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Ram Samudrala

Publications and source records attributed to Ram Samudrala.

At least 37 records · Page 2Linked to original sources
PubMed2005

Prediction of HIV-1 protease inhibitor resistance using a protein-inhibitor flexible docking approach.

Emergence of drug resistance remains one of the most challenging issues in the treatment of HIV-1 infection. Here we focus on resistance to HIV-1 protease inhibitors (PIs) at a molecular level, which can be analysed genotypically or phenotypically. Genotypic assays are based on the analysis of mutations associated with reduced drug susceptibility, but are problematic because of the numerous mutations and mutational patterns that confer drug resistance. Phenotypic resistance or susceptibility can be experimentally evaluated by measuring the amount of free drug bound to HIV-1 protease molecules, but this procedure is expensive and time-consuming. To overcome these problems, we have developed a docking protocol that takes protein-inhibitor flexibility into account to predict phenotypic drug resistance. For six FDA-approved Pls and a total of 1792 HIV-1 protease sequence mutants, we used a combination of inhibitor flexible docking and molecular dynamics (MD) simulations to calculate protein-inhibitor binding energies. Prediction results were expressed as fold changes of the calculated inhibitory constant (Ki), and the samples predicted to have fold-increase in calculated Ki above the fixed cut-off were defined as drug resistant. Our combined docking and MD protocol achieved accuracies ranging from 72-83% in predicting resistance/susceptibility for five of the six drugs evaluated. Evaluating the method only on samples where our predictions concurred with established knowledge-based methods resulted in increased accuracies of 83-94% for the six drugs. The results suggest that a physics-based approach, which is readily applicable to any novel PI and/or mutant, can be used judiciously with knowledge-based approaches that require experimental training data to devise accurate models of HIV-1 Pl resistance prediction.

Binding Sites↗
PubMed2005

Heptad-repeat-2 mutations enhance the stability of the enfuvirtide-resistant HIV-1 gp41 hairpin structure.

Enfuvirtide (T20) is a peptide-based fusion inhibitor derived from the heptad repeat 2 (HR2) region of HIV-1 glycoprotein 41 (gp41). The inhibitor binds to the gp41 heptad repeat 1 (HR1) region, thereby blocking viral HR1/HR2 association. Mutations in HR1 have been reported to cause enfuvirtide resistance and reduce viral fitness. In this study, we first showed that scores obtained by a residue-specific all-atom probability discriminatory function (RAPDF) may be used as a reliable predictor of structural stability of gp41 mutants by comparing it to experimentally determined melting temperatures, and as a reliable indicator of enfuvirtide resistance by comparing it to experimentally determined fusion inhibition and viral fitness levels. We then generated an initial set of 28 theoretical structures of the HR1/HR2 hairpin complex where each structure consists of one mutation on HR1 known to cause enfuvirtide resistance and a wild-type amino acid at the corresponding HR2 residue. Mutations were then introduced in the corresponding HR2 residue of each structure where the wild-type amino acid was changed to each of the other nineteen amino acids. The enfuvirtide-resistant HR1 mutants with compensatory mutations at the corresponding HR2 residues had better RAPDF scores than those HR1 mutants with wild-type HR2. This indicates that mutations in HR2 improve structural stability of the HR1/HR2 hairpin complex and may lead to enhanced enfuvirtide resistance when present with resistant HR1 mutations. Modification of the amino acid side chains that contribute to enfuvirtide resistance using the RAPDF scores as a guide may help design of a second generation of fusion inhibitors against the enfuvirtide-resistant strains.

Drug Resistance, Viral↗
PubMed2004

An enhanced Java graph applet interface for visualizing interactomes.

UNLABELLED: We have developed several new navigation features for a Java graph applet previously released for visualizing protein-protein interactions. This graph viewer can be used to navigate any molecular interactome dataset. We have successfully implemented this tool for exploring protein networks stored in the Bioverse interaction database. AVAILABILITY: http://bioverse.compbio.washington.edu/viewer CONTACT: ram@compbio.washington.edu.

Animals↗
PubMed2004

Improved protein structure selection using decoy-dependent discriminatory functions.

BACKGROUND: A key component in protein structure prediction is a scoring or discriminatory function that can distinguish near-native conformations from misfolded ones. Various types of scoring functions have been developed to accomplish this goal, but their performance is not adequate to solve the structure selection problem. In addition, there is poor correlation between the scores and the accuracy of the generated conformations. RESULTS: We present a simple and nonparametric formula to estimate the accuracy of predicted conformations (or decoys). This scoring function, called the density score function, evaluates decoy conformations by performing an all-against-all Calpha RMSD (Root Mean Square Deviation) calculation in a given decoy set. We tested the density score function on 83 decoy sets grouped by their generation methods (4state_reduced, fisa, fisa_casp3, lmds, lattice_ssfit, semfold and Rosetta). The density scores have correlations as high as 0.9 with the Calpha RMSDs of the decoy conformations, measured relative to the experimental conformation for each decoy. We previously developed a residue-specific all-atom probability discriminatory function (RAPDF), which compiles statistics from a database of experimentally determined conformations, to aid in structure selection. Here, we present a decoy-dependent discriminatory function called self-RAPDF, where we compiled the atom-atom contact probabilities from all the conformations in a decoy set instead of using an ensemble of native conformations, with a weighting scheme based on the density scores. The self-RAPDF has a higher correlation with Calpha RMSD than RAPDF for 76/83 decoy sets, and selects better near-native conformations for 62/83 decoy sets. Self-RAPDF may be useful not only for selecting near-native conformations from decoy sets, but also for fold simulations and protein structure refinement. CONCLUSIONS: Both the density score and the self-RAPDF functions are decoy-dependent scoring functions for improved protein structure selection. Their success indicates that information from the ensemble of decoy conformations can be used to derive statistical probabilities and facilitate the identification of near-native structures.

Computational Biology↗
PubMed2004

Genomic organization and sequence variation of the human integrin subunit alpha8 gene (ITGA8).

The integrin alpha8 is highly expressed during kidney and lung development. alpha8-deficient mice display abnormal renal development suggesting that alpha8 plays a critical role in organogenesis. Therefore, it would be of considerable interest to understand the genomic structure, localization and sequence variation of the alpha8 gene. Using FISH and genomic database analysis, we show that alpha8 gene maps to chromosome 10p13 and consists of >200 kbp organized into 30 exons. Examination of 47 individuals from two different ethnic groups (European and African descent) identified 286 varying sites. The diversity of alpha8 is comparable to that of other regions within the human genome. Eight of the varying sites were located in the coding regions: six resulted in nonsynonymous substitutions of which two lead to non-conservative changes in protein. None of the sites showed significant deviation from Hardy-Weinberg equilibrium. We mapped the coding region single nucleotide polymorphisms (SNPs) onto a model of the predicted alpha8 structure and found all the SNPs were located in the "calf" of the extracellular domain. In the European population, the linkage disequilibrium statistic D' showed three blocks of relatively non-recombinant regions in the alpha8 gene while the African population showed more evidence of recombination. The observed patterns of the linkage disequilibrium statistic R2 suggest that a large number of sites will need to be genotyped to ensure coverage of the entire gene for genetic association studies. Identification of the sequence variation will allow genetic association studies of alpha8 in kidney and lung disease.

Base Sequence↗
PubMed2004

Enhanced functional information from predicted protein networks.

Experimentally derived genome-wide protein interaction networks have been useful in the elucidation of functional information that is not evident from examining individual proteins but determination of these networks is complex and time consuming. To address this problem, several computational methods for predicting protein networks in novel genomes have been developed. A recent publication by Date and Marcotte describes the use of phylogenetic profiling for elucidating novel pathways in proteomes that have not been experimentally characterized. This method, in combination with other computational methods for generating protein-interaction networks, might help identify novel functional pathways and enhance functional annotation of individual proteins.

Energy Metabolism↗
PubMed2004

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↗
PubMed2004

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↗
PubMed2003

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↗
PubMed2003

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↗
PubMed2003

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↗
PubMed2003

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↗
PubMed2003

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↗