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In complex physiological systems, causal associations between protein concentration and outcomes can be difficult to determine, as proteins can be both effectors of outcomes and responders. This problem becomes especially difficult when the outcome of interest is death because this is often preceded by the failure of multiple physiological systems. Our aim in this study was to determine the extent to which variation in plasma protein abundance can predict all-cause and disease-specific mortality, and to assess the potential causal nature of any observed associations. To identify biomarkers for mortality, we analyzed the largest plasma proteome dataset compiled to date: 1,459 proteins measured in 54,306 individuals with over 12 years of prospective follow-up data from the UK Biobank (during which 5,032 mortality events occurred). We carried out a multivariate stability selection analysis of all biomarkers and identified 14 proteins that were robustly predictive of all-cause mortality. These biomarkers remained significant after accounting for pre-existing conditions and medication usage. Participants in the highest quintile of mortality risk prediction had ~25 times higher number of events as compared to participants in the lowest quintile. Moreover, these proteins are more robustly associated with mortality than established diagnostic biomarkers such as glycated hemoglobin (HbA1c) and cholesterol. To complement our epidemiological analyses, we quantified the genetic determinants of plasma protein abundance and assessed causality via Mendelian randomization and mediation analysis using polygenic scores. Integrating multiple lines of evidence, we prioritized eight plasma protein biomarkers causally associated with human lifespan.

We describe methodology for joint reconstruction of physiological-survival networks from observational data capable of identifying key survival-associated variables, inferring a minimal physiological network structure, and bridging this network to the Gompertzian survival layer. Using synthetic network structures, we show that the method is capable of identifying aging variables in cohorts as small as 5,000 participants. Applying the methodology to the observational human cohort, we find that interleukin-6, vascular calcification, and red-blood distribution width are strong predictors of baseline fitness. More important, we find that red blood cell counts, kidney function, and phosphate level are directly linked to the Gompertzian aging rate. Our model therefore enables discovery of processes directly linked to the aging rate of our species. We further show that this epidemiological framework can be applied as a causal inference engine to simulate the effects of interventions on physiology and longevity.

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.

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.

BackgroundCalcification of the abdominal artery is an important contributor to cardiovascular disease in diabetic and chronic kidney disease (CKD) populations. However, prevalence of the pathology, risk factors, and long term disease outcomes in a general population have not been systematically analyzed.MethodWe developed machine learning models to quantify levels of abdominal aortic calcification (AAC) in 29,957 whole body dual-energy X-ray absorptiometry (DEXA) scans from the UK Biobank cohort. Using regression techniques we associated severity of calcification across a wide range of physiological parameters, clinical biomarkers, and environmental risk factors (406 in total). We performed a common variant genetic association study spanning 9,572,557 single-nucleotide polymorphisms to identify genetic loci relevant to AAC. We evaluated the prognostic value of AAC across 151 disease classes using Cox proportional hazard models. We further examined an epidemiological model of calcification on cardiovascular morbidity with and without LDL interactions.FindingsWe find evidence for AAC in >10.4% of the cohort despite low prevalence of diabetes (2.5%) and CKD (0.5%). Increased level of AAC is a strong prognostic indicator of cardiovascular outcomes for stenosis of precerebral arteries (HR~1.5), Myocardial Infarction (HR~1.5), & Ischemic Heart Disease (HR~1.33). We find that AAC is genetically correlated with cardiovascular-related traits and that the genetic signals are enriched in vascular and adipose tissue. We report three loci associated with AAC, with the strongest association occuring at theTWIST1/HDAC9locus (beta=0.078, p-value=1.4e-11) in a region also associated with coronary artery disease. Surprisingly, we find that elevated but still within clinically normal levels of serum phosphate and glycated hemoglobin are linked to increased vascular calcification. Furthermore, we show AAC arises in the absence of hypercholesterolemia. By our estimate, AAC is an LDL-independent risk factor for cardiovascular outcomes, with risk similar to elevated LDL.DataThis research has been conducted using the UK Biobank Resource.

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. Cancer Res; 77(21); e3–6. ©2017 AACR.

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., ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.

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.

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.

Structure has traditionally been interrelated with sequence, usually in the framework of comparing sequences across species sharing a common structural 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 larger 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 – e.g. 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.

Author(s): Sethi, Anurag; Gu, Mengting; Gumusgoz, Emrah; Chan, Landon; Yan, Koon-Kiu; Rozowsky, Joel; Barozzi, Iros; Afzal, Veena; Akiyama, Jennifer; Plajzer-Frick, Ingrid; Yan, Chengfei; Pickle, Catherine; Kato, Momoe; Garvin, Tyler; Pham, Quan; Harrington, Anne; Mannion, Brandon; Lee, Elizabeth; Fukuda-Yuzawa, Yoko; Visel, Axel; Dickel, Diane; Yip, Kevin; Sutton, Richard; Pennacchio, Len; Gerstein, Mark | Abstract: Enhancers are important noncoding elements, but they have been traditionally hard to characterize experimentally. Only a few mammalian enhancers have been validated, making it difficult to train statistical models for their identification properly. Instead, postulated patterns of genomic features have been used heuristically for identification. The development of massively parallel assays allows for the characterization of large numbers of enhancers for the first time. Here, we developed a framework that uses Drosophila STARR-seq data to create shape-matching filters based on enhancer-associated meta-profiles of epigenetic features. We combined these features with supervised machine learning algorithms (e.g., support vector machines) to predict enhancers. We demonstrated that our model could be applied to predict enhancers in mammalian species (i.e., mouse and human). We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mouse and transduction-based reporter assays in human cell lines. Overall, the validations involved 153 enhancers in 6 mouse tissues and 4 human cell lines. 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 and promoters in human cell lines. We demonstrated that these patterns enable the construction of a secondary model effectively discriminating between enhancers and promoters.

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.

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 features1, 2, 3, 4, 5, 6. 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. In particular, this work was funded by a contract from the National Human Genome Research Institute modENCODE Project, contract U01 HG004271 and U54 HG006944, to S.E.C. (principal investigator) and P.C., T.R.G., R.A.H. and B.R.G. (co-principal investigators) with additional support from R01 GM076655 (S.E.C.) both under Department of Energy contract no. DE-AC02-05CH11231, and U54 HG007005 to B.R.G. J.B.B.’s work was supported by NHGRI K99 HG006698 and DOE DE-AC02-05CH11231. Work in P.J.B.’s group was supported by the modENCODE DAC sub award 5710003102, 1U01HG007031-01 and the ENCODE DAC 5U01HG004695-04. Work in M.B.G.’s group was supported by NIH grants HG007000 and HG007355. Work in Bloomington was supported in part by the Indiana METACyt Initiative of Indiana University, funded by an award from the Lilly Endowment, Inc. Work in E.C.L.’s group was supported by U01-HG004261 and RC2-HG005639. P.J.P. acknowledges support from the National Institutes of Health (grant no. U01HG004258). We thank the HAVANA team for providing annotation of the human reference genome, whose work is supported by National Institutes of Health (grant no. 5U54HG004555), the Wellcome Trust (grant no. WT098051). R.G. acknowledges support from the Spanish Ministry of Education (grant BIO2011-26205). We also acknowledge use of the Yale University Biomedical High Performance Computing Center. R.W.'s lab was supported by grant no. U01 HG 004263.

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.

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.

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.

Patient-Derived Xenografts (PDX) are powerful models to study tumors' drug-response in the context of personalized medicine. In the PDX model settings, by virtue of expanding the patient's tumor sample, testing multiple drug or drug-combinations can be executed rapidly and has no ethical limitations. However, there are major issues around standards that need to be addressed to make these models widely accessible and usable. The overarching goal of the PDXNet is to coordinate the development of appropriate PDX models and methods for preclinical drug testing to advance CTEP clinical development of new cancer agents. In an effort to standardize protocols for PDX generation as well as data analysis and metadata harmonization, we are building a data storage, sharing, and analysis platform that harmonizes PDXNet data with other large datasets and analysis workflows. The PDX Data Commons is built on top of existing NCI resources, leveraging the Cancer Genomics Cloud maintained by Seven Bridges Genomics, where PDXNet data is co-located with TCGA and other large-scale datasets. The PDCCC is co-led by experts from The Jackson Laboratory, providing scientific leadership in xenograft methods and cancer biology to ensure the promulgation of standards that are well-suited for the PDX community. In addition, the PDCCC is responsible for establishing studies to identify best-practices for PDX data analysis and metadata schemas. The data collected as part of the PDXNet is currently stored on the PDXNet portal that has a query interface for identifying models for pre-clinical trials. Simultaneously, we administer training activities and research pilots to build synergies within the PDXNet, enhancing the ability of the PDXNet to develop clinical trials from PDX studies. In PDXNet, besides the PDCCC, there are 4 PDX Development and Trial Centers (PDTCs) responsible for executing specific pre-clinical trials focused around cancer types including breast cancer, melanoma, and lung cancer. Data generated by the PDTCs will be hosted by the PDCCC, and metadata will be collected based on schemas developed by the network for systematic ontological analysis. These PDX models, in coordination with the NCI Patient-Derived Models Repository (PDMR) at the Frederick National Laboratory for Cancer Research (FNLCR) will be shared with the broader community. In addition, PDTC's will collaborate with non-PDXNet investigators for PDX studies through an administrative supplement program supported by the NCI. The PDXNet is a strong step toward building a consensus around PDX models, so that the power for discovery can be expanded by making multi-institutional PDX cohorts a reality. The PDCCC is a central part of this process to systematically capture and analyze the variables most influential to PDX models and share protocols and tools to make PDXs an interchangeable research currency for pre-clinical discovery. Citation Format: Anurag Sethi, Anuj Srivastava, Xingyi Woo, Vishal Sarsani, Ziming Zhao, Javad Noorbakhsh, Christian French, Jack DiGiovanna, Ogan D. Abaan, Steve Neuhauser, Peter Robinson, Yvonne A. Evrard, Carol J. Bult, Jeffrey A. Moscow, Brandi Davis-Dusenbery, Jeffrey H. Chuang. The PDX Data Commons and Coordinating Center (PDCCC) for PDXNet [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1029.

The Cancer Genome Atlas (TCGA)network has generated more than2.5petabytes of genomic dataover the last decade, with petabyte-scale additions of dataexpected inthe coming years. Access and analysis of this informationonalocal compute environment is challenging due tothe volume of dataand lack of sufficient computing resources at many research organizations. The Cancer Genomics Cloud Pilot project from the National Cancer Institute (NCI)has helped indemocratizing access toTCGA by co-localizing datawith computational resources onthe cloud. Funded as part of this project, the SevenBridges Cancer Genomics Cloud (CGC)hosts nearly 5petabytes of public datafrom TCGA, the Simons Genome Diversity Project, the Therapeutically Applicable Research toGenerate Effective Treatments (TARGET)initiative, The Cancer Imaging Archive (TCIA), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The CGCprovides academic researchers with asecure, scalable, cloud-based cancer research platform that includes collaborative tools for accessing, uploading, analyzing, and visualizing data. The platform uses resource descriptionframeworks, dataharmonization, and metadatacurationtofacilitate effective querying, and bioinformatics tools are implemented onthe CGCusing the CommonWorkflow Language (CWL), an emerging standard for describing computational workflows, tosupport computational reproducibility. Since its launch in2016, the CGChas enabled researchers from around the world tounderstand humangenetics and cancer biology through the analysis of large public datasets and private datainacloud computing environment. Inthis poster, we present anexample of analysis of TCGAdataonthe CGC. The OptiType tool for HumanLeukocyte Antigenclass I typing was used toprofile 8,872RNA-Seq samples present inthe TCGAdataset. All samples were accurately processed within2days using arobust, fault-tolerant, and cost-efficient CWL descriptionof OptiType that enabled analysis for less than50cents per sample onaverage. This case study demonstrates how cloud computing resources canfacilitate the successful analysis of large cohorts of datausing custom pipelines in a robust, scalable, and reproducible manner. Citation Format: Raunaq Malhotra, Alexandar Krasnitz, Anurag Sethi, Erik Lehnert, Elizabeth H. Williams, Davis-Dusenbery N. Brandi. Low-cost and accurate human leukocyte antigen (HLA) class I typing of The Cancer Genome Atlas on the Seven Bridges Cancer Genomics Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2348.

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.

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.

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.

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.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.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).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.).

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.

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.

We present an algorithm to generate complete evolutionary profiles that represent the topology of the molecular phylogenetic tree of the homologous group. The method, based on the multidimensional QR factorization of numerically encoded multiple sequence alignments, removes redundancy from the alignments and orders the protein sequences by increasing linear dependence, resulting in the identification of a minimal basis set of sequences that spans the evolutionary space of the homologous group of proteins. We observe a general trend that these smaller, more evolutionarily balanced profiles have comparable and, in many cases, better performance in database searches than conventional profiles containing hundreds of sequences, constructed in an iterative and computationally intensive procedure. For more diverse families or superfamilies, with sequence identity

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. Background: Molecular level mechanisms underlying cellulose hydrolysis by cellulases remain poorly understood. Results: The majority of cellobiohydrolase molecules on cellulose surfaces were stationary. Conclusion: There is a need for improved understanding of cellulose properties resulting in large fractions of stalled cellulases. Significance: Dynamic single-molecule imaging of cellulases provides insights on productive/nonproductive binding and surface diffusion on cellulose.

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 α-synuclein, 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.

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., Author Summary Since cases were first recognized in the United States in 1981, human immunodeficiency virus (HIV-1) has infected over one million Americans. Globally, this scale reaches into the tens of millions, but no effective vaccine exists. Of those infected, approximately 20–30% of patients will develop broadly neutralizing antibodies. The reasons for maturation of these potentially protective responses are presently unknown, but being able to elicit such antibodies via vaccination could curb the pandemic. Here, we defined the earliest neutralizing antibody targets and the consequent routes of viral escape in one subtype A HIV-1 infected subject who developed modest breadth. We also determined the genetic and structural characteristics of early neutralizing monoclonal antibodies circulating in this subject and found that subtle light chain alteration enhanced target contact and neutralization. Overall, our data support the idea that exposure to a specific sequence of viral variants, which have escaped from immune pressure, could program long-term potential for antibody breadth.

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., Author Summary The Env glycoproteins, gp120 and gp41, are the viral targets of HIV neutralizing antibodies. Accordingly, vaccine studies have focused on eliciting broadly neutralizing antibodies against epitopes in these proteins. Sequence diversity and the conformational flexibility of Env have made vaccine design efforts difficult. It is well documented that mutations distant from defined epitopes can lead to escape from neutralizing antibodies. In such cases, allostery within the Env protein could play a dominant role. In this study, we characterized the dynamical network in gp120 in terms of how spatially distant regions communicate with each other. We introduced an approach based on coupling computer simulations to compare gp120 core structures of three different virus strains from two clades, clade B and C. Our study finds that the long-distance collective motions in the protein are functionally relevant and are conserved across diverse strains of gp120, the communication pathways associated with these motions are sensitive to its sequence. Importantly, we find that gp120 exhibits communication modules (communities) with key residues (hotspots) serving as conduits for communication between different communities, a possible strategy to exploit in future vaccine design efforts.

Background The HIV-1 envelope spike, which consists of a compact, heterodimeric trimer of the glycoprotein gp120 and gp41, is the sole viral target of neutralizing antibodies. The gp120 component of the viral spike is known to be heavily glycosylated, and glycosylation can affect the conformation of envelope spikes. V1/V2 variable loops of gp120 are key target regions for a number of broadly neutralizing human antibodies, such as PG9 and PG16, CH01-CH04, and PGT141-145. Two glycosylation sites (N156 and N160) have been shown by mutagenesis studies to be important in forming the PG9 and PG16 epitopes. Recently, Peter Kwong and coworkers have resolved crystal structure of V1/V2 domain of HIV-1 gp120 from strains CAP45 and ZM109 complexes with antigen-binding fragment of PG9.

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.

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.

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., Author Summary Many biochemical interactions are mediated by multivalent binding where signaling proteins use relatively weak promiscuous interactions to increase the strength and specificity of complex formation. For a bivalent adaptor protein binding to a multivalent ligand, the tethering of one of the adaptors binding sites to a motif on a multivalent ligand constrains the adaptors second binding site to a region with a high local concentration of ligand binding motifs. Intramolecular equilibrium constants associated with multivalency are difficult to measure. Typically, polymer models are utilized to estimate the enhancement in local concentration and, when the biomolecular equilibrium constants for the individual sites are known, to obtain intramolecular equilibrium constants. However, flexibility of structured regions in proteins that contain the binding motifs restricts the application of simple polymer models for many systems. Here, we develop a hybrid method combining molecular dynamics simulations and polymer models to estimate the intramolecular equilibrium constants. We apply this method to study the multivalent interactions between the widely expressed adaptor protein growth factor receptor bound protein-2 (Grb2) and the nucleotide exchange factor son of sevenless 1 (Sos1).

Many recognition sites are located in regions of the proteins that are intrinsically disordered and undergo folding upon binding to their targets. The absence of well-defined structures and conformational flexibilities of these intrinsically disordered regions (IDRs) enable these proteins to bind to multiple partners with high specificity and affinity. However, structural and functional characteristics of such regions are not well understood. Here we compare molecular simulations to polymer models in order to characterize α-Synuclein conformational ensembles. α-Synuclein is ideal for such a study since it is small and possesses many of the unique characteristics of IDRs. Based on long timescale all-atom molecular dynamics simulations of α-synuclein in explicit water, a hierarchical approach is developed to break the problem into more tractable pieces that can be characterized using a combination of simulation and experimental methods. Integration of secondary structure profiles, clustering and network analysis from MD have been utilized to divide α-Synuclein into minimally interacting fragments. Based on these simulations, α-Synuclein is more globular than polymer model prediction due to contacts that prevent exposure of regions prone to aggregation. These contacts depend on the initial conformation and temperature of the MD simulation. Monomeric α-Synuclein also has a high propensity to form and break β-strands in the same regions that form β-sheets in fibrils associated with Parkinson's disease. Also, the solvent-induced highly collapsed structure of α-Synuclein is held together by transient contacts between distant regions of the protein upto 100s of nanosenconds. We generate the amide-I band of the infrared (IR) and compare it to measured band to further facilitate IR spectral characterization of disordered regions. Finally, we illustrate how the conformational properties of a disordered region could be severally biased by the chosen force field.

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.

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.

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.

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.

A short chain peptide has been isolated from the venom of a red scorpion of Indian origin, Buthus tamulus. This peptide was purified using ion exchange and reverse phase chromatography and was characterized by molecular weight determination and amino acid sequence. The primary structure analysis shows that BtITx3 is a short peptide of 35 amino acid residues having a molecular weight of 3796 Da. The toxin shows toxicity towards the Lepidopteran species of insect Heli- coverpa armigera causing flaccid paralysis and even death within 24 h. It shows more than 50% homology with the short insectotoxins having four disulfide bridges, which suggests that the toxin belongs to the class of short chain toxins blocking the chloride ion channels. This sequence homology study has also helped to bring out the structure-function relationship between the various short toxins. Homology modeling done by using template structure of a known toxin indicated that this toxin consists of a similar alpha/beta scaffold, as present in other scorpion toxins. (C) 2002 on behalf of the Federation of European Biochemical Societies.

Important immune responses are linked to Src-mediated phosphorylation of cytoplasmic tyrosine-based motifs (ITAMs). However, the mechanism of how receptor ligation translates into ITAM phosphorylation remains elusive. Here, we use molecular dynamic simulations to explore the potential regulatory involvement of lipid membranes and their influence on the structure and behavior of the cytoplasmic portion of the CD3ɛ chain of the T-cell receptor. It has been hypothesized that the accessibility of ITAM motifs, such as those in the CD3ɛ cytoplasmic tail, can be blocked by ionic interactions between positively charged amino acids in receptor tails and negatively charged lipid head groups. This interesting hypothesis represents a previously unrecognized mechanism for control of receptor activation. Our simulations support the notion that the net charge of the lipids present in the membrane can affect peptide-membrane interactions. Results are consistent with experimental findings that show increase interfacial absorption in negatively charged lipid bilayer. Our simulations revealed the conformational variability of the disordered tail, which led to an additional focus on quantifying the interaction by free energy calculations, combined with long time-scale simulations using coarse-grained (CG) approaches. These studies will be extended to address how changes in ionic conditions can modulate phosphorylation of ITAM motifs and lead to regulation of activation of the TCR and other ITAM-bearing immunoreceptors.

Approximately one quarter of HIV-1 infected individuals will generate broadly neutralizing antibodies, but the exact mechanisms for triggering and maturation of these responses are presently undefined. We consequently investigated the roots of neutralization breadth. In a subtype A HIV-1 infected Rwandan seroconverter, we pinpointed the primary neutralizing antibody target and the successive routes of viral escape using envelope glycoproteins from the transmitted/founder virus and longitudinal escape variants along with autologous plasma and monoclonal antibodies. Initially, a single mutation at one of three clustered residues proximal to gp120’s third hypervariable loop conferred viral escape. This putative epitope subsequently elicited at least two somatically related monoclonal antibodies, which bound and neutralized the established escape mutations. Resistance to this secondary wave of immune pressure then arose in later viral envelopes through introduction of two glycans that obscured this consistently targeted space. At 16-months post-infection, what had been a narrow, regional response evolved to force recognition and neutralization of distinct envelope portions, which resulted in moderate cross-clade humoral breadth. Our data suggest that unveiling a certain chain of envelope mutations could drive B cells toward the production of broadly neutralizing antibodies. Appreciating this knowledge during immunogen construction could positively impact HIV-1 vaccine design.

Methods Here we characterized the initial nAb response in a subtype A HIV-1-infected Rwandan seroconverter and investigated how consequent immune events influenced the downstream development of cross-clade breadth. Autologous envelope (Env) glycoproteins from the transmitted/ founder virus and twenty longitudinal nAb escape variants were utilized to define the neutralization targets of autologous plasma and monoclonal antibodies (mAbs), the latter of which were also examined genetically and structurally through crystallization. Heterologous Env glycoproteins from nine cross-clade variants were used to determine neutralization breadth.