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/.

/pdf/ucsf-chimera-a-visualization-system-for-exploratory-research-59xu54fhxg.pdf

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.

/pdf/the-protein-data-bank-4lbxxcio6v.pdf

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'.

/pdf/coot-model-building-tools-for-molecular-graphics-3xns4git70.pdf

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

Discovering chemical-protein interactions for millions of chemicals across the entire human and pathogen genomes is instrumental for chemical genomics, protein function prediction, drug discovery, and other applications. However, more than 90% of gene families remain dark, i.e., their small molecular ligands are undiscovered due to experimental limitations and human biases. Existing computational approaches typically fail when the unlabeled dark protein of interest differs from those with known ligands or structures. To address this challenge, we developed a deep learning framework PortalCG. PortalCG consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to represent the whole universe of protein sequences in recognition of evolutionary linkage of ligand binding sites across gene families, (ii) an end-to-end pretraining-fine-tuning strategy to simulate the folding process of protein-ligand interactions and reduce the impact of inaccuracy of predicted structures on function predictions under a sequence-structure-function paradigm, (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family, and (iv) stress model selection that uses different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for off-target predictions and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the human design. Our results also suggested that a differentiable sequence-structure-function deep learning framework where protein structure information serve as an intermediate layer could be superior to conventional methodology where the use of predicted protein structures for predicting protein functions from sequences. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of Dopamine receptors for the treatment of Opioid Use Disorder, and illuminating the undruggable human genome for targeting diseases that do not have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring the understudied protein functional space. Author Summary Many complex diseases such as Alzheimer’s disease, mental disorders, and substance use disorders do not have effective and safe therapeutics due to the polygenic nature of diseases and the lack of thoroughly validate drug targets and their ligands. Identifying small molecule ligands for all proteins encoded in the human genome will provide new opportunity for drug discovery of currently untreatable diseases. However, the small molecule ligand of more than 90% gene families is completely unknown. Existing protein-ligand docking and machine learning methods often fail when the protein of interest is dissimilar to those with known functions or structures. We develop a new deep learning framework PortalCG for efficiently and accurately predicting ligands of understudied proteins which are out of reach of existing methods. Our method achieves unprecedented accuracy over state-of-the-arts by incorporating ligand binding site information and sequence-to-structure-to-function paradigm into a novel deep meta-learning algorithms. In a case study, the performance of PortalCG surpassed the human design. The proposed computational framework will shed new light into how chemicals modulate biological system as demonstrated by applications to drug repurposing and designing polypharmacology. It will open a new door to developing effective and safe therapeutics for currently incurable diseases. PortalCG can be extended to other scientific inquiries such as predicting protein-protein interactions and protein-nucleic acid recognition.

/pdf/binding-site-enhanced-sequence-pretraining-and-out-of-ho0alb9m.pdf

Binding Site-enhanced Sequence Pretraining and Out-of-cluster Meta-learning Predict Genome-Wide Chemical-Protein Interactions for Dark Proteins

PLoS Computational Biology celebrated its fifth anniversary in 2010, and all in our community, either as readers, authors, or editors, should take pride in what has been accomplished in such a short space of time. In the past year we received 1,403 new Research Articles, a 295% increase from our first year of operation in 2005–2006 and a 17% increase over 2009. Of the articles submitted in 2010, 875 (62%) were rejected, and 70% of these were before review. We have seen growth not only in submissions, but in readership as well. Currently, around 16,000 readers receive the electronic table of contents, a 14% increase over the previous year. We published 392 Research Articles this year, along with 23 “front section” articles (Reviews, Perspectives, Education), down from 33 in the previous year. Eighty Associate Editors handled the combined submissions, with a total of 26 new editors joining this past year and six departing. We are proud to say that virtually every editor we asked to join accepted, a testament to how our community values the journal. These editors worked with more than 180 guest editors and 1,800 reviewers to handle the submissions, and we are of course very grateful for their support (Table S1).

Table 1 provides a list of Research Articles we have published since 2005 through October 2010 that have accrued over 10,000 downloads and shows the diversity of highly accessed papers published by the journal. Note that these are downloads from the PLoS Web site only, and do not include downloads from PubMed Central. Readers are free to review download statistics for all research and non-research articles published across the PLoS journals through the Microsoft Excel spreadsheet that can be found at http://www.ploscompbiol.org/static/plos-alm.zip. Individual article metrics and comments are available from the respective tabs associated with each article.



Table 1

List of published Research Articles that have accrued over 10,000 downloads since launch.



In 2010, we launched two new features to enrich the journal: “The Roots of Bioinformatics” and “PLoS Conference Postcards”. The Roots of Bioinformatics was eloquently introduced by the Series Editor, David B. Searls, in June [1] and was followed in July by Russell F. Doolittle’s insightful reflections on the roots of protein evolution, which went back as far as the 1950s when chemistry, rather than computers, ruled [2]. More such reflections will follow in 2011. Conference Postcards act as a counterpoint to the rich roots retrospectives by providing current views of the field of computational biology, as young scientists present crisp perspectives on what they perceive as conference highlights. We published Postcards from January’s Pacific Symposium on Biocomputing (PSB) meeting held in Hawaii [3] and from the Intelligent Systems for Molecular Biology (ISMB) meeting held in Boston in July [4]. At the latter we learnt about various sessions held at ISMB, namely the Highlights session, the ISCB Student Council Symposium’s “speed dating” event, and reports from Satellite meetings. We look forward to digging deeper and receiving Postcards from further afield in 2011.

PLoS Computational Biology continues its strong relationship with the International Society for Computational Biology (ISCB) through postings on its Web site and activities at ISMB. At ISMB 2010 in Boston, PLoS Computational Biology ran a Workshop entitled “Where and How to Get Published” in which we endeavored to make the path to getting published a little less inscrutable. The first half was led by journal co-founders Philip E. Bourne and Steven E. Brenner and provided guidelines on how to write a good paper, and the second half included questions and advice from editors and authors from a range of career stages, which resulted in a broad discussion of what journals want and the state of publishing today. Presenters’ materials from the Workshop are available on the new PLoS Blog (http://blogs.plos.org/plos/2010/10/materials-from-plos%E2%80%99-workshop-at-ismb-2010/).

We have three major goals for 2011. First, to reduce the time to decision for submitted manuscripts, which currently averages 10 days for those papers rejected before review and 40–50 days for those reviewed. Second, to introduce a new section called “Editors’ Outlook”, which are invited mini-reviews from members of our Editorial Board who will provide insights into their respective fields, discussing what is hot and what we can expect going forward. Collectively, these will provide an ongoing and insightful look into the broad and rapidly expanding field of computational biology—a field of endeavor the journal is proud to serve.

Specifically, current experimental techniques are leading to an unprecedented increase in the rate at which data are becoming available. When combined with the vast growth in computational power, we can expect rapid growth in computational papers. Computational biology is the area that helps in organizing the data, in making sense of observations, and in using these to make experimentally testable predictions. Our third goal is to keep abreast of these developments and keep PLoS Computational Biology the number one journal in the field.

/pdf/a-review-of-2010-for-plos-computational-biology-57i42w293u.pdf

A Review of 2010 for PLoS Computational Biology

Se proponen aqui diez acciones y actitudes que debe tomar todo estudiante graduado que planea entrar en un programa de postgrado.

Diez reglas simples para estudiantes que inician un postgrado

We propose a fold change visualization that demonstrates a combination of properties from log and linear plots of fold change. A useful fold change visualization can exhibit: (1) readability, where fold change values are recoverable from datapoint position; (2) proportionality, where fold change values of the same direction are proportionally distant from the point of no change; (3) symmetry, where positive and negative fold changes are equidistant to the point of no change; and (4) high dynamic range, where datapoint values are discernable across orders of magnitude. A linear visualization has readability and partial proportionality but lacks high dynamic range and symmetry (because negative direction fold changes are bound between [0, 1] while positive are between [1, $\infty$]). Log plots of fold change have partial readability, high dynamic range, and symmetry, but lack proportionality because of the log transform. We outline a new transform and visualization, named mirrored axis distortion of fold change (MAD-FC), that extends a linear visualization of fold change data to exhibit readability, proportionality, and symmetry (but still has the limited dynamic range of linear plots). We illustrate the use of MAD-FC with biomedical data using various fold change charts. We argue that MAD-FC plots may be a more useful visualization than log or linear plots for applications that require a limited dynamic range (approximately $\pm$2 orders of magnitude or $\pm$8 units in log2 space).

MAD-FC: A Fold Change Visualization with Readability, Proportionality, and Symmetry

Kinase-targeted drug design is challenging. It requires designing inhibitors that can bind to specific kinases when all kinase catalytic domains share a common folding scaffold that binds ATP. Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery. This begins with deciphering the kinase-ligand characteristics, analyzing the structure-activity relationships, and prioritizing the desired drug molecules across the whole kinome. Currently, there are more than 300 kinases with released PDB structures, which provides a substantial structural basis to gain these necessary insights. Here, we review in silico structure-based methods - notably, a function-site interaction fingerprint approach used in exploring the complete human kinome. In silico methods can be explored synergistically with multiple cell-based or protein-based assay platforms such as KINOMEscan. We conclude with new drug discovery opportunities associated with kinase signaling networks and using machine/deep learning techniques broadly referred to as structural biomedical data science.

/pdf/using-the-structural-kinome-to-systematize-kinase-drug-2tk81vrgiv.pdf

Philip E. Bourne

Papers

Binding Site-enhanced Sequence Pretraining and Out-of-cluster Meta-learning Predict Genome-Wide Chemical-Protein Interactions for Dark Proteins

A Review of 2010 for PLoS Computational Biology

Diez reglas simples para estudiantes que inician un postgrado

MAD-FC: A Fold Change Visualization with Readability, Proportionality, and Symmetry

Using the Structural Kinome to Systematize Kinase Drug Discovery