Now showing 1 - 10 of 24
  • Publication
    In Silico Protein Motif Discovery and Structural Analysis
    A wealth of in silico tools is available for protein motif discovery and structural analysis. The aim of this chapter is to collect some of the most common and useful tools and to guide the biologist in their use. A detailed explanation is provided for the use of Distill, a suite of web servers for the prediction of protein structural features and the prediction of full-atom 3D models from a protein sequence. Besides this, we also provide pointers to many other tools available for motif discovery and secondary and tertiary structure prediction from a primary amino acid sequence. The prediction of protein intrinsic disorder and the prediction of functional sites and SLiMs are also briefly discussed. Given that user queries vary greatly in size, scope and character, the trade-offs in speed, accuracy and scale need to be considered when choosing which methods to adopt.
      105Scopus© Citations 1
  • Publication
    Peptigram: a web-based application for peptidomics data visualization
    Tandem mass spectrometry (MS/MS) techniques, developed for protein identification, are increasingly being applied in the field of peptidomics. Using this approach, the set of protein fragments observed in a sample of interest can be determined to gain insights into important biological processes such as signaling and other bioactivities. As the peptidomics era progresses, there is a need for robust and convenient methods to inspect and analyze MS/MS derived data. Here, we present Peptigram, a novel tool dedicated to the visualization and comparison of peptides detected by MS/MS. The principal advantage of Peptigram is that it provides visualizations at both the protein and peptide level, allowing users to simultaneously visualize the peptide distributions of one or more samples of interest, mapped to their parent proteins. In this way rapid comparisons between samples can be made in terms of their peptide coverage and abundance. Moreover, Peptigram integrates and displays key sequence features from external databases and links with peptide analysis tools to offer the user a comprehensive peptide discovery resource. Here, we illustrate the use of Peptigram on a data set of milk hydrolysates. For convenience, Peptigram is implemented as a web application, and is freely available for academic use at
      862Scopus© Citations 65
  • Publication
    Design and Evaluation of Antimalarial Peptides Derived from Prediction of Short Linear Motifs in Proteins Related to Erythrocyte Invasion
    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.
      254Scopus© Citations 7
  • Publication
    Profile-based short linear protein motif discovery
    (BioMed Central, 2012-05-18) ;
    Background Short linear protein motifs are attracting increasing attention as functionally independent sites, typically 3-10 amino acids in length that are enriched in disordered regions of proteins. Multiple methods have recently been proposed to discover over-represented motifs within a set of proteins based on simple regular expressions. Here, we extend these approaches to profile-based methods, which provide a richer motif representation. Results The profile motif discovery method MEME performed relatively poorly for motifs in disordered regions of proteins. However, when we applied evolutionary weighting to account for redundancy amongst homologous proteins, and masked out poorly conserved regions of disordered proteins, the performance of MEME is equivalent to that of regular expression methods. However, the two approaches returned different subsets within both a benchmark dataset, and a more realistic discovery dataset. Conclusions Profile-based motif discovery methods complement regular expression based methods. Whilst profile-based methods are computationally more intensive, they are likely to discover motifs currently overlooked by regular expression methods.
      4630Scopus© Citations 15
  • Publication
    Prediction of short linear protein binding regions
    Short linear motifs in proteins (typically 3–12 residues in length) play key roles in protein–protein interactions by frequently binding specifically to peptide binding domains within interacting proteins. Their tendency to be found in disordered segments of proteins has meant that they have often been overlooked. Here we present SLiMPred (short linear motif predictor), the first general de novo method designed to computationally predict such regions in protein primary sequences independent of experimentally defined homologs and interactors. The method applies machine learning techniques to predict new motifs based on annotated instances from the Eukaryotic Linear Motif database, as well as structural, biophysical, and biochemical features derived from the protein primary sequence. We have integrated these data sources and benchmarked the predictive accuracy of the method, and found that it performs equivalently to a predictor of protein binding regions in disordered regions, in addition to having predictive power for other classes of motif sites such as polyproline II helix motifs and short linear motifs lying in ordered regions. It will be useful in predicting peptides involved in potential protein associations and will aid in the functional characterization of proteins, especially of proteins lacking experimental information on structures and interactions. We conclude that, despite the diversity of motif sequences and structures, SLiMPred is a valuable tool for prioritizing potential interaction motifs in proteins.
      9924Scopus© Citations 63
  • Publication
    Computational Approaches to Developing Short Cyclic Peptide Modulators of Protein-Protein Interactions
    Cyclic peptides are a promising class of bioactive molecules potentially capable of modulating 'difficult' targets, such as protein–protein interactions. Cyclic peptides have long been used as therapeutics derived from natural product derivatives, but remain an underexplored class of compounds from the perspective of rational drug design, possibly due to the known weaknesses of peptide drugs in general. While cyclic peptides are non 'druglike' by the accepted empirical rules, their unique structure may lend itself to both membrane permeability and proteolytic resistance—the main barriers to oral delivery. The constrained shape of cyclic peptides also lends itself better to virtual screening approaches, and new tools and successes in this area have been recently noted. An increasing number of strategies are available, both to generate and screen cyclic peptide libraries, and best practises and current successes are described within. This chapter will describe various computational strategies for virtual screening cyclic peptides, along with known implementations and applications. We will explore the generation and screening of diverse combinatorial virtual libraries, incorporating a range of cyclization strategies and structural modifications. More advanced approaches covered include evolutionary algorithms designed to aid in screening large structural libraries, machine learning approaches, and harnessing bioinformatics resources to bias cyclic peptide virtual libraries towards known bioactive structures.
    Scopus© Citations 13  1342
  • Publication
    Predictive modelling of angiotensin converting enzyme inhibitory dipeptides
    The ability of docking to predict angiotensin converting enzyme (ACE) inhibitory dipeptide sequences was assessed using AutoDock Vina. All potential dipeptides and phospho-dipeptides were docked and scored. Peptide intestinal stability was assessed using a prediction amino acid clustering model. Selected dipeptides, having AutoDock Vina scores −8.1 and predicted to be ‘stable’ intestinally, were characterised, using LIGPLOT and for ACE-inhibitory potency. Two newly identified ACE-inhibitory dipeptides, Asp-Trp and Trp-Pro, having Vina scores of −8.3 and −8.6 gave IC50 values of 258 ± 4.23 and 217 ± 15.7 μM, respectively. LIGPLOT analysis indicated no zinc interaction for these dipeptides. Phospho-dipeptides were predicted to have a good affinity for ACE. However, the experimentally determined IC50 results did not correlate since, for example, Trp-pThr and Pro-pTyr, having Vina scores of −8.5 and −8.1, respectively, displayed IC50 values of >500 μM. While docking allowed identification of new ACE inhibitory dipeptides, it may not be a fully reliable predictive tool in all cases.
      3549Scopus© Citations 70
  • Publication
    In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors
    Molecular docking of a library of all 8000 possible tripeptides to the active site of DPP-IV was used to determine their binding potential. A number of tripeptides were selected for experimental testing, however, there was no direct correlation between the Vina score and their in vitro DPP-IV inhibitory properties. While Trp-Trp-Trp, the peptide with the best docking score, was a moderate DPP-IV inhibitor (IC50 216 μM), Lineweaver and Burk analysis revealed its action to be non-competitive. This suggested that it may not bind to the active site of DPP-IV as assumed in the docking prediction. Furthermore, there was no significant link between DPP-IV inhibition and the physicochemical properties of the peptides (molecular mass, hydrophobicity, hydrophobic moment (μH), isoelectric point (pI) and charge). LIGPLOTs indicated that competitive inhibitory peptides were predicted to have both hydrophobic and hydrogen bond interactions with the active site of DPP-IV. DPP-IV inhibitory peptides generally had a hydrophobic or aromatic amino acid at the N-terminus, preferentially a Trp for non-competitive inhibitors and a broader range of residues for competitive inhibitors (Ile, Leu, Val, Phe, Trp or Tyr). Two of the potent DPP-IV inhibitors, Ile-Pro-Ile and Trp-Pro (IC 50 values of 3.5 and 44.2 μM, respectively), were predicted to be gastrointestinally/intestinally stable. This work highlights the needs to test the assumptions (i.e. competitive binding) of any integrated strategy of computational and experimental screening, in optimizing screening. Future strategies targeting allosteric mechanisms may need to rely more on structure-activity relationship modeling, rather than on docking, in computationally selecting peptides for screening.
      159Scopus© Citations 119
  • Publication
    Protein disorder and short conserved motifs in disordered regions are enriched near the cytoplasmic side of single-pass transmembrane proteins
    Intracellular juxtamembrane regions of transmembrane proteins play pivotal roles in cell signalling, mediated by protein-protein interactions. Disordered protein regions, and short conserved motifs within them, are emerging as key determinants of many such interactions. Here, we investigated whether disorder and conserved motifs are enriched in the juxtamembrane area of human single-pass transmembrane proteins. Conserved motifs were defined as short disordered regions that were much more conserved than the adjacent disordered residues. Human single-pass proteins had higher mean disorder in their cytoplasmic segments than their extracellular parts. Some, but not all, of this effect reflected the shorter length of the cytoplasmic tail. A peak of cytoplasmic disorder was seen at around 30 residues from the membrane. We noted a significant increase in the incidence of conserved motifs within the disordered regions at the same location, even after correcting for the extent of disorder. We conclude that elevated disorder within the cytoplasmic tail of many transmembrane proteins is likely to be associated with enrichment for signalling interactions mediated by conserved short motifs.
      396Scopus© Citations 16
  • Publication
    Potential utility of docking to identify protein-peptide binding regions
    (University College Dublin. School of Computer Science and Informatics, 2013-05) ; ; ; ;
    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 regions involved in binding. We set out to determine if docking peptides to peptide binding domains would assist in these predictions. First, we investigated the docking of known short peptides to their native and non-native peptide binding domains. We then investigated the docking of overlapping peptides adjacent to the native peptide. We found only weak discrimination of docking scores between native peptide and adjacent peptides in this context with similar results for both ordered and disordered regions. Finally, we trained a bidirectional recurrent neural network using as input the peptide sequence, predicted secondary structure, Vina docking score and Pepsite score.We conclude that docking has only modest power to define the location of a peptide within a larger protein region known to contain it. However, this information can be used in training machine learning methods which may allow for the identification of peptide binding regions within a protein sequence.