De novo peptide design with C3a receptor agonist and antagonist activities: theoretical predictions and experimental validation

Targeting the complement component 3a receptor (C3aR) with selective agonists or antagonists is believed to be a viable therapeutic option for several diseases such as stroke, heart attack, reperfusion injuries, and rheumatoid arthritis. We designed a number of agonists, partial agonists, and antagonists of C3aR using our two-stage de novo protein design framework. Of the peptides tested using a degranulation assay in C3aR-transfected rat basophilic leukemia cells, two were prominent agonists (EC(50) values of 25.3 and 66.2 nM) and two others were partial agonists (IC(50) values of 15.4 and 26.1 nM). Further testing of these lead compounds in a calcium flux assay in U937 cells yielded similar results although with reduced potencies compared to transfected cells. The partial agonists also displayed full antagonist activity when tested in a C3aR inhibition assay. In addition, the electrostatic potential profile was shown to potentially discriminate between full agonists and partial agonists.

ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction

The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.

Molecular dynamics in drug design: new generations of compstatin analogs

We report the computational and rational design of new generations of potential peptide-based inhibitors of the complement protein C3 from the compstatin family. The binding efficacy of the peptides is tested by extensive molecular dynamics-based structural and physicochemical analysis, using 32 atomic detail trajectories in explicit water for 22 peptides bound to human, rat or mouse target protein C3, with a total of 257 ns. The criteria for the new design are: (i) optimization for C3 affinity and for the balance between hydrophobicity and polarity to improve solubility compared to known compstatin analogs; and (ii) development of dual specificity, human-rat/mouse C3 inhibitors, which could be used in animal disease models. Three of the new analogs are analyzed in more detail as they possess strong and novel binding characteristics and are promising candidates for further optimization. This work paves the way for the development of an improved therapeutic for age-related macular degeneration, and other complement system-mediated diseases, compared to known compstatin variants.

β-sheet topology prediction with high precision and recall for β and mixed α/β proteins

The prediction of the correct -sheet topology for pure and mixed proteins is a critical intermediate step toward the three dimensional protein structure prediction. The predicted beta sheet topology provides distance constraints between sequentially separated residues, which reduces the three dimensional search space for a protein structure prediction algorithm. Here, we present a novel mixed integer linear optimization based framework for the prediction of -sheet topology in and mixed proteins. The objective is to maximize the total strand-to-strand contact potential of the protein. A large number of physical constraints are applied to provide biologically meaningful topology results. The formulation permits the creation of a rank-ordered list of preferred -sheet arrangements. Finally, the generated topologies are re-ranked using a fully atomistic approach involving torsion angle dynamics and clustering. For a large, non-redundant data set of 2102 and mixed proteins with at least 3 strands taken from the PDB, the proposed approach provides the top 5 solutions with average precision and recall greater than 78%. Consistent results are obtained in the -sheet topology prediction for blind targets provided during the CASP8 and CASP9 experiments, as well as for actual and predicted secondary structures. The -sheet topology prediction algorithm, BeST, is available to the scientific community at http://selene.princeton.edu/BeST/.

Novel protein identification methods for biomarker discovery via a proteomic analysis of periodontally healthy and diseased gingival crevicular fluid samples

AIM

To identify possible novel biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals using high-throughput proteomic analysis.

MATERIALS AND METHODS

Gingival crevicular fluid samples were collected from 12 CP and 12 periodontally healthy subjects. Samples were trypically digested with trypsin, eluted using high-performance liquid chromatography, and fragmented using tandem mass spectrometry (MS/MS). MS/MS spectra were analysed using PILOT_PROTEIN to identify all unmodified proteins within the samples.

RESULTS

Using the database derived from Homo sapiens taxonomy and all bacterial taxonomies, 432 human (120 new) and 30 bacterial proteins were identified. The human proteins, angiotensinogen, clusterin and thymidine phosphorylase were identified as biomarker candidates based on their high-scoring only in samples from periodontal health. Similarly, neutrophil defensin-1, carbonic anhydrase-1 and elongation factor-1 gamma were associated with CP. Candidate bacterial biomarkers include 33 kDa chaperonin, iron uptake protein A2 and phosphoenolpyruvate carboxylase (health-associated) and ribulose biphosphate carboxylase, a probable succinyl-CoA:3-ketoacid-coenzyme A transferase, or DNA-directed RNA polymerase subunit beta (CP-associated). Most of these human and bacterial proteins have not been previously evaluated as biomarkers of periodontal conditions and require further investigation.

CONCLUSIONS

The proposed methods for large-scale comprehensive proteomic analysis may lead to the identification of novel biomarkers of periodontal health or disease.

Design of a modified mouse protein with ligand binding properties of its human analog by molecular dynamics simulations: the case of C3 inhibition by compstatin

The peptide compstatin and its derivatives inhibit the complement-component protein C3 in primate mammals and are potential therapeutic agents against the unregulated activation of complement in humans, but are inactive against C3 from lower mammals. Recent molecular dynamics (MD) simulations showed that the most potent compstatin analog comprised entirely of natural amino acids (W4A9) had a smaller affinity for rat C3, due to reproducible changes in the rat protein structure with respect to the human protein, which eliminated or weakened specific protein-ligand interactions seen in the human C3:W4A9 complex. Here, we study by MD simulations three W4A9 complexes with the mouse C3 protein, and two "transgenic" mouse derivatives, containing a small number (6-9) of human C3 substitutions. The mouse complex experiences the conformational changes and affinity reduction of the rat complex. In the "transgenic" complexes, the conformation remains closer to that of the human complex, the protein-ligand interactions are improved, and the affinity for compstatin becomes "human-like." The present work creates new avenues for a compstatin-sensitive animal model. A similar strategy, involving the comparison of a series of complexes by MD simulations, could be used to design "transgenic" sequences in other systems.

Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database

Post-translational modifications (PTMs) broadly contribute to the recent explosion of proteomic data and possess a complexity surpassing that of protein design. PTMs are the chemical modification of a protein after its translation, and have wide effects broadening its range of functionality. Based on previous estimates, it is widely believed that more than half of proteins are glycoproteins. Whereas mutations can only occur once per position, different forms of post-translational modifications may occur in tandem. With the number and abundances of modifications constantly being discovered, there is no method to readily assess their relative levels. Here we report the relative abundances of each PTM found experimentally and putatively, from high-quality, manually curated, proteome-wide data, and show that at best, less than one-fifth of proteins are glycosylated. We make available to the academic community a continuously updated resource (http://selene.princeton.edu/PTMCuration) containing the statistics so scientists can assess “how many” of each PTM exists.

A new generation of potent complement inhibitors of the Compstatin family

Compstatin family peptides are potent inhibitors of the complement system and promising drug candidates against diseases involving under-regulated complement activation. Compstatin is a 13-residue cyclized peptide that inhibits cleavage of complement protein C3, preventing downstream complement activation. We present three new compstatin variants, characterized by tryptophan replacement at positions 1 and/or 13. Peptide design was based on physicochemical reasoning and was inspired by earlier work, which identified tryptophan substitutions at positions 1 and 13 in peptides with predicted C3c binding abilities [Bellows M.L., Fung H.K., Taylor M.S., Floudas C.A., López de Victoria A., Morikis D. (2010) Biophys J; 98: 2337-2346]. The new variants preserve distinct polar and nonpolar surfaces of compstatin, but have altered local interaction capabilities with C3. All three peptides exhibited potent C3 binding by surface plasmon resonance and potent complement inhibition by enzyme-linked immunosorbent assay. We also present enzyme-linked immunosorbent assay data and detailed surface plasmon resonance kinetic data of three peptides from previous computational design.

Discovery of entry inhibitors for HIV-1 via a new de novo protein design framework

A new (to our knowledge) de novo design framework with a ranking metric based on approximate binding affinity calculations is introduced and applied to the discovery of what we believe are novel HIV-1 entry inhibitors. The framework consists of two stages: a sequence selection stage and a validation stage. The sequence selection stage produces a rank-ordered list of amino-acid sequences by solving an integer programming sequence selection model. The validation stage consists of fold specificity and approximate binding affinity calculations. The designed peptidic inhibitors are 12-amino-acids-long and target the hydrophobic core of gp41. A number of the best-predicted sequences were synthesized and their inhibition of HIV-1 was tested in cell culture. All peptides examined showed inhibitory activity when compared with no drug present, and the novel peptide sequences outperformed the native template sequence used for the design. The best sequence showed micromolar inhibition, which is a 3-15-fold improvement over the native sequence, depending on the donor. In addition, the best sequence equally inhibited wild-type and Enfuvirtide-resistant virus strains.

Computational design of the lasso peptide antibiotic microcin J25

Microcin J25 (MccJ25) is a 21 amino acid (aa) ribosomally synthesized antimicrobial peptide with an unusual structure in which the eight N-terminal residues form a covalently cyclized macrolactam ring through which the remaining 13 aa tail is fed. An open question is the extent of sequence space that can occupy such an extraordinary, highly constrained peptide fold. To begin answering this question, here we have undertaken a computational redesign of the MccJ25 peptide using a two-stage sequence selection procedure based on both energy minimization and fold specificity. Eight of the most highly ranked sequences from the design algorithm, each of which contained two or three amino acid substitutions, were expressed in Escherichia coli and tested for production and antimicrobial activity. Six of the eight variants were successfully produced by E.coli at production levels comparable with that of the wild-type peptide. Of these six variants, three retain detectable antimicrobial activity, although this activity is reduced relative to wild-type MccJ25. The results here build upon previous findings that even rigid, constrained structures like the lasso architecture are amenable to redesign. Furthermore, this work provides evidence that a large amount of amino acid variation is tolerated by the lasso peptide fold.

Species specificity of the complement inhibitor compstatin investigated by all-atom molecular dynamics simulations

The development of compounds to regulate the activation of the complement system in non-primate species is of profound interest because it can provide models for human diseases. The peptide compstatin inhibits protein C3 in primate mammals and is a potential therapeutic agent against unregulated activation of complement in humans but is inactive against nonprimate species. Here, we elucidate this species specificity of compstatin by molecular dynamics simulations of complexes between the most potent natural compstatin analog and human or rat C3. The results are compared against an experimental conformation of the human complex, determined recently by X-ray diffraction at 2.4-A resolution. The human complex simulations provide information on the relative contributions to stability of specific C3 and compstatin residues. In the rat simulations, the protein undergoes reproducible conformational changes, which eliminate or weaken specific interactions and reduce the complex stability. The simulation insights can be used to design improved compstatin-based inhibitors for human C3 and active inhibitors against lower mammals.

A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix reordering and logistic regression

In this work, we combine the strengths of mixed-integer linear optimization (MILP) and logistic regression for predicting the in vivo toxicity of chemicals using only their measured in vitro assay data. The proposed approach utilizes a biclustering method based on iterative optimal reordering (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2008). Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies. BMC Bioinformatics 9, 458-474.; DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2010b). A network flow model for biclustering via optimal re-ordering of data matrices. J. Global. Optim. 47, 343-354.) to identify biclusters corresponding to subsets of chemicals that have similar responses over distinct subsets of the in vitro assays. The biclustering of the in vitro assays is shown to result in significant clustering based on assay target (e.g., cytochrome P450 [CYP] and nuclear receptors) and type (e.g., downregulated BioMAP and biochemical high-throughput screening protein kinase activity assays). An optimal method based on mixed-integer linear optimization for reordering sparse data matrices (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Li, G. Y., Rabinowitz, J. D., and Rabitz, H. A. (2010a). Enhancing molecular discovery using descriptor-free rearrangement clustering techniques for sparse data sets. AIChE J. 56, 405-418.; McAllister, S. R., DiMaggio, P. A., and Floudas, C. A. (2009). Mathematical modeling and efficient optimization methods for the distance-dependent rearrangement clustering problem. J. Global. Optim. 45, 111-129) is then applied to the in vivo data set (21.7% sparse) in order to cluster end points that have similar lowest effect level (LEL) values, where it is observed that the end points are effectively clustered according to (1) animal species (i.e., the chronic mouse and chronic rat end points were clearly separated) and (2) similar physiological attributes (i.e., liver- and reproductive-related end points were found to separately cluster together). As the liver and reproductive end points exhibited the largest degree of correlation, we further analyzed them using regularized logistic regression in a rank-and-drop framework to identify which subset of in vitro features could be utilized for in vivo toxicity prediction. It was observed that the in vivo end points that had similar LEL responses over the 309 chemicals (as determined by the sparse clustering results) also shared a significant subset of selected in vitro descriptors. Comparing the significant descriptors between the two different categories of end points revealed a specificity of the CYP assays for the liver end points and preferential selection of the estrogen/androgen nuclear receptors by the reproductive end points.