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Results for “In silico screening”

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PubMed2005

Paucity of chimeric gene-transposable element transcripts in the Drosophila melanogaster genome.

BACKGROUND: Recent analysis of the human and mouse genomes has shown that a substantial proportion of protein coding genes and cis-regulatory elements contain transposable element (TE) sequences, implicating TE domestication as a mechanism for the origin of genetic novelty. To understand the general role of TE domestication in eukaryotic genome evolution, it is important to assess the acquisition of functional TE sequences by host genomes in a variety of different species, and to understand in greater depth the population dynamics of these mutational events. RESULTS: Using an in silico screen for host genes that contain TE sequences, we identified a set of 63 mature "chimeric" transcripts supported by expressed sequence tag (EST) evidence in the Drosophila melanogaster genome. We found a paucity of chimeric TEs relative to expectations derived from non-chimeric TEs, indicating that the majority (approximately 80%) of TEs that generate chimeric transcripts are deleterious and are not observed in the genome sequence. Using a pooled-PCR strategy to assay the presence of gene-TE chimeras in wild strains, we found that over half of the observed chimeric TE insertions are restricted to the sequenced strain, and approximately 15% are found at high frequencies in North American D. melanogaster populations. Estimated population frequencies of chimeric TEs did not differ significantly from non-chimeric TEs, suggesting that the distribution of fitness effects for the observed subset of chimeric TEs is indistinguishable from the general set of TEs in the genome sequence. CONCLUSION: In contrast to mammalian genomes, we found that fewer than 1% of Drosophila genes produce mRNAs that include bona fide TE sequences. This observation can be explained by the results of our population genomic analysis, which indicates that most potential chimeric TEs in D. melanogaster are deleterious but that a small proportion may contribute to the evolution of novel gene sequences such as nested or intercalated gene structures. Our results highlight the need to establish the fixity of putative cases of TE domestication identified using genome sequences in order to demonstrate their functional importance, and reveal that the contribution of TE domestication to genome evolution may vary drastically among animal taxa.

Animals↗

PubMed2022

Reversal of cancer gene expression identifies repurposed drugs for diffuse intrinsic pontine glioma.

Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery.

Humans↗

PubMed2004

Insight into mutation-induced activation of the luteinizing hormone receptor: molecular simulations predict the functional behavior of engineered mutants at M398.

In this study, molecular simulations have been combined with site-directed mutagenesis experiments to explore M398(2.43), a LH (lutropin) receptor (LHR) site in helix 2 susceptible to spontaneous activating mutations, and to develop a computational tool for predicting the functionality (i.e. active or nonactive) of LHR mutants.Site-directed mutagenesis experiments engineered 15 different substitutions for M389(2.43), which resulted in variable levels of constitutive activity, inversely correlated with the size of the replacing amino acid. This inverse correlation is suggested to be mediated by I460(3.46), M571(6.37), and Y623(7.53), the tyrosine of the NPxxY motif. In fact, size reduction at position 398(2.43), which is concurrent with constitutive receptor activity, releases the van der Waals interactions found in the wild-type LHR between M398(2.43) and these three amino acids, resulting in structural modifications in the proximity to the E/DRY/W motif. An increment, above a threshold value, in the solvent accessibility of the cytosolic ends of helices 3 and 6 is the main structural feature shared by the active mutants of the LHR. This feature has been successfully used for predicting the functionality of the engineered mutants at M398(2.43), proving that molecular simulations can be useful for in silico screening of LHR mutants.

Amino Acid Motifs↗

PubMed2004

Identification of endogenous surrogate ligands for human P2Y receptors through an in silico search.

G protein-coupled receptors (GPCRs) are distributed widely throughout the human body, and nearly 50% of current medicines act on a GPCR. GPCRs are considered to consist of seven transmembrane alpha-helices that form an alpha-helical bundle in which agonists and antagonists bind. A 3D structure of the target GPCR is indispensable for designing novel medicines acting on a GPCR. We have previously constructed the 3D structure of human P2Y(1) (hP2Y(1)) receptor, a GPCR, by homology modeling with the 3D structure of bovine rhodopsin as a template. In the present study, we have employed an in silico screening for compounds that could bind to the hP2Y(1)-receptor model using AutoDock 3.0. We selected 21 of the 30 top-ranked compounds, and by measuring intracellular Ca(2+) concentration, we identified 12 compounds that activated or blocked the hP2Y(1) receptor stably expressed in recombinant CHO cells. 5-Phosphoribosyl-1-pyrophosphate (PRPP) was found to activate the hP2Y(1) receptor with a low ED(50) value of 15 nM. The Ca(2+) assays showed it had no significant effect on P2Y(2), P2Y(6), or P2X(2) receptors, but acted as a weak agonist on the P2Y(12) receptor. This is the first study to rationally identify surrogate ligands for the P2Y-receptor family.

Animals↗

PubMed2005

Identification and functional analyses of two cDNAs that encode fatty acid 9-/13-hydroperoxide lyase (CYP74C) in rice.

Fatty acid hydroperoxide lyase (HPL), a member of cytochrome P450 (CYP74), produces aldehydes and oxo-acids involved in plant defensive reactions. In monocots, HPL that cleaves 13-hydroperoxides of fatty acids has been reported, but HPL that cleaves 9-hydroperoxides is still unknown. To find this type of HPL, in silico screening of candidate cDNA clones and subsequent functional analyses of recombinant proteins were performed. We found that AK105964 and AK107161 (Genbank accession numbers), cDNAs previously annotated as allene oxide synthase (AOS) in rice, are distinctively grouped from AOS and 13-HPL. Recombinant proteins of these cDNAs produced in Escherichia. coli cleaved both 9- and 13-hydroperoxide of linoleic and linolenic into aldehydes, while having only a trace level of AOS activity and no divinyl ether synthase activity. Hence we designated AK105964 and AK107161 OsHPL1 and OsHPL2 respectively. They are the first CYP74C family cDNAs to be found in monocots.

Amino Acid Sequence↗

PubMed2005

Highly active mutants of carbonyl reductase S1 with inverted coenzyme specificity and production of optically active alcohols.

A wild type NADPH-dependent carbonyl reductase from Candida magnoliae (reductase S1) has been found not to utilize NADH as a coenzyme. A mutation to exchange the coenzyme specificity in reductase S1 has been designed by computer-aided methods, including three-dimensional structure modeling and in silico screening of enzyme mutants. Site-directed mutagenesis has been used to introduce systematic substitutions of seven or eight amino acid residues onto the adenosine-binding pocket of the enzyme according to rational computational design. The resulting S1 mutants show NADH-dependency and have lost their ability to utilize NADPH as a coenzyme, but retain those catalytic activities. Kinetic parameter V(max) and K(m) values of those mutants for NADH are 1/3- to 1/10-fold those of the wild type enzyme for NADPH. As a model system for industrial production of optically active alcohols, the S1 mutants can be applied to an asymmetric reduction of ketones, cooperating with a coenzyme-regeneration system that uses an NAD-dependent formate dehydrogenase.

Alcohol Oxidoreductases↗

PubMed2005

Homology modeling and SAR analysis of Schistosoma japonicum cathepsin D (SjCD) with statin inhibitors identify a unique active site steric barrier with potential for the design of specific inhibitors.

Proteases that digest the blood-meal of the parasitic fluke Schistosoma are potential targets for therapy of schistosomiasis, a disease of chronic morbidity in humans. We generated a three-dimensional model of the cathepsin D target protease of Schistosoma japonicum (SjCD) utilizing the crystal structure of human cathepsin D (huCD) in complex with pepstatin as template. A homology model was also generated for the related secreted aspartic protease 2 (SAP2) of the pathogenic yeast, Candida albicans . An initial panel of seven statin inhibitors, originally designed for huCD [Majer et al., Protein Sci. 6 (1997), pp. 1458-1466], was tested against the two pathogen proteases. One inhibitor showed poor reactivity with SjCD. Examination of the SjCD active-site cleft revealed that the poor inhibition was due to a unique steric barrier situated between the S2 and S4 subsites. An in silico screen of 20 potential statin scaffolds with the SjCD model and incorporating the steric barrier constraint was performed. Four inhibitors (SJ1-SJ4) were eventually synthesized and tested with SjCD, bovine CD and SAP2. Of these, SJ2 and SJ3 proved moderately more specific for SjCD over bovine CD, with IC 50 values of 15 and 60 nM, respectively. The unique steric barrier identified here provides a structural focus for further development of more specific SjCD inhibitors.

Amino Acid Sequence↗

PubMed2006

Small, nonpeptide p75NTR ligands induce survival signaling and inhibit proNGF-induced death.

Studies showing that neurotrophin binding to p75NTR can promote cell survival in the absence of Trk (tropomyosin-related kinase) receptors, together with recent structural data indicating that NGF may bind to p75NTR in a monovalent manner, raise the possibility that small molecule p75NTR ligands that positively regulate survival might be found. A pharmacophore designed to capture selected structural and physical chemical features of a neurotrophin domain known to interact with p75NTR was applied to in silico screening of small molecule libraries. Small, nonpeptide, monomeric compounds were identified that interact with p75NTR. In cells showing trophic responses to neurotrophins, the compounds promoted survival signaling through p75NTR-dependent mechanisms. In cells susceptible to proneurotrophin-induced death, compounds did not induce apoptosis but inhibited proneurotrophin-mediated death. These studies identify a unique range of p75NTR behaviors that can result from isolated receptor liganding and establish several novel therapeutic leads.

Animals↗

PubMed2002

Reliability of logP predictions based on calculated molecular descriptors: a critical review.

Correct QSAR analysis requires reliable measured or calculated logP values, being logP the most frequently utilized and most important physico-chemical parameter in such studies. Since the publication of theoretical fundamentals of logP prediction, many commercial software solutions are available. These programs are all based on experimental data of huge databases therefore the predicted logP values are mostly acceptable - especially for known structures and their derivatives. In this study we critically reviewed the published methods and compared the predictive power of commercial softwares (CLOGP, KOWWIN, SciLogP/ULTRA) to each other and to our recently developed automatic QS(P)AR program. We have selected a very diverse set of 625 known drugs (98%) and drug-like molecules with experimentally validated logP values. We have collected 78 reported "outliers" as well, which could not be predicted by the "traditional" methods. We used these data in the model building and validation. Finally, we used an external validation set of compounds missing from public databases. We emphasized the importance of data quality, descriptor calculation and selection, and presented a general, reliable descriptor selection and validation technique for such kind of studies. Our method is based on the strictest mathematical and statistical rules, fully automatic and after the initial settings there is no option for user intervention. Three approaches were applied: multiple linear regression, partial least squares analysis and artificial neural network. LogP predictions with a multiple linear regression model showed acceptable accuracy for new compounds therefore it can be used for "in-silico-screening" and/or planning virtual/combinatorial libraries.

Combinatorial Chemistry Techniques↗

PubMed2003

Chemical genomics strategy for the discovery of new anticancer agents.

Chemical genomics represents a cooperation of biology and chemistry to identify and intervene the biological targets. Small molecules with diverse structural characteristics should be used to validate the target through interfering with the biological processes. Because of the limitation of existing chemical libraries, the diversity can be exploited using both the molecular design techniques; structure-based design and ligand-based design. These methods can guide the selection of small molecules with optimal binding properties to desired biological targets. Studies of potential molecular targets for novel anticancer drug discovery including in silico screening, QSAR, and de novo design demonstrated the importance of chemical genomics strategy to find the chemical probes and drug lead compounds.

Animals↗

PubMed2004

Protein structure prediction in structure based drug design.

The human genome and other genome sequencing projects have generated huge amounts of protein sequence information. Recently, a structural genomics project that aims to determine at least one representative three-dimensional structure from every protein family experimentally has been started. Homology modeling will play an essential role in structure based drug design such as in silico screening; because based on these representative structures the three-dimensional structures of the remaining proteins encoded in the various genomes can be predicted by homology modeling. The results of the last Critical Assessment of Techniques for Protein Structure Prediction (CASP5) demonstrated that the quality of homology modeling prediction has improved; reaching a level of reliability that biologists can now confidently use homology modeling. With improvements in modeling software and the growing number of known protein structures, homology modeling is becoming a more and more powerful and reliable tool. The present paper discusses the features and roles of homology modeling in structure based drug design, and describes the CHIMERA and FAMS modeling systems as examples. For a sample application, homology modeling of non-structural proteins of the severe acute respiratory syndrome (SARS) coronavirus is discussed. Many biological functions involve formation of protein-protein complexes; in which the protein molecules behave dynamically in the course of binding. Therefore, an understanding of protein-protein interaction will be very important for structure based drug design. To this end, normal mode analysis is useful. The present paper discusses the prediction of protein-protein interaction using normal mode analysis and examples of applications are given.

Amino Acid Sequence↗

PubMed2003

Conformational flexibility models for the receptor in structure based drug design.

The problem of incorporating receptor flexibility in routine in silico screening of databases of small chemical compounds for the purposes of structure based drug design is still an unsolved problem. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered to represent receptor flexibility. In this paper we review protein flexibility models that have been developed to limit the number of additional search parameters. These models can be roughly divided into five different categories. These are a) use of soft receptors which relax energetic penalties due to steric clashes, b) selection of a few critical degrees of freedom in the receptor binding site, c) use of multiple receptor structures either individually or by combining them using an averaging scheme, d) use of modified molecular simulation methods, and e) use of collective degrees of freedom as a new basis of representation for protein flexibility. All these flexible receptor models strive to balance an improvement in the accuracy of the binding predictions with an increase in computational cost. In addition, other challenges such as the development of accurate solvation models and scoring functions make the receptor flexibility problem even harder.

Drug Design↗

PubMed2002

Scalable methods for the construction and analysis of virtual combinatorial libraries.

One can distinguish between two kinds of virtual combinatorial libraries: viable and accessible . Viable libraries are relatively small in size, are assembled from readily available reagents that have been filtered by the medicinal chemist, and often have a physical counterpart. Conversely, accessible libraries can encompass millions or billions of structures, typically include all possible reagents that are in principle compatible with a particular reaction scheme, and they can never be physically synthesized in their entirety. Although the analysis of viable virtual libraries is relatively straightforward, the handling of large accessible libraries requires methods that scale well with respect to library size. In this work, we present novel, efficient and scalable techniques for the construction, analysis, and in silico screening of massive virtual combinatorial libraries.

Combinatorial Chemistry Techniques↗

PubMed2004

Computational techniques for diversity analysis and compound classification.

Molecular similarity and diversity analysis has played a significant role in computer-aided drug discovery for more than a decade. Compound classification methods have also become increasingly important for the design and organization of compound databases and in silico screening. Here we review these related methodologies and discuss selected applications.

Cluster Analysis↗

PubMed2006

FAMS complex: a fully automated homology modeling system for protein complex structures.

The formation of a protein-protein complex is responsible for many biological functions; therefore, three-dimensional structures of protein complexes are essential for deeper understandings of protein functions and the mechanisms of diseases at the atomic level. However, compared with individual proteins, complex structures are difficult to solve experimentally because of technical limitations. Thus a method that can predict protein complex structures would be invaluable. In this study, we developed new software, FAMS Complex; a fully automated homology modeling system for protein complex structures consisting of two or more molecules. FAMS Complex requires only sequences and alignments of the target protein as input and constructs all molecules simultaneously and automatically. FAMS Complex is likely to become an essential tool for structure-based drug design, such as in silico screening to accelerate drug discovery before an experimental structure is solved. Moreover, in this post-genomic era when huge amounts of protein sequence information are available, a major goal is the determination of protein-protein interaction networks on a genomic scale. FAMS Complex will contribute to this goal, because its procedure is fully automated and so is suited for large-scale genome wide modeling.

Automation↗

PubMed2002

[CG17604 gene from Drosophila melanogaster--possible functional homolog of the yeast ZIP1 and SCP1 (SYCP1) mammalian genes, coding for synaptonemal complex proteins].

From data on the molecular organization of transverse filament proteins of the synaptonemal complex (SC)--Zip1 in yeast and SCP1 in mammals--and on the width of the central SC space in these organisms and in Drosophila, the putative molecular structure and size of a transverse filament protein of the SC in Drosophila has been inferred. Using genetic and molecular databases and software from the Internet, we carried out in silico screening for a candidate gene for the Drosophila transverse filament protein. The search in the 250-bp region overlapping the locus of this gene (sections 88E-89B) and containing 78 predicted genes has revealed only one gene, CG17604, whose protein meets all requirements for the transverse filament protein of the SC. It was suggested that gene CG17604 is gene c(3)G. In this case, gene c(3)G must be localized in section 89A7-8 of the cytological map of Drosophila melanogaster.

Animals↗

PubMed2002

Structural insights into the promiscuity and function of the human pregnane X receptor.

The pregnane X receptor (PXR) is a promiscuous nuclear receptor that responds to a wide variety of drugs, xenobiotics and endogenous compounds, and plays a critical role in mediating drug-drug interactions in humans. PXR is the master regulator of the expression of the CYP3A4 gene, which encodes for the most abundant and promiscuous drug-metabolizing enzyme in humans. PXR also regulates the expression of other genes involved in xenobiotic metabolism, including CYP2C8, CYP2C9, CYP2B6, GSTA2 and MDR1, as well as genes critical to bile acid metabolism. While PXR functions as a xenobiotic sensor in numerous vertebrates, its relatively low sequence conservation across species causes the PXRs from different organisms to respond to distinct subsets of xenobiotics. Thus, PXR promiscuity is directed and not random. The recent determination of crystal structures of the ligand binding domain of human PXR has provided the first detailed molecular view of this promiscuous receptor, and has advanced our understanding of its varied biological functions. We review the evidence establishing the binding promiscuity of PXR and its directed specificity in different species, and analyze the structural determinants of these characteristics. In addition, we examine the relationship between the interaction of PXR with ligands and the manner in which CYP3A4 is thought to bind to substrate molecules. The accumulating structural and functional data on PXR may facilitate the development of improved methods for in vitro, in vivo and in silico screening for PXR activation.

Animals↗

PubMed2003

Modeling the 3D structure of GPCRs: advances and application to drug discovery.

G protein-coupled receptors (GPCRs) are membrane-embedded proteins responsible for signal transduction; these receptors are, therefore, among the most important pharmaceutical drug targets. In the absence of X-ray structures, there have been numerous attempts to model the three-dimensional (3D) structure of GPCRs. In this review, the current status of GPCR modeling is evaluated, highlighting recent progress made in rhodopsin-based homology modeling and de novo modeling technology. Assessment of recent rhodopsin-based homology modeling studies indicates that, despite significant progress, these models do not yield hit rates that are sufficiently high for in silico screening (10 to 40% when screening for known binders). In contrast, the PREDICT modeling algorithm, which is independent of the rhodopsin structure, has now been fully validated in the context of drug discovery. PREDICT models are successfully used for drug discovery, yielding excellent hit rates (85 to 100% when screening for known binders), leading to the discovery of nanomolar-range new chemical entities for a variety of GPCR targets. Thus, 3D models of GPCRs should now allow the use of productive structure-based approaches for drug discovery.

Animals↗