The Protein Data Bank (PDB), its related services and software tools as key components for in silico guided drug discovery

Systems biology of Haemonchus contortus - Advancing biotechnology for parasitic nematode control

Parasitic nematodes represent a substantial global burden, impacting animal health, agriculture and economies worldwide. Of these worms, Haemonchus contortus - a blood-feeding nematode of ruminants - is a major pathogen and a model for molecular and applied parasitology research. This review synthesises some key advances in understanding the molecular biology, genetic diversity and host-parasite interactions of H. contortus, highlighting its value for comparative studies with the free-living nematode Caenorhabditis elegans. Key themes include recent developments in genomic, transcriptomic and proteomic technologies and resources, which are illuminating critical molecular pathways, including the ubiquitination pathway, protease/protease inhibitor systems and the secretome of H. contortus. Some of these insights are providing a foundation for identifying essential genes and exploring their potential as targets for novel anthelmintics or vaccines, particularly in the face of widespread anthelmintic resistance. Advanced bioinformatic tools, such as machine learning (ML) algorithms and artificial intelligence (AI)-driven protein structure prediction, are enhancing annotation capabilities, facilitating and accelerating analyses of gene functions, and biological pathways and processes. This review also discusses the integration of these tools with cutting-edge single-cell sequencing and spatial transcriptomics to dissect host-parasite interactions at the cellular level. The discussion emphasises the importance of curated databases, improved culture systems and functional genomics platforms to translate molecular discoveries into practical outcomes, such as novel interventions. New research findings and resources not only advance research on H. contortus and related nematodes but may also pave the way for innovative solutions to the global challenges with anthelmintic resistance.

In silico molecular docking and dynamic simulation of anti-cholinesterase compounds from the extract of Catunaregam spinosa for possible treatment of Alzheimer's disease

Alzheimer's disease (AD), is characterized by a progressive loss of cognitive abilities as well as behavioral symptoms including disorientation, trouble solving problems, personality and mood changes. Acetylcholinesterase (AChE) is a promising target for symptomatic improvement in AD due to its consistent and early cholinergic deficit. This research has investigated the potential compounds from Catunaregam spinosa as AChE inhibitors as a treatment option for AD, aiming to enhance cholinergic neurotransmission and alleviate cognitive decline. Tacrine, the FDA's first approved treatment for AD, is no longer in use due to its hepatotoxicity. Box-Behnken design (BBD) modelling was used to optimise the ultrasonic extraction of alkaloids from the dried fruits of C. spinosa. GC-MS analysis revealed the presence of ninety phytoconstituents in the extract. Among them, eighty-nine new phytoconstituents are reported in this plant fruit for the first time. Out of ninety phytoconstituents, eight phytoconstituents showed the best binding affinity against the AChE enzyme, i.e., PDB IDs 1GQR, 1QTI and 4PQE of AD targets using iGEMDOCK. The lead hits were tested for their drug-like properties and atomistic binding mechanisms using in silico ADMET prediction, LigPlot analysis, and molecular dynamics simulation. The results suggest four compounds such as 1,4,7,10,13,16-hexaoxacyclooctadecane; butanoic acid, 3-methyl-2-[(phenylmethoxy)imino]-, trime; butane-1,2,3,4-tetraol; and D-(+)-ribonic acid.gamma-lactone as potent inhibitors of AChE for the possible treatment of AD.

Midkine Interaction with Chondroitin Sulfate Model Synthetic Tetrasaccharides and Their Mimetics: The Role of Aromatic Interactions

Midkine (MK) is a neurotrophic factor that participates in the embryonic central nervous system (CNS) development and neural stem cell regulation, interacting with sulfated glycosaminoglycans (GAGs). Chondroitin sulfate (CS) is the natural ligand in the CNS. In this work, we describe the interactions between a library of synthetic models of CS-types and mimics. We did a structural study of this library by NMR and MD (Molecular Dynamics), concluding that the basic shape is controlled by similar geometry of the glycosidic linkages. Their 3D structures are a helix with four residues per turn, almost linear. We have studied the tetrasaccharide-midkine complexes by ligand observed NMR techniques and concluded that the shape of the ligands does not change upon binding. The ligand orientation into the complex is very variable. It is placed inside the central cavity of MK formed by the two structured beta-sheets domains linked by an intrinsically disordered region (IDR). Docking analysis confirmed the participation of aromatics residues from MK completed with electrostatic interactions. Finally, we test the biological activity by increasing the MK expression using CS tetrasaccharides and their capacity in enhancing the growth stimulation effect of MK in NIH3T3 cells.

A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Search for Potential Inducible Nitric Oxide Synthase Inhibitors with Favorable ADMET Profiles for the Therapy of Helicobacter pylori Infections

Background:

Helicobacter pylori is a gram-negative bacterium related to chronic gastritis, peptic ulcer and gastric carcinoma. During its infection process, promotes excessive inflammatory response, increasing the release of reactive species and inducing the production of pro-inflammatory mediators. Inducible Nitric Oxide Synthase (iNOS) plays a crucial role in the gastric carcinogenesis process and a key mediator of inflammation and host defense systems, which is expressed in macrophages induced by inflammatory stimuli. In chronic diseases such as Helicobacter pylori infections, the overproduction of NO due to the prolonged induction of iNOS is of major concern.

Objective:

In this sense, the search for potential iNOS inhibitors is a valuable strategy in the overall process of Helicobacter pylori pathogeny.

Method:

In silico techniques were applied in the search of interesting compounds against Inducible Nitric Oxide Synthase enzyme in a chemical space of natural products and derivatives from the Analyticon Discovery databases.

Results:

The five compounds with the best iNOS inhibition profile were selected for activity and toxicity predictions. Compound 9 (CAS 88198-99-6) displayed significant potential for iNOS inhibition, forming hydrogen bonds with residues from the active site and an ionic interaction with heme. This compound also displayed good bioavailability and absence of toxicity/or from its probable metabolites.

Conclusion:

The top-ranked compounds from the virtual screening workflow show promising results regarding the iNOS inhibition profile. The results evidenced the importance of the ionic bonding during docking selection, playing a crucial role in binding and positioning during ligand-target selection for iNOS.

In silico structural and functional modelling of Antifreeze protein (AFP) sequences of Ocean pout (Zoarces americanus, Bloch & Schneider 1801)

Antifreeze proteins (AFPs) are known to polypeptide components formed by certain plants, animals, fungi and bacteria which support to survive in sub-zero temperature. Current study highlighted the seven different antifreeze proteins of fish Ocean pout (Zoarces americanus), in which protein (amino acids sequence) were collected from National Centre for Biotechnology Information and finely characterized using several in silico tools. Such biocomputational techniques applied to figure out the physicochemical, functional and conformational characteristics of targeted AFPs. Multiple physicochemical properties such as Isoelectric Point, Extinction Coefficient and Instability Index, Aliphatic Index, Grand Average Hydropathy were calculated and analysed by ExPASy-ProtParam prediction web server. EMBOSS: pepwheel online tool was used to represent the protein sequences in a helical form. The primary structure analysis shows that most of the AFPs are hydrophobic in nature due to the high content of non-polar residues. The secondary structure of these proteins was calculated using SOPMA tool. SOSUI server and CYS_REC program also run for ideal prediction of transmembrane helices and disulfide bridges of experimental proteins respectively. The modelling of 3D structures of seven desired AFPs were executed by the homology modelling programmes; SWISS MODEL and ProSA web server. UCSF Chimera, Antheprot 3D, PyMOL and RAMPAGE were used to visualize and analysis of the structural variation of the predicted protein model. MEGA7.0.9 software used to know the phylogenetic relationship among these AFPs. These models offered excellent and reliable baseline information for functional characterization of the experimentally derived protein domain composition by using the advanced tools and techniques of Computational Biology.

Anti-Helicobacter pylori Activity of Isocoumarin Paepalantine: Morphological and Molecular Docking Analysis

The Helicobacterpylori bacterium is one of the main causes of chronic gastritis, peptic ulcers, and even gastric cancer. It affects an average of half of the world population. Its difficult eradication depends upon multi-drug therapy. Since its classification as a group 1 carcinogenic by International Agency for Research on Cancer (IARC), the importance of H. pylori eradication has obtained a novel meaning. There is considerable interest in alternative therapies for the eradication of H. pylori using compounds from a wide range of natural products. In the present study, we investigated the antibacterial property of the isocoumarin paepalantine against H. pylori and it exhibited significant anti-H. pylori activity at a minimum inhibitory concentration (MIC) of 128 μg/mL and at a minimum bactericidal concentration (MBC) of 256 μg/mL. The scanning electron microscopy (SEM) revealed significant morphological changes of the bacterial cell as a response to a sub-MIC of paepalantine, suggesting a penicillin-binding protein (PBP) inhibition. Computational studies were carried out in order to study binding modes for paepalantine in PBP binding sites, exploring the active and allosteric sites. The data from the present study indicates that paepalantine exhibits significant anti-H. pylori activity, most likely by inhibiting membrane protein synthesis.

Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design

Urea derivatives are ubiquitously found in many chemical disciplines. N,N'-substituted ureas may show different conformational preferences depending on their substitution pattern. The high energetic barrier for isomerization of the cis and trans state poses additional challenges on computational simulation techniques aiming at a reproduction of the biological properties of urea derivatives. Herein, we investigate energetics of urea conformations and their interconversion using a broad spectrum of methodologies ranging from data mining, via quantum chemistry to molecular dynamics simulation and free energy calculations. We find that the inversion of urea conformations is inherently slow and beyond the time scale of typical simulation protocols. Therefore, extra care needs to be taken by computational chemists to work with appropriate model systems. We find that both knowledge-driven approaches as well as physics-based methods may guide molecular modelers towards accurate starting structures for expensive calculations to ensure that conformations of urea derivatives are modeled as adequately as possible.

Conformational Sampling and Binding Site Assessment of Suppression of Tumorigenicity 2 Ectodomain

Suppression of Tumorigenicity 2 (ST2), a member of the interleukin-1 receptor (IL-1R) family, activates type 2 immune responses to pathogens and tissue damage via binding to IL-33. Dysregulated responses contribute to asthma, graft-versus-host and autoinflammatory diseases and disorders. To study ST2 structure for inhibitor development, we performed the principal component (PC) analysis on the crystal structures of IL1-1R1, IL1-1R2, ST2 and the refined ST2 ectodomain (ST2ECD) models, constructed from previously reported small-angle X-ray scattering data. The analysis facilitates mapping of the ST2ECD conformations to PC subspace for characterizing structural changes. Extensive coverage of ST2ECD conformations was then obtained using the accelerated molecular dynamics simulations started with the IL-33 bound ST2ECD structure as instructed by their projected locations on the PC subspace. Cluster analysis of all conformations further determined representative conformations of ST2ECD ensemble in solution. Alignment of the representative conformations with the ST2/IL-33 structure showed that the D3 domain of ST2ECD (containing D1-D3 domains) in most conformations exhibits no clashes with IL-33 in the crystal structure. Our experimental binding data informed that the D1-D2 domain of ST2ECD contributes predominantly to the interaction between ST2ECD and IL-33 underscoring the importance of the D1-D2 domain in binding. Computational binding site assessment revealed one third of the total detected binding sites in the representative conformations may be suitable for binding to potent small molecules. Locations of these sites include the D1-D2 domain ST2ECD and modulation sites conformed to ST2ECD conformations. Our study provides structural models and analyses of ST2ECD that could be useful for inhibitor discovery.

Identification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsets

Targeting Proviral integration-site of murine Moloney leukemia virus 1 kinase, hereafter called Pim-1 kinase, is a promising strategy for treating different kinds of human cancer. Headed for this a total list of 328 formerly reported Pim-1 kinase inhibitors has been explored and divided based on the pharmacophoric features of the most active molecules into 10 subsets projected to represent potential active binding manners accessible to ligands within the binding pocket of Pim-1 kinase. Discovery Studio 4.1 (DS 4.1) was employed to detect potential pharmacophoric active binding manners anticipated by Pim-1 Kinase inhibitors. The pharmacophoric models were then allowed to compete within Quantitative Structure Activity Relationship (QSAR) framework with other 2D descriptors. Accordingly Genetic algorithm and multiple linear regression investigation were engaged to find the finest QSAR equation that has the best predictive power r262(2) = 0.70, F = 119.14, rLOO(2) = 0.693, rPRESS(2) against 66 external test inhibitors = 0.71 q(2) = 0.55. Three different pharmacophores appeared in the successful QSAR equation this represents three different binding modes for inhibitors within the Pim-1 kinase binding pocket. Pharmacophoric models were later used to screen compounds within the National Cancer Institute database. Several low micromolar Pim-1 Kinase inhibitors were captured. The most potent hits show IC50 values of 0.77 and 1.03 µM. Also, upon analyzing the successful QSAR Equation we found that some polycyclic aromatic electron-rich structures namely 6-Chloro-2-methoxy-acridine can be considered as putative hits for Pim-1 kinase inhibition.

Protein secondary-structure description with a coarse-grained model

A coarse-grained geometrical model for protein secondary-structure description and analysis is presented which uses only the positions of the Cαatoms. A space curve connecting these positions by piecewise polynomial interpolation is constructed and the folding of the protein backbone is described by a succession of screw motions linking the Frenet frames at consecutive Cαpositions. Using the ASTRAL subset of the SCOPe database of protein structures, thresholds are derived for the screw parameters of secondary-structure elements and demonstrate that the latter can be reliably assigned on the basis of a Cαmodel. For this purpose, a comparative study with the widely usedDSSP(Define Secondary Structure of Proteins) algorithm was performed and it was shown that the parameter distribution corresponding to the ensemble of all pure Cαstructures in the RCSB Protein Data Bank matches that of the ASTRAL database. It is expected that this approach will be useful in the development of structure-refinement techniques for low-resolution data.

Validation of a computational docking methodology to identify the non-covalent binding site of ligands to DNA

Despite the biomedical consequences of carcinogen-DNA interactions and the potential of DNA as a drug target in medicinal chemistry, only a small number of studies have validated or used docking methods for the prediction of the physical binding of small molecules to DNA. Knowledge of the DNA-physically-bound ligand geometry can lead to the elucidation of the molecular-level mechanism of drugs as well as predicting the subsequent chemical interactions that lead to DNA damage from carcinogens. We sought to validate AutoDock 4.2, a docking method that includes a physics-based free energy function and a Lamarckian Genetic Algorithm, for the prediction of ligand geometries upon physical binding to DNA. We performed simulations by systematically changing the length of the search process for a comprehensive set of 32 ligand-DNA molecular systems with different physico-chemical properties, and we used a free-energy-based convergence criterion to terminate our simulations. For 11 out of 28 molecular systems for which convergence was achieved, the lowest binding free energy geometries were within 2 Å of the experimentally determined geometry. Considering all predicted sites with free energy changes within 20% of the lowest binding free energy site, we found a site within 2 Å of the experimentally determined geometry for 24 out of the 28 systems. However, the predicted hydrogen bonding interactions were different for most molecular systems compared to the same interactions in the experimentally determined geometry. We discuss reasons for the successes and failures, implications, and the importance of ensuring an adequate search in docking calculations. Overall, we concluded that AutoDock 4.2 can be used to predict the non-covalent binding geometry of a small molecule to DNA with some limitations.

Algorithmic approaches to protein-protein interaction site prediction

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

Protein structure prediction provides comparable performance to crystallographic structures in docking-based virtual screening

Structure based virtual screening has largely been limited to protein targets for which either an experimental structure is available or a strongly homologous template exists so that a high-resolution model can be constructed. The performance of state of the art protein structure predictions in virtual screening in systems where only weakly homologous templates are available is largely untested. Using the challenging DUD database of structural decoys, we show here that even using templates with only weak sequence homology (<30% sequence identity) structural models can be constructed by I-TASSER which achieve comparable enrichment rates to using the experimental bound crystal structure in the majority of the cases studied. For 65% of the targets, the I-TASSER models, which are constructed essentially in the apo conformations, reached 70% of the virtual screening performance of using the holo-crystal structures. A correlation was observed between the success of I-TASSER in modeling the global fold and local structures in the binding pockets of the proteins versus the relative success in virtual screening. The virtual screening performance can be further improved by the recognition of chemical features of the ligand compounds. These results suggest that the combination of structure-based docking and advanced protein structure modeling methods should be a valuable approach to the large-scale drug screening and discovery studies, especially for the proteins lacking crystallographic structures.

Sigma S-dependent antioxidant defense protects stationary-phase Escherichia coli against the bactericidal antibiotic gentamicin

Stationary-phase bacteria are important in disease. The σ(s)-regulated general stress response helps them become resistant to disinfectants, but the role of σ(s) in bacterial antibiotic resistance has not been elucidated. Loss of σ(s) rendered stationary-phase Escherichia coli more sensitive to the bactericidal antibiotic gentamicin (Gm), and proteomic analysis suggested involvement of a weakened antioxidant defense. Use of the psfiA genetic reporter, 3'-(p-hydroxyphenyl) fluorescein (HPF) dye, and Amplex Red showed that Gm generated more reactive oxygen species (ROS) in the mutant. HPF measurements can be distorted by cell elongation, but Gm did not affect stationary-phase cell dimensions. Coadministration of the antioxidant N-acetyl cysteine (NAC) decreased drug lethality particularly in the mutant, as did Gm treatment under anaerobic conditions that prevent ROS formation. Greater oxidative stress, due to insufficient quenching of endogenous ROS and/or respiration-linked electron leakage, therefore contributed to the greater sensitivity of the mutant; infection by a uropathogenic strain in mice showed this to be the case also in vivo. Disruption of antioxidant defense by eliminating the quencher proteins, SodA/SodB and KatE/SodA, or the pentose phosphate pathway proteins, Zwf/Gnd and TalA, which provide NADPH for ROS decomposition, also generated greater oxidative stress and killing by Gm. Thus, besides its established mode of action, Gm also kills stationary-phase bacteria by generating oxidative stress, and targeting the antioxidant defense of E. coli can enhance its efficacy. Relevant aspects of the current controversy on the role of ROS in killing by bactericidal drugs of exponential-phase bacteria, which represent a different physiological state, are discussed.

The Protein Data Bank archive as an open data resource

The Protein Data Bank archive was established in 1971, and recently celebrated its 40th anniversary (Berman et al. in Structure 20:391, 2012). An analysis of interrelationships of the science, technology and community leads to further insights into how this resource evolved into one of the oldest and most widely used open-access data resources in biology.

Identification of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists using molecular modeling method

Peroxisome proliferator-activated receptor-gamma (PPARγ) plays a critical role in lipid and glucose homeostasis. It is the target of many drug discovery studies, because of its role in various disease states including diabetes and cancer. Thiazolidinediones, a synthetic class of agents that work by activation of PPARγ, have been used extensively as insulin-sensitizers for the management of type 2 diabetes. In this study, a combination of QSAR and docking methods were utilised to perform virtual screening of more than 25 million compounds in the ZINC library. The QSAR model was developed using 1,517 compounds and it identified 42,378 potential PPARγ agonists from the ZINC library, and 10,000 of these were selected for docking with PPARγ based on their diversity. Several steps were used to refine the docking results, and finally 30 potentially highly active ligands were identified. Four compounds were subsequently tested for their in vitro activity, and one compound was found to have a K i values of <5 μM.

A chimeric SERM-histone deacetylase inhibitor approach to breast cancer therapy

Breast cancer remains a significant cause of death in women, and few therapeutic options exist for estrogen receptor negative (ER (-)) cancers. Epigenetic reactivation of target genes using histone deacetylase (HDAC) inhibitors has been proposed in ER (-) cancers to resensitize to therapy using selective estrogen receptor modulators (SERMs) that are effective in ER (+) cancer treatment. Based upon preliminary studies in ER (+) and ER (-) breast cancer cells treated with combinations of HDAC inhibitors and SERMs, hybrid drugs, termed SERMostats, were designed with computational guidance. Assay for inhibition of four type I HDAC isoforms and antagonism of estrogenic activity in two cell lines yielded a SERMostat with 1-3 μM potency across all targets. The superior hybrid caused significant cell death in ER (-) human breast cancer cells and elicited cell death at the same concentration as the parent SERM in combination treatment and at an earlier time point.

Data-driven medicinal chemistry in the era of big data

Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.

Design, synthesis, and optimization of novel epoxide incorporating peptidomimetics as selective calpain inhibitors

Hyperactivation of the calcium-dependent cysteine protease calpain 1 (Cal1) is implicated as a primary or secondary pathological event in a wide range of illnesses and in neurodegenerative states, including Alzheimer's disease (AD). E-64 is an epoxide-containing natural product identified as a potent nonselective, calpain inhibitor, with demonstrated efficacy in animal models of AD. By use of E-64 as a lead, three successive generations of calpain inhibitors were developed using computationally assisted design to increase selectivity for Cal1. First generation analogues were potent inhibitors, effecting covalent modification of recombinant Cal1 catalytic domain (Cal1cat), demonstrated using LC-MS/MS. Refinement yielded second generation inhibitors with improved selectivity. Further library expansion and ligand refinement gave three Cal1 inhibitors, one of which was designed as an activity-based protein profiling probe. These were determined to be irreversible and selective inhibitors by kinetics studies comparing full length Cal1 with the general cysteine protease papain.

MOTIF DISCOVERY WITH DATA MINING IN 3D PROTEIN STRUCTURE DATABASES: DISCOVERY, VALIDATION AND PREDICTION OF THE U-SHAPE ZINC BINDING ("HUF-ZINC") MOTIF

Data mining in protein databases, derivatives from more fundamental protein 3D structure and sequence databases, has considerable unearthed potential for the discovery of sequence motif—structural motif—function relationships as the finding of the U-shape (Huf-Zinc) motif, originally a small student's project, exemplifies. The metal ion zinc is critically involved in universal biological processes, ranging from protein-DNA complexes and transcription regulation to enzymatic catalysis and metabolic pathways. Proteins have evolved a series of motifs to specifically recognize and bind zinc ions. Many of these, so called zinc fingers, are structurally independent globular domains with discontinuous binding motifs made up of residues mostly far apart in sequence. Through a systematic approach starting from the BRIX structure fragment database, we discovered that there exists another predictable subset of zinc-binding motifs that not only have a conserved continuous sequence pattern but also share a characteristic local conformation, despite being included in totally different overall folds. While this does not allow general prediction of all Zn binding motifs, a HMM-based web server, Huf-Zinc, is available for prediction of these novel, as well as conventional, zinc finger motifs in protein sequences. The Huf-Zinc webserver can be freely accessed through this URL ( http://mendel.bii.a-star.edu.sg/METHODS/hufzinc/ ).

A combined bioinformatics and chemoinformatics approach for developing asymmetric bivalent AMPA receptor positive allosteric modulators as neuroprotective agents

PAMs new in town! An effective, combined bioinformatics and chemoinformatics approach was applied to the design of novel asymmetric bivalent α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor positive allosteric modulators (PAMs) with marked potency in vitro and efficacy in vivo for preventing neuroapoptosis. The novel chemotype could provide pharmacological probes and potential therapeutic agents for glutamatergic hypofunction and its related neurological and psychiatric disorders.

Dynamic regulation of phenylalanine hydroxylase by simulated redox manipulation

Recent clinical studies revealed increased phenylalanine levels and phenylalanine to tyrosine ratios in patients suffering from infection, inflammation and general immune activity. These data implicated down-regulation of activity of phenylalanine hydroxylase by oxidative stress upon in vivo immune activation. Though the structural damage of oxidative stress is expected to be comparably small, a structural rationale for this experimental finding was lacking. Hence, we investigated the impact of side chain oxidation at two vicinal cysteine residues on local conformational flexibility in the protein by comparative molecular dynamics simulations. Analysis of backbone dynamics revealed a highly flexible loop region (Tyr138-loop) in proximity to the active center of phenylalanine hydroxylase. We observed elevated loop dynamics in connection with a loop movement towards the active site in the oxidized state, thereby partially blocking access for the substrate phenylalanine. These findings were confirmed by extensive replica exchange molecular dynamics simulations and serve as a first structural explanation for decreased enzyme turnover in situations of oxidative stress.

DBD2BS: connecting a DNA-binding protein with its binding sites

By binding to short and highly conserved DNA sequences in genomes, DNA-binding proteins initiate, enhance or repress biological processes. Accurately identifying such binding sites, often represented by position weight matrices (PWMs), is an important step in understanding the control mechanisms of cells. When given coordinates of a DNA-binding domain (DBD) bound with DNA, a potential function can be used to estimate the change of binding affinity after base substitutions, where the changes can be summarized as a PWM. This technique provides an effective alternative when the chromatin immunoprecipitation data are unavailable for PWM inference. To facilitate the procedure of predicting PWMs based on protein-DNA complexes or even structures of the unbound state, the web server, DBD2BS, is presented in this study. The DBD2BS uses an atom-level knowledge-based potential function to predict PWMs characterizing the sequences to which the query DBD structure can bind. For unbound queries, a list of 1066 DBD-DNA complexes (including 1813 protein chains) is compiled for use as templates for synthesizing bound structures. The DBD2BS provides users with an easy-to-use interface for visualizing the PWMs predicted based on different templates and the spatial relationships of the query protein, the DBDs and the DNAs. The DBD2BS is the first attempt to predict PWMs of DBDs from unbound structures rather than from bound ones. This approach increases the number of existing protein structures that can be exploited when analyzing protein-DNA interactions. In a recent study, the authors showed that the kernel adopted by the DBD2BS can generate PWMs consistent with those obtained from the experimental data. The use of DBD2BS to predict PWMs can be incorporated with sequence-based methods to discover binding sites in genome-wide studies. Available at: http://dbd2bs.csie.ntu.edu.tw/, http://dbd2bs.csbb.ntu.edu.tw/, and http://dbd2bs.ee.ncku.edu.tw.

Novel inhibitor discovery through virtual screening against multiple protein conformations generated via ligand-directed modeling: a maternal embryonic leucine zipper kinase example

Kinase targets have been demonstrated to undergo major conformational reorganization upon ligand binding. Such protein conformational plasticity remains a significant challenge in structure-based virtual screening methodology and may be approximated by screening against an ensemble of diverse protein conformations. Maternal embryonic leucine zipper kinase (MELK), a member of serine-threonine kinase family, has been recently found to be involved in the tumerogenic state of glioblastoma, breast, ovarian, and colon cancers. We therefore modeled several conformers of MELK utilizing the available chemogenomic and crystallographic data of homologous kinases. We carried out docking pose prediction and virtual screening enrichment studies with these conformers. The performances of the ensembles were evaluated by their ability to reproduce known inhibitor bioactive conformations and to efficiently recover known active compounds early in the virtual screen when seeded with decoy sets. A few of the individual MELK conformers performed satisfactorily in reproducing the native protein-ligand pharmacophoric interactions up to 50% of the cases. By selecting an ensemble of a few representative conformational states, most of the known inhibitor binding poses could be rationalized. For example, a four conformer ensemble is able to recover 95% of the studied actives, especially with imperfect scoring function(s). The virtual screening enrichment varied considerably among different MELK conformers. Enrichment appears to improve by selection of a proper protein conformation. For example, several holo and unliganded active conformations are better to accommodate diverse chemotypes than ATP-bound conformer. These results prove that using an ensemble of diverse conformations could give a better performance. Applying this approach, we were able to screen a commercially available library of half a million compounds against three conformers to discover three novel inhibitors of MELK, one from each template. Among the three compounds validated via experimental enzyme inhibition assays, one is relatively potent (15; K(d) = 0.37 μM), one moderately active (12; K(d) = 3.2 μM), and one weak but very selective (9; K(d) = 18 μM). These novel hits may be utilized to assist in the development of small molecule therapeutic agents useful in diseases caused by deregulated MELK, and perhaps more importantly, the approach demonstrates the advantages of choosing an appropriate ensemble of a few conformers in pursuing compound potency, selectivity, and novel chemotypes over using single target conformation for structure-based drug design in general.

Insights from comprehensive multiple receptor docking to HDAC8

A systematic investigation of the available crystal structures of HDAC8 and of the influence of different receptor structures and docking protocols is presented. The study shows that the open conformation of HDAC8 may be preferred by ligands with flexible surface binding groups, as such a conformation allows the ligands to minimize their exposure to solvent upon binding. This observation allowed us to rationalize the excellent potency of pyrazole-based inhibitors compared to that of isoxazole-based inhibitors.

Metal-ligand interactions: an analysis of zinc binding groups using the Protein Data Bank

In the present study, we investigated zinc binding groups (ZBGs) using the coordinates of protein-ligand complex structures obtained from the Protein Data Bank. The distance from the zinc to the nearest ligand atom was measured to determine whether the atom was part of the ZBG. The most frequently found ZBG was carboxylate, followed by sulfonamide, hydroxamate, and phosphonate/phosphate. Because it was found that few heteroatoms, such as nitrogen, oxygen, and sulfur atoms, interacted with zinc, ideal distances between the zinc and these heteroatoms were identified. Whereas carboxylates bound to the zinc via both monodentate and bidentate interactions, the hydroxamates bound dominantly in a bidentate manner. These results will aid in the design of new inhibitors with the potential to interact with zinc in the target protein.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Predicting target DNA sequences of DNA-binding proteins based on unbound structures

DNA-binding proteins such as transcription factors use DNA-binding domains (DBDs) to bind to specific sequences in the genome to initiate many important biological functions. Accurate prediction of such target sequences, often represented by position weight matrices (PWMs), is an important step to understand many biological processes. Recent studies have shown that knowledge-based potential functions can be applied on protein-DNA co-crystallized structures to generate PWMs that are considerably consistent with experimental data. However, this success has not been extended to DNA-binding proteins lacking co-crystallized structures. This study aims at investigating the possibility of predicting the DNA sequences bound by DNA-binding proteins from the proteins' unbound structures (structures of the unbound state). Given an unbound query protein and a template complex, the proposed method first employs structure alignment to generate synthetic protein-DNA complexes for the query protein. Once a complex is available, an atomic-level knowledge-based potential function is employed to predict PWMs characterizing the sequences to which the query protein can bind. The evaluation of the proposed method is based on seven DNA-binding proteins, which have structures of both DNA-bound and unbound forms for prediction as well as annotated PWMs for validation. Since this work is the first attempt to predict target sequences of DNA-binding proteins from their unbound structures, three types of structural variations that presumably influence the prediction accuracy were examined and discussed. Based on the analyses conducted in this study, the conformational change of proteins upon binding DNA was shown to be the key factor. This study sheds light on the challenge of predicting the target DNA sequences of a protein lacking co-crystallized structures, which encourages more efforts on the structure alignment-based approaches in addition to docking- and homology modeling-based approaches for generating synthetic complexes.

A novel, customizable and optimizable parameter method using spherical harmonics for molecular shape similarity comparisons

A novel molecular shape similarity comparison method, namely SHeMS, derived from spherical harmonic (SH) expansion, is presented in this study. Through weight optimization using genetic algorithms for a customized reference set, the optimal combination of weights for the translationally and rotationally invariant (TRI) SH shape descriptor, which can specifically and effectively distinguish overall and detailed shape features according to the molecular surface, is obtained for each molecule. This method features two key aspects: firstly, the SH expansion coefficients from different bands are weighted to calculate similarity, leading to a distinct contribution of overall and detailed features to the final score, and thus can be better tailored for each specific system under consideration. Secondly, the reference set for optimization can be totally configured by the user, which produces great flexibility, allowing system-specific and customized comparisons. The directory of useful decoys (DUD) database was adopted to validate and test our method, and principal component analysis (PCA) reveals that SH descriptors for shape comparison preserve sufficient information to separate actives from decoys. The results of virtual screening indicate that the proposed method based on optimal SH descriptor weight combinations represents a great improvement in performance over original SH (OSH) and ultra-fast shape recognition (USR) methods, and is comparable to many other popular methods. Through combining efficient shape similarity comparison with SH expansion method, and other aspects such as chemical and pharmacophore features, SHeMS can play a significant role in this field and can be applied practically to virtual screening by means of similarity comparison with 3D shapes of known active compounds or the binding pockets of target proteins.

The Biomolecular Interaction Network Database in PSI-MI 2.5

The Biomolecular Interaction Network Database (BIND) is a major source of curated biomolecular interactions, which has been unmaintained for the last few years, a trend which will eventually result in the loss of a significant amount of unique biomolecular interaction information, mostly as database identifiers become out of date. To help reverse this trend, we converted BIND to a standard format, Proteomics Standard Initiative-Molecular Interaction 2.5, starting from the last curated data release (from 2005) available in a custom XML format and made the core components (interactions and complexes) plus additional valuable curated information available for download (http://download.baderlab.org/BINDTranslation/). Major work during the conversion process was required to update out of date molecule identifiers resulting in a more comprehensive conversion of BIND, by measures including number of species and interactor types covered, than what is currently accessible elsewhere. This work also highlights issues of data modeling, controlled vocabulary adoption and data cleaning that can serve as a general case study on the future compatibility of interaction databases. Database URL: http://download.baderlab.org/BINDTranslation/

The human rhinovirus: human-pathological impact, mechanisms of antirhinoviral agents, and strategies for their discovery

As the major etiological agent of the common cold, human rhinoviruses (HRV) cause millions of lost working and school days annually. Moreover, clinical studies proved an association between harmless upper respiratory tract infections and more severe diseases e.g. sinusitis, asthma, and chronic obstructive pulmonary disease. Both the medicinal and socio-economic impact of HRV infections and the lack of antiviral drugs substantiate the need for intensive antiviral research. A common structural feature of the approximately 100 HRV serotypes is the icosahedrally shaped capsid formed by 60 identical copies of viral capsid proteins VP1-4. The capsid protects the single-stranded, positive sense RNA genome of about 7,400 bases in length. Both structural as well as nonstructural proteins produced during the viral life cycle have been identified as potential targets for blocking viral replication at the step of attachment, entry, uncoating, RNA and protein synthesis by synthetic or natural compounds. Moreover, interferon and phytoceuticals were shown to protect host cells. Most of the known inhibitors of HRV replication were discovered as a result of empirical or semi-empirical screening in cell culture. Structure-activity relationship studies are used for hit optimization and lead structure discovery. The increasing structural insight and molecular understanding of viral proteins on the one hand and the advent of innovative computer-assisted technologies on the other hand have facilitated a rationalized access for the discovery of small chemical entities with antirhinoviral (anti-HRV) activity. This review will (i) summarize existing structural knowledge about HRV, (ii) focus on mechanisms of anti-HRV agents from synthetic and natural origin, and (iii) demonstrate strategies for efficient lead structure discovery.

CAZymes Analysis Toolkit (CAT): web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire nonredundant sequences of the CAZy database. The second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit, and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.

Pharmacophore screening of the protein data bank for specific binding site chemistry

A simple computational approach was developed to screen the Protein Data Bank (PDB) for putative pockets possessing a specific binding site chemistry and geometry. The method employs two commonly used 3D screening technologies, namely identification of cavities in protein structures and pharmacophore screening of chemical libraries. For each protein structure, a pocket finding algorithm is used to extract potential binding sites containing the correct types of residues, which are then stored in a large SDF-formatted virtual library; pharmacophore filters describing the desired binding site chemistry and geometry are then applied to screen this virtual library and identify pockets matching the specified structural chemistry. As an example, this approach was used to screen all human protein structures in the PDB and identify sites having chemistry similar to that of known methyl-lysine binding domains that recognize chromatin methylation marks. The selected genes include known readers of the histone code as well as novel binding pockets that may be involved in epigenetic signaling. Putative allosteric sites were identified on the structures of TP53BP1, L3MBTL3, CHEK1, KDM4A, and CREBBP.