Yossef Kliger

Discovery and Validation of Novel Peptide Agonists for G-protein-coupled Receptors

G-protein-coupled receptors (GPCRs) represent an important group of targets for pharmaceutical therapeutics. The completion of the human genome revealed a large number of putative GPCRs. However, the identification of their natural ligands, and especially peptides, suffers from low discovery rates, thus impeding development of therapeutics based on these potential drug targets. We describe the discovery of novel GPCR ligands encrypted in the human proteome. Hundreds of potential peptide ligands were predicted by machine learning algorithms. In vitro screening of selected 33 peptides on a set of 152 GPCRs, including a group of designated orphan receptors, was conducted by intracellular calcium measurements and cAMP assays. The screening revealed eight novel peptides as potential agonists that specifically activated six different receptors in a dose-dependent manner. Most of the peptides showed distinct stimulatory patterns targeted at designated and orphan GPCRs. Further analysis demonstrated a significant in vivo effect for one of the peptides in a mouse inflammation model.

Cloaked similarity between HIV-1 and SARS-CoV suggests an anti-SARS strategy

BackgroundSevere acute respiratory syndrome (SARS) is a febrile respiratory illness. The disease has been etiologically linked to a novel coronavirus that has been named the SARS-associated coronavirus (SARS-CoV), whose genome was recently sequenced. Since it is a member of the Coronaviridae, its spike protein (S2) is believed to play a central role in viral entry by facilitating fusion between the viral and host cell membranes. The protein responsible for viral-induced membrane fusion of HIV-1 (gp41) differs in length, and has no sequence homology with S2.ResultsSequence analysis reveals that the two viral proteins share the sequence motifs that construct their active conformation. These include (1) an N-terminal leucine/isoleucine zipper-like sequence, and (2) a C-terminal heptad repeat located upstream of (3) an aromatic residue-rich region juxtaposed to the (4) transmembrane segment.ConclusionsThis study points to a similar mode of action for the two viral proteins, suggesting that anti-viral strategy that targets the viral-induced membrane fusion step can be adopted from HIV-1 to SARS-CoV. Recently the FDA approved Enfuvirtide, a synthetic peptide corresponding to the C-terminal heptad repeat of HIV-1 gp41, as an anti-AIDS agent. Enfuvirtide and C34, another anti HIV-1 peptide, exert their inhibitory activity by binding to a leucine/isoleucine zipper-like sequence in gp41, thus inhibiting a conformational change of gp41 required for its activation. We suggest that peptides corresponding to the C-terminal heptad repeat of the S2 protein may serve as inhibitors for SARS-CoV entry.

Peptides modulating conformational changes in secreted chaperones: From in silico design to preclinical proof of concept

Blocking conformational changes in biologically active proteins holds therapeutic promise. Inspired by the susceptibility of viral entry to inhibition by synthetic peptides that block the formation of helix–helix interactions in viral envelope proteins, we developed a computational approach for predicting interacting helices. Using this approach, which combines correlated mutations analysis and Fourier transform, we designed peptides that target gp96 and clusterin, 2 secreted chaperones known to shift between inactive and active conformations. In human blood mononuclear cells, the gp96-derived peptide inhibited the production of TNFα, IL-1β, IL-6, and IL-8 induced by endotoxin by >80%. When injected into mice, the peptide reduced circulating levels of endotoxin-induced TNFα, IL-6, and IFNγ by >50%. The clusterin-derived peptide arrested proliferation of several neoplastic cell lines, and significantly enhanced the cytostatic activity of taxol in vitro and in a xenograft model of lung cancer. Also, the predicted mode of action of the active peptides was experimentally verified. Both peptides bound to their parent proteins, and their biological activity was abolished in the presence of the peptides corresponding to the counterpart helices. These data demonstrate a previously uncharacterized method for rational design of protein antagonists.

Reducing phylogenetic bias in correlated mutation analysis

Correlated mutation analysis (CMA) is a sequence-based approach for ab initio protein contact map prediction. The basis of this approach is the observed correlation between mutations in interacting amino acid residues. These correlations are often estimated by either calculating the Pearsons correlation coefficient (PCC) or the mutual information (MI) between columns in a multiple sequence alignment (MSA) of the protein of interest and its homologs. A major challenge of CMA is to filter out the background noise originating from phylogenetic relatedness between sequences included in the MSA. Recently, a procedure to reduce this background noise was demonstrated to improve an MI-based predictor. Herein, we tested whether a similar approach can also improve the performance of the classical PCC-based method. Indeed, performance improvements were achieved for all four major SCOP classes. Furthermore, the results reveal that the improved PCC-based method is superior to MI-based methods for proteins having MSAs of up to 100 sequences.

Optimal data collection for correlated mutation analysis

The main objective of correlated mutation analysis (CMA) is to predict intraprotein residue–residue interactions from sequence alone. Despite considerable progress in algorithms and computer capabilities, the performance of CMA methods remains quite low. Here we examine whether, and to what extent, the quality of CMA methods depends on the sequences that are included in the multiple sequence alignment (MSA). The results revealed a strong correlation between the number of homologs in an MSA and CMA prediction strength. Furthermore, many of the current methods include only orthologs in the MSA, we found that it is beneficial to include both orthologs and paralogs in the MSA. Remarkably, even remote homologs contribute to the improved accuracy. Based on our findings we put forward an automated data collection procedure, with a minimal coverage of 50% between the query protein and its orthologs and paralogs. This procedure improves accuracy even in the absence of manual curation. In this era of massive sequencing and exploding sequence data, our results suggest that correlated mutation‐based methods have not reached their inherent performance limitations and that the role of CMA in structural biology is far from being fulfilled. Proteins 2009.

From genome to antivirals: SARS as a test tube.

Abstract The severe acute respiratory syndrome (SARS) epidemic brought into the spotlight the need for rapid development of effective anti-viral drugs against newly emerging viruses. Researchers have leveraged the 20-year battle against AIDS into a variety of possible treatments for SARS. Most prominently, based solely on viral genome information, silencers of viral genes, viral-enzyme blockers and viral-entry inhibitors were suggested as potential therapeutic agents for SARS. In particular, inhibitors of viral entry, comprising therapeutic peptides, were based on the recently launched anti-HIV drug enfuvirtide. This could represent one of the most direct routes from genome sequencing to the discovery of antiviral drugs.

Large-scale analysis of secondary structure changes in proteins suggests a role for disorder-to-order transitions in nucleotide binding proteins

Conformational changes in proteins often involve secondary structure transitions. Such transitions can be divided into two types: disorder‐to‐order changes, in which a disordered segment acquires an ordered secondary structure (e.g., disorder to α‐helix, disorder to β‐strand), and order‐to‐order changes, where a segment switches from one ordered secondary structure to another (e.g., α‐helix to β‐strand, α‐helix to turn). In this study, we explore the distribution of these transitions in the proteome. Using a comprehensive, yet highly conservative method, we compared solved three‐dimensional structures of identical protein sequences, looking for differences in the secondary structures with which they were assigned. Protein chains in which such secondary structure transitions were detected, were classified into two sets according to the type of transition that is involved (disorder‐to‐order or order‐to‐order), allowing us to characterize each set by examining enrichment of gene ontology terms. The results reveal that the disorder‐to‐order set is significantly enriched with nucleotide binding proteins, whereas the order‐to‐order set is more diverse. Remarkably, further examination reveals that >22% of the purine nucleotide binding proteins include segments which undergo disorder‐to‐order transitions, suggesting that such transitions play an important role in this process. Proteins 2010.

Activation of Relaxin‐Related Receptors by Short, Linear Peptides Derived from a Collagen‐Containing Precursor

In a screening effort based on algorithmic predictions for novel G‐protein‐coupled receptor (GPCR) peptide activators, we were able to identify and examine two novel peptides (P59 and P74) which are short, linear, and derived from a natural, previously unidentified precursor protein containing a collagen‐like repeat. Both peptides seemed to show an apparent cAMP‐related effect on CHO‐K1 cells transiently transfected with either LGR7 or LGR8, usually after treatment with cAMP‐generating forskolin, compared to the same cells treated with forskolin plus relaxin. This activation was not found for the relaxin‐3 receptor (GPR135). In a set of follow‐up experiments, both peptides were found to stimulate cAMP production, mostly upon initial stimulation of cAMP production by 5 μM forskolin in cells transfected with either LGR7 or LGR8. In a dye‐free cell impedance GPCR activation assay, we were able to show that these peptides were also able to activate a cellular response mediated by these receptors. Although untransfected CHO‐K1 cells showed some cellular activation by both relaxin and at least one of our newly discovered peptides, both LGR7‐ and LGR8‐transfected cells showed a stronger response, indicating stimulation of a cellular pathway through activation of these receptors. In conclusion, we were able to show that these newly discovered peptides, which have no similarity to any member of the relaxin–insulin‐like peptide family, are potential ligands for the relaxin‐related family of receptors and as such might serve as novel candidates for relaxin‐related therapeutic indications. Both peptides are linear and were found to be active after being chemically synthesized.

Computational Approaches to Therapeutic Peptide Discovery

The development of peptides with therapeutic activities can be based on naturally occurring peptides or alternatively on de novo design. The discovery of natural peptides is often a matter of serendipity. In part, this is because natural peptides are typically proteolytically cleaved out from precursor proteins, a feature that averts the direct benefits of the genomic revolution. The first part of this review describes attempts to create a more systematic identification of natural peptides relying on a two step process. In the initial step, an in silico peptidome is predicted through the use of machine learning. Then, various computational biology tools are tailored to focus on peptides predicted to have the desired biological activity; for example, activating a GPCR or modulating the cellular arm of the immune system. The second part of the review is devoted to de novo peptide design and focuses on arguably the simplest scenario in which the designed peptide corresponds to a contiguous protein subsequence. Amongst these peptides, those corresponding to helical segments are prominent, mainly due to their relative ability to fold independently. Inspired by the clinical success of viral entry inhibitors, which are peptides corresponding to helical segments in viral envelope proteins, a computational tool for the identification of intramolecular helix-helix interactions was developed. Using this approach, peptides having anti-cancer, anti-angiogenic, and anti-inflammatory activities have been recently rationally designed and biologically characterized.

Predicting proteolytic sites in extracellular proteins

Abstract Motivation: Many secretory proteins are synthesized as inactive precursors that must undergo post-translational proteolysis in order to mature and become active. In the current study, we address the challenge of sequence-based discovery of proteolytic sites in secreted proteins using machine learning. Results: The results revealed that only half of the extracellular proteolytic sites are currently annotated, leaving over 3600 unannotated ones. Furthermore, we have found that only 6% of the unannotated sites are similar to known proteolytic sites, whereas the remaining 94% do not share significant similarity with any annotated proteolytic site. The computational challenges in these two cases are very different. While the precision in detecting the former group is close to perfect, only a mere 22% of the latter group were detected with a precision of 80%. The applicability of the classifier is demonstrated through members of the FGF family, in which we verified the conservation of physiologically-relevant proteolytic sites in homologous proteins. Contact: kliger@compugen.co.il; yossef.kliger@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.