On the persistence of supplementary resources in biomedical publications

BACKGROUND: Providing for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers. METHODS: We manually evaluated 655 supplementary data links extracted from PubMed abstracts published 1998–2005 (Method 1) as well as a further focused subset of 162 full-text manuscripts published within three representative high-impact biomedical journals between September and December 2004 (Method 2). RESULTS: For Method 1 we found that since 2001, only 71 – 92% of supplementary data were still accessible via the links provided, with 93% of these inaccessible links occurring where supplementary data was not stored with the publishing journal. Of the manuscripts evaluated in Method 2, we found that only 83% of these links were available approximately a year after publication, with 55% of these inaccessible links were at locations outside the journal of publication. CONCLUSION: We conclude that if supplemental data is required to support the publication, journals policies must take-on the responsibility to accept and store such data or require that it be maintained with a credible independent institution or under the terms of a strategic data storage plan specified by the authors. We further recommend that publishers provide automated systems to ensure that supplementary links remain persistent, and that granting bodies such as the NIH develop policies and funding mechanisms to maintain long-term persistent access to these data

A vision of the future for BMC Medicine: serving science, medicine and authors

In June 2009, BMC Medicine received its first official impact factor of 3.28 from Thomson Reuters. In recognition of this landmark event, the BMC Medicine editorial team present and discuss the vision and aims of the journal

In Vitro Recombination Catalyzed by Bacterial Class 1 Integron Integrase IntI1 Involves Cooperative Binding and Specific Oligomeric Intermediates

Gene transfer via bacterial integrons is a major pathway for facilitating the spread of antibiotic resistance genes across bacteria. Recently the mechanism underlying the recombination catalyzed by class 1 integron recombinase (IntI1) between attC and attI1 was highlighted demonstrating the involvement of a single-stranded intermediary on the attC site. However, the process allowing the generation of this single-stranded substrate has not been determined, nor have the active IntI1•DNA complexes been identified. Using the in vitro strand transfer assay and a crosslink strategy we previously described we demonstrated that the single-stranded attC sequences could be generated in the absence of other bacterial proteins in addition to IntI. This suggests a possible role for this protein in stabilizing and/or generating this structure. The mechanism of folding of the active IntI•DNA complexes was further analyzed and we show here that it involves a cooperative binding of the protein to each recombination site and the emergence of different oligomeric species specific for each DNA substrate. These findings provide further insight into the recombination reaction catalyzed by IntI1

VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines

BACKGROUND: Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. RESULTS: Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. CONCLUSION: VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods. It is freely-available online at the URL:

Ten Simple Rules for Organizing an Unconference

This is an open access article distributed under the terms of the Creative Commons Attribution License.Peer Reviewe

Investigating the Structural Impacts of I64T and P311S Mutations in APE1-DNA Complex: A Molecular Dynamics Approach

Elucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA.In this study, we developed a rapid and cost-effective method to analyze variants in APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis.Different parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis

Mathematical modeling and comparison of protein size distribution in different plant, animal, fungal and microbial species reveals a negative correlation between protein size and protein number, thus providing insight into the evolution of proteomes

<p>Abstract</p> <p>Background</p> <p>The sizes of proteins are relevant to their biochemical structure and for their biological function. The statistical distribution of protein lengths across a diverse set of taxa can provide hints about the evolution of proteomes.</p> <p>Results</p> <p>Using the full genomic sequences of over 1,302 prokaryotic and 140 eukaryotic species two datasets containing 1.2 and 6.1 million proteins were generated and analyzed statistically. The lengthwise distribution of proteins can be roughly described with a gamma type or log-normal model, depending on the species. However the shape parameter of the gamma model has not a fixed value of 2, as previously suggested, but varies between 1.5 and 3 in different species. A gamma model with unrestricted shape parameter described best the distributions in ~48% of the species, whereas the log-normal distribution described better the observed protein sizes in 42% of the species. The gamma restricted function and the sum of exponentials distribution had a better fitting in only ~5% of the species. Eukaryotic proteins have an average size of 472 aa, whereas bacterial (320 aa) and archaeal (283 aa) proteins are significantly smaller (33-40% on average). Average protein sizes in different phylogenetic groups were: Alveolata (628 aa), Amoebozoa (533 aa), Fornicata (543 aa), Placozoa (453 aa), Eumetazoa (486 aa), Fungi (487 aa), Stramenopila (486 aa), Viridiplantae (392 aa). Amino acid composition is biased according to protein size. Protein length correlated negatively with %C, %M, %K, %F, %R, %W, %Y and positively with %D, %E, %Q, %S and %T. Prokaryotic proteins had a different protein size bias for %E, %G, %K and %M as compared to eukaryotes.</p> <p>Conclusions</p> <p>Mathematical modeling of protein length empirical distributions can be used to asses the quality of small ORFs annotation in genomic releases (detection of too many false positive small ORFs). There is a negative correlation between average protein size and total number of proteins among eukaryotes but not in prokaryotes. The %GC content is positively correlated to total protein number and protein size in prokaryotes but not in eukaryotes. Small proteins have a different amino acid bias than larger proteins. Compared to prokaryotic species, the evolution of eukaryotic proteomes was characterized by increased protein number (massive gene duplication) and substantial changes of protein size (domain addition/subtraction).</p

Disorder Predictors Also Predict Backbone Dynamics for a Family of Disordered Proteins

Several algorithms have been developed that use amino acid sequences to predict whether or not a protein or a region of a protein is disordered. These algorithms make accurate predictions for disordered regions that are 30 amino acids or longer, but it is unclear whether the predictions can be directly related to the backbone dynamics of individual amino acid residues. The nuclear Overhauser effect between the amide nitrogen and hydrogen (NHNOE) provides an unambiguous measure of backbone dynamics at single residue resolution and is an excellent tool for characterizing the dynamic behavior of disordered proteins. In this report, we show that the NHNOE values for several members of a family of disordered proteins are highly correlated with the output from three popular algorithms used to predict disordered regions from amino acid sequence. This is the first test between an experimental measure of residue specific backbone dynamics and disorder predictions. The results suggest that some disorder predictors can accurately estimate the backbone dynamics of individual amino acids in a long disordered region

Structural Insights into the Evolution of a Non-Biological Protein: Importance of Surface Residues in Protein Fold Optimization

Phylogenetic profiling of amino acid substitution patterns in proteins has led many to conclude that most structural information is carried by interior core residues that are solvent inaccessible. This conclusion is based on the observation that buried residues generally tolerate only conserved sequence changes, while surface residues allow more diverse chemical substitutions. This notion is now changing as it has become apparent that both core and surface residues play important roles in protein folding and stability. Unfortunately, the ability to identify specific mutations that will lead to enhanced stability remains a challenging problem. Here we discuss two mutations that emerged from an in vitro selection experiment designed to improve the folding stability of a non-biological ATP binding protein. These mutations alter two solvent accessible residues, and dramatically enhance the expression, solubility, thermal stability, and ligand binding affinity of the protein. The significance of both mutations was investigated individually and together, and the X-ray crystal structures of the parent sequence and double mutant protein were solved to a resolution limit of 2.8 and 1.65 Å, respectively. Comparative structural analysis of the evolved protein to proteins found in nature reveals that our non-biological protein evolved certain structural features shared by many thermophilic proteins. This experimental result suggests that protein fold optimization by in vitro selection offers a viable approach to generating stable variants of many naturally occurring proteins whose structures and functions are otherwise difficult to study