1,720,967 research outputs found
MOBI: a web server to define and visualize structural mobility in NMR protein ensembles
No full textMotivation: MOBI is a web server for the identification of structurally mobile regions in NMR protein ensembles. It provides a binary mobility definition that is analogous to the commonly used definition of intrinsic disorder in X-ray crystallographic structures. At least three different use cases can be envisaged: ( i) visualization of NMR mobility for structural analysis; ( ii) definition of regions for reliable comparative modelling in protein structure prediction and (iii) definition of mobility in analogy to intrinsic disorder. MOBI uses structural superposition and local conformational differences to derive a robust binary mobility definition that is in excellent agreement with the manually curated definition used in the CASP8 experiment for intrinsic disorder in NMR structure. The output includes mobility-coloured PDB files, mobility plots and a FASTA formatted sequence file summarizing the mobility results
RUBI: rapid proteomic-scale prediction of lysine ubiquitination and factors influencing predictor performance
No full textAnalysis and consensus of currently available intrinsic protein disorder annotation sources in the MobiDB database.
Get PDFFELLS: fast estimator of latent local structure
No full textThe behavior of a protein is encoded in its sequence, which can be used to predict distinct features such as secondary structure, intrinsic disorder or amphipathicity. Integrating these and other features can help explain the context-dependent behavior of proteins. However, most tools focus on a single aspect, hampering a holistic understanding of protein structure. Here, we present Fast Estimator of Latent Local Structure (FELLS) to visualize structural features from the protein sequence. FELLS provides disorder, aggregation and low complexity predictions as well as estimated local propensities including amphipathicity. A novel fast estimator of secondary structure (FESS) is also trained to provide a fast response. The calculations required for FELLS are extremely fast and suited for large-scale analysis while providing a detailed analysis of difficult cases
RAPHAEL: Recognition, periodicity and insertion assignment of solenoid protein structures.
No full textMotivation: Repeat proteins form a distinct class of structures where folding is greatly simplified. Several classes have been defined, with solenoid repeats of periodicity between ca. 5 and 40 being the most challenging to detect. Such proteins evolve quickly and their periodicity may be rapidly hidden at sequence level. From a structural point of view, finding solenoids may be complicated by the presence of insertions or multiple domains. To the best of our knowledge, no automated methods are available to characterize solenoid repeats from structure.
Results: Here we introduce RAPHAEL, a novel method for the detection of solenoids in protein structures. It reliably solves three problems of increasing difficulty: (i) recognition of solenoid domains, (ii) determination of their periodicity and (iii) assignment of insertions. RAPHAEL uses a geometric approach mimicking manual classification, producing several numeric parameters which are optimized for maximum performance. The resulting method is very accurate, with 89.5% of solenoid proteins and 97.2% of non-solenoid proteins correctly classified. RAPHAEL periodicities have a Spearman correlation coefficient of 0.877 against the manually established ones. A baseline algorithm for insertion detection in identified solenoids has a Q2 value of 79.8%, suggesting room for further improvement. RAPHAEL finds 1,931 highly confident repeat structures not previously annotated as solenoids in the PDB records
PASTA 2.0: an improved server for protein aggregation prediction
No full textThe formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2
MobiDB 2.0: an improved database of intrinsically disordered and mobile proteins
Get PDFMobiDB (ext-link-type="uri" xlink:href="http://mobidb.bio.unipd.it/" xlink:type="simple">http://mobidb.bio.unipd.it/) is a database of intrinsically disordered and mobile proteins. Intrinsically disordered regions are key for the function of numerous proteins. Here we provide a new version of MobiDB, a centralized source aimed at providing the most complete picture on different flavors of disorder in protein structures covering all UniProt sequences (currently over 80 million). The database features three levels of annotation: manually curated, indirect and predicted. Manually curated data is extracted from the DisProt database. Indirect data is inferred from PDB structures that are considered an indication of intrinsic disorder. The 10 predictors currently included (three ESpritz flavors, two IUPred flavors, two DisEMBL flavors, GlobPlot, VSL2b and JRONN) enable MobiDB to provide disorder annotations for every protein in absence of more reliable data. The new version also features a consensus annotation and classification for long disordered regions. In order to complement the disorder annotations, MobiDB features additional annotations from external sources. Annotations from the UniProt database include post-translational modifications and linear motifs. Pfam annotations are displayed in graphical form and are link-enabled, allowing the user to visit the corresponding Pfam page for further information. Experimental protein-protein interactions from STRING are also classified for disorder content
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