San Diego Supercomputer Center

We present cosmological parameter results from the final full-mission Planck measurements of the cosmic microwave background (CMB) anisotropies, combining information from the temperature and polarization maps and the lensing reconstruction. Compared to the 2015 results, improved measurements of large-scale polarization allow the reionization optical depth to be measured with higher precision, leading to significant gains in the precision of other correlated parameters. Improved modelling of the small-scale polarization leads to more robust constraints on many parameters, with residual modelling uncertainties estimated to affect them only at the 0.5 σ level. We find good consistency with the standard spatially-flat 6-parameter ΛCDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted “base ΛCDM” in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density Ω c h 2 = 0.120 ± 0.001, baryon density Ω b h 2 = 0.0224 ± 0.0001, scalar spectral index n s = 0.965 ± 0.004, and optical depth τ = 0.054 ± 0.007 (in this abstract we quote 68% confidence regions on measured parameters and 95% on upper limits). The angular acoustic scale is measured to 0.03% precision, with 100 θ * = 1.0411 ± 0.0003. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-ΛCDM cosmology, the inferred (model-dependent) late-Universe parameters are: Hubble constant H 0 = (67.4 ± 0.5) km s −1 Mpc −1 ; matter density parameter Ω m = 0.315 ± 0.007; and matter fluctuation amplitude σ 8 = 0.811 ± 0.006. We find no compelling evidence for extensions to the base-ΛCDM model. Combining with baryon acoustic oscillation (BAO) measurements (and considering single-parameter extensions) we constrain the effective extra relativistic degrees of freedom to be N eff = 2.99 ± 0.17, in agreement with the Standard Model prediction N eff = 3.046, and find that the neutrino mass is tightly constrained to ∑ m ν < 0.12 eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base ΛCDM at over 2 σ , which pulls some parameters that affect the lensing amplitude away from the ΛCDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. The joint constraint with BAO measurements on spatial curvature is consistent with a flat universe, Ω K = 0.001 ± 0.002. Also combining with Type Ia supernovae (SNe), the dark-energy equation of state parameter is measured to be w 0 = −1.03 ± 0.03, consistent with a cosmological constant. We find no evidence for deviations from a purely power-law primordial spectrum, and combining with data from BAO, BICEP2, and Keck Array data, we place a limit on the tensor-to-scalar ratio r 0.002 < 0.06. Standard big-bang nucleosynthesis predictions for the helium and deuterium abundances for the base-ΛCDM cosmology are in excellent agreement with observations. The Planck base-ΛCDM results are in good agreement with BAO, SNe, and some galaxy lensing observations, but in slight tension with the Dark Energy Survey’s combined-probe results including galaxy clustering (which prefers lower fluctuation amplitudes or matter density parameters), and in significant, 3.6 σ , tension with local measurements of the Hubble constant (which prefer a higher value). Simple model extensions that can partially resolve these tensions are not favoured by the Planck data.

Understanding the evolutionary history of living organisms is a central problem in biology. Until recently the ability to infer evolutionary relationships was limited by the amount of DNA sequence data available, but new DNA sequencing technologies have largely removed this limitation. As a result, DNA sequence data are readily available or obtainable for a wide spectrum of organisms, thus creating an unprecedented opportunity to explore evolutionary relationships broadly and deeply across the Tree of Life. Unfortunately, the algorithms used to infer evolutionary relationships are NP-hard, so the dramatic increase in available DNA sequence data has created a commensurate increase in the need for access to powerful computational resources. Local laptop or desktop machines are no longer viable for analysis of the larger data sets available today, and progress in the field relies upon access to large, scalable high-performance computing resources. This paper describes development of the CIPRES Science Gateway, a web portal designed to provide researchers with transparent access to the fastest available community codes for inference of phylogenetic relationships, and implementation of these codes on scalable computational resources. Meeting the needs of the community has included developing infrastructure to provide access, working with the community to improve existing community codes, developing infrastructure to insure the portal is scalable to the entire systematics community, and adopting strategies that make the project sustainable by the community. The CIPRES Science Gateway has allowed more than 1800 unique users to run jobs that required 2.5 million Service Units since its release in December 2009. (A Service Unit is a CPU-hour at unit priority).

Despite recent advances achieved by application of high-performance computing methods and novel algorithmic techniques to maximum likelihood (ML)-based inference programs, the major computational bottleneck still consists in the computation of bootstrap support values. Conducting a probably insufficient number of 100 bootstrap (BS) analyses with current ML programs on large datasets-either with respect to the number of taxa or base pairs-can easily require a month of run time. Therefore, we have developed, implemented, and thoroughly tested rapid bootstrap heuristics in RAxML (Randomized Axelerated Maximum Likelihood) that are more than an order of magnitude faster than current algorithms. These new heuristics can contribute to resolving the computational bottleneck and improve current methodology in phylogenetic analyses. Computational experiments to assess the performance and relative accuracy of these heuristics were conducted on 22 diverse DNA and AA (amino acid), single gene as well as multigene, real-world alignments containing 125 up to 7764 sequences. The standard BS (SBS) and rapid BS (RBS) values drawn on the best-scoring ML tree are highly correlated and show almost identical average support values. The weighted RF (Robinson-Foulds) distance between SBS- and RBS-based consensus trees was smaller than 6% in all cases (average 4%). More importantly, RBS inferences are between 8 and 20 times faster (average 14.73) than SBS analyses with RAxML and between 18 and 495 times faster than BS analyses with competing programs, such as PHYML or GARLI. Moreover, this performance improvement increases with alignment size. Finally, we have set up two freely accessible Web servers for this significantly improved version of RAxML that provide access to the 200-CPU cluster of the Vital-IT unit at the Swiss Institute of Bioinformatics and the 128-CPU cluster of the CIPRES project at the San Diego Supercomputer Center. These Web servers offer the possibility to conduct large-scale phylogenetic inferences to a large part of the community that does not have access to, or the expertise to use, high-performance computing resources.

Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

We present an implementation of explicit solvent all atom classical molecular dynamics (MD) within the AMBER program package that runs entirely on CUDA-enabled GPUs. First released publicly in April 2010 as part of version 11 of the AMBER MD package and further improved and optimized over the last two years, this implementation supports the three most widely used statistical mechanical ensembles (NVE, NVT, and NPT), uses particle mesh Ewald (PME) for the long-range electrostatics, and runs entirely on CUDA-enabled NVIDIA graphics processing units (GPUs), providing results that are statistically indistinguishable from the traditional CPU version of the software and with performance that exceeds that achievable by the CPU version of AMBER software running on all conventional CPU-based clusters and supercomputers. We briefly discuss three different precision models developed specifically for this work (SPDP, SPFP, and DPDP) and highlight the technical details of the approach as it extends beyond previously reported work [Götz et al., J. Chem. Theory Comput. 2012, DOI: 10.1021/ct200909j; Le Grand et al., Comp. Phys. Comm. 2013, DOI: 10.1016/j.cpc.2012.09.022].We highlight the substantial improvements in performance that are seen over traditional CPU-only machines and provide validation of our implementation and precision models. We also provide evidence supporting our decision to deprecate the previously described fully single precision (SPSP) model from the latest release of the AMBER software package.

Computing in science and engineering is now ubiquitous: digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Access to an array of integrated and well-supported high-end digital services is critical for the advancement of knowledge. Driven by community needs, the Extreme Science and Engineering Discovery Environment (XSEDE) project substantially enhances the productivity of a growing community of scholars, researchers, and engineers (collectively referred to as "scientists"' throughout this article) through access to advanced digital services that support open research. XSEDE's integrated, comprehensive suite of advanced digital services federates with other high-end facilities and with campus-based resources, serving as the foundation for a national e-science infrastructure ecosystem. XSEDE's e-science infrastructure has tremendous potential for enabling new advancements in research and education. XSEDE's vision is a world of digitally enabled scholars, researchers, and engineers participating in multidisciplinary collaborations to tackle society's grand challenges.

Abstract Molecular dynamics (MD) allows the study of biological and chemical systems at the atomistic level on timescales from femtoseconds to milliseconds. It complements experiment while also offering a way to follow processes difficult to discern with experimental techniques. Numerous software packages exist for conducting MD simulations of which one of the widest used is termed Amber. Here, we outline the most recent developments, since version 9 was released in April 2006, of the Amber and AmberTools MD software packages, referred to here as simply the Amber package. The latest release represents six years of continued development, since version 9, by multiple research groups and the culmination of over 33 years of work beginning with the first version in 1979. The latest release of the Amber package, version 12 released in April 2012, includes a substantial number of important developments in both the scientific and computer science arenas. We present here a condensed vision of what Amber currently supports and where things are likely to head over the coming years. Figure 1 shows the performance in ns/day of the Amber package version 12 on a single‐core AMD FX‐8120 8‐Core 3.6GHz CPU, the Cray XT5 system, and a single GPU GTX680. © 2012 John Wiley & Sons, Ltd. This article is categorized under: Software > Molecular Modeling

The Protein Data Bank [PDB; Berman, Westbrook et al. (2000), Nucleic Acids Res. 28, 235-242; http://www.pdb.org/] is the single worldwide archive of primary structural data of biological macromolecules. Many secondary sources of information are derived from PDB data. It is the starting point for studies in structural bioinformatics. This article describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource. The reader should come away with an understanding of the scope of the PDB and what is provided by the resource.

We report on the implications for cosmic inflation of the 2018 release of the Planck cosmic microwave background (CMB) anisotropy measurements. The results are fully consistent with those reported using the data from the two previous Planck cosmological releases, but have smaller uncertainties thanks to improvements in the characterization of polarization at low and high multipoles. Planck temperature, polarization, and lensing data determine the spectral index of scalar perturbations to be n s = 0.9649 ± 0.0042 at 68% CL. We find no evidence for a scale dependence of n s , either as a running or as a running of the running. The Universe is found to be consistent with spatial flatness with a precision of 0.4% at 95% CL by combining Planck with a compilation of baryon acoustic oscillation data. The Planck 95% CL upper limit on the tensor-to-scalar ratio, r 0.002 < 0.10, is further tightened by combining with the BICEP2/Keck Array BK15 data to obtain r 0.002 < 0.056. In the framework of standard single-field inflationary models with Einstein gravity, these results imply that: (a) the predictions of slow-roll models with a concave potential, V ″( ϕ ) < 0, are increasingly favoured by the data; and (b) based on two different methods for reconstructing the inflaton potential, we find no evidence for dynamics beyond slow roll. Three different methods for the non-parametric reconstruction of the primordial power spectrum consistently confirm a pure power law in the range of comoving scales 0.005 Mpc −1 ≲ k ≲ 0.2 Mpc −1 . A complementary analysis also finds no evidence for theoretically motivated parameterized features in the Planck power spectra. For the case of oscillatory features that are logarithmic or linear in k , this result is further strengthened by a new combined analysis including the Planck bispectrum data. The new Planck polarization data provide a stringent test of the adiabaticity of the initial conditions for the cosmological fluctuations. In correlated, mixed adiabatic and isocurvature models, the non-adiabatic contribution to the observed CMB temperature variance is constrained to 1.3%, 1.7%, and 1.7% at 95% CL for cold dark matter, neutrino density, and neutrino velocity, respectively. Planck power spectra plus lensing set constraints on the amplitude of compensated cold dark matter-baryon isocurvature perturbations that are consistent with current complementary measurements. The polarization data also provide improved constraints on inflationary models that predict a small statistically anisotropic quadupolar modulation of the primordial fluctuations. However, the polarization data do not support physical models for a scale-dependent dipolar modulation. All these findings support the key predictions of the standard single-field inflationary models, which will be further tested by future cosmological observations.

We present an implementation of generalized Born implicit solvent all-atom classical molecular dynamics (MD) within the AMBER program package that runs entirely on CUDA enabled NVIDIA graphics processing units (GPUs). We discuss the algorithms that are used to exploit the processing power of the GPUs and show the performance that can be achieved in comparison to simulations on conventional CPU clusters. The implementation supports three different precision models in which the contributions to the forces are calculated in single precision floating point arithmetic but accumulated in double precision (SPDP), or everything is computed in single precision (SPSP) or double precision (DPDP). In addition to performance, we have focused on understanding the implications of the different precision models on the outcome of implicit solvent MD simulations. We show results for a range of tests including the accuracy of single point force evaluations and energy conservation as well as structural properties pertainining to protein dynamics. The numerical noise due to rounding errors within the SPSP precision model is sufficiently large to lead to an accumulation of errors which can result in unphysical trajectories for long time scale simulations. We recommend the use of the mixed-precision SPDP model since the numerical results obtained are comparable with those of the full double precision DPDP model and the reference double precision CPU implementation but at significantly reduced computational cost. Our implementation provides performance for GB simulations on a single desktop that is on par with, and in some cases exceeds, that of traditional supercomputers.

A new algorithm is reported which builds an alignment between two protein structures. The algorithm involves a combinatorial extension (CE) of an alignment path defined by aligned fragment pairs (AFPs) rather than the more conventional techniques using dynamic programming and Monte Carlo optimization. AFPs, as the name suggests, are pairs of fragments, one from each protein, which confer structure similarity. AFPs are based on local geometry, rather than global features such as orientation of secondary structures and overall topology. Combinations of AFPs that represent possible continuous alignment paths are selectively extended or discarded thereby leading to a single optimal alignment. The algorithm is fast and accurate in finding an optimal structure alignment and hence suitable for database scanning and detailed analysis of large protein families. The method has been tested and compared with results from Dali and VAST using a representative sample of similar structures. Several new structural similarities not detected by these other methods are reported. Specific one-on-one alignments and searches against all structures as found in the Protein Data Bank (PDB) can be performed via the Web at http://cl.sdsc.edu/ce.html.

Lipids are produced, transported, and recognized by the concerted actions of numerous enzymes, binding proteins, and receptors. A comprehensive analysis of lipid molecules, “lipidomics,” in the context of genomics and proteomics is crucial to understanding cellular physiology and pathology; consequently, lipid biology has become a major research target of the postgenomic revolution and systems biology. To facilitate international communication about lipids, a comprehensive classification of lipids with a common platform that is compatible with informatics requirements has been developed to deal with the massive amounts of data that will be generated by our lipid community. As an initial step in this development, we divide lipids into eight categories (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides) containing distinct classes and subclasses of molecules, devise a common manner of representing the chemical structures of individual lipids and their derivatives, and provide a 12 digit identifier for each unique lipid molecule. The lipid classification scheme is chemically based and driven by the distinct hydrophobic and hydrophilic elements that compose the lipid.This structured vocabulary will facilitate the systematization of lipid biology and enable the cataloging of lipids and their properties in a way that is compatible with other macromolecular databases. Lipids are produced, transported, and recognized by the concerted actions of numerous enzymes, binding proteins, and receptors. A comprehensive analysis of lipid molecules, “lipidomics,” in the context of genomics and proteomics is crucial to understanding cellular physiology and pathology; consequently, lipid biology has become a major research target of the postgenomic revolution and systems biology. To facilitate international communication about lipids, a comprehensive classification of lipids with a common platform that is compatible with informatics requirements has been developed to deal with the massive amounts of data that will be generated by our lipid community. As an initial step in this development, we divide lipids into eight categories (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides) containing distinct classes and subclasses of molecules, devise a common manner of representing the chemical structures of individual lipids and their derivatives, and provide a 12 digit identifier for each unique lipid molecule. The lipid classification scheme is chemically based and driven by the distinct hydrophobic and hydrophilic elements that compose the lipid. This structured vocabulary will facilitate the systematization of lipid biology and enable the cataloging of lipids and their properties in a way that is compatible with other macromolecular databases. The goal of collecting data on lipids using a “systems biology” approach to lipidomics requires the development of a comprehensive classification, nomenclature, and chemical representation system to accommodate the myriad lipids that exist in nature. Lipids have been loosely defined as biological substances that are generally hydrophobic in nature and in many cases soluble in organic solvents (1Smith A. Oxford Dictionary of Biochemistry and Molecular Biology. 2nd edition. Oxford University Press, Oxford, UK2000Google Scholar). These chemical properties cover a broad range of molecules, such as fatty acids, phospholipids, sterols, sphingolipids, terpenes, and others (2Christie W.W. Lipid Analysis. 3rd edition. Oily Press, Bridgewater, UK2003Google Scholar). The LIPID MAPS (LIPID Metabolites And Pathways Strategy; http://www.lipidmaps.org), Lipid Library (http://lipidlibrary.co.uk), Lipid Bank (http://lipidbank.jp), LIPIDAT (http://www.lipidat.chemistry.ohio-state.edu), and Cyberlipids (http://www.cyberlipid.org) websites provide useful online resources for an overview of these molecules and their structures. More accurate definitions are possible when lipids are considered from a structural and biosynthetic perspective, and many different classification schemes have been used over the years. However, for the purpose of comprehensive classification, we define lipids as hydrophobic or amphipathic small molecules that may originate entirely or in part by carbanion-based condensations of thioesters (fatty acids, polyketides, etc.) and/or by carbocation-based condensations of isoprene units (prenols, sterols, etc.). Additionally, lipids have been broadly subdivided into “simple” and “complex” groups, with simple lipids being those yielding at most two types of products on hydrolysis (e.g., fatty acids, sterols, and acylglycerols) and complex lipids (e.g., glycerophospholipids and glycosphingolipids) yielding three or more products on hydrolysis. The classification scheme presented here organizes lipids into well-defined categories that cover eukaryotic and prokaryotic sources and that is equally applicable to archaea and synthetic (manmade) lipids. Lipids may be categorized based on their chemically functional backbone as polyketides, acylglycerols, sphingolipids, prenols, or saccharolipids. However, for historical and bioinformatics advantages, we chose to separate fatty acyls from other polyketides, the glycerophospholipids from the other glycerolipids, and sterol lipids from other prenols, resulting in a total of eight primary categories. An important aspect of this scheme is that it allows for subdivision of the main categories into classes and subclasses to handle the existing and emerging arrays of lipid structures. Although any classification scheme is in part subjective as a result of the structural and biosynthetic complexity of lipids, it is an essential prerequisite for the organization of lipid research and the development of systematic methods of data management. The classification scheme presented here is chemically based and driven by the distinct hydrophobic and hydrophilic elements that constitute the lipid. Biosynthetically related compounds that are not technically lipids because of their water solubility are included for completeness in this classification scheme. The proposed lipid categories listed in Table 1 have names that are, for the most part, well accepted in the literature. The fatty acyls (FA) are a diverse group of molecules synthesized by chain elongation of an acetyl-CoA primer with malonyl-CoA (or methylmalonyl-CoA) groups that may contain a cyclic functionality and/or are substituted with heteroatoms. Structures with a glycerol group are represented by two distinct categories: the glycerolipids (GL), which include acylglycerols but also encompass alkyl and 1Z-alkenyl variants, and the glycerophospholipids (GP), which are defined by the presence of a phosphate (or phosphonate) group esterified to one of the glycerol hydroxyl groups. The sterol lipids (ST) and prenol lipids (PR) share a common biosynthetic pathway via the polymerization of dimethylallyl pyrophosphate/isopentenyl pyrophosphate but have obvious differences in terms of their eventual structure and function. Another well-defined category is the sphingolipids (SP), which contain a long-chain base as their core structure. This classification does not have a glycolipids category per se but rather places glycosylated lipids in appropriate categories based on the identity of their core lipids. It also was necessary to define a category with the term “saccharolipids” (SL) to account for lipids in which fatty acyl groups are linked directly to a sugar backbone. This SL group is distinct from the term “glycolipid” that was defined by the International Union of Pure and Applied Chemists (IUPAC) as a lipid in which the fatty acyl portion of the molecule is present in a glycosidic linkage. The final category is the polyketides (PK), which are a diverse group of metabolites from plant and microbial sources. Protein modification by lipids (e.g., fatty acyl, prenyl, cholesterol) occurs in nature; however, these proteins are not included in this database but are listed in protein databases such as GenBank (http://www.ncbi.nlm.nih.gov) and SwissProt (http://www.ebi.ac.uk/swissprot/).TABLE 1Lipid categories and examplesCategoryAbbreviationExampleFatty acyls FAdodecanoic acidGlycerolipids GL1-hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycerolGlycerophospholipids GP1-hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycero-3-phosphocholineSphingolipids SPN-(tetradecanoyl)-sphing-4-enineSterol lipids STcholest-5-en-3β-olPrenol lipids PR2E,6E-farnesolSaccharolipids SLUDP-3-O-(3R-hydroxy-tetradecanoyl)-αd-N-acetylglucosaminePolyketides PKaflatoxin B1 Open table in a new tab A naming scheme must unambiguously define a lipid structure in a manner that is amenable to chemists, biologists, and biomedical researchers. The issue of lipid nomenclature was last addressed in detail by the International Union of Pure and Applied Chemists and the International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) Commission on Biochemical Nomenclature in 1976, which subsequently published its recommendations (3IUPAC-IUB Commission on Biochemical Nomenclature (CBN). The nomenclature of lipids (recommendations 1976). 1977. Eur. 1977. 1977. 1977. Lipid Scholar). a of to the naming of glycolipids Commission on Biochemical Nomenclature Nomenclature of glycolipids (recommendations Eur. Pure Commission on Biochemical Nomenclature Nomenclature of (recommendations Eur. and Commission on Biochemical Nomenclature Nomenclature of (recommendations Eur. have been by this and on the A of lipid classes have been the last three that have not been The present classification these new lipids and a with our proposed classification we provide of systematic (or names for the classes and subclasses of lipids. 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Lipid classes and base and base and base etc.) Open table in a new tab The major base of is to as because that was by the to this A on the of and Scholar). is is also and This is one of many in which in alkyl chain and the and of the presence of hydroxyl groups, and other The structural has functional for in the have at and/or that with molecules, the of are in a of derivatives, the as as well as and and a of such as and compounds with structural to of which as and the are of of are a major of base with an fatty The fatty are or with chain from to the presence of a hydroxyl group on is have fatty acids, as by the in which has a fatty with a hydroxyl group on the are generally of more complex The major of are contain and have and structures for Commission on Biochemical Nomenclature Nomenclature of glycolipids (recommendations Eur. Pure are on the of contain one or more such as and which are into based on the nature of the as in the contain functional groups or to or sugar such as or The are and the of is with a or a the that a names as and group structures are in common (e.g., and contain sphingolipids in which the phosphate is by a or The other category sphingolipids that are to for and are to proteins of and are used as for proteins in a manner to the that are to proteins in other of structures are in The sterol category is subdivided on the of biological function. The sterols, of which and its are the most in constitute an important of lipids, with the glycerophospholipids and of Scholar). are many of unique from and sources that are as distinct subclasses in this The which also contain the core have different biological as and molecules Molecular of of Scholar). These are subdivided on the of the of in the core The include the the the such as and The containing a two chain at the the as well as the and The of are by of the of the core the understanding of the actions of Scholar). classes the category are the The enzymes, and of which in are of synthesized from in the and their and lipid structures are in lipids classes and and and and and and and and and and and acids, and acids, and acids, and acids, and Open table in a new tab structures for sterol are synthesized from the and dimethylallyl that are via the pathway of the of the isoprene Scholar). (e.g., and are by the phosphate pathway The a new target for the development of and Scholar). the simple etc.) are by the of it is to in this manner with a for those structures containing more of Scholar). that A and its and and its are are important simple that as and as of A W.W. in and Scholar). Another important of molecules is by the and which contain an to a core of and A. of a of and a on as well as the in are of this lipids classes and and Open table in a new tab and their important in the of phosphate and in in polymerization in to and its and in eukaryotic protein The and of phosphate and in the of Scholar). The and of phosphate from those of the we have in separate in which the to in the is are to 12 units in of to isoprene the of the three units are of prenol lipid structures are in structures for prenol have the term “glycolipid” in the classification scheme to a on lipid structures. eight lipid categories in the present scheme include important derivatives, the term with the goal of lipid in the term “saccharolipids” to compounds in which fatty are linked directly to a sugar structures that are compatible with the a sugar for the glycerol backbone that is present in glycerolipids and as or as The most are the of the lipid A of the in Scholar). lipid A molecules are of which are with as many as fatty acyl structure of Lipid in structural analysis of in and Scholar). that in naming these the total of fatty acyl groups are of the nature of the or The for in is a lipid A that is glycosylated with two the backbone of lipid A is by and of the of by from in the of lipid A molecules with acyl the has been also in this are the of and by such as The are of that are with a fatty acyl include fatty of which are by the units of lipids The of Scholar). of and also have been in fatty and in Scholar). structures are in classes and Open table in a new tab are synthesized by as well as and with that share with the fatty the of acyl proteins and and and of however, a of many of which have the of lipids. The in from to and complex systems are by and/or other polyketides are linked with synthesized to of the three classes are in used and are polyketides or of these include and polyketides are The of and the that polyketides has the for in the The of and of classes and polyketides polyketides Open table in a new tab this classification of polyketides as the step in a more scheme. It will be important to include as many structures as possible in a lipid database that be for and chemical The of the LIPID MAPS are to lipids and new to and in lipids that with cellular and to bioinformatics that lipid the of Lipid Bank are to and lipid structures and the with and the of the are to and and with lipid To the from three and to facilitate a comprehensive classification of lipids with a common platform that is compatible with informatics requirements must be developed to deal with the massive amounts of data that will be generated by the lipid community. The proposed classification, nomenclature, and chemical representation system was to accommodate the massive data that will result from the LIPID MAPS but it has been to accommodate as many lipids as also have to the system compatible with existing lipid databases and the lipids in It is to be new or subclasses be in the and will be on the LIPID MAPS The development of this system has been by with the in the that this system will be accepted and The the of the International Lipids and Nomenclature to on the of these This A. and representing LIPID representing the International on the of representing the and and representing the of the on the Biochemistry of The are most of and of this with is the of the Nomenclature of and the Commission on Biochemical The the for by at the of for with their nomenclature for glycosylated structures. are to at the of for to this This was by the LIPID MAPS from the of

In 2005, the International Lipid Classification and Nomenclature Committee under the sponsorship of the LIPID MAPS Consortium developed and established a “Comprehensive Classification System for Lipids” based on well-defined chemical and biochemical principles and using an ontology that is extensible, flexible, and scalable. This classification system, which is compatible with contemporary databasing and informatics needs, has now been accepted internationally and widely adopted. In response to considerable attention and requests from lipid researchers from around the globe and in a variety of fields, the comprehensive classification system has undergone significant revisions over the last few years to more fully represent lipid structures from a wider variety of sources and to provide additional levels of detail as necessary. The details of this classification system are reviewed and updated and are presented here, along with revisions to its suggested nomenclature and structure-drawing recommendations for lipids. In 2005, the International Lipid Classification and Nomenclature Committee under the sponsorship of the LIPID MAPS Consortium developed and established a “Comprehensive Classification System for Lipids” based on well-defined chemical and biochemical principles and using an ontology that is extensible, flexible, and scalable. This classification system, which is compatible with contemporary databasing and informatics needs, has now been accepted internationally and widely adopted. In response to considerable attention and requests from lipid researchers from around the globe and in a variety of fields, the comprehensive classification system has undergone significant revisions over the last few years to more fully represent lipid structures from a wider variety of sources and to provide additional levels of detail as necessary. The details of this classification system are reviewed and updated and are presented here, along with revisions to its suggested nomenclature and structure-drawing recommendations for lipids. In an effort to support the growing field of lipidomics and establish the importance of lipids as a major class of biomolecules, the International Lipid Classification and Nomenclature Committee (ILCNC) developed a “Comprehensive Classification System for Lipids” that was published in 2005 (1Fahy E. Subramaniam S. Brown H.A. Glass C.K. Merrill Jr., A.H. Murphy R.C. Raetz C.R. Russell D.W. Seyama Y. Shaw W. al et A comprehensive classification system for lipids.J. Lipid Res. 2005; 46: 839-862Abstract Full Text Full Text PDF PubMed Scopus (1141) Google Scholar). For the purpose of classification, we define lipids as hydrophobic or amphipathic small molecules that may originate entirely or in part by carbanion-based condensations of thioesters (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids, and polyketides) and/or by carbocation-based condensations of isoprene units (prenol lipids and sterol lipids). The comprehensive classification system organizes lipids into these eight well-defined categories (Table 1) that cover eukaryotic and prokaryotic sources. It has been adopted internationally and widely accepted by the lipidomics community. The system is also available online on the LIPID MAPS (2Schmelzer K. Fahy E. Subramaniam S. Dennis E.A. The lipid maps initiative in lipidomics.Methods Enzymol. 2007; 432: 171-183Crossref PubMed Scopus (115) Google Scholar) website (http://www.lipidmaps.org). The comprehensive classification system has been under the guidance of the ILCNC, 3The ILCNC currently consists of Dr. Edward A. Dennis, Chair, (US), Dr. Robert C. Murphy (US), Dr. Masahiro Nishijima (Japan), Dr. Christian R. H. Raetz (US), Dr. Takao Shimizu (Japan), Dr. Friedrich Spener (Austria), Dr. Gerrit van Meer (The Netherlands), and Dr. Michael Wakelam (UK). Dr. Shankar Subramaniam serves as Informatics Advisor, and Dr. Eoin Fahy serves as Director. Meetings were held May 7, 2006 and May 4, 2008 in La Jolla, CA. which meets periodically to propose changes and updates to classification, nomenclature, and structural representation.TABLE 1Lipid categories of the comprehensive classification system and the number of structures in the LIPID MAPS databaseCategoryAbbreviationStructures in DatabaseFatty acylsFA2678GlycerolipidsGL3009GlycerophospholipidsGP1970SphingolipidsSP620Sterol LipidsST1744Prenol LipidsPR610SaccharolipidsSL11PolyketidesPK132 Open table in a new tab The initial version of the comprehensive classification system was more heavily focused on mammalian lipids, reflecting a bias toward the experimental interests of the LIPID MAPS Consortium (2Schmelzer K. Fahy E. Subramaniam S. Dennis E.A. The lipid maps initiative in lipidomics.Methods Enzymol. 2007; 432: 171-183Crossref PubMed Scopus (115) Google Scholar). However, due to considerable attention and requests from lipid researchers in a variety of fields, the classification system has now been extended to more fully represent lipid structures from nonmammalian sources, such as plants, bacteria, and fungi. For example, two new main classes (Glycosyldiradylglycerols and Glycosylmonoradylglycerols) have been added to the Glycerolipids category to accommodate key plant structural lipids, such as the sulfoquinovosyldiacylglycerols (3Norman H.A. Mischke C.F. Allen B. Vincent J.S. Semi-preparative isolation of plant sulfoquinovosyldiacylglycerols by solid phase extraction and HPLC procedures.J. Lipid Res. 1996; 37: 1372-1376Abstract Full Text PDF PubMed Google Scholar) found in chloroplasts. Also, the list of subclasses under the Sterols main class has been expanded to include a set of 15 different core structures (Ergosterols, Gorgosterols, Furostanols, etc.), which provide a structure-based classification of these molecules that span multiple phyla. Another key development has been the adoption of existing hierarchies (4Buckingham J. Dictionary of Natural Products on CD-ROM, Version 6.1. Chapman & Hall, London1998Crossref Google Scholar) for the Polyketide category and Prenol Lipids/Isoprenoids subclasses where the majority of these molecules are derived from natural product sources and have been studied intensively from a pharmaceutical and ecological standpoint. This in turn has necessitated the expansion of the number of existing classification levels (category, main class, and subclass) to accommodate an additional level of stratification in the case of the C10 to C30 isoprenoid subclasses that now contain entries at a fourth level of detail. The “LM_ID” identifier, whose format provides a systematic means of assigning a unique identification to each lipid molecule, has accordingly been expanded in length in these particular cases, with an additional two characters being used to describe the fourth level. A detailed overview of the changes and updates to the comprehensive classification system is presented below. As a consequence of adding an extra level of classification detail, the length of the LM_ID identifier was lengthened from 12 characters to 14 in cases where a lipid defined with four levels of classification is being described (Table 2). In this case, characters 9 and 10 specify the level-4 class. It should be emphasized that all lipids that do not require a fourth level of detail (i.e., the vast majority of them) still use a 12-digit LM_ID identifier.TABLE 2Format of LIPID MAPS identifier (LM_ID) in the comprehensive classification systemCharactersDescriptionExampleComments1–2Fixed “LM” designationLMAlways LM3–4Two-letter category codePROne of eight categories5–6Two-digit class code01—7–8Two-digit subclass code03“00” when no subclass9–10Two-digit fourth level code06Only used for lipids with four levelsLast four digitsUnique four-character identifier within subclass or within fourth level0002First two of the last four digits are letters in the case of the Glycosphingolipid subclasses Open table in a new tab In keeping with the theme of having a classification system dictated by molecular structure and function, the sterol lipid subclasses Phytosterols, Marine sterols, and Fungal sterols were retired because these refer to the lipid source (marine) or biological kingdom (plants and fungi). It is possible to identify a particular sterol in more than one of these three sources. These subclasses have been replaced by a new set of subclasses based on the carbon skeleton of the sterol core structure (Ergosterols, Gorgosterols, Furostanols, etc.). The details are outlined under the Sterol Lipids section below, and the complete description of this category can be found on the LIPID MAPS website 4Supplementary tables that provide the complete list of the classes, subclasses, and fourth class level (where applicable) of each of the eight categories of lipids are available on the LIPID MAPS website at http://www.lipidmaps.org. (http://www.lipidmaps.org). The natural products chemistry and medicinal chemistry literature describes tens of thousands of molecules that fall under the scope of lipids, based on their biosynthetic origin. In particular, isoprenoids and polyketides from diverse sources, such as plant, fungi, algae, bacteria, and marine invertebrates, are well documented and have been reviewed and classified in detail. The Dictionary of Natural Products (4Buckingham J. Dictionary of Natural Products on CD-ROM, Version 6.1. Chapman & Hall, London1998Crossref Google Scholar), a database available from Chapman and Hall/CRC (http://dnp.chemnetbase.com), has a classification hierarchy that covers polyketides and isoprenoids in depth. The LIPID MAPS comprehensive classification system has now incorporated some of these hierarchies relevant to natural products, with a view to covering both mammalian and nonmammalian lipids comprehensively. 3The ILCNC currently consists of Dr. Edward A. Dennis, Chair, (US), Dr. Robert C. Murphy (US), Dr. Masahiro Nishijima (Japan), Dr. Christian R. H. Raetz (US), Dr. Takao Shimizu (Japan), Dr. Friedrich Spener (Austria), Dr. Gerrit van Meer (The Netherlands), and Dr. Michael Wakelam (UK). Dr. Shankar Subramaniam serves as Informatics Advisor, and Dr. Eoin Fahy serves as Director. Meetings were held May 7, 2006 and May 4, 2008 in La Jolla, CA. It was recognized that additional levels of stratification were required to classify certain types of lipids and that the current three-level system of category/main class/subclass needed to be expanded. For example, in the Prenol Lipids category, 3The ILCNC currently consists of Dr. Edward A. Dennis, Chair, (US), Dr. Robert C. Murphy (US), Dr. Masahiro Nishijima (Japan), Dr. Christian R. H. Raetz (US), Dr. Takao Shimizu (Japan), Dr. Friedrich Spener (Austria), Dr. Gerrit van Meer (The Netherlands), and Dr. Michael Wakelam (UK). Dr. Shankar Subramaniam serves as Informatics Advisor, and Dr. Eoin Fahy serves as Director. Meetings were held May 7, 2006 and May 4, 2008 in La Jolla, CA. the Sesquiterpene C15 subclass contains ∼90 known variants based on their carbon skeletons (Bisabolanes, Germacranes, etc.). A fourth level of detail has been added to the LIPID MAPS comprehensive classification system to handle cases such as these. In response to worldwide interest in the comprehensive classification system for lipids, the scope has been expanded to cover lipids from nonmammalian sources, such as plants, bacteria, fungi, algae, and marine organisms. To accomplish this, several new lipid classes have been added, such as fatty acyl glycosides, glycosyldiradylglycerols, and various sterol skeletons. The Polyketide category has also been revised comprehensively. 3The ILCNC currently consists of Dr. Edward A. Dennis, Chair, (US), Dr. Robert C. Murphy (US), Dr. Masahiro Nishijima (Japan), Dr. Christian R. H. Raetz (US), Dr. Takao Shimizu (Japan), Dr. Friedrich Spener (Austria), Dr. Gerrit van Meer (The Netherlands), and Dr. Michael Wakelam (UK). Dr. Shankar Subramaniam serves as Informatics Advisor, and Dr. Eoin Fahy serves as Director. Meetings were held May 7, 2006 and May 4, 2008 in La Jolla, CA. The nomenclature of lipids falls into two main categories: systematic names and common or trivial names. The latter includes abbreviations that are a convenient way to define acyl/alkyl chains in acylglycerols, sphingolipids, and glycerophospholipids and synonyms such as “phosphatidyl” for “glycerophospho.” The generally accepted guidelines for lipid systematic names have been defined by the International Union of Pure and Applied Chemists and the International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) Commission on Biochemical Nomenclature (http://www.chem.qmul.ac.uk/iupac/) (5IUPAC-IUB Commission on Biochemical Nomenclature (CBN). The nomenclature of lipids (Recommendations 1976). 1977. Eur. J. Biochem. 79: 11–21; 1977. Hoppe-Seylers Z. Physiol. Chem. 358: 617–631; 1977. Lipids 12: 455–468; 1977. Mol. Cell. Biochem. 17: 157–171; 1978. Chem. Phys. Lipids 21: 159–173; 1978. J. Lipid Res. 19: 114–128; 1978. Biochem. J. 171: 21–35. (http://www.chem.qmul.ac.uk/iupac/lipid/).Google Scholar, 6I. U. P. A. C-I. U. B. Joint Commission on Biochemical Nomenclature (JCBN). Nomenclature of glycolipids. (Recommendations 1997) 2000. Adv. Carbohydr. Chem. Biochem. 55: 311–326; 1988. Carbohydr. Res. 312: 167–175; 1998. Eur. J. Biochem. 257: 293–298; 1999. Glycoconjugate J. 16:1–6; 1999. J. Mol. Biol. 286: 963–970; 1997. Pure Appl. Chem. 69: 2475–2487. (http://www.chem.qmul.ac.uk/iupac/misc/glylp.html)Google Scholar, 7I. U. P. A. C-I. U. B. Joint Commission on Biochemical Nomenclature (JCBN). Nomenclature of prenols. (Recommendations 1987) 1987. Eur. J. Biochem. 167: 181–184. (http://www.chem.qmul.ac.uk/iupac/misc/prenol.html)Google Scholar, 8I. U. P. A. C-I. U. B. Joint Commission on Biochemical Nomenclature (JCBN). Nomenclature of steroids (Recommendations 1989) 1989. Eur. J. Biochem. 186: 429–458. (http://www.chem.qmul.ac.uk/iupac/steroid/).Google Scholar). In response to several requests from knowledgeable lipid experts, abbreviations for Glycerophospholipid classes (see http://www.lipidmaps.org for GP category 3The ILCNC currently consists of Dr. Edward A. Dennis, Chair, (US), Dr. Robert C. Murphy (US), Dr. Masahiro Nishijima (Japan), Dr. Christian R. H. Raetz (US), Dr. Takao Shimizu (Japan), Dr. Friedrich Spener (Austria), Dr. Gerrit van Meer (The Netherlands), and Dr. Michael Wakelam (UK). Dr. Shankar Subramaniam serves as Informatics Advisor, and Dr. Eoin Fahy serves as Director. Meetings were held May 7, 2006 and May 4, 2008 in La Jolla, CA.) have been changed now in the comprehensive classification system to the more universally used two-letter “PC/PE/PS/PA/PI” format. Consequently, glycerophospholipids in the LIPID MAPS structure database and LIPID MAPS standards database as well as all the Glycerophospholipids drawing tools and mass spectrometry prediction tools have been updated to conform to this new abbreviation format (Table 3).TABLE 3Changes in abbreviations for Glycerophospholipids in the comprehensive classification systemClassSynonymOldNewGlycerophosphocholinesPhosphatidylcholinesGPChoPCaFor abbreviations of monoradyglycerophospholipids (lysophospholipids), LPX may be used, for example, LPC, LPE, LPA, etc.GlycerophosphoethanolaminesPhosphatidylethanolaminesGPEtnPEGlycerophosphoserinesPhosphatidylserinesGPSerPSGlycerophosphoglycerolsPhosphatidylglycerolsGPGroPGGlycerophosphoglycerophosphatesPhosphatidylglycerol phosphatesGPGroPPGPGlycerophosphoinositolsPhosphatidylinositolsGPInsPIGlycerophosphoinositol monophosphatesPhosphatidylinositol phosphatesGPInsPPIPGlycerophosphoinositol bis-phosphatesPhosphatidylinositol bis-phosphatesGPInsP2PIP2Glycerophosphoinositol For abbreviations of monoradyglycerophospholipids (lysophospholipids), LPX may be used, for example, LPC, LPE, LPA, Open table in a new tab The LIPID MAPS Consortium has considerable effort to establish guidelines for drawing lipid structures in a and and lipids are to which to the use of unique that more than the lipid community. the structure-drawing is the in molecular of lipids. However, classes of lipids well as for structure-drawing due to their A of structure-drawing tools has been developed and that of systematic and abbreviations E. Subramaniam S. LIPID MAPS online tools for lipid Res. 2007; PubMed Scopus Google Scholar). The structures may be and in a variety of of the structure-drawing tools for fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, and sterols are available in the section of the LIPID MAPS website (http://www.lipidmaps.org). of the structures are in the importance of these molecules in and is to have a database of lipids with a defined ontology that is extensible, flexible, and scalable. The ontology of lipids classification, nomenclature, structure and structural of all in the have developed a available comprehensive database of lipid structures of lipid molecules from existing and from the LIPID MAPS This database Fahy E. Brown A. Dennis E.A. Glass C.K. Merrill Jr., A.H. Murphy R.C. Raetz C.R. Russell D.W. al et LIPID MAPS structure Res. 2007; PubMed Scopus Google Scholar, E. R. A. J. Y. Subramaniam S. for lipidomics.Methods Enzymol. 2007; 432: PubMed Scopus Google Scholar), in to as the to and of lipid also contains systematic classification, nomenclature, and structure of lipids along with mass where than lipid molecules are now available on the LIPID MAPS and these have been adopted by the for as well as the of and database structures have been classified and to LIPID MAPS A number of different molecular such as and the and are and nomenclature of these molecules are also The database is and the include and structure-based the category, the subclasses and have been changed to and to accommodate The names of the fatty subclasses and have been to and A new acyl main class has been added to cover the number of found in bacteria, and marine of natural of fatty and PubMed Scopus Google Scholar, and of the natural PubMed Scopus Google Scholar). subclasses include acyl of and and The Glycerolipids category was to include two new main classes (Glycosyldiradylglycerols and Glycosylmonoradylglycerols) that contain key plant structural lipids, such as the found in chloroplasts. The existing subclasses were to the that is to the of on the for and of structures in the LIPID MAPS structure database have been For with two different two different structural are for with three different different of drawing all possible structural an is used as a A along with the number of possible is to the abbreviation and and a unique LM_ID is of this format is the The structure to the LM_ID on the LIPID MAPS website to the in the and the is to all in the are also cases within the and classes where are due to by certain of both the or to and where the of the at is by the acyl from the of to the at or can an In such cases when a is can be with a for example, or the is with a for example, It should be that the two-letter abbreviation or all possible types of lipid for example, having and The is by the for example, and the by the for example, The to the classes within the Glycerophospholipids In cases where is and is abbreviations such as and may be used, where the within refer to the number of and of all the For the Glycerophospholipids category, the subclass has been replaced by the more due to the that in are the in H.A. J.S. S. in and PubMed Scopus Google Scholar). updates have been for the Glycerophospholipid The lipids class has been replaced by the class to of with than As we have changed to two-letter abbreviations to describe glycerophospholipids in are abbreviations for all molecular of their These names to and are used widely in lipid as to systematic names. This format one or two chains where the structures of the chains are within is at the carbon of and the is at the In cases of molecules with at of the and of the at the the of is to the abbreviation and the abbreviation format is for molecules with at the carbon of the the of is to the and the structure is with In cases where is and is such as may be used to of and for all and are by an or identifier, as in and In the latter case, the an at of and a at the or may be with a in the for example, The “phosphatidyl” is used to refer to classes all types of chains and not acyl as was by guidelines (5IUPAC-IUB Commission on Biochemical Nomenclature (CBN). The nomenclature of lipids (Recommendations 1976). 1977. Eur. J. Biochem. 79: 11–21; 1977. Hoppe-Seylers Z. Physiol. Chem. 358: 617–631; 1977. Lipids 12: 455–468; 1977. Mol. Cell. Biochem. 17: 157–171; 1978. Chem. Phys. Lipids 21: 159–173; 1978. J. Lipid Res. 19: 114–128; 1978. Biochem. J. 171: 21–35. (http://www.chem.qmul.ac.uk/iupac/lipid/).Google Scholar). The classification is from that in the in this (2Schmelzer K. Fahy E. Subramaniam S. Dennis E.A. The lipid maps initiative in lipidomics.Methods Enzymol. 2007; 432: 171-183Crossref PubMed Scopus (115) Google Scholar). of is that for of the Glycosphingolipid subclasses, the structure of the is known the structure of the is In these cases, the last two digits of the LIPID MAPS LM_ID identifier are as to an and the and fourth last digits are a different two-letter identifier for unique within that For example, in the subclass the structure is an LM_ID of where the digits specify the unique and the digits a the has a and is a LM_ID of The Sterol lipids subclasses Phytosterols, Marine sterols, and Fungal sterols have been and replaced with a set of subclasses (Ergosterols, sterols, Gorgosterols, Furostanols, and that in the of their sterol core structures and cover multiple A. Sterols in marine Scopus Google Scholar, Phytosterols, and their in structural and Lipid Res. PubMed Scopus Google Scholar). The class has been with the and the class now includes and The class has been to the Prenol Lipids category the of the core structure is at with the of the of the Sterol Lipids The subclass of the Prenol lipids category has been added to the class. are a of that are to A. have in of and As the C10 to C30 isoprenoid subclasses now contain entries at a fourth level of detail. The LM_ID contain an extra two digits that specify the fourth level class, for example, the is an LM_ID of The class has been to the Prenol Lipids category the Sterol Lipids For the the main class acyl has been added to cover a variety of from plants, bacteria, and fungi. is the from the plant and from the of Scopus Google Scholar). It should be that this category covers structures in which fatty acyl/alkyl are to a lipids to a are found in their The category was revised and on the classification hierarchy used by the Dictionary of Natural Products (4Buckingham J. Dictionary of Natural Products on CD-ROM, Version 6.1. Chapman & Hall, London1998Crossref Google Scholar). are from bacteria, fungi, plants, and and have been heavily studied by natural products and for The new classification format provides a of the structural within this The toward classification of lipids is the of an ontology that is extensible, flexible, and scalable. be to and represent these molecules in a that is to databasing and The ILCNC the comprehensive classification system in 2005 and has been in and on a to considerable attention and requests from lipid researchers in a variety of fields, the classification system has been extended to more fully represent lipid structures from nonmammalian sources, such as plants, bacteria, and fungi. This system has been internationally accepted and is now widely used in and for The LIPID MAPS classification system has also been adopted by where hierarchies lipids, and have been and by the in format of the In an effort to LIPID MAPS lipid structures are now available on website where have been The classification system is available online where has been with an database of lipids. This in to as the and of lipid also contains systematic classification, nomenclature, and structure of lipids along with mass where structures have been classified and to LIPID MAPS The format of the LM_ID identifier (Table provides a systematic means of the classification hierarchy and assigning a unique identification to each lipid It also for the of new classification in the The database is and the include and structure-based This database is described in detail Fahy E. Brown A. Dennis E.A. Glass C.K. Merrill Jr., A.H. Murphy R.C. Raetz C.R. Russell D.W. al et LIPID MAPS structure Res. 2007; PubMed Scopus Google Scholar, E. R. A. J. Y. Subramaniam S. for lipidomics.Methods Enzymol. 2007; 432: PubMed Scopus Google Scholar). A of lipid structure-drawing tools in the section of the LIPID MAPS has been developed to structure with LIPID MAPS These tools are also of systematic names and detailed and databasing of lipid and has been to and database and to classify and LIPID MAPS These tools be expanded and as the scope of the classification system and over the The the of lipid researchers around the have and to attention in the Classification System for which to be to new and in the lipid The are also to the LIPID MAPS Consortium for their and to Dr. at the of for to this

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Chemokine receptors are critical regulators of cell migration in the context of immune surveillance, inflammation, and development. The G protein-coupled chemokine receptor CXCR4 is specifically implicated in cancer metastasis and HIV-1 infection. Here we report five independent crystal structures of CXCR4 bound to an antagonist small molecule IT1t and a cyclic peptide CVX15 at 2.5 to 3.2 angstrom resolution. All structures reveal a consistent homodimer with an interface including helices V and VI that may be involved in regulating signaling. The location and shape of the ligand-binding sites differ from other G protein-coupled receptors and are closer to the extracellular surface. These structures provide new clues about the interactions between CXCR4 and its natural ligand CXCL12, and with the HIV-1 glycoprotein gp120.

The crystal structure of the catalytic subunit of cyclic adenosine monophosphate-dependent protein kinase complexed with a 20-amino acid substrate analog inhibitor has been solved and partially refined at 2.7 A resolution to an R factor of 0.212. The magnesium adenosine triphosphate (MgATP) binding site was located by difference Fourier synthesis. The enzyme structure is bilobal with a deep cleft between the lobes. The cleft is filled by MgATP and a portion of the inhibitor peptide. The smaller lobe, consisting mostly of amino-terminal sequence, is associated with nucleotide binding, and its largely antiparallel beta sheet architecture constitutes an unusual nucleotide binding motif. The larger lobe is dominated by helical structure with a single beta sheet at the domain interface. This lobe is primarily involved in peptide binding and catalysis. Residues 40 through 280 constitute a conserved catalytic core that is shared by more than 100 protein kinases. Most of the invariant amino acids in this conserved catalytic core are clustered at the sites of nucleotide binding and catalysis.

BACKGROUND: Reliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the protein's globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes. RESULTS: Here we present ElliPro, a web-tool that implements Thornton's method and, together with a residue clustering algorithm, the MODELLER program and the Jmol viewer, allows the prediction and visualization of antibody epitopes in a given protein sequence or structure. ElliPro has been tested on a benchmark dataset of discontinuous epitopes inferred from 3D structures of antibody-protein complexes. In comparison with six other structure-based methods that can be used for epitope prediction, ElliPro performed the best and gave an AUC value of 0.732, when the most significant prediction was considered for each protein. Since the rank of the best prediction was at most in the top three for more than 70% of proteins and never exceeded five, ElliPro is considered a useful research tool for identifying antibody epitopes in protein antigens. ElliPro is available at http://tools.immuneepitope.org/tools/ElliPro. CONCLUSION: The results from ElliPro suggest that further research on antibody epitopes considering more features that discriminate epitopes from non-epitopes may further improve predictions. As ElliPro is based on the geometrical properties of protein structure and does not require training, it might be more generally applied for predicting different types of protein-protein interactions.

Abstract Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery ‘pipelines’. A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. ‘the Grid’). However, this infrastructure is only a means to an end and ideally scientists should not be too concerned with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows . These are networks of analytical steps that may involve, e.g., database access and querying steps, data analysis and mining steps, and many other steps including computationally intensive jobs on high‐performance cluster computers. In this paper we describe characteristics of and requirements for scientific workflows as identified in a number of our application projects. We then elaborate on Kepler, a particular scientific workflow system, currently under development across a number of scientific data management projects. We describe some key features of Kepler and its underlying Ptolemy II system, planned extensions, and areas of future research. Kepler is a community‐driven, open source project, and we always welcome related projects and new contributors to join. Copyright © 2005 John Wiley & Sons, Ltd.

In the original version, the bounds given in Eqs. (87a) and (87b) on the contribution to the early-time optical depth, (15,30), contained a numerical error in deriving the 95th percentile from the Monte Carlo samples. The corrected 95% upper bounds are: τ(15,30) < 0:018 (lowE, flat τ(15, 30), FlexKnot), (1) τ(15, 30) < 0:023 (lowE, flat knot, FlexKnot): (2) These bounds are a factor of 3 larger than the originally reported results. Consequently, the new bounds do not significantly improve upon previous results from Planck data presented in Millea & Bouchet (2018) as was stated, but are instead comparable. Equations (1) and (2) give results that are now similar to those of Heinrich & Hu (2021), who used the same Planck 2018 data to derive a 95% upper bound of 0.020 using the principal component analysis (PCA) model and uniform priors on the PCA mode amplitudes.

The European Space Agency’s Planck satellite, which was dedicated to studying the early Universe and its subsequent evolution, was launched on 14 May 2009. It scanned the microwave and submillimetre sky continuously between 12 August 2009 and 23 October 2013, producing deep, high-resolution, all-sky maps in nine frequency bands from 30 to 857 GHz. This paper presents the cosmological legacy of Planck , which currently provides our strongest constraints on the parameters of the standard cosmological model and some of the tightest limits available on deviations from that model. The 6-parameter ΛCDM model continues to provide an excellent fit to the cosmic microwave background data at high and low redshift, describing the cosmological information in over a billion map pixels with just six parameters. With 18 peaks in the temperature and polarization angular power spectra constrained well, Planck measures five of the six parameters to better than 1% (simultaneously), with the best-determined parameter ( θ * ) now known to 0.03%. We describe the multi-component sky as seen by Planck , the success of the ΛCDM model, and the connection to lower-redshift probes of structure formation. We also give a comprehensive summary of the major changes introduced in this 2018 release. The Planck data, alone and in combination with other probes, provide stringent constraints on our models of the early Universe and the large-scale structure within which all astrophysical objects form and evolve. We discuss some lessons learned from the Planck mission, and highlight areas ripe for further experimental advances.