AmyLoad: website dedicated to amyloidogenic protein fragments

Wozniak, Pawel P.; Kotulska, Małgorzata

doi:10.1093/bioinformatics/btv375

Cited by 51 publications

(43 citation statements)

References 10 publications

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“…We have utilized 700 experimentally known aggregating peptides (Amyloid700) to identify experimentally validated aggregation prone regions in human proteins. This dataset includes peptides of length >5, collected from CPAD (https://www.iitm.ac.in/bioinfo/CPAD/), WALTZ‐DB (http://waltzdb.switchlab.org/) and AmyLoad (http://comprec-lin.iiar.pwr.edu.pl/amyload/) databases and 34 experimentally validated peptides from amyloidogenic proteins . The links to all databases as well as the primary datasets on protein proteome used in the present study are given at https://www.iitm.ac.in/bioinfo/AgginHprot/.…”

Section: Methodsmentioning

confidence: 99%

Aggregation prone regions in human proteome: Insights from large‐scale data analyses

et al. 2017

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Protein aggregation leads to several burdensome human maladies, but a molecular level understanding of how human proteome has tackled the threat of aggregation is currently lacking. In this work, we survey the human proteome for incidence of aggregation prone regions (APRs), by using sequences of experimentally validated amyloid-fibril forming peptides and via computational predictions. While approximately 30 human proteins are currently known to be amyloidogenic, we found that 260 proteins (∼1% of human proteome) contain at least one experimentally validated amyloid-fibril forming segment. Computer predictions suggest that more than 80% of the human proteins contain at least one potential APR and approximately two-thirds (65%) contain two or more APRs; spanning 3-5% of their sequences. Sequence randomizations show that this apparently high incidence of APRs has been actually significantly reduced by unique amino acid composition and sequence patterning of human proteins. The human proteome has utilized a wide repertoire of sequence-structural optimization strategies, most of them already known, to minimize deleterious consequences due to the presence of APRs while simultaneously taking advantage of their order promoting properties. This survey also found that APRs tend to be located near the active and ligand binding sites in human proteins, but not near the post translational modification sites. The APRs in human proteins are also preferentially found at heterotypic interfaces rather than homotypic ones. Interestingly, this survey reveals that APRs play multiple, often opposing, roles in the human protein sequence-structure-function relationships. Insights gained from this work have several interesting implications towards novel drug discovery and development. Proteins 2017; 85:1099-1118. © 2017 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

Aggregation prone regions in human proteome: Insights from large‐scale data analyses

et al. 2017

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“…In this sample case (Listing 2), populations of amyloidogenic and non-amyloidogenic peptides in the AmyLoad database [27] are compared in the feature space defined by hydropathy and volume of amino acids. Import statements in Listing 2 are omitted.…”

Section: Resultsmentioning

confidence: 99%

Quantiprot - a Python package for quantitative analysis of protein sequences

2017

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BackgroundThe field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted.ResultsQuantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf’s law coefficient.ConclusionsWe propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.

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“…In addition to this, the set was enriched with proteins that form amyloid fibrils in vitro [41]. To expand this dataset, AmyLoad [7] was used as a source of supplementary proteins, characterized to form amyloid-like fibrils in vitro at experimental level. A final addition included several UniProtKB [42] entries, gathered elaborately to incorporate reviewed proteins related to amyloid fibrils.…”

Section: Methodsmentioning

confidence: 99%

“…Noteworthy, proteins, ranging from bacteria to humans, have been also found to adopt the same amyloid architecture, as part of their nature [5, 6]. A vast amount of data, regarding amyloid fibril formation, present both in pathological and physiological conditions, is currently organized into freely available databases [7–11]. …”

Section: Introductionmentioning

confidence: 99%

The amyloid interactome: Exploring protein aggregation

et al. 2017

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Protein-protein interactions are the quintessence of physiological activities, but also participate in pathological conditions. Amyloid formation, an abnormal protein-protein interaction process, is a widespread phenomenon in divergent proteins and peptides, resulting in a variety of aggregation disorders. The complexity of the mechanisms underlying amyloid formation/amyloidogenicity is a matter of great scientific interest, since their revelation will provide important insight on principles governing protein misfolding, self-assembly and aggregation. The implication of more than one protein in the progression of different aggregation disorders, together with the cited synergistic occurrence between amyloidogenic proteins, highlights the necessity for a more universal approach, during the study of these proteins. In an attempt to address this pivotal need we constructed and analyzed the human amyloid interactome, a protein-protein interaction network of amyloidogenic proteins and their experimentally verified interactors. This network assembled known interconnections between well-characterized amyloidogenic proteins and proteins related to amyloid fibril formation. The consecutive extended computational analysis revealed significant topological characteristics and unraveled the functional roles of all constituent elements. This study introduces a detailed protein map of amyloidogenicity that will aid immensely towards separate intervention strategies, specifically targeting sub-networks of significant nodes, in an attempt to design possible novel therapeutics for aggregation disorders.

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AmyLoad: website dedicated to amyloidogenic protein fragments

Abstract: malgorzata.kotulska@pwr.edu.pl.

Cited by 51 publications

References 10 publications

Aggregation prone regions in human proteome: Insights from large‐scale data analyses

Aggregation prone regions in human proteome: Insights from large‐scale data analyses

Quantiprot - a Python package for quantitative analysis of protein sequences

The amyloid interactome: Exploring protein aggregation

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