scholarly journals Unsupervised mining of HLA-I peptidomes reveals new binding motifs and substantial false positives in community database

2021 ◽  
Author(s):  
Chatchapon Sricharoensuk ◽  
Tanupat Boonchalermvichien ◽  
Phijitra Muanwien ◽  
Poorichaya Somparn ◽  
Trairak Pisitkun ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Prediction Models ◽  
De Novo ◽  
Current Knowledge ◽  
Peptide Binding ◽  
Binding Specificity ◽  
Peptide Sequencing ◽  
False Positives ◽  
Binding Motifs ◽  
Hla Binding

AbstractModern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.

Algorithm Development of de novo Peptide Sequencing Via Tandem Mass Spectrometry

2011 ◽  
Vol 37 (12) ◽  
pp. 1278-1288 ◽  
Author(s):  
Han-Chang SUN ◽  
Ji-Yang ZHANG ◽  
Hui LIU ◽  
Wei ZHANG ◽  
Chang-Ming XU ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
De Novo ◽  
Peptide Sequencing ◽  
Tandem Mass ◽  
Algorithm Development

Barcoded Rational AAV Vector Evolution Enables Systematic In Vivo Mapping of Peptide Binding Motifs

2018 ◽  
Author(s):  
Marcus Davidsson ◽  
Gang Wang ◽  
Patrick Aldrin-Kirk ◽  
Tiago Cardoso ◽  
Sara Nolbrant ◽  
...  
Keyword(s):  
Peptide Binding ◽  
Aav Vector ◽  
Binding Motifs

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

2019 ◽  
Vol 20 (3) ◽  
pp. 170-176 ◽  
Author(s):  
Zhongyan Li ◽  
Qingqing Miao ◽  
Fugang Yan ◽  
Yang Meng ◽  
Peng Zhou
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Peptide Binding ◽  
Machine Learning Techniques ◽  
Future Perspective ◽  
Peptide Design ◽  
Human Interactome ◽  
Therapeutic Peptide ◽  
Peptide Affinity ◽  
Peptide Binding Affinity

Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

scholarly journals The L1-dependant and Pol III transcribed Alu retrotransposon, from its discovery to innate immunity

2021 ◽  
Vol 48 (3) ◽  
pp. 2775-2789
Author(s):  
Ludwig Stenz
Keyword(s):  
Human Genome ◽  
Viral Infections ◽  
De Novo ◽  
Current Knowledge ◽  
Innate Immune ◽  
De Novo Mutation ◽  
Neuronal Diversity ◽  
Alu Sequences ◽  
Pol Iii ◽  
The Brain

AbstractThe 300 bp dimeric repeats digestible by AluI were discovered in 1979. Since then, Alu were involved in the most fundamental epigenetic mechanisms, namely reprogramming, pluripotency, imprinting and mosaicism. These Alu encode a family of retrotransposons transcribed by the RNA Pol III machinery, notably when the cytosines that constitute their sequences are de-methylated. Then, Alu hijack the functions of ORF2 encoded by another transposons named L1 during reverse transcription and integration into new sites. That mechanism functions as a complex genetic parasite able to copy-paste Alu sequences. Doing that, Alu have modified even the size of the human genome, as well as of other primate genomes, during 65 million years of co-evolution. Actually, one germline retro-transposition still occurs each 20 births. Thus, Alu continue to modify our human genome nowadays and were implicated in de novo mutation causing diseases including deletions, duplications and rearrangements. Most recently, retrotransposons were found to trigger neuronal diversity by inducing mosaicism in the brain. Finally, boosted during viral infections, Alu clearly interact with the innate immune system. The purpose of that review is to give a condensed overview of all these major findings that concern the fascinating physiology of Alu from their discovery up to the current knowledge.

From the discovery to molecular understanding of cellular iron-sulfur protein biogenesis

2020 ◽  
Vol 401 (6-7) ◽  
pp. 855-876 ◽  
Author(s):  
Roland Lill
Keyword(s):  
De Novo ◽  
Current Knowledge ◽  
Protein Stabilization ◽  
S Protein ◽  
Cellular Iron ◽  
Iron Sulfur ◽  
Protein Biogenesis ◽  
Sulfur Protein ◽  
Initial Discovery ◽  
S Proteins

AbstractProtein cofactors often are the business ends of proteins, and are either synthesized inside cells or are taken up from the nutrition. A cofactor that strictly needs to be synthesized by cells is the iron-sulfur (Fe/S) cluster. This evolutionary ancient compound performs numerous biochemical functions including electron transfer, catalysis, sulfur mobilization, regulation and protein stabilization. Since the discovery of eukaryotic Fe/S protein biogenesis two decades ago, more than 30 biogenesis factors have been identified in mitochondria and cytosol. They support the synthesis, trafficking and target-specific insertion of Fe/S clusters. In this review, I first summarize what led to the initial discovery of Fe/S protein biogenesis in yeast. I then discuss the function and localization of Fe/S proteins in (non-green) eukaryotes. The major part of the review provides a detailed synopsis of the three major steps of mitochondrial Fe/S protein biogenesis, i.e. the de novo synthesis of a [2Fe-2S] cluster on a scaffold protein, the Hsp70 chaperone-mediated transfer of the cluster and integration into [2Fe-2S] recipient apoproteins, and the reductive fusion of [2Fe-2S] to [4Fe-4S] clusters and their subsequent assembly into target apoproteins. Finally, I summarize the current knowledge of the mechanisms underlying the maturation of cytosolic and nuclear Fe/S proteins.

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