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1.  An inverse docking approach for identifying new potential anti-cancer targets 
Inverse docking is a relatively new technique that has been used to identify potential receptor targets of small molecules. Our docking software package MDock is well suited for such an application as it is both computationally efficient, yet simultaneously shows adequate results in binding affinity predictions and enrichment tests. As a validation study, we present the first stage results of an inverse-docking study which seeks to identify potential direct targets of PRIMA-1. PRIMA-1 is well known for its ability to restore mutant p53's tumor suppressor function, leading to apoptosis in several types of cancer cells. For this reason, we believe that potential direct targets of PRIMA-1 identified in silico should be experimentally screened for their ability to inhibit cancer cell growth. The highest-ranked human protein of our PRIMA-1 docking results is oxidosqualene cyclase (OSC), which is part of the cholesterol synthetic pathway. The results of two followup experiments which treat OSC as a possible anti-cancer target are promising. We show that both PRIMA-1 and Ro 48-8071, a known potent OSC inhibitor, significantly reduce the viability of BT-474 breast cancer cells relative to normal mammary cells. In addition, like PRIMA-1, we find that Ro 48-8071 results in increased binding of mutant p53 to DNA in BT-474 cells (which highly express p53). For the first time, Ro 48-8071 is shown as a potent agent in killing human breast cancer cells. The potential of OSC as a new target for developing anticancer therapies is worth further investigation.
doi:10.1016/j.jmgm.2011.01.002
PMCID: PMC3068237  PMID: 21315634
Inverse Docking; In Silico Screening; Protein-Ligand Interactions; Molecular Docking
2.  Multipose Binding in Molecular Docking 
Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking.
doi:10.3390/ijms15022622
PMCID: PMC3958872  PMID: 24534807
multipose binding; high-throughput docking; scoring optimization; binding affinity prediction
3.  Towards Complete Sets of Farnesylated and Geranylgeranylated Proteins 
PLoS Computational Biology  2007;3(4):e66.
Three different prenyltransferases attach isoprenyl anchors to C-terminal motifs in substrate proteins. These lipid anchors serve for membrane attachment or protein–protein interactions in many pathways. Although well-tolerated selective prenyltransferase inhibitors are clinically available, their mode of action remains unclear since the known substrate sets of the various prenyltransferases are incomplete. The Prenylation Prediction Suite (PrePS) has been applied for large-scale predictions of prenylated proteins. To prioritize targets for experimental verification, we rank the predictions by their functional importance estimated by evolutionary conservation of the prenylation motifs within protein families. The ranked lists of predictions are accessible as PRENbase (http://mendel.imp.univie.ac.at/sat/PrePS/PRENbase) and can be queried for verification status, type of modifying enzymes (anchor type), and taxonomic distribution. Our results highlight a large group of plant metal-binding chaperones as well as several newly predicted proteins involved in ubiquitin-mediated protein degradation, enriching the known functional repertoire of prenylated proteins. Furthermore, we identify two possibly prenylated proteins in Mimivirus. The section HumanPRENbase provides complete lists of predicted prenylated human proteins—for example, the list of farnesyltransferase targets that cannot become substrates of geranylgeranyltransferase 1 and, therefore, are especially affected by farnesyltransferase inhibitors (FTIs) used in cancer and anti-parasite therapy. We report direct experimental evidence verifying the prediction of the human proteins Prickle1, Prickle2, the BRO1 domain–containing FLJ32421 (termed BROFTI), and Rab28 (short isoform) as exclusive farnesyltransferase targets. We introduce PRENbase, a database of large-scale predictions of protein prenylation substrates ranked by evolutionary conservation of the motif. Experimental evidence is presented for the selective farnesylation of targets with an evolutionary conserved modification site.
Author Summary
Various cellular functions require reversible membrane localization of proteins. This is often facilitated by attaching lipids to the respective proteins, thus anchoring them to the membrane. For example, addition of prenyl lipid anchors (prenylation) is directed by a motif in the protein sequence that can be predicted using a recently developed method. We describe the prediction of protein prenylation in all currently known proteins. The annotated results are available as an online database: PRENbase. A ranking of the predictions is introduced, assuming that existence of a prenylation sequence motif in related proteins from different species (evolutionary conservation) relates to functional importance of the lipid anchor. We present experimental evidence for high-ranked human proteins predicted to be affected by anticancer drugs inhibiting prenylation.
doi:10.1371/journal.pcbi.0030066
PMCID: PMC1847700  PMID: 17411337
4.  Towards Complete Sets of Farnesylated and Geranylgeranylated Proteins 
PLoS Computational Biology  2007;3(4):e66.
Three different prenyltransferases attach isoprenyl anchors to C-terminal motifs in substrate proteins. These lipid anchors serve for membrane attachment or protein–protein interactions in many pathways. Although well-tolerated selective prenyltransferase inhibitors are clinically available, their mode of action remains unclear since the known substrate sets of the various prenyltransferases are incomplete. The Prenylation Prediction Suite (PrePS) has been applied for large-scale predictions of prenylated proteins. To prioritize targets for experimental verification, we rank the predictions by their functional importance estimated by evolutionary conservation of the prenylation motifs within protein families. The ranked lists of predictions are accessible as PRENbase (http://mendel.imp.univie.ac.at/sat/PrePS/PRENbase) and can be queried for verification status, type of modifying enzymes (anchor type), and taxonomic distribution. Our results highlight a large group of plant metal-binding chaperones as well as several newly predicted proteins involved in ubiquitin-mediated protein degradation, enriching the known functional repertoire of prenylated proteins. Furthermore, we identify two possibly prenylated proteins in Mimivirus. The section HumanPRENbase provides complete lists of predicted prenylated human proteins—for example, the list of farnesyltransferase targets that cannot become substrates of geranylgeranyltransferase 1 and, therefore, are especially affected by farnesyltransferase inhibitors (FTIs) used in cancer and anti-parasite therapy. We report direct experimental evidence verifying the prediction of the human proteins Prickle1, Prickle2, the BRO1 domain–containing FLJ32421 (termed BROFTI), and Rab28 (short isoform) as exclusive farnesyltransferase targets. We introduce PRENbase, a database of large-scale predictions of protein prenylation substrates ranked by evolutionary conservation of the motif. Experimental evidence is presented for the selective farnesylation of targets with an evolutionary conserved modification site.
Author Summary
Various cellular functions require reversible membrane localization of proteins. This is often facilitated by attaching lipids to the respective proteins, thus anchoring them to the membrane. For example, addition of prenyl lipid anchors (prenylation) is directed by a motif in the protein sequence that can be predicted using a recently developed method. We describe the prediction of protein prenylation in all currently known proteins. The annotated results are available as an online database: PRENbase. A ranking of the predictions is introduced, assuming that existence of a prenylation sequence motif in related proteins from different species (evolutionary conservation) relates to functional importance of the lipid anchor. We present experimental evidence for high-ranked human proteins predicted to be affected by anticancer drugs inhibiting prenylation.
doi:10.1371/journal.pcbi.0030066
PMCID: PMC1847700  PMID: 17411337
5.  Tandem affinity purification of miRNA target mRNAs (TAP-Tar) 
Nucleic Acids Research  2009;38(4):e20.
MicroRNAs (miRNAs) bind to Argonaute proteins, and together they form the RISC complex and regulate target mRNA translation and/or stability. Identification of mRNA targets is key to deciphering the physiological functions and mode of action of miRNAs. In mammals, miRNAs are generally poorly homologous to their target sequence, and target identification cannot be based solely on bioinformatics. Here, we describe a biochemical approach, based on tandem affinity purification, in which mRNA/miRNA complexes are sequentially pulled down, first via the Argonaute moiety and then via the miRNA. Our ‘TAP-Tar’ procedure allows the specific pull down of mRNA targets of miRNA. It is useful for validation of targets predicted in silico, and, potentially, for discovery of previously uncharacterized targets.
doi:10.1093/nar/gkp1100
PMCID: PMC2831319  PMID: 19955234
6.  In silico selection of RNA aptamers 
Nucleic Acids Research  2009;37(12):e87.
In vitro selection of RNA aptamers that bind to a specific ligand usually begins with a random pool of RNA sequences. We propose a computational approach for designing a starting pool of RNA sequences for the selection of RNA aptamers for specific analyte binding. Our approach consists of three steps: (i) selection of RNA sequences based on their secondary structure, (ii) generating a library of three-dimensional (3D) structures of RNA molecules and (iii) high-throughput virtual screening of this library to select aptamers with binding affinity to a desired small molecule. We developed a set of criteria that allows one to select a sequence with potential binding affinity from a pool of random sequences and developed a protocol for RNA 3D structure prediction. As verification, we tested the performance of in silico selection on a set of six known aptamer–ligand complexes. The structures of the native sequences for the ligands in the testing set were among the top 5% of the selected structures. The proposed approach reduces the RNA sequences search space by four to five orders of magnitude—significantly accelerating the experimental screening and selection of high-affinity aptamers.
doi:10.1093/nar/gkp408
PMCID: PMC2709588  PMID: 19465396
7.  Specificity quantification of biomolecular recognition and its implication for drug discovery 
Scientific Reports  2012;2:309.
Highly efficient and specific biomolecular recognition requires both affinity and specificity. Previous quantitative descriptions of biomolecular recognition were mostly driven by improving the affinity prediction, but lack of quantification of specificity. We developed a novel method SPA (SPecificity and Affinity) based on our funneled energy landscape theory. The strategy is to simultaneously optimize the quantified specificity of the “native” protein-ligand complex discriminating against “non-native” binding modes and the affinity prediction. The benchmark testing of SPA shows the best performance against 16 other popular scoring functions in industry and academia on both prediction of binding affinity and “native” binding pose. For the target COX-2 of nonsteroidal anti-inflammatory drugs, SPA successfully discriminates the drugs from the diversity set, and the selective drugs from non-selective drugs. The remarkable performance demonstrates that SPA has significant potential applications in identifying lead compounds for drug discovery.
doi:10.1038/srep00309
PMCID: PMC3298884  PMID: 22413060
8.  Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques 
Bioinformatics  2009;25(5):621-627.
Motivation: The binding sites of proteins generally contain smaller regions that provide major contributions to the binding free energy and hence are the prime targets in drug design. Screening libraries of fragment-sized compounds by NMR or X-ray crystallography demonstrates that such ‘hot spot’ regions bind a large variety of small organic molecules, and that a relatively high ‘hit rate’ is predictive of target sites that are likely to bind drug-like ligands with high affinity. Our goal is to determine the ‘hot spots’ computationally rather than experimentally.
Results: We have developed the FTMAP algorithm that performs global search of the entire protein surface for regions that bind a number of small organic probe molecules. The search is based on the extremely efficient fast Fourier transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids, but can use only sums of correlation functions for scoring and hence is generally restricted to very simple energy expressions. The novelty of FTMAP is that we were able to incorporate and represent on grids a detailed energy expression, resulting in a very accurate identification of low-energy probe clusters. Overlapping clusters of different probes are defined as consensus sites (CSs). We show that the largest CS is generally located at the most important subsite of the protein binding site, and the nearby smaller CSs identify other important subsites. Mapping results are presented for elastase whose structure has been solved in aqueous solutions of eight organic solvents, and we show that FTMAP provides very similar information. The second application is to renin, a long-standing pharmaceutical target for the treatment of hypertension, and we show that the major CSs trace out the shape of the first approved renin inhibitor, aliskiren.
Availability: FTMAP is available as a server at http://ftmap.bu.edu/.
Contact: vajda@bu.edu
Supplementary information: Supplementary Material is available at Bioinformatics online.
doi:10.1093/bioinformatics/btp036
PMCID: PMC2647826  PMID: 19176554
9.  Pharmacophore-based discovery of FXR-agonists. Part II: Identification of bioactive triterpenes from Ganoderma lucidum 
Bioorganic & Medicinal Chemistry  2011;19(22):6779-6791.
Graphical abstract
The farnesoid X receptor (FXR) belonging to the metabolic subfamily of nuclear receptors is a ligand-induced transcriptional activator. Its central function is the physiological maintenance of bile acid homeostasis including the regulation of glucose and lipid metabolism. Accessible structural information about its ligand-binding domain renders FXR an attractive target for in silico approaches. Integrated to natural product research these computational tools assist to find novel bioactive compounds showing beneficial effects in prevention and treatment of, for example, the metabolic syndrome, dyslipidemia, atherosclerosis, and type 2 diabetes. Virtual screening experiments of our in-house Chinese Herbal Medicine database with structure-based pharmacophore models, previously generated and validated, revealed mainly lanostane-type triterpenes of the TCM fungus Ganoderma lucidum Karst. as putative FXR ligands. To verify the prediction of the in silico approach, two Ganoderma fruit body extracts and compounds isolated thereof were pharmacologically investigated. Pronounced FXR-inducing effects were observed for the extracts at a concentration of 100 μg/mL. Intriguingly, five lanostanes out of 25 secondary metabolites from G. lucidum, that is, ergosterol peroxide (2), lucidumol A (11), ganoderic acid TR (12), ganodermanontriol (13), and ganoderiol F (14), dose-dependently induced FXR in the low micromolar range in a reporter gene assay. To rationalize the binding interactions, additional pharmacophore profiling and molecular docking studies were performed, which allowed establishing a first structure–activity relationship of the investigated triterpenes.
doi:10.1016/j.bmc.2011.09.039
PMCID: PMC3254236  PMID: 22014750
CDCA, chenodeoxycholic acid; CHM, 3D structural database of Chinese Herbal Medicine; DMEM, Dulbecco’s Modified Eagle’s Medium; FBS, fetal bovine serum; FXR, farnesoid X receptor; HEK-293, human embryonic kidney-293; PDB, protein data bank; RXR, 9-cis-retionic acid receptor; SHP-1, small heterodimer partner 1; VH, virtual hit; Farnesoid X receptor; Ganoderma lucidum; Lanostane triterpenes; Ganoderic acids; Molecular modeling; Virtual screening; Natural products
10.  idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach 
Nucleic Acids Research  2012;40(Web Server issue):W393-W399.
Identification of possible protein targets of small chemical molecules is an important step for unravelling their underlying causes of actions at the molecular level. To this end, we construct a web server, idTarget, which can predict possible binding targets of a small chemical molecule via a divide-and-conquer docking approach, in combination with our recently developed scoring functions based on robust regression analysis and quantum chemical charge models. Affinity profiles of the protein targets are used to provide the confidence levels of prediction. The divide-and-conquer docking approach uses adaptively constructed small overlapping grids to constrain the searching space, thereby achieving better docking efficiency. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB). We show that idTarget is able to reproduce known off-targets of drugs or drug-like compounds, and the suggested new targets could be prioritized for further investigation. idTarget is freely available as a web-based server at http://idtarget.rcas.sinica.edu.tw.
doi:10.1093/nar/gks496
PMCID: PMC3394295  PMID: 22649057
11.  Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles 
Nuclear receptors (NRs) are ligand dependent transcriptional factors and play a key role in reproduction, development, and homeostasis of organism. NRs are potential targets for treatment of cancer and other diseases such as inflammatory diseases, and diabetes. In this study, we present a comprehensive library of pocket conformational ensembles of thirteen human nuclear receptors (NRs), and test the ability of these ensembles to recognize their ligands in virtual screening, as well as predict their binding geometry, functional type, and relative binding affinity. 157 known NR modulators and 66 structures were used as a benchmark. Our pocket ensemble library correctly predicted the ligand binding poses in 94% of the cases. The models were also highly selective for the active ligands in virtual screening, with the areas under the ROC curves ranging from 82 to a remarkable 99%. Using the computationally determined receptor-specific binding energy offsets, we showed that the ensembles can be used for predicting selectivity profiles of NR ligands. Our results evaluate and demonstrate the advantages of using receptor ensembles for compound docking, screening, and profiling.
Electronic supplementary material
The online version of this article (doi:10.1007/s10822-010-9362-4) contains supplementary material, which is available to authorized users.
doi:10.1007/s10822-010-9362-4
PMCID: PMC2881208  PMID: 20455005
Nuclear receptor; Multiple receptor conformers; Virtual ligand screening; Ligands profiling; Internal Coordinate Mechanics
12.  FINDSITEX: A structure based, small molecule virtual screening approach with application to all identified human GPCRs 
Molecular Pharmaceutics  2012;9(6):1775-1784.
We have developed FINDSITEX, an extension of FINDSITE, a protein threading based algorithm for the inference of protein binding sites, biochemical function and virtual ligand screening, that removes the limitation that holo protein structures (those containing bound ligands) of a sufficiently large set of distant evolutionarily related proteins to the target be solved; rather, predicted protein structures and experimental ligand binding information are employed. To provide the predicted protein structures, a fast and accurate version of our recently developed TASSERVMT, TASSERVMT-lite, for template-based protein structural modeling applicable up to 1000 residues is developed and tested, with comparable performance to the top CASP9 servers. Then, a hybrid approach that combines structure alignments with an evolutionary similarity score for identifying functional relationships between target and proteins with binding data has been developed. By way of illustration, FINDSITEX is applied to 998 identified human G-protein coupled receptors (GPCRs). First, TASSERVMT-lite provides updates of all human GPCR structures previously modeled in our lab. We then use these structures and the new function similarity detection algorithm to screen all human GPCRs against the ZINC8 non-redundant (TC<0.7) ligand set combined with ligands from the GLIDA database (a total of 88,949 compounds). Testing (excluding GPCRs whose sequence identity > 30% to the target from the binding data library) on a 168 human GPCR set with known binding data, the average enrichment factor in the top 1% of the compound library (EF0.01) is 22.7, whereas EF0.01 by FINDSITE is 7.1. For virtual screening when just the target and its native ligands are excluded, then the average EF0.01 reaches 41.4. We also analyze off-target interactions for the 168 protein test set. All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html.
doi:10.1021/mp3000716
PMCID: PMC3396429  PMID: 22574683
TASSERVMT; FINDSITE; GPCR modeling; template-based modeling; virtual screening
13.  Binding-Site Assessment by Virtual Fragment Screening 
PLoS ONE  2010;5(4):e10109.
The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules) would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock ∼11000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors.
doi:10.1371/journal.pone.0010109
PMCID: PMC2852417  PMID: 20404926
14.  Virtual Target Screening: Validation Using Kinase Inhibitors 
Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term “Virtual Target Screening (VTS)”, a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.
doi:10.1021/ci300073m
PMCID: PMC3488111  PMID: 22747098
virtual target screening; virtual counter-screening; computational inverse docking; in silico inverse docking; drug retargeting; drug repurposing; off-target effects
15.  The Importance of Discerning Shape in Molecular Pharmacology 
Shape is a fundamentally important molecular feature that often determines the fate of a compound in terms of molecular interactions with preferred and non-preferred biological targets. Complementarity of binding in small molecule-protein, peptide-receptor, antigen-antibody and protein-protein interactions is key to life and survival, but also to targeting molecules with bioactivity. We review the application of shape in various biological systems such as substrate recognition, ligand specificity / selectivity and antibody recognition in the context of computational methods such as docking, quantitative structure activity relationships, classification models and similarity search algorithms. These in silico pharmacology methods have recently demonstrated the importance and applicability of determining molecular shape in drug discovery, virtual screening and predictive toxicology. The results from recently published studies show that shape and shape-based descriptors are at least as useful as other traditional molecular descriptors.
doi:10.1016/j.tips.2008.12.001
PMCID: PMC2854656  PMID: 19187977
Antibody; Depth; Descriptors; Dopamine receptors; Molecular shape; Nuclear hormone receptor; Pharmacophore
16.  Non-canonical peroxisome targeting signals: identification of novel PTS1 tripeptides and characterization of enhancer elements by computational permutation analysis 
BMC Plant Biology  2012;12:142.
Background
High-accuracy prediction tools are essential in the post-genomic era to define organellar proteomes in their full complexity. We recently applied a discriminative machine learning approach to predict plant proteins carrying peroxisome targeting signals (PTS) type 1 from genome sequences. For Arabidopsis thaliana 392 gene models were predicted to be peroxisome-targeted. The predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal.
Results
In this study, we experimentally validated the predictions in greater depth by focusing on the most challenging Arabidopsis proteins with unknown non-canonical PTS1 tripeptides and prediction scores close to the threshold. By in vivo subcellular targeting analysis, three novel PTS1 tripeptides (QRL>, SQM>, and SDL>) and two novel tripeptide residues (Q at position −3 and D at pos. -2) were identified. To understand why, among many Arabidopsis proteins carrying the same C-terminal tripeptides, these proteins were specifically predicted as peroxisomal, the residues upstream of the PTS1 tripeptide were computationally permuted and the changes in prediction scores were analyzed. The newly identified Arabidopsis proteins were found to contain four to five amino acid residues of high predicted targeting enhancing properties at position −4 to −12 in front of the non-canonical PTS1 tripeptide. The identity of the predicted targeting enhancing residues was unexpectedly diverse, comprising besides basic residues also proline, hydroxylated (Ser, Thr), hydrophobic (Ala, Val), and even acidic residues.
Conclusions
Our computational and experimental analyses demonstrate that the plant PTS1 tripeptide motif is more diverse than previously thought, including an increasing number of non-canonical sequences and allowed residues. Specific targeting enhancing elements can be predicted for particular sequences of interest and are far more diverse in amino acid composition and positioning than previously assumed. Machine learning methods become indispensable to predict which specific proteins, among numerous candidate proteins carrying the same non-canonical PTS1 tripeptide, contain sufficient enhancer elements in terms of number, positioning and total strength to cause peroxisome targeting.
doi:10.1186/1471-2229-12-142
PMCID: PMC3487989  PMID: 22882975
17.  Life Beyond Kinases: Structure-based Discovery of Sorafenib as Nanomolar Antagonist of 5-HT Receptors 
Journal of Medicinal Chemistry  2012;55(12):5749-5759.
Of great interest in recent years has been computationally predicting the novel polypharmacology of drug molecules. Here, we applied an “induced-fit” protocol to improve the homology models of 5-HT2A receptor, and we assessed the quality of these models in retrospective virtual screening. Subsequently, we computationally screened the FDA approved drug molecules against the best induced-fit 5-HT2A models, and chose six top scoring hits for experimental assays. Surprisingly, one well-known kinase inhibitor, sorafenib has shown unexpected promiscuous 5-HTRs binding affinities, Ki = 1959, 56 and 417 nM against 5-HT2A, 5-HT2B and 5-HT2C, respectively. Our preliminary SAR exploration supports the predicted binding mode, and further suggests sorafenib to be a novel lead compound for 5HTR ligand discovery. Although it has been well known that sorafenib produces anticancer effects through targeting multiple kinases, carefully designed experimental studies are desirable to fully understand whether its “off-target” 5-HTR binding activities contribute to its therapeutic efficacy or otherwise undesirable side effects.
doi:10.1021/jm300338m
PMCID: PMC3402552  PMID: 22694093
GPCR; 5-HTR; induced-fit; molecular docking; molecular dynamics; sorafenib
18.  Computational redesign of endonuclease DNA binding and cleavage specificity 
Nature  2006;441(7093):656-659.
The reprogramming of DNA-binding specificity is an important challenge for computational protein design that tests current understanding of protein–DNA recognition, and has considerable practical relevance for biotechnology and medicine1–6. Here we describe the computational redesign of the cleavage specificity of the intron-encoded homing endonuclease I-MsoI7 using a physically realistic atomic-level forcefield8,9. Using an in silico screen, we identified single base-pair substitutions predicted to disrupt binding by the wild-type enzyme, and then optimized the identities and conformations of clusters of amino acids around each of these unfavourable substitutions using Monte Carlo sampling10. A redesigned enzyme that was predicted to display altered target site specificity, while maintaining wild-type binding affinity, was experimentally characterized. The redesigned enzyme binds and cleaves the redesigned recognition site ~10,000 times more effectively than does the wild-type enzyme, with a level of target discrimination comparable to the original endonuclease. Determination of the structure of the redesigned nuclease- recognition site complex by X-ray crystallography confirms the accuracy of the computationally predicted interface. These results suggest that computational protein design methods can have an important role in the creation of novel highly specific endonucleases for gene therapy and other applications.
doi:10.1038/nature04818
PMCID: PMC2999987  PMID: 16738662
19.  In silico Target Fishing for Rationalized Ligand Discovery Exemplified on Constituents of Ruta graveolens 
Planta medica  2008;75(3):195-204.
The identification of targets whose interaction is likely to result in the successful treatment of a disease is of growing interest for natural product scientists. In the current study we performed an exemplary application of a virtual parallel screening approach to identify potential targets for 16 secondary metabolites isolated and identified from the aerial parts of the medicinal plant Ruta graveolens L. Low energy conformers of the isolated constituents were simultaneously screened against a set of 2208 pharmacophore models generated in-house for the in silico prediction of putative biological targets, i. e., target fishing. Based on the predicted ligand-target interactions, we focused on three biological targets, namely acetylcholinesterase (AChE), the human rhinovirus (HRV) coat protein and the cannabinoid receptor type-2 (CB2). For a critical evaluation of the applied parallel screening approach, virtual hits and non-hits were assayed on the respective targets. For AChE the highest scoring virtual hit, arborinine, showed the best inhibitory in vitro activity on AChE (IC50 34.7 μM). Determination of the anti-HRV-2 effect revealed 6,7,8-trimethoxycoumarin and arborinine to be the most active antiviral constituents with IC50 values of 11.98 μM and 3.19 μM, respectively. Of these, arborinine was predicted virtually. Of all the molecules subjected to parallel screening, one virtual CB2 ligand was obtained, i.e., rutamarin. Interestingly, in experimental studies only this compound showed a selective activity to the CB2 receptor (Ki of 7.4 μM) by using a radioligand displacement assay. The applied parallel screening paradigm with constituents of R. graveolens on three different proteins has shown promise as an in silico tool for rational target fishing and pharmacological profiling of extracts and single chemical entities in natural product research.
doi:10.1055/s-0028-1088397
PMCID: PMC3525952  PMID: 19096995
Ruta graveolens L; Rutaceae; pharmacophore modelling; virtual parallel screening; acetylcholinesterase; cannabinoid receptor 2; human rhinovirus coat protein
20.  Identification of small molecular weight inhibitors of Src homology 2 domain-containing tyrosine phosphatase 2 (SHP-2) via in silico database screening combined with experimental assay 
Journal of medicinal chemistry  2008;51(23):7396-7404.
Virtual screening methods combined with experimental assays were used to identify low molecular weight inhibitors for Src homology 2 domain-containing phosphatase 2 (SHP-2) that is mutated and hyperactivated in Noonan syndrome and a significant portion of childhood leukemias. Virtual screening included multiple conformations of the protein, score normalization procedures, and chemical similarity considerations. As the catalytic core of SHP-2 shares extremely high homology to those of the related SHP-1 phosphatase and other tyrosine phosphatases, in order to identify selective inhibitors, we chose to target an adjacent protein surface pocket that is predicted to be important for binding to phospho-peptides and that has structural features unique to SHP-2. From a database of 1.3 million compounds, 9 out of 165 computationally selected compounds were shown to inhibit SHP-2 activity with IC50 values of ≈ 100 μM. Two of the active compounds were further verified for their ability to inhibit SHP-2-mediated cellular functions. Fluorescence titration experiments confirmed their direct binding to SHP-2. Because of their simple chemical structures, these small organic compounds have the potential to act as lead compounds for the development of novel anti-SHP-2 drugs.
doi:10.1021/jm800229d
PMCID: PMC2645921  PMID: 19007293
21.  FINDSITEcomb: A threading/structure-based, proteomic-scale virtual ligand screening approach 
Virtual ligand screening is an integral part of the modern drug discovery process. Traditional ligand-based, virtual screening approaches are fast but require a set of structurally diverse ligands known to bind to the target. Traditional structure-based approaches require high-resolution target protein structures and are computationally demanding. In contrast, the recently developed threading/structure-based FINDSITE-based approaches have the advantage that they are as fast as traditional ligand-based approaches and yet overcome the limitations of traditional ligand- or structure-based approaches. These new methods can use predicted low-resolution structures and infer the likelihood of a ligand binding to a target by utilizing ligand information excised from the target’s remote or close homologous proteins and/or libraries of ligand binding databases. Here, we develop an improved version of FINDSITE, FINDSITEfilt, that filters out false positive ligands in threading identified templates by a better binding site detection procedure that includes information about the binding site amino acid similarity. We then combine FINDSITEfilt with FINDSITEX that uses publicly available binding databases ChEMBL and DrugBank for virtual ligand screening. The combined approach, FINDSITEcomb, is compared to two traditional docking methods, AUTODOCK Vina and DOCK 6, on the DUD benchmark set. It is shown to be significantly better in terms of enrichment factor, dependence on target structure quality and speed. FINDSITEcomb is then tested for virtual ligand screening on a large set of 3,576 generic targets from the DrugBank database as well as a set of 168 Human GPCRs. Excluding close homologues, FINDSITEcomb gives an average enrichment factor of 52.1 for generic targets and 22.3 for GPCRs within the top 1% of the screened compound library. Around 65% of the targets have better than random enrichment factors. The performance is insensitive to target structure quality, as long as it has a TM-score ≥ 0.4 to native. Thus, FINDSITEcomb makes the screening of millions of compounds across entire proteomes feasible. The FINDSITEcomb web service is freely available for academic users at http://cssb.biology.gatech.edu/skolnick/webservice/FINDSITE-COMB/index.html
doi:10.1021/ci300510n
PMCID: PMC3557555  PMID: 23240691
Virtual ligand screening; FINDSITE; FINDSITEX; FINDSITEcomb
22.  Nonsense-Mediated mRNA Decay Immunity Can Help Identify Human Polycistronic Transcripts 
PLoS ONE  2014;9(3):e91535.
Eukaryotic polycistronic transcription units are rare and only a few examples are known, mostly being the outcome of serendipitous discovery. We claim that nonsense-mediated mRNA decay (NMD) immune structure is a common characteristic of polycistronic transcripts, and that this immunity is an emergent property derived from all functional CDSs. The human RefSeq transcriptome was computationally screened for transcripts capable of eliciting NMD, and which contain an additional ORF(s) potentially capable of rescuing the transcript from NMD. Transcripts were further analyzed implementing domain-based strategies in order to estimate the potential of the candidate ORF to encode a functional protein. Consequently, we predict the existence of forty nine novel polycistronic transcripts.
Experimental verification was carried out utilizing two different types of analyses. First, five Gene Expression Omnibus (GEO) datasets from published NMD-inhibition studies were used, aiming to explore whether a given mRNA is indeed insensitive to NMD. All known bicistronic transcripts and eleven out of the twelve predicted genes that were analyzed, displayed NMD insensitivity using various NMD inhibitors. For three genes, a mixed expression pattern was observed presenting both NMD sensitivity and insensitivity in different cell types. Second, we used published global translation initiation sequencing data from HEK293 cells to verify the existence of translation initiation sites in our predicted polycistronic genes. In five of our genes, the predicted rescuing uORFs are indeed identified as translation initiation sites, and in two additional genes, one of two predicted rescuing uORF is verified. These results validate our computational analysis and reinforce the possibility that NMD-immune architecture is a parameter by which polycistronic genes can be identified. Moreover, we present evidence for NMD-mediated regulation controlling the production of one or more proteins encoded in the polycistronic transcript.
doi:10.1371/journal.pone.0091535
PMCID: PMC3951408  PMID: 24621851
23.  Development of a Novel Virtual Screening Cascade Protocol to Identify Potential Trypanothione Reductase Inhibitors 
Journal of Medicinal Chemistry  2009;52(6):1670-1680.
The implementation of a novel sequential computational approach that can be used effectively for virtual screening and identification of prospective ligands that bind to trypanothione reductase (TryR) is reported. The multistep strategy combines a ligand-based virtual screening for building an enriched library of small molecules with a docking protocol (AutoDock, X-Score) for screening against the TryR target. Compounds were ranked by an exhaustive conformational consensus scoring approach that employs a rank-by-rank strategy by combining both scoring functions. Analysis of the predicted ligand−protein interactions highlights the role of bulky quaternary amine moieties for binding affinity. The scaffold hopping (SHOP) process derived from this computational approach allowed the identification of several chemotypes, not previously reported as antiprotozoal agents, which includes dibenzothiepine, dibenzooxathiepine, dibenzodithiepine, and polycyclic cationic structures like thiaazatetracyclo-nonadeca-hexaen-3-ium. Assays measuring the inhibiting effect of these compounds on T. cruzi and T. brucei TryR confirm their potential for further rational optimization.
doi:10.1021/jm801306g
PMCID: PMC2659691  PMID: 19296695
24.  SPdb – a signal peptide database 
BMC Bioinformatics  2005;6:249.
Background
The signal peptide plays an important role in protein targeting and protein translocation in both prokaryotic and eukaryotic cells. This transient, short peptide sequence functions like a postal address on an envelope by targeting proteins for secretion or for transfer to specific organelles for further processing. Understanding how signal peptides function is crucial in predicting where proteins are translocated. To support this understanding, we present SPdb signal peptide database , a repository of experimentally determined and computationally predicted signal peptides.
Results
SPdb integrates information from two sources (a) Swiss-Prot protein sequence database which is now part of UniProt and (b) EMBL nucleotide sequence database. The database update is semi-automated with human checking and verification of the data to ensure the correctness of the data stored. The latest release SPdb release 3.2 contains 18,146 entries of which 2,584 entries are experimentally verified signal sequences; the remaining 15,562 entries are either signal sequences that fail to meet our filtering criteria or entries that contain unverified signal sequences.
Conclusion
SPdb is a manually curated database constructed to support the understanding and analysis of signal peptides. SPdb tracks the major updates of the two underlying primary databases thereby ensuring that its information remains up-to-date.
doi:10.1186/1471-2105-6-249
PMCID: PMC1276010  PMID: 16221310
25.  A Discovery Funnel for Nucleic Acid Binding Drug Candidates 
Drug development research  2011;72(2):178-186.
Computational approaches are becoming increasingly popular for the discovery of drug candidates against a target of interest. Proteins have historically been the primary targets of many virtual screening efforts. While in silico screens targeting proteins has proven successful, other classes of targets, in particular DNA, remain largely unexplored using virtual screening methods. With the realization of the functional importance of many non-cannonical DNA structures such as G-quadruplexes, increased efforts are underway to discover new small molecules that can bind selectively to DNA structures. Here, we describe efforts to build an integrated in silico and in vitro platform for discovering compounds that may bind to a chosen DNA target. Millions of compounds are initially screened in silico for selective binding to a particular structure and ranked to identify several hundred best hits. An important element of our strategy is the inclusion of an array of possible competing structures in the in silico screen. The best hundred or so hits are validated experimentally for binding to the actual target structure by a high-throughput 96-well thermal denaturation assay to yield the top ten candidates. Finally, these most promising candidates are thoroughly characterized for binding to their DNA target by rigorous biophysical methods, including isothermal titration calorimetry, differential scanning calorimetry, spectroscopy and competition dialysis.This platform was validated using quadruplex DNA as a target and a newly discovered quadruplex binding compound with possible anti-cancer activity was discovered. Some considerations when embarking on virtual screening and in silico experiments are also discussed.
doi:10.1002/ddr.20414
PMCID: PMC3090163  PMID: 21566705
drug discovery; in silico screening; SURFLEX-DOCK; DNA; G-quadruplex; high-throughput screening

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