CycloPs: Generating Virtual Libraries of Cyclized and Constrained Peptides Including Nonnatural Amino Acids

Duffy, Fergal J.; Verniere, Mélanie; Devocelle, Marc; Bernard, Elise; Shields, Denis C.; Chubb, Anthony J.

doi:10.1021/ci100431r

Cited by 36 publications

(28 citation statements)

References 24 publications

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“…We chose regions where the motif was predicted to be on the cytoplasmic side of a TM protein, close to the membrane. These peptides were then checked for solubility and synthesisability [ 29 ]. Finally five peptides were selected for testing from four protein sequences (see Table 1 , Set 1).…”

Section: Methodsmentioning

confidence: 99%

Design and Evaluation of Antimalarial Peptides Derived from Prediction of Short Linear Motifs in Proteins Related to Erythrocyte Invasion

et al. 2015

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The purpose of this study was to investigate the blood stage of the malaria causing parasite, Plasmodium falciparum, to predict potential protein interactions between the parasite merozoite and the host erythrocyte and design peptides that could interrupt these predicted interactions. We screened the P. falciparum and human proteomes for computationally predicted short linear motifs (SLiMs) in cytoplasmic portions of transmembrane proteins that could play roles in the invasion of the erythrocyte by the merozoite, an essential step in malarial pathogenesis. We tested thirteen peptides predicted to contain SLiMs, twelve of them palmitoylated to enhance membrane targeting, and found three that blocked parasite growth in culture by inhibiting the initiation of new infections in erythrocytes. Scrambled peptides for two of the most promising peptides suggested that their activity may be reflective of amino acid properties, in particular, positive charge. However, one peptide showed effects which were stronger than those of scrambled peptides. This was derived from human red blood cell glycophorin-B. We concluded that proteome-wide computational screening of the intracellular regions of both host and pathogen adhesion proteins provides potential lead peptides for the development of anti-malarial compounds.

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Section: Methodsmentioning

confidence: 99%

Design and Evaluation of Antimalarial Peptides Derived from Prediction of Short Linear Motifs in Proteins Related to Erythrocyte Invasion

et al. 2015

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“…Peptides were converted into the SMILES format using CycloPs [48], and from SMILES to PDB format using Open Babel [49]. AutoDock 4.2 [47] was used to prepare the peptides as ligand files for Vina.…”

Section: Methodsmentioning

confidence: 99%

Predicting Binding within Disordered Protein Regions to Structurally Characterised Peptide-Binding Domains

et al. 2013

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Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif) containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58).Next, we trained a bidirectional recurrent neural network (BRNN) using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72) showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods) clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.

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“…l -amino acids are annotated using capital letters, whereas d -amino acids—using small letters. Such a layout is used in, e.g., CycloPS program (University College Dublin, Ireland) [ 69 , 70 ], and the SATPdb database (Institute of Microbial Technology, Chandigargh, India) [ 42 , 43 ]. The last database annotates protein amino acids and their D-enantiomers using a single letter code, whereas non-protein amino acids—using a multi-letter code.…”

Section: Codes For Annotation Of Peptide Sequences and Structuresmentioning

confidence: 99%

“…The order of symbols, as proposed by Siani et al [ 2 ], and presented in Figure 1 , i.e., α-amine group; asymmetric carbon atom C2; side chain, and carboxyl group, is sufficient for the construction of SMILES representations of peptides. Such SMILES codes are presented in, e.g., source codes to the Cyclops program [ 69 , 70 ] and in SwissSidechain database [ 65 , 66 ]. SMILES strings presented in the PubChem and ChemSpider databases do not maintain the above order.…”

Section: Codes For Annotation Of Peptide Sequences and Structuresmentioning

confidence: 99%

Annotation of Peptide Structures Using SMILES and Other Chemical Codes–Practical Solutions

2017

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Contemporary peptide science exploits methods and tools of bioinformatics, and cheminformatics. These approaches use different languages to describe peptide structures—amino acid sequences and chemical codes (especially SMILES), respectively. The latter may be applied, e.g., in comparative studies involving structures and properties of peptides and peptidomimetics. Progress in peptide science “in silico” may be achieved via better communication between biologists and chemists, involving the translation of peptide representation from amino acid sequence into SMILES code. Recent recommendations concerning good practice in chemical information include careful verification of data and their annotation. This publication discusses the generation of SMILES representations of peptides using existing software. Construction of peptide structures containing unnatural and modified amino acids (with special attention paid on glycosylated peptides) is also included. Special attention is paid to the detection and correction of typical errors occurring in SMILES representations of peptides and their correction using molecular editors. Brief recommendations for training of staff working on peptide annotations, are discussed as well.

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CycloPs: Generating Virtual Libraries of Cyclized and Constrained Peptides Including Nonnatural Amino Acids

Cited by 36 publications

References 24 publications

Design and Evaluation of Antimalarial Peptides Derived from Prediction of Short Linear Motifs in Proteins Related to Erythrocyte Invasion

Design and Evaluation of Antimalarial Peptides Derived from Prediction of Short Linear Motifs in Proteins Related to Erythrocyte Invasion

Predicting Binding within Disordered Protein Regions to Structurally Characterised Peptide-Binding Domains

Annotation of Peptide Structures Using SMILES and Other Chemical Codes–Practical Solutions

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