On the border of the amyloidogenic sequences: prefix analysis of the parallel beta sheets in the PDB_Amyloid collection

Takacs, Kristof; Grolmusz, Vince

doi:10.1515/jib-2020-0043

Cited by 4 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we established the patterns of amyloidicity and nonamyloidicity in the case of hexapeptides, based on a Support Vector Machine-based predictor, available at https:// pitgroup.org/bap. Because there are 20 6 , that is, 64 million hexapeptides, formed from the 20 proteinogenic amino acids, it is worthwhile to show succinct patterns of both amyloidforming and nonforming hexapeptides, based on the BAP predictor. First, in the literature, we have introduced hexapeptide patterns with free-to-choose positions, denoted by "x", describing hundreds, or even tens of thousands of hexapeptides with the same predicted amyloidogenecity, each with only six characters.…”

Section: ■ Conclusionmentioning

confidence: 99%

See 1 more Smart Citation

Succinct Amyloid and Nonamyloid Patterns in Hexapeptides

et al. 2022

Self Cite

View full text Add to dashboard Cite

Hexapeptides are widely applied as a model system for studying the amyloid-forming properties of polypeptides, including proteins. Recently, large experimental databases have become publicly available with amyloidogenic labels. Using these data sets for training and testing purposes, one may build artificial intelligence (AI)-based classifiers for predicting the amyloid state of peptides. In our previous work (Biomolecules 2021, 11, 500), we described the Support Vector Machine (SVM)-based Budapest Amyloid Predictor (https://pitgroup.org/bap). Here, we apply the Budapest Amyloid Predictor for discovering numerous amyloidogenic and nonamyloidogenic hexapeptide patterns with accuracy between 80% and 84%, as surprising and succinct novel rules for further understanding the amyloid state of peptides. For example, we have shown that for any independently mutated residue (position marked by "x"), the patterns CxFLWx, FxFLFx, or xxIVIV are predicted to be amyloidogenic, while those of PxDxxx, xxKxEx, and xxPQxx are nonamyloidogenic. We note that each amyloidogenic pattern with two x's (e.g.,CxFLWx) describes succinctly 20 2 = 400 hexapeptides, while the nonamyloidogenic patterns comprising four point mutations (e.g.,PxDxxx) give 20 4 = 160 000 hexapeptides in total. We also examine the restricted substitutions for positions "x" from subclasses of proteinogenic amino acid residues; for example, if "x" is substituted with hydrophobic amino acids, then there exist patterns containing three x's, like MxVVxx, predicted to be amyloidogenic. If we can choose for the x positions any hydrophobic amino acids, except the "structure breaker" proline, then we get amyloid patterns with five x positions, for example, xxxFxx, each corresponding to 32 768 hexapeptides. To our knowledge, no similar applications of artificial intelligence tools or succinct amyloid patterns were described before the present work.

show abstract

Section: ■ Conclusionmentioning

confidence: 99%

“…Amyloids are misfolded proteins , with a well-defined and periodic 3D structure, comprising mostly parallel β-sheets. , While amyloids are only seldom present in healthy human tissues, they were reported to be connected with several neurodegenerative diseases, most importantly with Alzheimer’s disease.…”

Section: Introductionmentioning

confidence: 99%

Succinct Amyloid and Nonamyloid Patterns in Hexapeptides

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Amyloids are misfolded proteins [3,4] with well-defined and periodic 3D structure, comprising mostly parallel β-sheets [5,6]. While amyloids are only seldom present in healthy human tissues [7], they were reported to be connected with several neurodegenerative diseases [8], most importantly with Alzheimer's disease.…”

Section: Introductionmentioning

confidence: 99%

Succinct Amyloid and Non-Amyloid Patterns in Hexapeptides

Keresztes¹,

Evelin²,

Varga³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Hexapeptides are widely applied as a model system for studying amyloidforming properties of polypeptides, including proteins. Recently, large experimental databases have become publicly available with amyloidogenic labels. Using these datasets for training and testing purposes, one may build artificial intelligence (AI)-based classifiers for predicting the amyloid state of peptides. In our previous work (Biomolecules, 11(4) 500, ( 2021)) we described the Support Vector Machine (SVM)-based Budapest Amyloid Predictor (https://pitgroup.org/bap). Here we apply the Budapest Amyloid Predictor for discovering numerous amyloidogenic and non-amyloidogenic hexapeptide patterns with accuracy between 80% and 84%, as surprising and succinct novel rules for further understanding the amyloid state of peptides. For example, we have shown that for any independently mutated residue (position marked by "x"), the patterns CxFLWx, FxFLFx, or xxIVIV are predicted to be amyloidogenic, while those of PxDxxx, xxKxEx, and xxPQxx non-amyloidogenic at all. We note that each amyloidogenic pattern with two x's (e.g.,CxFLWx) describes succinctly 20 2 = 400 hexapeptides, while the non-amyloidogenic patterns comprising four point mutations (e.g.,PxDxxx) gives 20 4 = 160, 000 hexapeptides in total. We also examine restricted substitutions for positions "x" from subclasses of proteinogenic amino acid residues; for example, if "x" is substituted with hydrophobic amino acids, then there exist patterns containing three x's, like MxVVxx, predicted to be amyloidogenic. If we can choose for the x positions any hydrophobic amino acids, except the "structure breaker" proline, then we get amyloid patterns with five x positions, e.g., xxxFxx, each corresponding to 32,768 hexapeptides. To our knowledge, no similar applications of artificial intelligence tools or succinct amyloid patterns were described before the present work.

show abstract

“…Here we consider the amyloid predictors: tools, which tell us if a given amino acid sequence has or has not the propensity to become amyloid. Amyloids are misfolded protein aggregates (Horváth et al, 2019;Taricska et al, 2020), which -in contrast with the unstructured aggregates -have a well-defined structure, comprising parallel β-sheets (Takács et al, 2019;Takacs and Grolmusz, 2020). Amyloids are present in numerous organisms in biology: for example, in healthy human pituitary secretory granules (Maji et al, 2009), in the immune system of certain insects (Falabella et al, 2012), the silkmoth chorion and some fish choria (Iconomidou and Hamodrakas, 2008), in human amyloidoses and several neuro-degenerative diseases (Soto et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

The Budapest Amyloid Predictor and its Applications

Keresztes¹,

Evelin²,

Varga³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The amyloid state of proteins is widely studied with relevancy in neurology, biochemistry, and biotechnology. In contrast with amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and anti-parallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed by using mostly artificial neural networks (ANNs) as the underlying computational techniques. From a good neural network-based predictor, it is a very difficult task to identify those attributes of the input amino acid sequence, which implied the decision of the network. Here we present a Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the published ANN-based tools. Unlike the artificial neural networks, the decision of the SVMs are much easier to analyze, and from a good predictor, we can infer rich biochemical knowledge.

show abstract

On the border of the amyloidogenic sequences: prefix analysis of the parallel beta sheets in the PDB_Amyloid collection

Cited by 4 publications

References 25 publications

Succinct Amyloid and Nonamyloid Patterns in Hexapeptides

Succinct Amyloid and Nonamyloid Patterns in Hexapeptides

Succinct Amyloid and Non-Amyloid Patterns in Hexapeptides

The Budapest Amyloid Predictor and its Applications

Contact Info

Product

Resources

About