Publications by authors named "Anurag Sethi"

32 Publications

Supervised enhancer prediction with epigenetic pattern recognition and targeted validation.

Nat Methods 2020 08 29;17(8):807-814. Epub 2020 Jul 29.

Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.

Enhancers are important non-coding elements, but they have traditionally been hard to characterize experimentally. The development of massively parallel assays allows the characterization of large numbers of enhancers for the first time. Here, we developed a framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features. We integrated these features with supervised machine-learning algorithms to predict enhancers. We further demonstrated that our model could be transferred to predict enhancers in mammals. We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mice and transduction-based reporter assays in human cell lines (153 enhancers in total). The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription factor binding patterns at predicted enhancers versus promoters. We demonstrated that these patterns enable the construction of a secondary model that effectively distinguishes enhancers and promoters.
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http://dx.doi.org/10.1038/s41592-020-0907-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073243PMC
August 2020

An integrative ENCODE resource for cancer genomics.

Nat Commun 2020 07 29;11(1):3696. Epub 2020 Jul 29.

Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06520, USA.

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.
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http://dx.doi.org/10.1038/s41467-020-14743-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391744PMC
July 2020

The promise and reality of therapeutic discovery from large cohorts.

J Clin Invest 2020 02;130(2):575-581

Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.
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http://dx.doi.org/10.1172/JCI129196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994121PMC
February 2020

Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data.

Curr Protoc Bioinformatics 2017 12 8;60:11.16.1-11.16.32. Epub 2017 Dec 8.

Seven Bridges Genomics Inc, Cambridge, Massachusetts.

Next-generation sequencing has produced petabytes of data, but accessing and analyzing these data remain challenging. Traditionally, researchers investigating public datasets like The Cancer Genome Atlas (TCGA) would download the data to a high-performance cluster, which could take several weeks even with a highly optimized network connection. The National Cancer Institute (NCI) initiated the Cancer Genomics Cloud Pilots program to provide researchers with the resources to process data with cloud computational resources. We present protocols using one of these Cloud Pilots, the Seven Bridges Cancer Genomics Cloud (CGC), to find and query public datasets, bring your own data to the CGC, analyze data using standard or custom workflows, and benchmark tools for accuracy with interactive analysis features. These protocols demonstrate that the CGC is a data-analysis ecosystem that fully empowers researchers with a variety of areas of expertise and interests to collaborate in the analysis of petabytes of data. © 2017 by John Wiley & Sons, Inc.
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http://dx.doi.org/10.1002/cpbi.39DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5726550PMC
December 2017

The Cancer Genomics Cloud: Collaborative, Reproducible, and Democratized-A New Paradigm in Large-Scale Computational Research.

Cancer Res 2017 11;77(21):e3-e6

Seven Bridges Genomics, Cambridge, Massachusetts.

The Seven Bridges Cancer Genomics Cloud (CGC; www.cancergenomicscloud.org) enables researchers to rapidly access and collaborate on massive public cancer genomic datasets, including The Cancer Genome Atlas. It provides secure on-demand access to data, analysis tools, and computing resources. Researchers from diverse backgrounds can easily visualize, query, and explore cancer genomic datasets visually or programmatically. Data of interest can be immediately analyzed in the cloud using more than 200 preinstalled, curated bioinformatics tools and workflows. Researchers can also extend the functionality of the platform by adding their own data and tools via an intuitive software development kit. By colocalizing these resources in the cloud, the CGC enables scalable, reproducible analyses. Researchers worldwide can use the CGC to investigate key questions in cancer genomics. .
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http://dx.doi.org/10.1158/0008-5472.CAN-17-0387DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832960PMC
November 2017

Identifying Allosteric Hotspots with Dynamics: Application to Inter- and Intra-species Conservation.

Structure 2016 05 7;24(5):826-837. Epub 2016 Apr 7.

Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Computer Science, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA. Electronic address:

The rapidly growing volume of data being produced by next-generation sequencing initiatives is enabling more in-depth analyses of conservation than previously possible. Deep sequencing is uncovering disease loci and regions under selective constraint, despite the fact that intuitive biophysical reasons for such constraint are sometimes absent. Allostery may often provide the missing explanatory link. We use models of protein conformational change to identify allosteric residues by finding essential surface pockets and information-flow bottlenecks, and we develop a software tool that enables users to perform this analysis on their own proteins of interest. Though fundamentally 3D-structural in nature, our analysis is computationally fast, thereby allowing us to run it across the PDB and to evaluate general properties of predicted allosteric residues. We find that these tend to be conserved over diverse evolutionary time scales. Finally, we highlight examples of allosteric residues that help explain poorly understood disease-associated variants.
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http://dx.doi.org/10.1016/j.str.2016.03.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4883016PMC
May 2016

Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs.

Cell Syst 2016 Mar;2(3):147-157

Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520; Department of Computer Science, Yale University, New Haven, CT 06520.

Biological systems are complex. In particular, the interactions between molecular components often form dense networks that, more often than not, are criticized for being inscrutable 'hairballs'. We argue that one way of untangling these hairballs is through cross-disciplinary network comparison-leveraging advances in other disciplines to obtain new biological insights. In some cases, such comparisons enable the direct transfer of mathematical formalism between disciplines, precisely describing the abstract associations between entities and allowing us to apply a variety of sophisticated formalisms to biology. In cases where the detailed structure of the network does not permit the transfer of complete formalisms between disciplines, comparison of mechanistic interactions in systems for which we have significant day-to-day experience can provide analogies for interpreting relatively more abstruse biological networks. Here, we illustrate how these comparisons benefit the field with a few specific examples related to network growth, organizational hierarchies, and the evolution of adaptive systems.
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http://dx.doi.org/10.1016/j.cels.2016.02.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4817108PMC
March 2016

Reads meet rotamers: structural biology in the age of deep sequencing.

Curr Opin Struct Biol 2015 Dec 1;35:125-34. Epub 2015 Dec 1.

Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, United States; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States. Electronic address:

Structure has traditionally been interrelated with sequence, usually in the framework of comparing sequences across species sharing a common fold. However, the nature of information within the sequence and structure databases is evolving, changing the type of comparisons possible. In particular, we now have a vast amount of personal genome sequences from human populations and a greater fraction of new structures contain interacting proteins within large complexes. Consequently, we have to recast our conception of sequence conservation and its relation to structure-for example, focusing more on selection within the human population. Moreover, within structural biology there is less emphasis on the discovery of novel folds and more on relating structures to networks of protein interactions. We cover this changing mindset here.
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http://dx.doi.org/10.1016/j.sbi.2015.11.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4751031PMC
December 2015

Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma.

N Engl J Med 2016 Jan 4;374(2):135-45. Epub 2015 Nov 4.

Background: Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.

Methods: We performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis.

Results: Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH).

Conclusions: Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.).
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http://dx.doi.org/10.1056/NEJMoa1505917DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775252PMC
January 2016

Membrane-mediated regulation of the intrinsically disordered CD3ϵ cytoplasmic tail of the TCR.

Biophys J 2015 May;108(10):2481-2491

Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico; New Mexico Consortium, Los Alamos, New Mexico. Electronic address:

The regulation of T-cell-mediated immune responses depends on the phosphorylation of immunoreceptor tyrosine-based activation motifs (ITAMs) on T-cell receptors. Although many details of the signaling cascades are well understood, the initial mechanism and regulation of ITAM phosphorylation remains unknown. We used molecular dynamics simulations to study the influence of different compositions of lipid bilayers on the membrane association of the CD3ϵ cytoplasmic tails of the T-cell receptors. Our results show that binding of CD3ϵ to membranes is modulated by both the presence of negatively charged lipids and the lipid order of the membrane. Free-energy calculations reveal that the protein-membrane interaction is favored by the presence of nearby basic residues and the ITAM tyrosines. Phosphorylation minimizes membrane association, rendering the ITAM motif more accessible to binding partners. In systems mimicking biological membranes, the CD3ϵ chain localization is modulated by different facilitator lipids (e.g., gangliosides or phosphoinositols), revealing a plausible regulatory effect on activation through the regulation of lipid composition in cell membranes.
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http://dx.doi.org/10.1016/j.bpj.2015.03.059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457001PMC
May 2015

Comparative analysis of the transcriptome across distant species.

Nature 2014 Aug;512(7515):445-8

Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA.

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters.
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http://dx.doi.org/10.1038/nature13424DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155737PMC
August 2014

Deducing conformational variability of intrinsically disordered proteins from infrared spectroscopy with Bayesian statistics.

Chem Phys 2013 Aug;422

Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA ; Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.

As it remains practically impossible to generate ergodic ensembles for large intrinsically disordered proteins (IDP) with molecular dynamics (MD) simulations, it becomes critical to compare spectroscopic characteristics of the theoretically generated ensembles to corresponding measurements. We develop a Bayesian framework to infer the ensemble properties of an IDP using a combination of conformations generated by MD simulations and its measured infrared spectrum. We performed 100 different MD simulations totaling more than 10 µs to characterize the conformational ensemble of αsynuclein, a prototypical IDP, in water. These conformations are clustered based on solvent accessibility and helical content. We compute the amide-I band for these clusters and predict the thermodynamic weights of each cluster given the measured amide-I band. Bayesian analysis produces a reproducible and non-redundant set of thermodynamic weights for each cluster, which can then be used to calculate the ensemble properties. In a rigorous validation, these weights reproduce measured chemical shifts.
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http://dx.doi.org/10.1016/j.chemphys.2013.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810979PMC
August 2013

Binding and movement of individual Cel7A cellobiohydrolases on crystalline cellulose surfaces revealed by single-molecule fluorescence imaging.

J Biol Chem 2013 Aug 1;288(33):24164-72. Epub 2013 Jul 1.

Material Physics and Applications, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.

The efficient catalytic conversion of biomass to bioenergy would meet a large portion of energy requirements in the near future. A crucial step in this process is the enzyme-catalyzed hydrolysis of cellulose to glucose that is then converted into fuel such as ethanol by fermentation. Here we use single-molecule fluorescence imaging to directly monitor the movement of individual Cel7A cellobiohydrolases from Trichoderma reesei (TrCel7A) on the surface of insoluble cellulose fibrils to elucidate molecular level details of cellulase activity. The motion of multiple, individual TrCel7A cellobiohydrolases was simultaneously recorded with ∼15-nm spatial resolution. Time-resolved localization microscopy provides insights on the activity of TrCel7A on cellulose and informs on nonproductive binding and diffusion. We measured single-molecule residency time distributions of TrCel7A bound to cellulose both in the presence of and absence of cellobiose the major product and a potent inhibitor of Cel7A activity. Combining these results with a kinetic model of TrCel7A binding provides microscopic insight into interactions between TrCel7A and the cellulose substrate.
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http://dx.doi.org/10.1074/jbc.M113.455758DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3745362PMC
August 2013

Increased enzyme binding to substrate is not necessary for more efficient cellulose hydrolysis.

Proc Natl Acad Sci U S A 2013 Jul 19;110(27):10922-7. Epub 2013 Jun 19.

Biomass Conversion Research Laboratory (BCRL), Chemical Engineering and Materials Science, Michigan State University, Lansing, MI 48910, USA.

Substrate binding is typically one of the rate-limiting steps preceding enzyme catalytic action during homogeneous reactions. However, interfacial-based enzyme catalysis on insoluble crystalline substrates, like cellulose, has additional bottlenecks of individual biopolymer chain decrystallization from the substrate interface followed by its processive depolymerization to soluble sugars. This additional decrystallization step has ramifications on the role of enzyme-substrate binding and its relationship to overall catalytic efficiency. We found that altering the crystalline structure of cellulose from its native allomorph I(β) to III(I) results in 40-50% lower binding partition coefficient for fungal cellulases, but surprisingly, it enhanced hydrolytic activity on the latter allomorph. We developed a comprehensive kinetic model for processive cellulases acting on insoluble substrates to explain this anomalous finding. Our model predicts that a reduction in the effective binding affinity to the substrate coupled with an increase in the decrystallization procession rate of individual cellulose chains from the substrate surface into the enzyme active site can reproduce our anomalous experimental findings.
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http://dx.doi.org/10.1073/pnas.1213426110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703979PMC
July 2013

A mechanistic understanding of allosteric immune escape pathways in the HIV-1 envelope glycoprotein.

PLoS Comput Biol 2013 16;9(5):e1003046. Epub 2013 May 16.

Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.

The HIV-1 envelope (Env) spike, which consists of a compact, heterodimeric trimer of the glycoproteins gp120 and gp41, is the target of neutralizing antibodies. However, the high mutation rate of HIV-1 and plasticity of Env facilitates viral evasion from neutralizing antibodies through various mechanisms. Mutations that are distant from the antibody binding site can lead to escape, probably by changing the conformation or dynamics of Env; however, these changes are difficult to identify and define mechanistically. Here we describe a network analysis-based approach to identify potential allosteric immune evasion mechanisms using three known HIV-1 Env gp120 protein structures from two different clades, B and C. First, correlation and principal component analyses of molecular dynamics (MD) simulations identified a high degree of long-distance coupled motions that exist between functionally distant regions within the intrinsic dynamics of the gp120 core, supporting the presence of long-distance communication in the protein. Then, by integrating MD simulations with network theory, we identified the optimal and suboptimal communication pathways and modules within the gp120 core. The results unveil both strain-dependent and -independent characteristics of the communication pathways in gp120. We show that within the context of three structurally homologous gp120 cores, the optimal pathway for communication is sequence sensitive, i.e. a suboptimal pathway in one strain becomes the optimal pathway in another strain. Yet the identification of conserved elements within these communication pathways, termed inter-modular hotspots, could present a new opportunity for immunogen design, as this could be an additional mechanism that HIV-1 uses to shield vulnerable antibody targets in Env that induce neutralizing antibody breadth.
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http://dx.doi.org/10.1371/journal.pcbi.1003046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3656115PMC
March 2014

Viral escape from neutralizing antibodies in early subtype A HIV-1 infection drives an increase in autologous neutralization breadth.

PLoS Pathog 2013 Feb 28;9(2):e1003173. Epub 2013 Feb 28.

Immunology and Molecular Pathogenesis Graduate Program, Emory University, Atlanta, Georgia, United States of America.

Antibodies that neutralize (nAbs) genetically diverse HIV-1 strains have been recovered from a subset of HIV-1 infected subjects during chronic infection. Exact mechanisms that expand the otherwise narrow neutralization capacity observed during early infection are, however, currently undefined. Here we characterized the earliest nAb responses in a subtype A HIV-1 infected Rwandan seroconverter who later developed moderate cross-clade nAb breadth, using (i) envelope (Env) glycoproteins from the transmitted/founder virus and twenty longitudinal nAb escape variants, (ii) longitudinal autologous plasma, and (iii) autologous monoclonal antibodies (mAbs). Initially, nAbs targeted a single region of gp120, which flanked the V3 domain and involved the alpha2 helix. A single amino acid change at one of three positions in this region conferred early escape. One immunoglobulin heavy chain and two light chains recovered from autologous B cells comprised two mAbs, 19.3H-L1 and 19.3H-L3, which neutralized the founder Env along with one or three of the early escape variants carrying these mutations, respectively. Neither mAb neutralized later nAb escape or heterologous Envs. Crystal structures of the antigen-binding fragments (Fabs) revealed flat epitope contact surfaces, where minimal light chain mutation in 19.3H-L3 allowed for additional antigenic interactions. Resistance to mAb neutralization arose in later Envs through alteration of two glycans spatially adjacent to the initial escape signatures. The cross-neutralizing nAbs that ultimately developed failed to target any of the defined V3-proximal changes generated during the first year of infection in this subject. Our data demonstrate that this subject's first recognized nAb epitope elicited strain-specific mAbs, which incrementally acquired autologous breadth, and directed later B cell responses to target distinct portions of Env. This immune re-focusing could have triggered the evolution of cross-clade antibodies and suggests that exposure to a specific sequence of immune escape variants might promote broad humoral responses during HIV-1 infection.
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http://dx.doi.org/10.1371/journal.ppat.1003173DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3585129PMC
February 2013

Taste of sugar at the membrane: thermodynamics and kinetics of the interaction of a disaccharide with lipid bilayers.

Biophys J 2013 Feb;104(3):622-32

Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.

Sugar recognition at the membrane is critical in various physiological processes. Many aspects of sugar-membrane interaction are still unknown. We take an integrated approach by combining conventional molecular-dynamics simulations with enhanced sampling methods and analytical models to understand the thermodynamics and kinetics of a di-mannose molecule in a phospholipid bilayer system. We observe that di-mannose has a slight preference to localize at the water-phospholipid interface. Using umbrella sampling, we show the free energy bias for this preferred location to be just -0.42 kcal/mol, which explains the coexistence of attraction and exclusion mechanisms of sugar-membrane interaction. Accurate estimation of absolute entropy change of water molecules with a two-phase model indicates that the small energy bias is the result of a favorable entropy change of water molecules. Then, we incorporate results from molecular-dynamics simulation in two different ways to an analytical diffusion-reaction model to obtain association and dissociation constants for di-mannose interaction with membrane. Finally, we verify our approach by predicting concentration dependence of di-mannose recognition at the membrane that is consistent with experiment. In conclusion, we provide a combined approach for the thermodynamics and kinetics of a weak ligand-binding system, which has broad implications across many different fields.
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http://dx.doi.org/10.1016/j.bpj.2012.12.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566452PMC
February 2013

Identification of minimally interacting modules in an intrinsically disordered protein.

Biophys J 2012 Aug;103(4):748-57

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.

The conformational characterization of intrinsically disordered proteins (IDPs) is complicated by their conformational heterogeneity and flexibility. If an IDP could somehow be divided into smaller fragments and reconstructed later, theoretical and spectroscopic studies could probe its conformational variability in detail. Here, we used replica molecular-dynamics simulations and network theory to explore whether such a divide-and-conquer strategy is feasible for α-synuclein, a prototypical IDP. We characterized the conformational variability of α-synuclein by conducting >100 unbiased all-atom molecular-dynamics simulations, for a total of >10 μs of trajectories. In these simulations, α-synuclein formed a heterogeneous ensemble of collapsed coil states in an aqueous environment. These states were stabilized by heterogeneous contacts between sequentially distant regions. We find that α-synuclein contains residual secondary structures in the collapsed states, and the heterogeneity in the collapsed state makes it feasible to split α-synuclein into sequentially contiguous minimally interacting fragments. This study reveals previously unknown characteristics of α-synuclein and provides a new (to our knowledge) approach for studying other IDPs.
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http://dx.doi.org/10.1016/j.bpj.2012.06.052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3443776PMC
August 2012

A coarse-grained model for synergistic action of multiple enzymes on cellulose.

Biotechnol Biofuels 2012 Aug 1;5(1):55. Epub 2012 Aug 1.

Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

Background: Degradation of cellulose to glucose requires the cooperative action of three classes of enzymes, collectively known as cellulases. Endoglucanases randomly bind to cellulose surfaces and generate new chain ends by hydrolyzing β-1,4-D-glycosidic bonds. Exoglucanases bind to free chain ends and hydrolyze glycosidic bonds in a processive manner releasing cellobiose units. Then, β-glucosidases hydrolyze soluble cellobiose to glucose. Optimal synergistic action of these enzymes is essential for efficient digestion of cellulose. Experiments show that as hydrolysis proceeds and the cellulose substrate becomes more heterogeneous, the overall degradation slows down. As catalysis occurs on the surface of crystalline cellulose, several factors affect the overall hydrolysis. Therefore, spatial models of cellulose degradation must capture effects such as enzyme crowding and surface heterogeneity, which have been shown to lead to a reduction in hydrolysis rates.

Results: We present a coarse-grained stochastic model for capturing the key events associated with the enzymatic degradation of cellulose at the mesoscopic level. This functional model accounts for the mobility and action of a single cellulase enzyme as well as the synergy of multiple endo- and exo-cellulases on a cellulose surface. The quantitative description of cellulose degradation is calculated on a spatial model by including free and bound states of both endo- and exo-cellulases with explicit reactive surface terms (e.g., hydrogen bond breaking, covalent bond cleavages) and corresponding reaction rates. The dynamical evolution of the system is simulated by including physical interactions between cellulases and cellulose.

Conclusions: Our coarse-grained model reproduces the qualitative behavior of endoglucanases and exoglucanases by accounting for the spatial heterogeneity of the cellulose surface as well as other spatial factors such as enzyme crowding. Importantly, it captures the endo-exo synergism of cellulase enzyme cocktails. This model constitutes a critical step towards testing hypotheses and understanding approaches for maximizing synergy and substrate properties with a goal of cost effective enzymatic hydrolysis.
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http://dx.doi.org/10.1186/1754-6834-5-55DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3475064PMC
August 2012

Characterization of a disordered protein during micellation: interactions of α-synuclein with sodium dodecyl sulfate.

J Phys Chem B 2012 Apr 6;116(15):4417-24. Epub 2012 Apr 6.

Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

To better understand the interaction of α-synuclein (αSyn) with lipid membranes, we carried out self-assembly molecular dynamics simulations of αSyn with monomeric and micellar sodium dodecyl sulfate (SDS), a widely used membrane mimic. We find that both electrostatic and hydrophobic forces contribute to the interactions of αSyn with SDS. In the presence of αSyn, our simulations suggest that SDS aggregates along the protein chain and forms small-size micelles at very early times. Aggregation is followed by formation of a collapsed protein-SDS micelle complex, which is consistent with experimental results. Finally, interaction of αSyn with preformed micelles induces alterations in the shape of the micelle, and the N-terminal helix (residues 3 through 37) tends to associate with micelles. Overall, our simulations provide an atomistic description of the early time scale αSyn-SDS interaction during the self-assembly of SDS into micelles.
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http://dx.doi.org/10.1021/jp210339fDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3357070PMC
April 2012

Quantifying intramolecular binding in multivalent interactions: a structure-based synergistic study on Grb2-Sos1 complex.

PLoS Comput Biol 2011 Oct 13;7(10):e1002192. Epub 2011 Oct 13.

Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.

Numerous signaling proteins use multivalent binding to increase the specificity and affinity of their interactions within the cell. Enhancement arises because the effective binding constant for multivalent binding is larger than the binding constants for each individual interaction. We seek to gain both qualitative and quantitative understanding of the multivalent interactions of an adaptor protein, growth factor receptor bound protein-2 (Grb2), containing two SH3 domains interacting with the nucleotide exchange factor son-of-sevenless 1 (Sos1) containing multiple polyproline motifs separated by flexible unstructured regions. Grb2 mediates the recruitment of Sos1 from the cytosol to the plasma membrane where it activates Ras by inducing the exchange of GDP for GTP. First, using a combination of evolutionary information and binding energy calculations, we predict an additional polyproline motif in Sos1 that binds to the SH3 domains of Grb2. This gives rise to a total of five polyproline motifs in Sos1 that are capable of binding to the two SH3 domains of Grb2. Then, using a hybrid method combining molecular dynamics simulations and polymer models, we estimate the enhancement in local concentration of a polyproline motif on Sos1 near an unbound SH3 domain of Grb2 when its other SH3 domain is bound to a different polyproline motif on Sos1. We show that the local concentration of the Sos1 motifs that a Grb2 SH3 domain experiences is approximately 1000 times greater than the cellular concentration of Sos1. Finally, we calculate the intramolecular equilibrium constants for the crosslinking of Grb2 on Sos1 and use thermodynamic modeling to calculate the stoichiometry. With these equilibrium constants, we are able to predict the distribution of complexes that form at physiological concentrations. We believe this is the first systematic analysis that combines sequence, structure, and thermodynamic analyses to determine the stoichiometry of the complexes that are dominant in the cellular environment.
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http://dx.doi.org/10.1371/journal.pcbi.1002192DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3192808PMC
October 2011

The B cell response is redundant and highly focused on V1V2 during early subtype C infection in a Zambian seroconverter.

J Virol 2011 Jan 27;85(2):905-15. Epub 2010 Oct 27.

Emory Vaccine Center, Emory University, 954 Gatewood Rd., Atlanta, GA 30329, USA.

High-titer autologous neutralizing antibody responses have been demonstrated during early subtype C human immunodeficiency virus type 1 (HIV-1) infection. However, characterization of this response against autologous virus at the monoclonal antibody (MAb) level has only recently begun to be elucidated. Here we describe five monoclonal antibodies derived from a subtype C-infected seroconverter and their neutralizing activities against pseudoviruses that carry envelope glycoproteins from 48 days (0 month), 2 months, and 8 months after the estimated time of infection. Sequence analysis indicated that the MAbs arose from three distinct B cell clones, and their pattern of neutralization compared to that in patient plasma suggested that they circulated between 2 and 8 months after infection. Neutralization by MAbs representative of each B cell clone was mapped to two residues: position 134 in V1 and position 189 in V2. Mutational analysis revealed cooperative effects between glycans and residues at these two positions, arguing that they contribute to a single epitope. Analysis of the cognate gp120 sequence through homology modeling places this potential epitope near the interface between the V1 and V2 loops. Additionally, the escape mutation R189S in V2, which conferred resistance against all three MAbs, had no detrimental effect on virus replication in vitro. Taken together, our data demonstrate that independent B cells repeatedly targeted a single structure in V1V2 during early infection. Despite this assault, a single amino acid change was sufficient to confer complete escape with minimal impact on replication fitness.
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http://dx.doi.org/10.1128/JVI.02006-10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3020014PMC
January 2011

Genetic signatures in the envelope glycoproteins of HIV-1 that associate with broadly neutralizing antibodies.

PLoS Comput Biol 2010 Oct 7;6(10):e1000955. Epub 2010 Oct 7.

Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.

A steady increase in knowledge of the molecular and antigenic structure of the gp120 and gp41 HIV-1 envelope glycoproteins (Env) is yielding important new insights for vaccine design, but it has been difficult to translate this information to an immunogen that elicits broadly neutralizing antibodies. To help bridge this gap, we used phylogenetically corrected statistical methods to identify amino acid signature patterns in Envs derived from people who have made potently neutralizing antibodies, with the hypothesis that these Envs may share common features that would be useful for incorporation in a vaccine immunogen. Before attempting this, essentially as a control, we explored the utility of our computational methods for defining signatures of complex neutralization phenotypes by analyzing Env sequences from 251 clonal viruses that were differentially sensitive to neutralization by the well-characterized gp120-specific monoclonal antibody, b12. We identified ten b12-neutralization signatures, including seven either in the b12-binding surface of gp120 or in the V2 region of gp120 that have been previously shown to impact b12 sensitivity. A simple algorithm based on the b12 signature pattern was predictive of b12 sensitivity/resistance in an additional blinded panel of 57 viruses. Upon obtaining these reassuring outcomes, we went on to apply these same computational methods to define signature patterns in Env from HIV-1 infected individuals who had potent, broadly neutralizing responses. We analyzed a checkerboard-style neutralization dataset with sera from 69 HIV-1-infected individuals tested against a panel of 25 different Envs. Distinct clusters of sera with high and low neutralization potencies were identified. Six signature positions in Env sequences obtained from the 69 samples were found to be strongly associated with either the high or low potency responses. Five sites were in the CD4-induced coreceptor binding site of gp120, suggesting an important role for this region in the elicitation of broadly neutralizing antibody responses against HIV-1.
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http://dx.doi.org/10.1371/journal.pcbi.1000955DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2951345PMC
October 2010

Exit strategies for charged tRNA from GluRS.

J Mol Biol 2010 Apr 13;397(5):1350-71. Epub 2010 Feb 13.

Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

For several class I aminoacyl-tRNA synthetases (aaRSs), the rate-determining step in aminoacylation is the dissociation of charged tRNA from the enzyme. In this study, the following factors affecting the release of the charged tRNA from aaRSs are computationally explored: the protonation states of amino acids and substrates present in the active site, and the presence and the absence of AMP and elongation factor Tu. Through molecular modeling, internal pK(a) calculations, and molecular dynamics simulations, distinct, mechanistically relevant post-transfer states with charged tRNA bound to glutamyl-tRNA synthetase from Thermus thermophilus (Glu-tRNA(Glu)) are considered. The behavior of these nonequilibrium states is characterized as a function of time using dynamical network analysis, local energetics, and changes in free energies to estimate transitions that occur during the release of the tRNA. The hundreds of nanoseconds of simulation time reveal system characteristics that are consistent with recent experimental studies. Energetic and network results support the previously proposed mechanism in which the transfer of amino acid to tRNA is accompanied by the protonation of AMP to H-AMP. Subsequent migration of proton to water reduces the stability of the complex and loosens the interface both in the presence and in the absence of AMP. The subsequent undocking of AMP or tRNA then proceeds along thermodynamically competitive pathways. Release of the tRNA acceptor stem is further accelerated by the deprotonation of the alpha-ammonium group on the charging amino acid. The proposed general base is Glu41, a residue binding the alpha-ammonium group that is conserved in both structure and sequence across nearly all class I aaRSs. This universal handle is predicted through pK(a) calculations to be part of a proton relay system for destabilizing the bound charging amino acid following aminoacylation. Addition of elongation factor Tu to the aaRS.tRNA complex stimulates the dissociation of the tRNA core and the tRNA acceptor stem.
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http://dx.doi.org/10.1016/j.jmb.2010.02.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232055PMC
April 2010

Fast folding of an RNA tetraloop on a rugged energy landscape detected by a stacking-sensitive probe.

Biophys J 2009 Sep;97(5):1418-27

Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

We investigate the microsecond-timescale kinetics of the RNA hairpin ga*cUUCGguc. The fluorescent nucleotide 2-aminopurine (a*) reports mainly on base stacking. Ten kinetic traces and the temperature denaturation curve are globally fitted to four-state models of the free-energy surface. In the best-fitting sequential model, the hairpin unfolds over successively larger barriers in at least three stages: stem fraying and increased base-stacking fluctuations; concerted loss of hydrogen bonding and partial unstacking; and additional unstacking of single strands at the highest temperatures. Parallel and trap models also provide adequate fits: such pathways probably also play a role in the complete free-energy surface of the hairpin. To interpret the model states structurally, 200 ns of molecular dynamics, including six temperature-jump simulations, were run. Although the sampling is by no means comprehensive, five different states were identified using hydrogen bonding and base stacking as reaction coordinates. The four to five states required to explain the experiments or simulations set a lower limit on the complexity of this small RNA hairpin's energy landscape.
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http://dx.doi.org/10.1016/j.bpj.2009.06.035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2749769PMC
September 2009

Dynamical networks in tRNA:protein complexes.

Proc Natl Acad Sci U S A 2009 Apr 7;106(16):6620-5. Epub 2009 Apr 7.

Department of Chemistry,University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Community network analysis derived from molecular dynamics simulations is used to identify and compare the signaling pathways in a bacterial glutamyl-tRNA synthetase (GluRS):tRNA(Glu) and an archaeal leucyl-tRNA synthetase (LeuRS):tRNA(Leu) complex. Although the 2 class I synthetases have remarkably different interactions with their cognate tRNAs, the allosteric networks for charging tRNA with the correct amino acid display considerable similarities. A dynamic contact map defines the edges connecting nodes (amino acids and nucleotides) in the physical network whose overall topology is presented as a network of communities, local substructures that are highly intraconnected, but loosely interconnected. Whereas nodes within a single community can communicate through many alternate pathways, the communication between monomers in different communities has to take place through a smaller number of critical edges or interactions. Consistent with this analysis, there are a large number of suboptimal paths that can be used for communication between the identity elements on the tRNAs and the catalytic site in the aaRS:tRNA complexes. Residues and nucleotides in the majority of pathways for intercommunity signal transmission are evolutionarily conserved and are predicted to be important for allosteric signaling. The same monomers are also found in a majority of the suboptimal paths. Modifying these residues or nucleotides has a large effect on the communication pathways in the protein:RNA complex consistent with kinetic data.
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http://dx.doi.org/10.1073/pnas.0810961106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2672494PMC
April 2009

Molecular signatures of ribosomal evolution.

Proc Natl Acad Sci U S A 2008 Sep 3;105(37):13953-8. Epub 2008 Sep 3.

Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Ribosomal signatures, idiosyncrasies in the ribosomal RNA (rRNA) and/or proteins, are characteristic of the individual domains of life. As such, insight into the early evolution of the domains can be gained from a comparative analysis of their respective signatures in the translational apparatus. In this work, we identify signatures in both the sequence and structure of the rRNA and analyze their contributions to the universal phylogenetic tree using both sequence- and structure-based methods. Domain-specific ribosomal proteins can be considered signatures in their own right. Although it is commonly assumed that they developed after the universal ribosomal proteins, we present evidence that at least one may have been present before the divergence of the organismal lineages. We find correlations between the rRNA signatures and signatures in the ribosomal proteins showing that the rRNA signatures coevolved with both domain-specific and universal ribosomal proteins. Finally, we show that the genomic organization of the universal ribosomal components contains these signatures as well. From these studies, we propose the ribosomal signatures are remnants of an evolutionary-phase transition that occurred as the cell lineages began to coalesce and so should be reflected in corresponding signatures throughout the fabric of the cell and its genome.
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http://dx.doi.org/10.1073/pnas.0804861105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2528867PMC
September 2008

Dynamics of Recognition between tRNA and elongation factor Tu.

J Mol Biol 2008 Apr 4;377(5):1382-405. Epub 2008 Feb 4.

Center for Biophysics and Computational Biology, Urbana, IL, USA.

Elongation factor Tu (EF-Tu) binds to all standard aminoacyl transfer RNAs (aa-tRNAs) and transports them to the ribosome while protecting the ester linkage between the tRNA and its cognate amino acid. We use molecular dynamics simulations to investigate the dynamics of the EF-Tu.guanosine 5'-triphosphate.aa-tRNA(Cys) complex and the roles played by Mg2+ ions and modified nucleosides on the free energy of protein.RNA binding. Individual modified nucleosides have pronounced effects on the structural dynamics of tRNA and the EF-Tu.Cys-tRNA(Cys) interface. Combined energetic and evolutionary analyses identify the coevolution of residues in EF-Tu and aa-tRNAs at the binding interface. Highly conserved EF-Tu residues are responsible for both attracting aa-tRNAs as well as providing nearby nonbonded repulsive energies that help fine-tune molecular attraction at the binding interface. In addition to the 3' CCA end, highly conserved tRNA nucleotides G1, G52, G53, and U54 contribute significantly to EF-Tu binding energies. Modification of U54 to thymine affects the structure of the tRNA common loop resulting in a change in binding interface contacts. In addition, other nucleotides, conserved within certain tRNA specificities, may be responsible for tuning aa-tRNA binding to EF-Tu. The trend in EF-Tu.Cys-tRNA(Cys) binding energies observed as the result of mutating the tRNA agrees with experimental observation. We also predict variations in binding free energies upon misacylation of tRNA(Cys) with d-cysteine or O-phosphoserine and upon changing the protonation state of l-cysteine. Principal components analysis in each case reveals changes in the communication network across the protein.tRNA interface and is the basis for the entropy calculations.
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http://dx.doi.org/10.1016/j.jmb.2008.01.073DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232051PMC
April 2008

A network of conserved interactions regulates the allosteric signal in a glutamine amidotransferase.

Biochemistry 2007 Feb 30;46(8):2156-73. Epub 2007 Jan 30.

Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.

We have combined equilibrium and steered molecular dynamics (SMD) simulations with principal component and correlation analyses to probe the mechanism of allosteric regulation in imidazole glycerol phosphate (IGP) synthase. An evolutionary analysis of IGP synthase revealed a conserved network of interactions leading from the effector binding site to the glutaminase active site, forming conserved communication pathways between the remote active sites. SMD simulations of the undocking of the ribonucleotide effector N1-[(5'-phosphoribulosyl)-formino]-5'-aminoimidazole carboxamide ribonucleotide (PRFAR) resulted in a large scale hinge-opening motion at the interface. Principal component analysis and a correlation analysis of the equilibration protein motion indicate that the dynamics involved in the allosteric transition are mediated by coupled motion between sites that are more than 25 A apart. Furthermore, conserved residues at the substrate-binding site, within the barrel, and at the interface were found to exhibit highly correlated motion during the allosteric transition. The coupled motion between PRFAR unbinding and the directed opening of the interface is interpreted in combination with kinetic assays for the wild-type and mutant systems to develop a model of allosteric regulation in IGP synthase that is monitored and investigated with atomic resolution.
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http://dx.doi.org/10.1021/bi061708eDOI Listing
February 2007

The evolutionary history of Cys-tRNACys formation.

Proc Natl Acad Sci U S A 2005 Dec;102(52):19003-8

Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

The recent discovery of an alternate pathway for indirectly charging tRNA(Cys) has stimulated a re-examination of the evolutionary history of Cys-tRNA(Cys) formation. In the first step of the pathway, O-phosphoseryl-tRNA synthetase charges tRNA(Cys) with O-phosphoserine (Sep), a precursor of the cognate amino acid. In the following step, Sep-tRNA:Cys-tRNA synthase (SepCysS) converts Sep to Cys in a tRNA-dependent reaction. The existence of such a pathway raises several evolutionary questions, including whether the indirect pathway is a recent evolutionary invention, as might be implied from its localization to the Euryarchaea, or, as evidence presented here indicates, whether this pathway is more ancient, perhaps already in existence at the time of the last universal common ancestral state. A comparative phylogenetic approach is used, combining evolutionary information from protein sequences and structures, that takes both the signature of horizontal gene transfer and the recurrence of the full canonical phylogenetic pattern into account, to document the complete evolutionary history of cysteine coding and understand the nature of this process in the last universal common ancestral state. Resulting from the historical study of tRNA(Cys) aminoacylation and the integrative perspective of sequence, structure, and function are 3D models of O-phosphoseryl-tRNA synthetase and SepCysS, which provide experimentally testable predictions regarding the identity and function of key active-site residues in these proteins. The model of SepCysS is used to suggest a sulfhydrylation reaction mechanism, which is predicted to occur at the interface of a SepCysS dimer.
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http://dx.doi.org/10.1073/pnas.0509617102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1323144PMC
December 2005