NDLI: On the amyloid datasets used for training PAFIG how (not) to extend the experimental dataset of hexapeptides

Content Provider	Springer Nature : BioMed Central
Author	Kotulska, Malgorzata Unold, Olgierd
Abstract	Background Amyloids are proteins capable of forming aberrant intramolecular contact sites, characteristic of beta zipper configuration. Amyloids can underlie serious health conditions, e.g. Alzheimer’s or Parkinson’s diseases. It has been proposed that short segments of amino acids can be responsible for protein amyloidogenicity, but no more than two hundred such hexapeptides have been experimentally found. The authors of the computational tool Pafig published in BMC Bioinformatics a method for extending the amyloid hexapeptide dataset that could be used for training and testing models. They assumed that all hexapeptides belonging to an amyloid protein can be regarded as amylopositive, while those from proteins never reported as amyloid are always amylonegative. Here we show why the above described method of extending datasets is wrong and discuss the reasons why the incorrect data could lead to falsely correct classification. Results The amyloid classification of hexapeptides by Pafig was confronted with the classification results from different state of the art computational methods and the outputs of all methods were studied by clustering analysis. The clustering methods show that Pafig is an outlier with regard to other approaches. Our study of the statistical patterns of its training and testing datasets showed a strong bias towards STVIIE hexapeptide in their positive part. Different statistical patterns of seemingly amylo -positive and -negative hexapeptides allow for a repeatable classification, which is not related to amyloid propensity of the hexapetides. Conclusions Our study on recognition of amyloid hexapeptides showed that occurrence of incidental patterns in wrongly selected datasets can produce falsely correct results of classification. The assumption that all hexapeptides belonging to amyloid protein can be regarded as amylopositive and those from proteins never reported as amyloid are always amylonegative is not supported by any other computational method. This is in line with experimental observations that amyloid propensity of a full protein can result from only one amyloidogenic fragment in this protein, while the occurrence of amyliodogenic part that is well hidden inside the protein may never lead to fibril formation. This leads to the conclusion that Pafig does not provide correct classification with regard to amyloidogenicity.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-14-351.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/1471-2105-14-351
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	14
Language	English
Publisher	BioMed Central
Publisher Date	2013-12-04
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Machine learning Amyloid Intramolecular contact sites Hot spot Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Correspondence
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

On the amyloid datasets used for training PAFIG how (not) to extend the experimental dataset of hexapeptides

FISH Amyloid – a new method for finding amyloidogenic segments in proteins based on site specific co-occurence of aminoacids

Improve hot region prediction by analyzing different machine learning algorithms

Computational identification of ubiquitylation sites from protein sequences

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

SVM-based prediction of caspase substrate cleavage sites

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties

Identification of hot-spot residues in protein-protein interactions by computational docking

On the amyloid datasets used for training PAFIG how (not) to extend the experimental dataset of hexapeptides

Similar Documents

On the amyloid datasets used for training PAFIG ­ how (not) to extend the experimental dataset of hexapeptides

FISH Amyloid – a new method for finding amyloidogenic segments in proteins based on site specific co-occurence of aminoacids

Improve hot region prediction by analyzing different machine learning algorithms

Computational identification of ubiquitylation sites from protein sequences

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

SVM-based prediction of caspase substrate cleavage sites

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties

Identification of hot-spot residues in protein-protein interactions by computational docking

On the amyloid datasets used for training PAFIG ­ how (not) to extend the experimental dataset of hexapeptides

On the amyloid datasets used for training PAFIG how (not) to extend the experimental dataset of hexapeptides

On the amyloid datasets used for training PAFIG how (not) to extend the experimental dataset of hexapeptides