We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the logexpectation score, and refinement using treedependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

MUSCLE: multiple sequence alignment with high accuracy and high throughput

The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.

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UCSF Chimera--a visualization system for exploratory research and analysis.

The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.

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The Protein Data Bank

CCP4mg is a project that aims to provide a general-purpose tool for structural biologists, providing tools for X-ray structure solution, structure comparison and analysis, and publication-quality graphics. The map-fitting tools are available as a stand-alone package, distributed as `Coot'.

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Coot: model-building tools for molecular graphics.

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Structural genomics has been heralded as the follow on to the human genome project. We interpret that to mean a large-scale project with scientific, engineering and technological components and the potential to have a large impact on the life sciences. A project that goes beyond the blueprint for life to the buildings defined by that blueprint — the three-dimensional protein structures. Whereas the human genome project was relatively well defined, with the aim to sequence the three billion nucleotides comprising the human genome, what constitutes structural genomics is open to different interpretations. To some, it aims to characterize all the protein structures in a given genome — Arabidopsis thaliana, Thermotoga maritima and Mycobacterium tuberculosis are examples under scrutiny. To others, the goal of structural genomics is to provide sufficient coverage of fold space to facilitate accurate homology modeling of the majority of proteins of biological interest. As structure determination has already taught us so much about biological function when undertaken as a functionally driven initiative, undertaking structure determination in a broader genomic sense is also likely to bring significant new understanding of living systems. Further, it is likely to lead to advances in the process of structure determination, whether by X-ray crystallography or NMR. With such promise, and with some projects already in their third or fourth year, an obvious question is, how are we doing?

The status of structural genomics

Our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and understand biological systems—from protein structure comparison and classification to function prediction and evolutionary analyses. For instance, is there an optimal granularity at which to view protein structural similarities (e.g., architecture, topology or some other level)? If so, how does it vary with the type of question being asked? Similarly, the discrete/continuous dichotomy of fold space is central in structural bioinformatics, but remains unresolved. Discrete views of fold space bin ‘similar’ folds into distinct, non-overlapping groups; unfortunately, such binning may inherently miss many remote relationships. While hierarchical databases like CATH, SCOP and ECOD represent major steps forward in protein classification, a scalable, objective and conceptually flexible method, with less reliance on assumptions and heuristics, could enable a more systematic and nuanced exploration of fold space, particularly as regards evolutionarily-distant relationships. Building upon a recent ‘Urfold’ model of protein structure, we have developed a new approach to analyze protein structure relationships. Termed ‘DeepUrfold’, this method is rooted in deep generative modeling via variational Bayesian inference, and we find it to be useful for comparative analysis across the protein universe. Critically, DeepUrfold leverages its deep generative model’s embeddings, which represent a distilled, lower-dimensional space of a given protein and its amalgamation of sequence, structure and biophysical properties. Notably, DeepUrfold is structure-guided, versus being purely structure-based, and its architecture allows each trained model to learn protein features (structural and otherwise) that, in a sense, ‘define’ different superfamilies. Deploying DeepUrfold with CATH suggests a new, mostly-continuous view of fold space—a view that extends beyond simple 3D structural/geometric similarity, towards the realm of integrated sequence↔structure↔function properties. We find that such an approach can quantitatively represent and detect evolutionarily-remote relationships that evade existing methods. Availability Our detailed results can be explored at https://bournelab.org/research/DeepUrfold/; the DeepUrfold code is available at http://www.github.com/bouralab/DeepUrfold and data are available at https://doi.org/10.5281/zenodo.6916524.

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Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space

The comparison of results from 3-D structure alignments obtained by a variety of different algorithms and those determined by domain experts reveals significant differences. Simply finding the best RMSD between the corresponding C α positions in two structures is not enough to match biologically meaningful features, and hence provide the most meaningful structure alignment. Yet an accurate comparative analysis of structure reveals a great deal about the biological function of related proteins. A recently described combinatorial extension algorithm has been further refined by the use of protein properties relevant to structural and functional features to provide good structure alignments. The resulting database of automated pairwise alignments is reported and contains acetylcholinesterases (9), lipases (15), haloalkane dehalogenases (11), and cholesterol esterases (2) structures for which more than 200 residues could be aligned with an RMSD of less than 4.0A. Results for the alignment of a specific acetylcholinesterase to a lipase is compared to the previously performed manual alignment. The database and associated tools for visualizing the structure alignments are available via the Web at the URL http://cl.sdsc.edu/align_db.html.

Philip E. Bourne

Papers

The status of structural genomics

Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space

A Database of Pairwise Aligned 3-D Structures for the Acetylcholinesterases, Lipases and Other Homologous Proteins

Catalysis and regulation. Proteins. Web alert.

The RCSB Protein Data Bank: site functionality and bioinformatics use cases