AVID: An integrative framework for discovering functional relationships among proteins

BACKGROUND: Determining the functions of uncharacterized proteins is one of the most pressing problems in the post-genomic era. Large scale protein-protein interaction assays, global mRNA expression analyses and systematic protein localization studies provide experimental information that can be used for this purpose. The data from such experiments contain many false positives and false negatives, but can be processed using computational methods to provide reliable information about protein-protein relationships and protein function. An outstanding and important goal is to predict detailed functional annotation for all uncharacterized proteins that is reliable enough to effectively guide experiments. RESULTS: We present AVID, a computational method that uses a multi-stage learning framework to integrate experimental results with sequence information, generating networks reflecting functional similarities among proteins. We illustrate use of the networks by making predictions of detailed Gene Ontology (GO) annotations in three categories: molecular function, biological process, and cellular component. Applied to the yeast Saccharomyces cerevisiae, AVID provides 37,451 pair-wise functional linkages between 4,191 proteins. These relationships are ~65–78% accurate, as assessed by cross-validation testing. Assignments of highly detailed functional descriptors to proteins, based on the networks, are estimated to be ~67% accurate for GO categories describing molecular function and cellular component and ~52% accurate for terms describing biological process. The predictions cover 1,490 proteins with no previous annotation in GO and also assign more detailed functions to many proteins annotated only with less descriptive terms. Predictions made by AVID are largely distinct from those made by other methods. Out of 37,451 predicted pair-wise relationships, the greatest number shared in common with another method is 3,413. CONCLUSION: AVID provides three networks reflecting functional associations among proteins. We use these networks to generate new, highly detailed functional predictions for roughly half of the yeast proteome that are reliable enough to drive targeted experimental investigations. The predictions suggest many specific, testable hypotheses. All of the data are available as downloadable files as well as through an interactive website at . Thus, AVID will be a valuable resource for experimental biologists

Correlation of Influenza Virus Excess Mortality with Antigenic Variation: Application to Rapid Estimation of Influenza Mortality Burden

The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention

NCACO-score: An effective main-chain dependent scoring function for structure modeling

<p>Abstract</p> <p>Background</p> <p>Development of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.</p> <p>Results</p> <p>Based on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.</p> <p>Conclusions</p> <p>Although NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.</p

Conjugating drug candidates to polymeric chains does not necessarily enhance anti-influenza activity

Using the plaque reduction assay, relatively simple bicyclic quinone molecules, as well as multiple copies thereof covalently attached to a long polyglutamate-based polymeric chain, were examined as new inhibitors of various naturally occurring strains of influenza A virus. The polymer-conjugated inhibitors were found to have a far greater potency (for some as high as two orders of magnitude when a long spacer arm was employed) than their corresponding parent molecules against the human Wuhan influenza strain. However, such polymeric inhibitors failed to exhibit higher potency compared with their small molecule predecessors against the human Puerto Rico and avian turkey influenza strains. These observations, further explored by means of molecular modeling, reveal the previously unrecognized unpredictability of the benefits of multivalency, possibly because of poor accessibility of the viral targets to polymeric agentsNational Institutes of Health (U.S.) (Grant U01-AI074443

Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China.

An in-depth annotation of the newly discovered coronavirus (2019-nCoV) genome has revealed differences between 2019-nCoV and severe acute respiratory syndrome (SARS) or SARS-like coronaviruses. A systematic comparison identified 380 amino acid substitutions between these coronaviruses, which may have caused functional and pathogenic divergence of 2019-nCoV

Rapid Estimation of Binding Activity of Influenza Virus Hemagglutinin to Human and Avian Receptors

A critical step for avian influenza viruses to infect human hosts and cause epidemics or pandemics is acquisition of the ability of the viral hemagglutinin (HA) to bind to human receptors. However, current global influenza surveillance does not monitor HA binding specificity due to a lack of rapid and reliable assays. Here we report a computational method that uses an effective scoring function to quantify HA-receptor binding activities with high accuracy and speed. Application of this method reveals receptor specificity changes and its temporal relationship with antigenicity changes during the evolution of human H3N2 viruses. The method predicts that two amino acid differences at 222 and 225 between HAs of A/Fujian/411/02 and A/Panama/2007/99 viruses account for their differences in binding to both avian and human receptors; this prediction was verified experimentally. The new computational method could provide an urgently needed tool for rapid and large-scale analysis of HA receptor specificities for global influenza surveillance.National Key Project (2008ZX10004-013)National Institutes of Health (U.S.) (grant AI07443)Singapore-MIT Alliance for Research and TechnologyMassachusetts Institute of Technology. International Science and Technology Initiatives Global Seed FundNational Basic Research Program (973 Program) (2009CB918503)National Basic Research Program (973 Program) (2006CB911002

Sequential Reassortments Underlie Diverse Influenza H7N9 Genotypes in China

Initial genetic characterizations have suggested that the influenza A (H7N9) viruses responsible for the current outbreak in China are novel reassortants. However, little is known about the pathways of their evolution and, in particular, the generation of diverse viral genotypes. Here we report an in-depth evolutionary analysis of whole-genome sequence data of 45 H7N9 and 42 H9N2 viruses isolated from humans, poultry, and wild birds during recent influenza surveillance efforts in China. Our analysis shows that the H7N9 viruses were generated by at least two steps of sequential reassortments involving distinct H9N2 donor viruses in different hosts. The first reassortment likely occurred in wild birds and the second in domestic birds in east China in early 2012. Our study identifies the pathways for the generation of diverse H7N9 genotypes in China and highlights the importance of monitoring multiple sources for effective surveillance of potential influenza outbreaks.National Natural Science Foundation (China) (31125016)National Natural Science Foundation (China) (31371338)National Center for Biotechnology Information (U.S.) (Major National Earmark Project for Infectious Diseases, 2013ZX10004611-002)National Basic Research Program of China (973 Program)National Basic Research Program of China (973 Program, grant, 2009CB918503)National Science and Technology Major Projects (2012ZX10004214001002)Jiangsu Sheng (China) (Priority Academic Program Development of Jiangsu Higher Education Institutions)National Natural Science Foundation (China) (31100950)MIT International Science and Technology Initiative

Whole-genome sequencing and comparative genomics analysis of a newly emerged multidrug-resistant Klebsiella pneumoniae isolate of ST967

Whole-genome sequencing and population genetics analysis of K. pneumoniae are scarce from LMICs, and none has been reported for Armenia. Multilevel comparative analysis revealed that ARM01 (an isolate belonging to a newly emerged K. pneumoniae ST967 lineage) was genetically similar to two isolates recovered from Qatar

Intracellular CD24 disrupts the ARF–NPM interaction and enables mutational and viral oncogene-mediated p53 inactivation

CD24 is overexpressed in nearly 70% human cancers, whereas TP53 is the most frequently mutated tumour-suppressor gene that functions in a context-dependent manner. Here we show that both targeted mutation and short hairpin RNA (shRNA) silencing of CD24 retard the growth, progression and metastasis of prostate cancer. CD24 competitively inhibits ARF binding to NPM, resulting in decreased ARF, increase MDM2 and decrease levels of p53 and the p53 target p21/CDKN1A. CD24 silencing prevents functional inactivation of p53 by both somatic mutation and viral oncogenes, including the SV40 large T antigen and human papilloma virus 16 E6-antigen. In support of the functional interaction between CD24 and p53, in silico analyses reveal that TP53 mutates at a higher rate among glioma and prostate cancer samples with higher CD24 mRNA levels. These data provide a general mechanism for functional inactivation of ARF and reveal an important cellular context for genetic and viral inactivation of TP53. P53 is a tumour suppressor that is frequently mutated or downregulated in cancer. Here, Wang et al. show that CD24, a molecule frequently overexpressed in cancer, promotes p53 degradation by disrupting a regulatory ARF–MDM2 interaction, and silencing CD24 prevents the downregulation of p53

Incorporation of Local Structural Preference Potential Improves Fold Recognition

Fold recognition, or threading, is a popular protein structure modeling approach that uses known structure templates to build structures for those of unknown. The key to the success of fold recognition methods lies in the proper integration of sequence, physiochemical and structural information. Here we introduce another type of information, local structural preference potentials of 3-residue and 9-residue fragments, for fold recognition. By combining the two local structural preference potentials with the widely used sequence profile, secondary structure information and hydrophobic score, we have developed a new threading method called FR-t5 (fold recognition by use of 5 terms). In benchmark testings, we have found the consideration of local structural preference potentials in FR-t5 not only greatly enhances the alignment accuracy and recognition sensitivity, but also significantly improves the quality of prediction models