Amyloidogenecity of Epidermal proteins: Unfolding the mysteries.

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[This is a paper on cutaneous amyloidosis that I did not publish. I presented it in a conference though. The discussion is posted in my dermatology blog. Poster can be downloaded here: http://www.box.com/s/uae8yijuohefphzoh4cx under
Amyloidogenecity of Epidermal proteins by Bell Raj Eapen is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.


Introduction
Amyloidosis is the process by which biologically unrelated proteins fold in a non-native way thereby acquiring certain novel but generic properties. Amyloid cannot be always classified as a misfolded protein as amyloid deposits can have physiological properties. Ultrastructurally amyloid has antiparallel, beta-pleated sheet configuration. Amyloid deposits occur in several organs including skin. Cutaneous amyloidosis occurs in two main patterns called macular and lichen amyloidosis. The discernible properties of amyloid important for dermatologists include apple-green birefringence under polarizing light and congo-red staining. Since several unrelated proteins can form amyloid deposits, it is not easy to identify the source of amyloid deposits. Since cutaneous amyloid deposits bind to antikeratin antibodies it is presumed to be a keratin derivative. However cutaneous amyloidosis being innocuous has not received as much attention as neuro-degenerative disorders.

The propensity for a protein to form amyloid is determined by short segments of amyloidogenic fragments composed mostly of either hydrophobic residues or a combination of Gln and Asn residues. Extrinsic factors like pH and the concentration of polypeptide may also influence rate of aggregation. Several computational methods for amyloidogenic fragments are described. Though keratin is widely accepted as the source of amyloid in cutaneous amyloidosis, the probable involvement of other proteins like collagen and elastin is suspected but not proved. We have used a web-based ‘FoldAmyloid server’ to predict amyloidogenic regions on these proteins to have a better understanding of their behaviour. The FoldAmyloid algorithm utilizes the expected probability of hydrogen bonds formation and expected packing density of residues to predict amyloidogenic regions on a protein and has shown good success rates in predicting amyloidogenic regions on polypeptides. We have also analysed V324A mutation on KRT5 in a Weber Cockayne variant of Epidermolysis Bullosa reported to be associated with amyloid deposits. We also discuss the probable molecular mechanisms of common but ill-explained symptoms of cutaneous amyloidosis like pruritus and hyperpigmentation. Finally we analyse the mechanism of action of few existing treatment strategies and the future perspectives.

Materials and Methods
The web based FoldAmyloid server was used to predict the amyloidogenic regions on keratin, collagen and elastin. FoldAmyloid server does not support batch submission of multiple sequences. Hence a perl script was written to retrieve specified sequences from GenBank and submit the sequences one by one to FoldAmyloid server. All available isoforms of Keratins 1 to 20, collagen 1 to 25 and elastin were submitted for prediction of amyloidogenic areas using the script. The rare mutation, V324A on KRT 5 reported to be associated with amyloid deposits was submitted separately. The default settings of foldAmyloid were used for prediction.

Related articles

• Formation of immunoglobulin light chain amyloid oligomers in primary cutaneous nodular amyloidosis (dx.doi.org)
• Noncore Residues Influence the Kinetics of Functional TTR(105-)(115)-Based Amyloid Fibril Assembly. (ncbi.nlm.nih.gov)
• Slow Amyloid Nucleation via alpha-Helix-Rich Oligomeric Intermediates in Short Polyglutamine-Containing Huntingtin Fragments. (ncbi.nlm.nih.gov)
• Inhibiting the Nucleation of Amyloid Structure in a Huntingtin Fragment by Targeting alpha-Helix-Rich Oligomeric Intermediates. (ncbi.nlm.nih.gov)

The 4th Virtual Training Workshop on Bioinformatics in 2010, organized by Asian Bioinformatics Research and Education Network (ABREN) was a great success, drawing 1,869 participants from 76 countries. Now they are planning to hold the 5th Workshop for two months starting from December, 2011. The registration will start on Nov. 4th.

Workshop and registration site: http://www.abren.net/workshop/

3rd Pan Arab Human Genetics Conference

The things I learnt from the conference.

Papillon Lefvre syndrome is common in Soudi Population.

South Indians, especially Keralites have high incidence of certain SNPs in cytochromes responsible for Cyclosporine and Tacrolimus metabolism. Both these drugs are commonly used by dermatologists. Has anyone noticed higher efficacy of Tacrolimus in Keralites, especially when used for vitiligo?

Copy number variations (CNV) are as frequent as SNPs and have high impact on the phenotype.

Elaborating on the last point, I have recently been doing some work on SNPs in hair disorders. But after hearing the lecture on CNVs I feel we are probably giving undue importance to SNPs. If I understand it correctly we have a pair of each chromosome (except sex chromosomes) and hence have a pair of most genes. So an SNP in a single gene may not have any effect as there is another pair of same gene without the SNP. Now consider a situation where a chunk of this chromosome is deleted or duplicated. All the SNPs in the corresponding region have a higher chance of having a phenotypic manifestation. These CNVs are not rare as I used to think. Do we have a database of CNVs?

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Delayed-Type hypersensitivity to latex: Computational prediction of MHC class II epitopes on latex allergens

This article was rejected by several journals. Hence I feel the concept is fundamentally wrong. I have posted the full article here. The referee comment from the last journal is also included.

• The object of the study is laudable. However, the data presented here is too preliminary for publication.
• Class II MHC molecules bind short peptides of 12 to 20 residues long. The concept of MHC binding to whole antigen is grossly not acceptable as given in Table 1.
• Moreover, studies of this nature require experimental verification for publication.
• It is strongly advised to look at MHCBN, MHCPEP and SYFPETHI for the type of peptides MHC molecules bind.
• The design of the study should be narrowed down to a single antigen with overlapping peptides to multiple class 2 alleles for a reasonable argument.

Key words
Natural Rubber Latex, MHC Class II.

Abstract
Delayed type hypersensitivity to natural rubber latex is rare compared to IgE mediated immediate reactions. Binding of allergens to MHC Class II is a crucial step in the presentation of antigens to CD4+ T Cells responsible for delayed reactions. Computational prediction of MHC class II epitopes on thirteen known latex allergens using SMM-align method revealed strong binding with several alleles. This shows that latex allergens are capable of initiating delayed type hypersensitivity in susceptible individuals.

Background
Natural rubber latex (NRL) derived from the rubber tree (Hevea brasiliensis) is an important allergen causing mostly IgE mediated immediate reactions like urticaria, angioedema and asthma. [1] The existence of a true NRL induced delayed type hypersensitivity (type IV) is still debated. Delayed type hypersensitivity reactions are attributed to additives like thiurams and carbamates. [2] The coexistence of the type I and type IV patterns and entities like protein contact dermatitis [3] further complicate the issue. However incidence of true type IV hypersensitivities ranging from 1.2 to 6% has been reported following the introduction of an NRL containing patch test series. [4]

During the sensitization phase of contact hypersensitivity, epidermal Langerhans cells internalise the allergen, migrate to the regional lymph node and present the processed allergen bound to MHC Class II molecules to CD4 T cells. MHC Class II binding site consists of a groove open at both ends and several pockets binding peptides with 13 to 25 residues. [5] MHC molecules exhibit high degree of genetic variation which enables them to bind variety of peptides.

As IgE from latex-sensitive patients bind to several proteins, there is still no consensus on which latex proteins are antigenic. However information on thirteen officially accepted latex allergens designated serially from Hev b 1 to 13 is available from the online repository www.allergen.org and are summarized in Table 1. [6,[7]

We tried to computationally predict the MHC class II epitopes on these antigens using SMM-align method. SMM-align is a novel stabilization matrix alignment method based on scoring matrices that evaluate the contribution to binding of different residues in a peptide. [8] T cells are likely to recognize MHC binding peptides and initiate a cellular response. [9]

Methodology
The protein sequences of all thirteen allergens were downloaded from www.allergen.org and were added to a single file. This file was used for MHC Class II epitope prediction with the publicly available SMM-align server using default parameters. The server returns IC50 prediction scores (concentration of competing ligand which displaces 50% of the specific ligand) covering fourteen HLA DR alleles for each nanomer within the query peptide. Allergens with at least one nanomer with IC50 value less than fifty were considered as binders.

Results
Strong binding to one or more alleles was shown by all allergens except Hev b 5. Hev b 2 was the most promiscuous allergen binding several alleles. DRB1*0101 was the most commonly bound allele. Results are summarized in Table 1.

Discussion
NRL is an allergen causing mostly IgE mediated immediate type hypersensitivity. Though there have been significant advances in molecular biology of latex allergens, their ability to initiate delayed-type hypersensitivity has been subject to debate. Computational prediction of MHC class II binding regions on latex allergens adds credence to reports of type IV contact hypersensitivity to NRL. [4]

Prediction of MHC binding peptides is a commonly employed step in the identification of T cell epitopes. [9] MHC epitope related data is available from several databases like SYFPEITHI, [10] MHCBN [11] and IEDB [12]. Several algorithms like ARB [13] and SMM-align [8] are available for prediction of MHC class II binding peptides. Algorithms for scanning promiscuous peptides that can bind multiple MHC class II molecules are also available. [14] Promiscuous peptides are important for vaccine development and immunotherapy. [5] In our study Hev b 2 bound to six out of fourteen tested HLA DR alleles.

Conclusion
Most of the characterized latex allergens contain segments which can strongly bind MHC class II alleles and are capable of initiating delayed type hypersensitivity without a hapten in susceptible individuals. Certain promiscuous allergens like Hev b 2 bind to several MHC alleles making them ideal candidates for immunotherapy. [15] However computational prediction of MHC binding has limited accuracy and clinical validation is essential.

References

1 Reunala T et al.,. Latex allergy and skin. Curr Opin Allergy Clin Immunol 4: 397-401 (2004) [PMID: 15349039]
2 Miri S et al.,. Prevalence of type I allergy to natural rubber latex and type IV allergy to latex and rubber additives in operating room staff with glove-related symptoms. Allergy Asthma Proc 28: 557-63 (2007) [PMID: 18034975]
3 Doutre MS. Occupational contact urticaria and protein contact dermatitis. Eur J Dermatol 15: 419-24 (2005) [PMID: 16280292]
4 Sommer S et al.,. Type IV hypersensitivity reactions to natural rubber latex: results of a multicentre study. Br J Dermatol 146: 114-7 (2002) [PMID: 11841376]
5 Wang P et al.,. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol 4: e1000048 (2008) [PMID: 18389056]
6 Breiteneder H et al.,. Molecular and immunological characteristics of latex allergens. Int Arch Allergy Immunol 116: 83-92 (1998) [PMID: 9652300]
7 Wagner S et al.,. Hevea brasiliensis latex allergens: current panel and clinical relevance. Int Arch Allergy Immunol 136: 90-7 (2005) [PMID: 15627782]
8 Nielsen M et al.,. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 8: 238 (2007) [PMID: 17608956]
9 Sette A et al.,. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J Immunol 153: 5586-92 (1994) [PMID: 7527444]
10 Schuler MM et al.,. SYFPEITHI: database for searching and T-cell epitope prediction. Methods Mol Biol 409: 75-93 (2007) [PMID: 18449993]
11 Bhasin M et al.,. MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19: 665-6 (2003) [PMID: 12651731]
12 Peters B et al.,. The immune epitope database and analysis resource: from vision to blueprint. PLoS Biol 3: e91 (2005) [PMID: 15760272]
13 Bui HH et al.,. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 57: 304-14 (2005) [PMID: 15868141]
14 Zhang GL et al.,. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res 33: W172-9 (2005) [PMID: 15980449]
15 Tabar AI et al.,. Specific immunotherapy with standardized latex extract versus placebo in latex-allergic patients. Int Arch Allergy Immunol 141: 369-76 (2006) [PMID: 16943675]

Protein-Protein Docking Problem.

I am looking for a solution to the following problem. Any insight will be greatly appreciated.

A membrane receptor (A) has an extracellular domain (AE), transmembrane domain (AT) and intracellular domain (AI).

A bacteria (B) binds to (AE) leading to dimerization of (A) at (AT) and subsequent downstream signaling through (AI).

(A) has no known natural ligands.

(A) has one known inhibitor (I) binding to (AE)

The structure of (AE) bound to (I) is available from PDB.

How do we identify which protein in (B) binds to (AE).

The obvious solution is to dock all proteins with known structures in (B) to all known pockets in AE. Any better solutions?

The art of taking online help:

I am not a big time researcher with lots of international experience. However I would like to make an attempt to suggest few guidelines for the young Indian bioinformatician, seeking online help for project or showcasing their profile online.

How to address a researcher online? Generally in research community, people are not bothered much about show of respect. Hence sir, respected sir, the most adorable etc can be translated to lack of confidence or to too much submissiveness. Hence it is appropriate to address anybody by the second name adding the appropriate title. Just using the first name is also OK. However title is often taken seriously and addressing a Dr/Prof as Mr is a cardinal sin even if you add a liberal dose of sir/almighty to that.

Career guidance is often done face to face or over the phone or through forums specifically dedicated for that. However before posting career guidance questions to forums search the forum for similar questions unless your profile is unique. Questions like I am going to finish my Kinder Garden What should I do next to become a successful bioinformatician is unlikely to fetch many answers. If you dont have enough time to search the forum, dont expect anybody else to send a personal two page letter to you.

The same applies to very broad, open ended questions. Questions like How is bioinformatics important for clinical medicine? is unlikely to get much attention. Be as specific as possible. Do not expect others to provide complete answers in a platter. Answers will be mostly very short, incomplete and often cryptic (because you may not know what the other person is talking about). Be ready to do some background research on the answer rather than asking for more information.

Posting your profile in online forums is also an art. Bioinformatics is a very broad field and employers look for certain specific skills which you may not always have. I often see sequence analysis, genomics, proteiomics, computer programming, PERL, RUBY, EMERALD, systems biology, drug designing, structural and molecular biology, talking, reading and sleeping in the skill set, everyone competing to make the complete list. In reality, no body can be a complete bioinformatician and it is better to showcase your core competency which needs to be substantiated by your projects or publications.

Please post your comments / criticisms / suggestions here.