GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies

Thangakani, A. Mary; Kumar, Sandeep; Raju, Nagarajan; Velmurugan, D.; Gromiha, M. Michael

doi:10.1093/bioinformatics/btu167

Cited by 53 publications

(35 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The description of GAP states that ~90 % true positives were obtained by using the peptides database (Thangakani et al 2014 ). However, our test of the proteins listed in Table 9.1 suggests that GAP signifi cantly overpredicts the amyloidogenic regions; for example, GAP predicted ~150 amyloidogenic regions for the fi rst target protein, prolactin, which is unrealistically high.…”

Section: Performance Of Prediction Algorithms Using 30 Amyloidogenic mentioning

confidence: 85%

“…In spite of its relatively poor performance on amyloidogenic proteins (described in the next section), the method yielded several interesting results. For example, the propensities for β-structure formation in globular proteins differed from those in amyloid-forming peptides (Thangakani et al 2014 ). The authors also found that, in spite of the partial overlap, there was a distinct difference in the pairing propensities of the amino acids for the peptides forming amyloid fi brils versus amorphous β-structured aggregates.…”

Section: Other Prediction Methodsmentioning

confidence: 97%

“…One of the recently proposed methods, termed GAP, is a result of advances in machine learning (Thangakani et al 2014 ). The method is based on the idea of "paired frequencies" postulating that, since the N and N + 2 side chains are similarly oriented in any β-strand, amyloid-forming sequences are expected to have position-specifi c amino acid propensities similar to those found in the secondary structures of globular proteins.…”

Section: Other Prediction Methodsmentioning

confidence: 99%

“…Another reason for the limited performance may be attributed to particular amino acid sequences giving rise to various types of aggregates, e.g. β-sheet rich amorphous aggregates versus fi brils, as suggested by the peptide studies (Thangakani et al 2014 ).…”

Section: Performance Of Prediction Algorithms Using 30 Amyloidogenic mentioning

confidence: 99%

See 3 more Smart Citations

Computational Approaches to Identification of Aggregation Sites and the Mechanism of Amyloid Growth

Dovidchenko

Galzitskaya

2015

Advances in Experimental Medicine and Biology

View full text Add to dashboard Cite

This chapter describes computational approaches to study amyloid formation. The first part addresses identification of potential amyloidogenic regions in the amino acid sequences of proteins and peptides. Next, we discuss nucleation and aggregation sites in protein folding and misfolding. The last part describes up-to-date kinetic models of amyloid fibrils formation. Numerous studies show that protein misfolding is initiated by specific amino acid segments with high amyloid-forming propensity. The ability to identify and, ultimately, block such segments is very important. To this end, many prediction algorithms have been developed which vary greatly in their effectiveness. We compared the predictions for 30 proteins by using different methods and found that, at best, only 50% of residues in amyloidogenic segments were predicted correctly. The best results were obtained by using the meta-servers that combine several independent approaches, and by the method PASTA2. Thus, correct prediction of amyloidogenic segments remains a difficult task. Additional data and new algorithms that are becoming available are expected to improve the accuracy of the prediction methods, particularly if they use 3D structural information on the target proteins. At the same time, our understanding of the kinetics of fibril formation is more advanced. The current kinetic models outlined in this chapter adequately describe the key features of amyloid nucleation and growth. However, the underlying structural details are less clear, not least because of the apparently different mechanisms of amyloid fibril formation which are discussed. Ultimately, the detailed understanding of the structural basis for amyloidogenesis should help develop rational therapies to block this pathogenic process.

show abstract

Section: Performance Of Prediction Algorithms Using 30 Amyloidogenic mentioning

confidence: 85%

Section: Other Prediction Methodsmentioning

confidence: 97%

Section: Other Prediction Methodsmentioning

confidence: 99%

Section: Performance Of Prediction Algorithms Using 30 Amyloidogenic mentioning

confidence: 99%

See 2 more Smart Citations

Computational Approaches to Identification of Aggregation Sites and the Mechanism of Amyloid Growth

Dovidchenko

Galzitskaya

2015

Advances in Experimental Medicine and Biology

View full text Add to dashboard Cite

show abstract

“…Excellent papers on theoretical calculations on A␤aggregation inhibitors were published: 2013: [546], 2014: [547][548][549][550].…”

Section: Khg-26377mentioning

confidence: 99%

Cognitive Enhancers (Nootropics). Part 3: Drugs Interacting with Targets other than Receptors or Enzymes. Disease-Modifying Drugs. Update 2014

Froestl

Pfeifer

Muhs

2014

JAD

View full text Add to dashboard Cite

Scientists working in the field of Alzheimer's disease and, in particular, cognitive enhancers, are very productive. The review "Drugs interacting with Targets other than Receptors or Enzymes. Disease-modifying Drugs" was accepted in October 2012. In the last 20 months, new targets for the potential treatment of Alzheimer's disease were identified. Enormous progress was realized in the pharmacological characterization of natural products with cognitive enhancing properties. This review covers the evolution of research in this field through May 2014.

show abstract

Understanding and predicting protein misfolding and aggregation: Insights from proteomics

Pallarès

Ventura

2016

Proteomics

View full text Add to dashboard Cite

Protein misfolding and aggregation are being found to be associated with an increasing number of human diseases and premature aging, either because they promote a loss of protein function or, more frequently, because the aggregated species gain a toxic activity. Despite potentially harmful, aggregation seems to be a generic property of polypeptide chains and aggregation-prone protein sequences seem to be ubiquitous, which, counterintuitively, suggests that they serve evolutionary conserved functions. The in vitro study of individual aggregation reactions of a large number of proteins has provided important insights on the structural and sequential determinants of this process. However, it is clear that understanding the role played by protein aggregation and its regulation in health and disease at the cellular, developmental, and evolutionary levels require more global approaches. The use of model organisms and their proteomic analysis hold the power to provide answers to such issues. In the present review, we address how, initially, computational large-scale analysis and, more recently, experimental proteomics are helping us to rationalize how, why and when proteins aggregate, as well as to decipher the strategies organisms have developed to control proteins aggregation propensities.

show abstract

GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies

Abstract: http://www.iitm.ac.in/bioinfo/GAP/.

Cited by 53 publications

References 38 publications

Computational Approaches to Identification of Aggregation Sites and the Mechanism of Amyloid Growth

Computational Approaches to Identification of Aggregation Sites and the Mechanism of Amyloid Growth

Cognitive Enhancers (Nootropics). Part 3: Drugs Interacting with Targets other than Receptors or Enzymes. Disease-Modifying Drugs. Update 2014

Understanding and predicting protein misfolding and aggregation: Insights from proteomics

Contact Info

Product

Resources

About