PVRIG is Expressed on Stem-Like T Cells in Dendritic Cell-Rich Niches in Tumors and Its Blockade May Induce Immune Infiltration in Non-Inflamed Tumors.

Cancers that are poorly immune infiltrated pose a substantial challenge, with current immunotherapies yielding limited clinical success. Stem-like memory T cells (TSCM) have been identified as a subgroup of T cells that possess strong proliferative capacity and that can expand and differentiate following interactions with dendritic cells (DCs). In this study, we explored the pattern of expression of a recently discovered inhibitory receptor poliovirus receptor-related immunoglobulin domain protein (PVRIG) and its ligand, poliovirus receptor-related ligand 2 (PVRL2), in the human tumor microenvironment. Using spatial and single-cell RNA transcriptomics data across diverse cancer indications, we found that among the T-cell checkpoints, PVRIG is uniquely expressed on TSCM and PVRL2 is expressed on DCs in immune aggregate niches in tumors. PVRIG blockade could therefore enhance TSCM-DC interactions and efficiently drive T-cell infiltration to tumors. Consistent with these data, following PVRIG blockade in patients with poorly infiltrated tumors, we observed immune modulation including increased tumor T-cell infiltration, T-cell receptor (TCR) clonality, and intratumoral T-cell expansion, all of which were associated with clinical benefit. These data suggest PVRIG blockade as a promising strategy to induce potent antitumor T-cell responses, providing a novel approach to overcome resistance to immunotherapy in immune-excluded tumors.

Gp96 Peptide Antagonist gp96-II Confers Therapeutic Effects in Murine Intestinal Inflammation.

BACKGROUND: The expression of heat shock protein gp96 is strongly correlated with the degree of tissue inflammation in ulcerative colitis and Crohn's disease, thereby leading us to the hypothesis that inhibition of expression via gp96-II peptide prevents intestinal inflammation. METHODS: We employed daily injections of gp96-II peptide in two murine models of intestinal inflammation, the first resulting from five daily injections of IL-12/IL-18, the second via a single intrarectal application of TNBS (2,4,6-trinitrobenzenesulfonic acid). We also assessed the effectiveness of gp96-II peptide in murine and human primary cell culture. RESULTS: In the IL-12/IL-18 model, all gp96-II peptide-treated animals survived until day 5, whereas 80% of placebo-injected animals died. gp96-II peptide reduced IL-12/IL-18-induced plasma IFNγ by 89%, IL-1ß by 63%, IL-6 by 43% and tumor necrosis factor (TNF) by 70% compared to controls. The clinical assessment Disease Activity Index of intestinal inflammation severity was found to be significantly lower in the gp96-II-treated animals when compared to vehicle-injected mice. gp96-II peptide treatment in the TNBS model limited weight loss to 5% on day 7 compared with prednisolone treatment, whereas placebo-treated animals suffered a 20% weight loss. Histological disease severity was reduced equally by prednisolone (by 40%) and gp96-II peptide (35%). Mice treated with either gp96-II peptide or prednisolone exhibited improved endoscopic scores compared with vehicle-treated control mice: vascularity, fibrin, granularity, and translucency scores were reduced by up to 49% by prednisolone and by up to 30% by gp96-II peptide. In vitro, gp96-II peptide reduced TLR2-, TLR4- and IL-12/IL-18-induced cytokine expression in murine splenocytes, with declines in constitutive IL-6 (54%), lipopolysaccharide-induced TNF (48%), IL-6 (81%) and in Staphylococcus epidermidis-induced TNF (67%) and IL-6 (81%), as well as IL-12/IL-18-induced IFNγ (75%). gp96-II peptide reduced IL-1ß, IL-6, TNF and GM-CSF in human peripheral blood mononuclear cells to a similar degree without affecting cell viability, whereas RANTES, IL-25 and MIF were twofold to threefold increased. CONCLUSION: gp96-II peptide protects against murine intestinal inflammation by regulating inflammation in vivo and in vitro, pointing to its promise as a novel treatment for inflammatory bowel disease.

Computational identification of natural peptides based on analysis of molecular evolution.

MOTIVATION: Many secretory peptides are synthesized as inactive precursors that must undergo post-translational processing to become biologically active peptides. Attempts to predict natural peptides are limited by the low performance of proteolytic site predictors and by the high combinatorial complexity of pairing such sites. To overcome these limitations, we analyzed the site-wise evolutionary mutation rates of peptide hormone precursors, calculated using the Rate4Site algorithm. RESULTS: Our analysis revealed that within their precursors, peptide residues are significantly more conserved than the pro-peptide residues. This disparity enables the prediction of peptides with a precision of ∼60% at a recall of 40% [receiver-operating characteristic curve (ROC) AUC 0.79]. Subsequently, combining the Rate4Site score with additional features and training a Random Forest classifier enable the prediction of natural peptides hidden within secreted human proteins at a precision of ∼90% at a recall of 50% (ROC AUC 0.96). The high performance of our method allows it to be applied to full secretomes and to predict naturally occurring active peptides. Our prediction on Homo sapiens revealed several putative peptides in the human secretome that are currently unannotated. Furthermore, the unique expression of some of these peptides implies a potential hormone function, including peptides that are highly expressed in endocrine glands. AVAILABILITY AND IMPLEMENTATION: A pseudocode is available in the SUPPLEMENTARY INFORMATION. CONTACT: doron.gerber@biu.ac.il or kliger@cgen.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving classical substructure-based virtual screening to handle extrapolation challenges.

Target-oriented substructure-based virtual screening (sSBVS) of molecules is a promising approach in drug discovery. Yet, there are doubts whether sSBVS is suitable also for extrapolation, that is, for detecting molecules that are very different from those used for training. Herein, we evaluate the predictive power of classic virtual screening methods, namely, similarity searching using Tanimoto coefficient (MTC) and Naive Bayes (NB). As could be expected, these classic methods perform better in interpolation than in extrapolation tasks. Consequently, to enhance the predictive ability for extrapolation tasks, we introduce the Shadow approach, in which inclusion relations between substructures are considered, as opposed to the classic sSBVS methods that assume independence between substructures. Specifically, we discard contributions from substructures included in ("shaded" by) others which are, in turn, included in the molecule of interest. Indeed, the Shadow classifier significantly outperforms both MTC (pValue = 3.1 × 10(-16)) and NB (pValue = 3.5 × 10(-9)) in detecting hits sharing low similarity with the training active molecules.

Hidden conformations in protein structures.

MOTIVATION: Prediction of interactions between protein residues (contact map prediction) can facilitate various aspects of 3D structure modeling. However, the accuracy of ab initio contact prediction is still limited. As structural genomics initiatives move ahead, solved structures of homologous proteins can be used as multiple templates to improve contact prediction of the major conformation of an unsolved target protein. Furthermore, multiple templates may provide a wider view of the protein's conformational space. However, successful usage of multiple structural templates is not straightforward, due to their variable relevance to the target protein, and because of data redundancy issues. RESULTS: We present here an algorithm that addresses these two limitations in the use of multiple structure templates. First, the algorithm unites contact maps extracted from templates sharing high sequence similarity with each other in a fashion that acknowledges the possibility of multiple conformations. Next, it weights the resulting united maps in inverse proportion to their evolutionary distance from the target protein. Testing this algorithm against CASP8 targets resulted in high precision contact maps. Remarkably, based solely on structural data of remote homologues, our algorithm identified residue-residue interactions that account for all the known conformations of calmodulin, a multifaceted protein. Therefore, employing multiple templates, which improves prediction of contact maps, can also be used to reveal novel conformations. As multiple templates will soon be available for most proteins, our scheme suggests an effective procedure for their optimal consideration. AVAILABILITY: A Perl script implementing the WMC algorithm described in this article is freely available for academic use at http://tau.ac.il/~haimash/WMC.

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.

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 Pearson's 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.

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 alpha-helix, disorder to beta-strand), and order-to-order changes, where a segment switches from one ordered secondary structure to another (e.g., alpha-helix to beta-strand, alpha-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.

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 TNFalpha, IL-1beta, IL-6, and IL-8 induced by endotoxin by >80%. When injected into mice, the peptide reduced circulating levels of endotoxin-induced TNFalpha, IL-6, and IFNgamma 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.

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 micro M 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.

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.

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.

Predicting proteolytic sites in extracellular proteins: only halfway there.

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.

Towards alignment independent quantitative assessment of homology detection.

Identification of homologous proteins provides a basis for protein annotation. Sequence alignment tools reliably identify homologs sharing high sequence similarity. However, identification of homologs that share low sequence similarity remains a challenge. Lowering the cutoff value could enable the identification of diverged homologs, but also introduces numerous false hits. Methods are being continuously developed to minimize this problem. Estimation of the fraction of homologs in a set of protein alignments can help in the assessment and development of such methods, and provides the users with intuitive quantitative assessment of protein alignment results. Herein, we present a computational approach that estimates the amount of homologs in a set of protein pairs. The method requires a prevalent and detectable protein feature that is conserved between homologs. By analyzing the feature prevalence in a set of pairwise protein alignments, the method can estimate the number of homolog pairs in the set independently of the alignments' quality. Using the HomoloGene database as a standard of truth, we implemented this approach in a proteome-wide analysis. The results revealed that this approach, which is independent of the alignments themselves, works well for estimating the number of homologous proteins in a wide range of homology values. In summary, the presented method can accompany homology searches and method development, provides validation to search results, and allows tuning of tools and methods.

Discovering hidden viral piracy.

MOTIVATION: Viruses and developers of anti-inflammatory therapies share a common interest in proteins that manipulate the immune response. Large double-stranded DNA viruses acquire host proteins to evade host defense mechanisms. Hence, viral pirated proteins may have a therapeutic potential. Although dozens of viral piracy events have already been identified, we hypothesized that sequence divergence impedes the discovery of many others. RESULTS: We developed a method to assess the number of viral/human homologs and discovered that at least 917 highly diverged homologs are hidden in low-similarity alignment hits that are usually ignored. However, these low-similarity homologs are masked by many false alignment hits. We therefore applied a filtering method to increase the proportion of viral/human homologous proteins. The homologous proteins we found may facilitate functional annotation of viral and human proteins. Furthermore, some of these proteins play a key role in immune modulation and are therefore therapeutic protein candidates.

From genome to antivirals: SARS as a test tube.

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.

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

BACKGROUND: Severe 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. RESULTS: Sequence 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. CONCLUSIONS: This 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.