Weak agreement between Antivirogram and Phenosense assays in predicting reduced susceptibility to antiretroviral drugs.
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Anti-HIV Agents↗
Biomedical subjects
Ram Samudrala
Publications and source records attributed to Ram Samudrala.
Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance.
Drug resistance is a major obstacle to the successful treatment of HIV-1 infection. Genotypic assays are used widely to provide indirect evidence of drug resistance, but the performance of these assays has been mixed. We used standard stepwise linear regression to construct drug resistance models for seven protease inhibitors and 10 reverse transcriptase inhibitors using data obtained from the Stanford HIV drug resistance database. We evaluated these models by hold-one-out experiments and by tests on an independent dataset. Our linear model outperformed other publicly available genotypic interpretation algorithms, including decision tree, support vector machine and four rules-based algorithms (HIVdb, VGI, ANRS and Rega) under both tests. Interestingly, our model did well despite the absence of any terms for interactions between different residues in protease or reverse transcriptase. The resulting linear models are easy to understand and can potentially assist in choosing combination therapy regimens.
Algorithms↗
Antivirogram or phenosense: a comparison of their reproducibility and an analysis of their correlation.
The Antivirogram and PhenoSense assays are widely used phenotypic tests for HIV drug resistance. There are limited data on the reproducibility of each assay, and little is known about the correlation between the two. Using data from the Stanford HIV drug resistance database, we performed a comprehensive analysis of the reproducibility of each assay, and calculated the correlation and concordance of the two assays using both general IC50 fold change cutoff values and drug-specific cutoff values. Although the within-assay correlations were high (rank correlation coefficients r=0.94 and r=0.95 for the Antivirogram and PhenoSense assays, respectively), the between-assay correlation was considerably lower (r=0.36). Using drug-specific cutoff values for viruses classified as resistant by the Antivirogram or PhenoSense assays, respectively, only 71.4% [95% confidence intervals (95% CI): 58.7-82.1%] and 57.0% (95% CI: 45.3-68.1%) of the samples were classified as resistant using the other assay. The poor agreement between the assays was primarily due to the extremely poor correlation between these assays for samples with low resistance values (r=0.02 and r=0.61 for samples with the Antivirogram measurements lower or higher than 2.0, respectively). Since the cutoff values for both assays are relatively low, our analysis suggests that one should be very careful when interpreting measurements that are near the cutoff values for drug resistance.
Anti-HIV Agents↗
Identifying inhibitors of the SARS coronavirus proteinase.
The Severe Acute Respiratory Syndrome (SARS) is a serious respiratory illness that has recently been reported in parts of Asia and Canada. In this study, we use molecular dynamics (MD) simulations and docking techniques to screen 29 approved and experimental drugs against the theoretical model of the SARS CoV proteinase as well as the experimental structure of the transmissible gastroenteritis virus (TGEV) proteinase. Our predictions indicate that existing HIV-1 protease inhibitors, L-700,417 for instance, have high binding affinities and may provide good starting points for designing SARS CoV proteinase inhibitors.
Antiviral Agents↗
PROTINFO: Secondary and tertiary protein structure prediction.
Information about the secondary and tertiary structure of a protein sequence can greatly assist biologists in the generation and testing of hypotheses, as well as design of experiments. The PROTINFO server enables users to submit a protein sequence and request a prediction of the three-dimensional (tertiary) structure based on comparative modeling, fold generation and de novo methods developed by the authors. In addition, users can submit NMR chemical shift data and request protein secondary structure assignment that is based on using neural networks to combine the chemical shifts with secondary structure predictions. The server is available at http://protinfo.compbio.washington.edu.
Internet↗
Bioverse: Functional, structural and contextual annotation of proteins and proteomes.
Functional annotation is routinely performed for large-scale genomics projects and databases. Researchers working on more specific problems, for instance on an individual pathway or complex, also need to be able to quickly, completely and accurately annotate sequences. The Bioverse sequence annotation server (http://bioverse.compbio.washington.edu) provides a web-based interface to allow users to submit protein sequences to the Bioverse framework. Sequences are functionally and structurally annotated and potential contextual annotations are provided. Researchers can also submit candidate genomes for annotation of all proteins encoded by the genome (proteome).
Internet↗
Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations.
BACKGROUND: The accurate prediction of enzyme-substrate interaction energies is one of the major challenges in computational biology. This study describes the improvement of protein-ligand binding energy prediction by incorporating protein flexibility through the use of molecular dynamics (MD) simulations. RESULTS: Docking experiments were undertaken using the program AutoDock for twenty-five HIV-1 protease-inhibitor complexes determined by x-ray crystallography. Protein-rigid docking without any dynamics produced a low correlation of 0.38 between the experimental and calculated binding energies. Correlations improved significantly for all time scales of MD simulations of the receptor-ligand complex. The highest correlation coefficient of 0.87 between the experimental and calculated energies was obtained after 0.1 picoseconds of dynamics simulation. CONCLUSION: Our results indicate that relaxation of protein complexes by MD simulation is useful and necessary to obtain binding energies that are representative of the experimentally determined values.
Computer Simulation↗
Accurate and automated classification of protein secondary structure with PsiCSI.
PsiCSI is a highly accurate and automated method of assigning secondary structure from NMR data, which is a useful intermediate step in the determination of tertiary structures. The method combines information from chemical shifts and protein sequence using three layers of neural networks. Training and testing was performed on a suite of 92 proteins (9437 residues) with known secondary and tertiary structure. Using a stringent cross-validation procedure in which the target and homologous proteins were removed from the databases used for training the neural networks, an average 89% Q3 accuracy (per residue) was observed. This is an increase of 6.2% and 5.5% (representing 36% and 33% fewer errors) over methods that use chemical shifts (CSI) or sequence information (Psipred) alone. In addition, PsiCSI improves upon the translation of chemical shift information to secondary structure (Q3 = 87.4%) and is able to use sequence information as an effective substitute for sparse NMR data (Q3 = 86.9% without (13)C shifts and Q3 = 86.8% with only H(alpha) shifts available). Finally, errors made by PsiCSI almost exclusively involve the interchange of helix or strand with coil and not helix with strand (<2.5 occurrences per 10000 residues). The automation, increased accuracy, absence of gross errors, and robustness with regards to sparse data make PsiCSI ideal for high-throughput applications, and should improve the effectiveness of hybrid NMR/de novo structure determination methods. A Web server is available for users to submit data and have the assignment returned.
Automation↗
Genes for the cytoskeletal protein tubulin in the bacterial genus Prosthecobacter.
Tubulins, the protein constituents of the microtubule cytoskeleton, are present in all known eukaryotes but have never been found in the Bacteria or Archaea. Here we report the presence of two tubulin-like genes [bacterial tubulin a (btuba) and bacterial tubulin b (btubb)] in bacteria of the genus Prosthecobacter (Division Verrucomicrobia). In this study, we investigated the organization and expression of these genes and conducted a comparative analysis of the bacterial and eukaryotic protein sequences, focusing on their phylogeny and 3D structures. The btuba and btubb genes are arranged as adjacent loci within the genome along with a kinesin light chain gene homolog. RT-PCR experiments indicate that these three genes are cotranscribed, and a probable promoter was identified upstream of btuba. On the basis of comparative modeling data, we predict that the Prosthecobacter tubulins are monomeric, unlike eukaryotic alpha and beta tubulins, which form dimers and are therefore unlikely to form microtubule-like structures. Phylogenetic analyses indicate that the Prosthecobacter tubulins are quite divergent and do not support recent horizontal transfer of the genes from a eukaryote. The discovery of genes for tubulin in a bacterial genus may offer new insights into the evolution of the cytoskeleton.
Base Sequence↗
A comprehensive analysis of 40 blind protein structure predictions.
BACKGROUND: We thoroughly analyse the results of 40 blind predictions for which an experimental answer was made available at the fourth meeting on the critical assessment of protein structure methods (CASP4). Using our comparative modelling and fold recognition methodologies, we made 29 predictions for targets that had sequence identities ranging from 50% to 10% to the nearest related protein with known structure. Using our ab initio methodologies, we made eleven predictions for targets that had no detectable sequence relationships. RESULTS: For 23 of these proteins, we produced models ranging from 1.0 to 6.0 A root mean square deviation (RMSD) for the Calpha atoms between the model and the corresponding experimental structure for all or large parts of the protein, with model accuracies scaling fairly linearly with respect to sequence identity (i.e., the higher the sequence identity, the better the prediction). We produced nine models with accuracies ranging from 4.0 to 6.0 A Calpha RMSD for 60-100 residue proteins (or large fragments of a protein), with a prediction accuracy of 4.0 A Calpha RMSD for residues 1-80 for T110/rbfa. CONCLUSIONS: The areas of protein structure prediction that work well, and areas that need improvement, are discernable by examining how our methods have performed over the past four CASP experiments. These results have implications for modelling the structure of all tractable proteins encoded by the genome of an organism.
Amino Acid Sequence↗
Identification of amino acids in the Dr adhesin required for binding to decay-accelerating factor.
Members of the Dr family of adhesins of Escherichia coli recognize as a receptor the Dr(a) blood-group antigen present on the complement regulatory and signalling molecule, decay-accelerating factor (DAF). One member of this family, the Dr haemagglutinin, also binds to a second receptor, type IV collagen. Structure/function information regarding these adhesins has been limited and domains directly involved in the interaction with DAF have not been determined. We devised a strategy to identify amino acids in the Dr haemagglutinin that are specifically involved in the interaction with DAF. The gene encoding the adhesive subunit, draE, was subjected to random mutagenesis and used to complement a strain defective for its expression. The resulting mutants were enriched and screened to obtain those that do not bind to DAF, but retain binding to type IV collagen. Individual amino acid changes at positions 10, 63, 65, 75, 77, 79 and 131 of the mature DraE sequence significantly reduced the ability of the DraE adhesin to bind DAF, but not collagen. Over half of the mutants obtained had substitutions within amino acids 63-81. Analysis of predicted structures of DraE suggest that these proximal residues may cluster to form a binding domain for DAF.
Adhesins, Bacterial↗