from the large libraries of small compounds now readily available through combinatorial chemistry. Despite a shaky early history, computer-aided drug design techniques can now be effective in reducing costs and speeding up drug discovery. This happy outcome results from development of more accurate and reliable algorithms, use of more thoughtfully planned strategies to apply them, and greatly increased computer power to allow studies with the necessary reliability to be performed. Our review focuses on applications and protocols, with the main emphasis on critical analysis of recent studies where docking calculations and molecular dynamics (MD) simulations were combined to dock small molecules into protein receptors. We highlight successes to demonstrate what is possible now, but also point out drawbacks and future directions. The review is structured to lead the reader from the simpler to more compute-intensive methods. Thus, while inexpensive and fast docking algorithms can be used to scan large compound libraries and reduce their size, more accurate but expensive MD simulations can be applied when a few selected ligand candidates remain. MD simulations can be used: during the preparation of the protein receptor before docking, to optimize its structure and account for protein flexibility; for the refinement of docked complexes, to include solvent effects and account for induced fit; to calculate binding free energies, to provide an accurate ranking of the potential ligands; and in the latest developments, during the docking process itself to find the binding site and correctly dock the ligand a priori.

Multiple ligand-binding modes in bacterial R67 dihydrofolate reductase

R67 dihydrofolate reductase (DHFR), a bacterial plasmid-encoded enzyme associated with resistance to the drug trimethoprim, shows neither sequence nor structural homology with the chromosomal DHFR. It presents a highly symmetrical toroidal structure, where four identical monomers contribute to the unique central active-site pore. Two reactants (dihydrofolate, DHF), two cofactors (NADPH) or one of each (R67*DHF*NADPH) can be found simultaneously within the active site, the last one being the reactive ternary complex. As the positioning of the ligands has proven elusive to empirical determination, we addressed the problem from a theoretical perspective. Several potential structures of the ternary complex were generated using the docking programs AutoDock and FlexX. The variability among the final poses, many of which conformed to experimental data, prompted us to perform a comparative scoring analysis and molecular dynamics simulations to assess the stability of the complexes. Analysis of ligand-ligand and ligand-protein interactions along the 4 ns trajectories of eight different structures allowed us to identify important inter-ligand contacts and key protein residues. Our results, combined with published empirical data, clearly suggest that multipe binding modes of the ligands are possible within R67 DHFR. While the pterin ring of DHF and the nicotinamide ring of NADPH assume a stacked endo-conformation at the centre of the pore, probably assisted by V66, Q67 and I68, the tails of the molecules extend towards opposite ends of the cavity, adopting multiple configurations in a solvent rich-environment where hydrogen-bond interactions with K32 and Y69 may play important roles.

Virtual screening of biogenic amine-binding G-protein coupled receptors: comparative evaluation of protein- and ligand-based virtual screening protocols

In this paper, we compare protein- and ligand-based virtual screening techniques for identifying the ligands of four biogenic amine-binding G-protein coupled receptors (GPCRs). For the screening of the virtual compound libraries, we used (1) molecular docking into GPCR homology models, (2) ligand-based pharmacophore and Feature Tree models, (3) three-dimensional (3D)-similarity searches, and (4) statistical methods [partial least squares (PLS) and partial least squares discriminant analysis (PLS-DA) models] based on two-dimensional (2D) molecular descriptors. The comparison of the different methods in retrieving known antagonists from virtual libraries shows that in our study the ligand-based pharmacophore-, Feature Tree-, and 2D quantitative structure-activity relationship (QSAR)-based screening techniques provide enrichment factors that are higher than those provided by molecular docking into the GPCR homology models. Nevertheless, the hit rates achieved when docking with GOLD and ranking the ligands with GoldScore (up to 60% among the top-ranked 1% of the screened databases) are still satisfying. These results suggest that docking into GPCR homology models can be a useful approach for lead finding by virtual screening when either little or no information about the active ligands is available.

Searching sequence space: two different approaches to dihydrofolate reductase catalysis

There are numerous examples of proteins that catalyze the same reaction while possessing different structures. This review focuses on two dihydrofolate reductases (DHFRs) that have disparate structures and discusses how the catalytic strategies of these two DHFRs are driven by their respective scaffolds. The two proteins are E. coli chromosomal DHFR (Ec DHFR) and a type II R-plasmid-encoded DHFR, typified by R67 DHFR. The former has been described as a very well evolved enzyme with an efficiency of 0.15, while the latter has been suggested to be a model for a "primitive" enzyme that has not yet been optimized by evolution. This comparison underlines what is important to catalysis in these two enzymes and concurrently highlights fundamental issues in enzyme catalysis.

Modeling correlated main-chain motions in proteins for flexible molecular recognition

We describe a new method for modeling protein and ligand main-chain flexibility, and show its ability to model flexible molecular recognition. The goal is to sample the full conformational space, including large-scale motions that typically cannot be reached in molecular dynamics simulations due to the computational intensity, as well as conformations that have not been observed yet by crystallography or NMR. A secondary goal is to assess the degree of flexibility consistent with protein-ligand recognition. Flexibility analysis of the target protein is performed using the graph-theoretic algorithm FIRST, which also identifies coupled networks of covalent and noncovalent bonds within the protein. The available conformations of the flexible regions are then explored with ROCK by random-walk sampling of the rotatable bonds. ROCK explores correlated motions by only sampling dihedral angles that preserve the coupled bond networks in the protein and generates conformers with good stereochemistry, without using a computationally expensive potential function. A representative set of the conformational ensemble generated this way can be used as targets for docking with SLIDE, which handles the flexibility of protein and ligand side-chains. The realism of this protein main-chain conformational sampling is assessed by comparison with time-resolved NMR studies of cyclophilin A motions. ROCK is also effective for modeling the flexibility of large cyclic and polycyclic ligands, as demonstrated for cyclosporin and zearalenol. The use of this combined approach to perform docking with main-chain flexibility is illustrated for the cyclophilin A-cyclosporin complex and the estrogen receptor in complex with zearalenol, while addressing the question of how much flexibility is allowed without hindering molecular recognition.

Comparative evaluation of eight docking tools for docking and virtual screening accuracy

Eight docking programs (DOCK, FLEXX, FRED, GLIDE, GOLD, SLIDE, SURFLEX, and QXP) that can be used for either single-ligand docking or database screening have been compared for their propensity to recover the X-ray pose of 100 small-molecular-weight ligands, and for their capacity to discriminate known inhibitors of an enzyme (thymidine kinase) from randomly chosen "drug-like" molecules. Interestingly, both properties are found to be correlated, since the tools showing the best docking accuracy (GLIDE, GOLD, and SURFLEX) are also the most successful in ranking known inhibitors in a virtual screening experiment. Moreover, the current study pinpoints some physicochemical descriptors of either the ligand or its cognate protein-binding site that generally lead to docking/scoring inaccuracies.