The misuse of terms in scientific literature

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Outlier Profiles of Atomic Structures Derived From X-ray Crystallography and From Cryo-electron Microscopy

Background: As more protein atomic structures are determined from cryo-electron microscopy (cryo-EM) density maps, validation of such structures is an important task. Methods: We applied a histogram-based outlier score (HBOS) to six sets of cryo-EM atomic structures and five sets of X-ray atomic structures, including one derived from X-ray data with better than 1.5 Å resolution. Cryo-EM data sets contain structures released by December 2016 and those released between 2017 and 2019, derived from resolution ranges 0–4 Å and 4–6 Å respectively. Results: The distribution of HBOS values in five sets of X-ray structures show that HBOS is sensitive distinguishing sets of X-ray structures derived from different resolution ranges-higher than 1.5 Å, 1.5–2.0 Å, 2.0–2.5 Å, 2.5–3.0 Å, and 3.0–3.5 Å. The overall quality of cryo-EM structures is likely improved, as shown in a comparison of cryo-EM structures released before the end of 2016, those between 2017 and 2018, and those between 2018 and 2019. Our investigation shows that leucine (LEU) has a significantly higher rate of HBOS outliers than that of the reference data set (X-ray-1.5) and of other residue types in the cryo-EM data sets. HBOS was able to detect outliers for those residues that are currently marked as green in PDB validation reports. Conclusions: The HBOS profile of a dataset is a potential method to characterize the overall structural quality of the set. Residue LEU deserves special attention since it has a significantly higher HBOS outlier rate in sets of cryo-EM structures and those X-ray structures derived from X-ray data of lower than 2.5 Å resolutions. Most HBOS outlier residues from the EM-0-4-2019 set are located on loops for most types of residues

Molecular modelling of co-receptor CD8αα and its complex with MHC class I and T-cell receptor in sea bream (Sparus aurata)

T-cells are the main actors of cell-mediated immune defence; they recognize and respond to peptide antigens associated with MHC class I and class II molecules. In this paper, we investigated by molecular modelling methods in the teleost sea bream (Sparus aurata) the interaction among the molecules of the tertiary complex CD8/MHC-I/TCR, which determines the T-cell-mediated immunological response to foreign molecules. First, we predicted the three-dimensional structure of CD8αα dimer and MHC-I, and, successively, we simulated the CD8αα/MHC-I complex. Finally, the 3D structure of the CD8/MHC-I/TCR complex was simulated in order to investigate the possible changes that can influence TCR signalling events.L'articolo è disponibile sul sito dell'editore http://www.sciencedirect.com

Evaluation of the structural quality of modeled proteins by using globularity criteria

BACKGROUND: The knowledge of the three-dimensional structure of globular proteins is fundamental for a detailed investigation of their functional properties. Experimental methods are too slow for structure investigation on a large scale, while computational prediction methods offer alternatives that are continuously being improved. The international Comparative Assessment of Structure Prediction (CASP), an "a posteriori" evaluation of the quality of theoretical models when the experimental structure becomes available, demonstrates that predictions can be successful as well as unsuccessful, and this suggests the necessity for evaluations able to discard "a priori" the wrong models. RESULTS: We analyzed different structural properties of globular proteins for experimentally solved proteins belonging to the four different structural classes: "mainly alpha", "mainly beta", "alpha/beta" and "alpha+beta". The properties were found to be linearly correlated to protein molecular weight, but with some differences among the four classes. These results were applied to develop an evaluation test of theoretical models based on the expected globular properties of proteins. To verify the success of our test, we applied it to several protein models submitted to the sixth edition of CASP. The best theoretical models, as judged by CASP assessors, were in agreement with the expected properties, while most of the low-quality models had not passed our evaluations. CONCLUSION: This study supports the need for careful checks to avoid the diffusion of incorrect structural models. Our test allows the evaluation of models in the absence of experimental reference structures, thereby preventing the diffusion of incorrect structural models and the formulation of incorrect functional hypotheses. It can be used to check the globularity of predicted models, and to supplement other methods already used to evaluate their quality

Identification of a novel domain of fibroblast growth factor 2 controlling its angiogenic properties.

Fibroblast growth factor 2 (FGF-2) is a potent factor modulating the activity of many cell types. Its dimerization and binding to high affinity receptors are considered to be necessary steps to induce FGF receptor phosphorylation and signaling activation. A structural analysis was carried out and a region encompassing residues 48-58 of human FGF-2 was identified, as potentially involved in FGF-2 dimerization. A peptide (FREG-48-58) derived from this region strongly and specifically inhibited FGF-2 induced proliferation and migration of primary bovine aorta endothelial cells (BAEC) in vitro, and markedly reduced FGF-2-dependent angiogenesis in two distinct in vivo assays. To further investigate the role of region 48-58, a polyclonal antibody raised against FREG-(48-58) was tested and was found to block FGF-2 action in vitro. Human FGF-2 has three histidine residues, one falling within the region 48-58. Chemical modification of histidine residues blocked FGF-2 activity and FREG-(48-58) inhibitory effect in vitro, indicating that histidine residues, in particular the one within FREG-(48-58) region, play a crucial role in the observed activity. Additional experiments showed that FREG-(48-58) specifically interacted with FGF-2, impaired FGF-2-interaction with itself, with heparin and with FGF receptor 1, and inhibited FGF-2-induced receptor phosphorylation and FGF-2 internalization. These data indicate for the first time that region 48-58 of FGF-2 is a functional domain controlling FGF-2 activity

FASMA: A Service to Format and Analyze Sequences in Multiple Alignments

Multiple sequence alignments are successfully applied in many studies for understanding the structural and functional relations among single nucleic acids and protein sequences as well as whole families. Because of the rapid growth of sequence databases, multiple sequence alignments can often be very large and difficult to visualize and analyze. We offer a new service aimed to visualize and analyze the multiple alignments obtained with different external algorithms, with new features useful for the comparison of the aligned sequences as well as for the creation of a final image of the alignment. The service is named FASMA and is available at http://bioinformatica.isa.cnr.it/FASMA/

A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry spectra, widely used in proteomics studies as a screening tool for protein profiling and to detect discriminatory signals, are high dimensional data. A large number of local maxima (a.k.a. <it>peaks</it>) have to be analyzed as part of computational pipelines aimed at the realization of efficient predictive and screening protocols. With this kind of data dimensions and samples size the risk of over-fitting and selection bias is pervasive. Therefore the development of bio-informatics methods based on unsupervised feature extraction can lead to general tools which can be applied to several fields of predictive proteomics.</p> <p>Results</p> <p>We propose a method for feature selection and extraction grounded on the theory of multi-scale spaces for high resolution spectra derived from analysis of serum. Then we use support vector machines for classification. In particular we use a database containing 216 samples spectra divided in 115 cancer and 91 control samples. The overall accuracy averaged over a large cross validation study is 98.18. The area under the ROC curve of the best selected model is 0.9962.</p> <p>Conclusion</p> <p>We improved previous known results on the problem on the same data, with the advantage that the proposed method has an unsupervised feature selection phase. All the developed code, as MATLAB scripts, can be downloaded from <url>http://medeaserver.isa.cnr.it/dacierno/spectracode.htm</url></p