Statistical molecular design of peptoid libraries

Application of fourier transform and proteochemometrics principles to protein engineering

Background

Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.

Results

We show that the digitalized representation of the protein sequence in the form of a Fourier spectrum can be used as an efficient descriptor to model the sequence-activity relationship of proteins. The iSAR methodology that we have developed identifies high fitness mutants from mutant libraries relying on physicochemical properties of the amino acids, digital signal processing and regression techniques. iSAR correlates variations caused by mutations in spectra with biological activity/fitness. It takes into account the impact of mutations on the whole spectrum and does not focus on local fitness alone. The utility of the method is illustrated on 4 datasets: cytochrome P450 for thermostability, TNF-alpha for binding affinity, GLP-2 for potency and enterotoxins for thermostability. The choice of the datasets has been made such as to illustrate the ability of the method to perform when limited training data is available and also when novel mutations appear in the test set, that have not been featured in the training set.

Conclusion

The combination of Fast Fourier Transform and Partial Least Squares regression is efficient in capturing the effects of mutations on the function of the protein. iSAR is a fast algorithm which can be implemented with limited computational resources and can make effective predictions even if the training set is limited in size.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2407-8) contains supplementary material, which is available to authorized users.

Validated and predictive processing of gas chromatography-mass spectrometry based metabolomics data for large scale screening studies, diagnostics and metabolite pattern verification

The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern.

Design of glycopeptides used to investigate class II MHC binding and T-cell responses associated with autoimmune arthritis

The glycopeptide fragment CII259–273 from type II collagen (CII) binds to the murine A^q and human DR4 class II Major Histocompatibility Complex (MHC II) proteins, which are associated with development of murine collagen-induced arthritis (CIA) and rheumatoid arthritis (RA), respectively. It has been shown that CII259–273 can be used in therapeutic vaccination of CIA. This glycopeptide also elicits responses from T-cells obtained from RA patients, which indicates that it has an important role in RA as well. We now present a methodology for studies of (glyco)peptide-receptor interactions based on a combination of structure-based virtual screening, ligand-based statistical molecular design and biological evaluations. This methodology included the design of a CII259–273 glycopeptide library in which two anchor positions crucial for binding in pockets of A^q and DR4 were varied. Synthesis and biological evaluation of the designed glycopeptides provided novel structure-activity relationship (SAR) understanding of binding to A^q and DR4. Glycopeptides that retained high affinities for these MHC II proteins and induced strong responses in panels of T-cell hybridomas were also identified. An analysis of all the responses revealed groups of glycopeptides with different response patterns that are of high interest for vaccination studies in CIA. Moreover, the SAR understanding obtained in this study provides a platform for the design of second-generation glycopeptides with tuned MHC affinities and T-cell responses.

Protein-protein interaction antagonists as novel inhibitors of non-canonical polyubiquitylation

BACKGROUND

Several pathways that control cell survival under stress, namely RNF8-dependent DNA damage recognition and repair, PCNA-dependent DNA damage tolerance and activation of NF-kappaB by extrinsic signals, are regulated by the tagging of key proteins with lysine 63-based polyubiquitylated chains, catalyzed by the conserved ubiquitin conjugating heterodimeric enzyme Ubc13-Uev.

METHODOLOGY/PRINCIPAL FINDINGS

By applying a selection based on in vivo protein-protein interaction assays of compounds from a combinatorial chemical library followed by virtual screening, we have developed small molecules that efficiently antagonize the Ubc13-Uev1 protein-protein interaction, inhibiting the enzymatic activity of the heterodimer. In mammalian cells, they inhibit lysine 63-type polyubiquitylation of PCNA, inhibit activation of NF-kappaB by TNF-alpha and sensitize tumor cells to chemotherapeutic agents. One of these compounds significantly inhibited invasiveness, clonogenicity and tumor growth of prostate cancer cells.

CONCLUSIONS/SIGNIFICANCE

This is the first development of pharmacological inhibitors of non-canonical polyubiquitylation that show that these compounds produce selective biological effects with potential therapeutic applications.

Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

BACKGROUND

Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity.

RESULTS

We applied proteochemometric modelling to correlate the properties of 317 wild-type and mutated kinases and 38 inhibitors (12,046 inhibitor-kinase combinations) to the respective combination's interaction dissociation constant (Kd). We compared six approaches for description of protein kinases and several linear and non-linear correlation methods. The best performing models encoded kinase sequences with amino acid physico-chemical z-scale descriptors and used support vector machines or partial least- squares projections to latent structures for the correlations. Modelling performance was estimated by double cross-validation. The best models showed high predictive ability; the squared correlation coefficient for new kinase-inhibitor pairs ranging P2 = 0.67-0.73; for new kinases it ranged P2kin = 0.65-0.70. Models could also separate interacting from non-interacting inhibitor-kinase pairs with high sensitivity and specificity; the areas under the ROC curves ranging AUC = 0.92-0.93. We also investigated the relationship between the number of protein kinases in the dataset and the modelling results. Using only 10% of all data still a valid model was obtained with P2 = 0.47, P2kin = 0.42 and AUC = 0.83.

CONCLUSIONS

Our results strongly support the applicability of proteochemometrics for kinome-wide interaction modelling. Proteochemometrics might be used to speed-up identification and optimization of protein kinase targeted and multi-targeted inhibitors.

Quantitative Structure−Activity Relationship of Peptides Binding to the Class II Major Histocompatibility Complex Molecule Aq Associated with Autoimmune Arthritis

Presentation of (glyco)peptides by the class II major histocompatibility complex molecule Aq to T cells plays a central role in collagen-induced arthritis, an animal model for the autoimmune disease rheumatoid arthritis. A peptide library was designed using statistical molecular design in amino acid space in which five positions in the minimal mouse collagen type II binding epitope CII260-267 were varied. A substantially reduced peptide library of 24 peptides with diverse and representative molecular characteristics was selected, synthesized, and evaluated for the binding strength to Aq. A multivariate QSAR model was established by correlating calculated descriptors, compressed to its principle properties, with the binding data using partial least-square regression. The model was successfully validated by an external test set. Interpretation of the model provided a molecular property binding motif for peptides interacting with Aq. The information may be useful in future research directed toward new treatments of rheumatoid arthritis.

Extended peptoids: a new class of oligomers based on aromatic building blocks

Peptoids (N-substituted polyglycines) represent a class of bioinspired oligomers that have unique physical and structural properties. Here we report the construction of "extended peptoids" based on aromatic building blocks, in which the N-alkylaminoacetyl group of the peptoid backbone has been replaced by an N-alkylaminomethylbenzoyl spacer. Both meta- and para-bromomethylbenzoic acids were synthesized, providing access to a new class of peptoids. Further, inclusion of hydrophilic side chains confers water solubility to these compounds, showing that, like simple peptoids, extended peptoids add an extra dimension to synthetic polyamide oligomers with potential application in a variety of biological contexts.

Side-chain and backbone amide bond requirements for glycopeptide stimulation of T-cells obtained in a mouse model for rheumatoid arthritis

Collagen induced arthritis (CIA) is the most studied animal model for rheumatoid arthritis and is associated with the MHC class II molecule Aq. T-cell recognition of a peptide from type II collagen, CII256-270, bound to Aq is a requirement for development of CIA. Lysine 264 is the major T-cell recognition site of CII256-270 and CIA is in particular associated with recognition of lysine 264 after posttranslational hydroxylation and subsequent attachment of a beta-D-galactopyranosyl moiety. In this paper we have studied the structural requirements of collagenous glycopeptides required for T-cell stimulation, as an extension of earlier studies of the recognition of the galactose moiety. Synthesis and evaluation of alanine substituted glycopeptides revealed that there are T-cells that only recognise the galactosylated hydroxylysine 264, and no other amino acid side chains in the peptide. Other T-cells also require glutamic acid 266 as a T-cell contact point. Introduction of a methylene ether isostere instead of the amide bond between residues 260 and 261 allowed weaker recognition by some, but not all, of the T-cells. Altogether, these results allowed us to propose a model for glycopeptide recognition by the T-cells, where recognition from one or the other side of the galactose moiety could explain the different binding patterns of the T-cells.

Multivariate design and evaluation of a set of RGRPQ-derived innate immunity peptides

Oral commensal Streptococcus gordonii proteolytically cleave the salivary PRP-1 polypeptide into an RGRPQ innate peptide. The Arg and Gln termini are crucial for RGRPQ-mediated ammonia production and proliferation by S. gordonii SK12 and adhesion inhibition and desorption by Actinomyces naeslundii T14V, respectively. Here we have applied (i) a multivariate approach using RGRPQ-related peptides varied at amino acids 2, 3, and 4 simultaneously and (ii) size and N- and C-terminal modifications of RGRPQ to generate structure activity information. While the N-terminal arginine motif mediated ammonia production independent of peptide size, other responses required more or less full-length peptide motifs. The motifs for adhesion inhibition and desorption were the same. The adhesion and proliferation motifs required similarly a hydrophobic/low polarity amino acid 4 but differentially a hydrophilic or hydrophobic character of amino acids 2/3, respectively; polar peptides with small/hydrophilic and hydrophilic amino acids 2 and 3, respectively, had high adhesion inhibition/desorption activity, and lipophilic peptides with large/hydrophobic amino acids 2 and 3 had high proliferation activity. Accordingly, while RIWWQ had increased proliferation but abolished adhesion/desorption activity, peptides designed with hydrophilic amino acids 2 and 3 were predicted to behave in the opposite way. Moreover, a RGRPQ mimetic for all three responses should mimic small hydrophilic, large nitrogen-containing, and hydrophobic/low polarity amino acids 2, 3, and 4, respectively. Peptides fulfilling these criteria were 1-1.6-fold improved in all three responses. Thus, both mimetics and peptides with differential proliferation and adhesion activities may be generated for evaluation in biofilm models.

Prediction of indirect interactions in proteins

BACKGROUND

Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect interactions can be defined as effects on recognition controlled from distant sites in the proteins, e.g. by changes in protein conformation and mobility, whereas direct interactions occur in close proximity of the protein's amino acids and the ligand. Molecular recognition is traditionally studied using three-dimensional methods, but with such techniques it is difficult to predict the effects caused by mutational changes of amino acids located far away from the ligand-binding site. We recently developed an approach, proteochemometrics, to the study of molecular recognition that models the chemical effects involved in the recognition of ligands by proteins using statistical sampling and mathematical modelling.

RESULTS

A proteochemometric model was built, based on a statistically designed protein library's (melanocortin receptors') interaction with three peptides and used to predict which amino acids and sequence fragments that are involved in direct and indirect ligand interactions. The model predictions were confirmed by directed mutagenesis. The predicted presumed direct interactions were in good agreement with previous three-dimensional studies of ligand recognition. However, in addition the model could also correctly predict the location of indirect effects on ligand recognition arising from distant sites in the receptors, something that three-dimensional modelling could not afford.

CONCLUSION

We demonstrate experimentally that proteochemometric modelling can be used with high accuracy to predict the site of origin of direct and indirect effects on ligand recognitions by proteins.

Multivariate design, synthesis, and biological evaluation of peptide inhibitors of FimC/FimH protein-protein interactions in uropathogenic Escherichia coli

A peptide library targeting protein-protein interactions crucial for pilus assembly in Gram negative bacteria has been designed using statistical molecular design. A nonamer peptide scaffold was used, with seven positions being varied. The selection was performed in the building block space, and previously known structure-activity data were included in the design procedure. This resulted in a heavily reduced library consisting of 32 peptides which was prepared by solid-phase synthesis. The ability of the peptides to inhibit the protein-protein interaction between the periplasmic chaperone FimC and the pilus adhesin FimH was then determined in an ELISA. Novel peptides with the capability to inhibit the FimC/FimH protein-protein interaction to the same extent as the native FimC peptides were discovered. Multivariate QSAR studies of the response in the ELISA gave valuable information on the properties of amino acids which were preferred at the seven positions in the nonamer scaffold. This information can be used in attempts to develop optimized peptides and peptidomimetics that inhibit pilus assembly in pathogenic bacteria.