Identification and rational redesign of peptide ligands to CRIP1, a novel biomarker for cancers

Rational peptide design for targeting cancer cell invasion

Cell invasion is an important process in cancer progression and recurrence. Invasion and implantation of cancer cells from their original place to other tissues, by disabling vital organs, challenges the treatment of cancer patients. Given the importance of the matter, many molecular treatments have been developed to inhibit cancer cell invasion. Because of their low production cost and ease of production, peptides are valuable therapeutic molecules for inhibiting cancer cell invasion. In recent years, advances in the field of computational biology have facilitated the design of anti-cancer peptides. In our investigation, using computational biology approaches such as evolutionary analysis, residue scanning, protein-peptide interaction analysis, molecular dynamics, and free energy analysis, our team designed a peptide library with about 100 000 candidates based on A6 (acetyl-KPSSPPEE-amino) sequence which is an anti-invasion peptide. During computational studies, two of the designed peptides that give the highest scores and showed the greatest sequence similarity to A6 were entered into the experimental analysis workflow for further analysis. In experimental analysis steps, the anti-metastatic potency and other therapeutic effects of designed peptides were evaluated using MTT assay, RT-qPCR, zymography analysis, and invasion assay. Our study disclosed that the IK1 (acetyl-RPSFPPEE-amino) peptide, like A6, has great potency to inhibit the invasion of cancer cells.

Host microRNA interactions with the SARS-CoV-2 viral genome 3'-untranslated region

The 2019 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has marked the spread of a novel human coronavirus. While the viral life cycle is well understood, most of the interactions at the virus-host interface remain elusive. Furthermore, the molecular mechanisms behind disease severity and immune evasion are still largely unknown. Conserved elements of the viral genome such as secondary structures within the 5'- and 3'-untranslated regions (UTRs) serve as attractive targets of interest and could prove crucial in furthering our understanding of virus-host interactions. It has been proposed that microRNA (miR) interactions with viral components could be used by both the virus and host for their own benefit. Analysis of the SARS-CoV-2 viral genome 3'-UTR has revealed the potential for host cellular miR binding sites, providing sites for specific interactions with the virus. In this study, we demonstrate that the SARS-CoV-2 genome 3'-UTR binds the host cellular miRNAs miR-760-3p, miR-34a-5p, and miR-34b-5p, which have been shown to influence translation of interleukin-6 (IL-6), the IL-6 receptor (IL-6R), as well as progranulin (PGRN), respectively, proteins that have roles in the host immune response and inflammatory pathways. Furthermore, recent work suggests the potential of miR-34a-5p and miR-34b-5p to target and inhibit translation of viral proteins. Native gel electrophoresis and steady-state fluorescence spectroscopy were utilized to characterize the binding of these miRs to their predicted sites within the SARS-CoV-2 genome 3'-UTR. Additionally, we investigated 2'-fluoro-D-arabinonucleic acid (FANA) analogs of these miRNAs as competitive binding inhibitors for these miR binding interactions. The mechanisms detailed in this study have the potential to drive the development of antiviral treatments for SARS-CoV-2 infection, and provide a potential molecular basis for cytokine release syndrome and immune evasion which could implicate the host-virus interface.

CRIP1 supports the growth and migration of AML-M5 subtype cells by activating Wnt/β-catenin pathway

Acute myeloid leukemia (AML) is a clinically and molecularly heterogeneous hematopoietic disorder. To effectively eradicate AML, it is urgent to develop new therapeutic approaches and identify novel molecular targets. In silico analysis indicated that the expression of cysteine-rich intestinal protein 1 (CRIP1) was significantly elevated in AML cells and correlated with worse overall survival of the AML patients. However, its specific roles in AML remain elusive. Here we demonstrated that CRIP1 acted as a key oncogene to support AML cell survival and migration. Using a loss-of-function analysis, we found that CRIP1 silencing in U937 and THP1 cells by lentivirus-mediated shRNAs resulted in a decrease in cell growth, migration and colony formation, and an increase in chemosensitivity to Ara-C. CRIP1 silencing induced cell apoptosis and G1/S transition arrest. Mechanically, CRIP1 silencing caused inactivation of Wnt/β-catenin pathway through upregulating axin1 protein. The Wnt/β-catenin agonist SKL2001 markedly rescued the cell growth and migration defect induced by CRIP1 silencing. Our findings reveals that CRIP1 may contribute to AML-M5 pathogenesis and represent a novel target for AML-M5 treatment.

Genome-wide DNA methylation and transcription analysis in psoriatic epidermis

Aim: To identify DNA methylation and transcription biomarkers in the psoriatic epidermis. Materials & methods: Gene transcription and DNA methylation datasets of psoriatic epidermal tissue were obtained from the Gene Expression Omnibus. Machine learning algorithm analysis and weighted gene coexpression network analysis were carried out to screen hub genes. Results: Differentially methylated and expressed genes were identified in the psoriatic epidermis. Six hub genes were selected - GZMB, CRIP1, S100A12, ISG15, CRABP2 and VNN1 - whose transcript levels showed a significant correlation with Psoriasis Area and Severity Index scores and immune infiltration. Conclusion: Psoriatic epidermis is primarily in a hypermethylated status. Epidermis-specific hub differentially methylated and expressed genes are potential biomarkers to help judge the condition of psoriasis.

What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders?

Protein therapeutics have been widely used to treat hematological disorders. With the advent of de novo protein design, protein therapeutics are not limited to ameliorating natural proteins but also produce novel protein sequences, folds, and functions with shapes and functions customized to bind to the therapeutic targets. De novo protein techniques have been widely used biomedically to design novel diagnostic and therapeutic drugs, novel vaccines, and novel biological materials. In addition, de novo protein design has provided new options for treating hematological disorders. Scientists have designed protein switches called Colocalization-dependent Latching Orthogonal Cage-Key pRoteins (Co-LOCKR) that perform computations on the surface of cells. De novo designed molecules exhibit a better capacity than the currently available tyrosine kinase inhibitors in chronic myeloid leukemia therapy. De novo designed protein neoleukin-2/15 enhances chimeric antigen receptor T-cell activity. This new technique has great biomedical potential, especially in exploring new treatment methods for hematological disorders. This review discusses the development of de novo protein design and its biological applications, with emphasis on the treatment of hematological disorders.

Peptidomimetics in cancer targeting

The low efficiency of treatment strategies is one of the main obstacles to developing cancer inhibitors. Up to now, various classes of therapeutics have been developed to inhibit cancer progression. Peptides due to their small size and easy production compared to proteins are highly regarded in designing cancer vaccines and oncogenic pathway inhibitors. Although peptides seem to be a suitable therapeutic option, their short lifespan, instability, and low binding affinity for their target have not been widely applicable against malignant tumors. Given the peptides' disadvantages, a new class of agents called peptidomimetic has been introduced. With advances in physical chemistry and biochemistry, as well as increased knowledge about biomolecule structures, it is now possible to chemically modify peptides to develop efficient peptidomimetics. In recent years, numerous studies have been performed to the evaluation of the effectiveness of peptidomimetics in inhibiting metastasis, angiogenesis, and cancerous cell growth. Here, we offer a comprehensive review of designed peptidomimetics to diagnose and treat cancer.

Peptide probes with high affinity to target protein selection by phage display and characterization using biophysical approaches

Herein, phage display was utilized to screen the affinity of peptides against dihydrofolate reductase and a positive peptide was obtained, and the verification of the affinity was tested by multiple in vitro biophysical methods.

Phage Display Selection, Identification, and Characterization of Novel Pancreatic Cancer Targeting Peptides

Pancreatic cancer is characterized by a 5-year survival rate of 3%, in part due to inadequate detection methods. The small size of peptides offers advantages regarding molecular targeting. Thus, peptides may be used in detection of pancreatic cancer. Here, peptides that target pancreatic cancer cells were selected using phage display technology using a 15-mer fUSE5 library. Phage were pre-cleared against immortalized pancreatic cells (hTERT-HPNE), followed by selections against pancreatic cancer (Mia Paca-2) cells. Next-generation sequencing identified two peptides, MCA1 and MCA2, with a Log2 fold change (Mia Paca-2/ hTERT-HPNE) >1.5. Modified ELISA and fluorescent microscopy showed that both peptides bound significantly higher to Mia Paca-2 cells, and not to hTERT-HPNE, embryonic kidney (HEK 293), ovarian (SKOV-3) and prostate cancer (LNCaP) cell lines. Further characterization of MCA1 and MCA2 revealed EC₅₀ values of 16.11 µM (95% CI [9.69, 26.31 µM]) and 97.01 µM (95% CI [58.64, 166.30 µM]), respectively. Based on these results, MCA1 was selected for further studies. A competitive dose response assay demonstrated specific binding and an IC₅₀ value of 2.15 µM (95% CI [1.28, 3.62 µM]). Taken together, this study suggests that MCA1 may be used as a pancreatic cancer targeting ligand for detection of the disease.

DisruPPI: structure-based computational redesign algorithm for protein binding disruption

Motivation

Disruption of protein–protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein–Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.

Results

Two existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins.

Availability and implementation

DisruPPI is implemented in the EpiSweep package, freely available under an academic use license.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational design of cyclic peptides for the customized oriented immobilization of globular proteins

The oriented immobilization of proteins, key for the development of novel responsive biomaterials, relies on the availability of effective probes. These are generally provided by standard approaches based on in vivo maturation and in vitro selection of antibodies and/or aptamers. These techniques can suffer technical problems when a non-immunogenic epitope needs to be targeted. Here we propose a strategy to circumvent this issue by in silico design. In our method molecular binders, in the form of cyclic peptides, are computationally evolved by stochastically exploring their sequence and structure space to identify high-affinity peptides for a chosen epitope of a target globular protein: here a solvent-exposed site of β2-microglobulin (β2m). Designed sequences were screened by explicit solvent molecular dynamics simulations (MD) followed by experimental validation. Five candidates gave dose-response surface plasmon resonance signals with dissociation constants in the micromolar range. One of them was further analyzed by means of isothermal titration calorimetry, nuclear magnetic resonance, and 250 ns of MD. Atomic-force microscopy imaging showed that this peptide is able to immobilize β2m on a gold surface. In short, we have shown by a variety of experimental techniques that it is possible to capture a protein through an epitope of choice by computational design.

Computational Protein Design Through Grafting and Stabilization

Computational grafting of target residues onto existing protein scaffolds is a powerful method for the design of proteins with novel function. In the grafting method side chain mutations are introduced into a preexisting protein scaffold to recreate a target functional motif. The success of this approach relies on two primary criteria: (1) the availability of compatible structural scaffolds, and (2) the introduction of mutations that do not affect the protein structure or stability. To identify compatible structural motifs we use the Erebus webserver, to search the protein data bank (PDB) for user-defined structural scaffolds. To identify potential design mutations we use the Eris webserver, which accurately predicts changes in protein stability resulting from mutations. Mutations that increase the protein stability are more likely to maintain the protein structure and therefore produce the desired function. Together these tools provide effective methods for identifying existing templates and guiding further design experiments. The software tools for scaffold searching and design are available at http://dokhlab.org .

Dynamic Docking of Conformationally Constrained Macrocycles: Methods and Applications

Many natural products consist of large and flexible macrocycles that engage their targets via multiple contact points. This combination of contained flexibility and large contact area often allows natural products to bind at target surfaces rather than deep pockets, making them attractive scaffolds for inhibiting protein-protein interactions and other challenging therapeutic targets. The increasing ability to manipulate such compounds either biosynthetically or via semisynthetic modification means that these compounds can now be considered as starting points for medchem campaigns rather than solely as ends. Modern medchem benefits substantially from rational improvements made on the basis of molecular docking. As such, docking methods have been enhanced in recent years to deal with the complicated binding modalities and flexible scaffolds of macrocyclic natural products and natural product-like structures. Here, we comprehensively review methods for treating and docking these large macrocyclic scaffolds and discuss some of the resulting advances in medicinal chemistry.

Characterization of Self-Assembled Monolayers of Peptide Mimotopes of CD20 Antigen and Their Binding with Rituximab

CD20, expressed in greater than 90% of B-lymphocytic lymphomas, is a target for antibody therapy. Rituximab is a chimeric therapeutic monoclonal antibody (mAb) against the protein CD20, allowing it to destroy B cells and to treat lymphoma, leukemia, transplant rejection, and autoimmune disorder. In this work, the binding of rituximab to self-assembled monolayers (SAMs) of peptide mimotopes of CD20 antigen was systematically characterized. Four peptide mimotopes of CD 20 antigen were selected from the literature and redesigned to allow their SAM immobilizations on gold electrodes through a peptide linker with cysteine. The bindings of these peptides with rituximab and control mAbs (trastuzumab and bevacizumab) were characterized by quartz crystal microbalance (QCM). Among the four peptide mimotopes initially selected, the peptide designated as CN-14 (CGSGSGSWPRWLEN) was the most selective and sensitive for rituximab binding. The CN-14 SAM was further characterized by ellipsometry and atomic force microscopy. The thickness of the CN-14 SAM film was approximately 32 Å, and the CN-14 SAM is suggested to be stabilized by a salt bridge of Arg-10 and Glu-13 between CN-14 peptides. The CN-14 salt bridge was evaluated by a series of modifications to the CN-14 peptide sequence and characterized by QCM. The CN-14 amide variant produced a better affinity to rituximab than CN-14 without a significant impact on selectivity. As the pKa of the Glu residue of CN-14 increased, the affinity of the SAM to rituximab increased, whereas the selectivity decreased. This was attributed to the weakening of the salt bridge between the CN-14 Arg-10 and Glu-13 at higher pKa values for Glu-13. Our study shows that peptide mimotopes have potential benefits in sensor applications, as the peptide-peptide interactions in the SAMs can be manipulated by the addition of functional groups to the peptide to influence the binding of target proteins.

Graphene-Gold Metasurface Architectures for Ultrasensitive Plasmonic Biosensing

Graphene-gold metasurface architectures that can provide significant gains in plasmonic detection sensitivity for trace-amount target analytes are reported. Benefiting from extreme phase singularities of reflected light induced by strong plasmon-mediated energy confinements, the metasurface demonstrates a much-improved sensitivity to molecular bindings nearby and achieves an ultralow detection limit of 1 × 10(-18) m for 7.3 kDa 24-mer single-stranded DNA.

Characterization of the MUC1-C Cytoplasmic Domain as a Cancer Target

Mucin 1 (MUC1) is a heterodimeric protein that is aberrantly expressed in diverse human carcinomas and certain hematologic malignancies. The oncogenic MUC1 transmembrane C-terminal subunit (MUC1-C) functions in part by transducing growth and survival signals from cell surface receptors. However, little is known about the structure of the MUC1-C cytoplasmic domain as a potential drug target. Using methods for structural predictions, our results indicate that a highly conserved CQCRRK sequence, which is adjacent to the cell membrane, forms a small pocket that exposes the two cysteine residues for forming disulfide bonds. By contrast, the remainder of the MUC1-C cytoplasmic domain has no apparent structure, consistent with an intrinsically disordered protein. Our studies thus focused on targeting the MUC1 CQCRRK region. The results show that L- and D-amino acid CQCRRK-containing peptides bind directly to the CQC motif. We further show that the D-amino acid peptide, designated GO-203, blocks homodimerization of the MUC1-C cytoplasmic domain in vitro and in transfected cells. Moreover, GO-203 binds directly to endogenous MUC1-C in breast and lung cancer cells. Colocalization studies further demonstrate that GO-203 predominantly binds to MUC1-C at the cell membrane. These findings support the further development of agents that target the MUC1-C cytoplasmic domain CQC motif and thereby MUC1-C function in cancer cells.

The involvement of cysteine-rich intestinal protein in early development and innate immunity of Asiatic hard clam, Meretrix meretrix

Cysteine-rich intestinal protein (CRIP), a Zn(2+)-binding protein, contains a single copy of the highly conserved double-zinc-finger structure known as the LIM (lin-11-isl-1-mec-3) motif. In this paper, a cDNA encoding MmCRIP was isolated from the Asiatic hard clam Meretrix meretrix. The full-length cDNA of MmCRIP consists of a 237-bp open reading frame that encodes a polypeptide of 78 amino acids with a predicted molecular weight (MW) of 8635.8 Da and theoretical isoelectric point (pI) of 9.01. Bioinformatics analysis showed that it belonged to a new member of the CRIP subfamily. Relationship analysis revealed that MmCRIP has high-levels of sequence similarity to many CRIPs reported in other animals, particularly in invertebrates. Real-time PCR analysis showed that the highest level of MmCRIP expression was in hemocyte tissue and at pediveligers stage. To investigate immune function, mature clams were challenged with Aeromonas hydrophila. During A. hydrophila infection, up-regulation of MmCRIP transcript in clam's hemocyte, gill and hepatopancreas was detected. DsRNAi (double-strand RNA interference) approach was employed to study the function of MmCRIP and the data showed that inactivation of the MmCRIP gene blocked larvae development and caused mass mortalities. The probable roles of MmCRIP in clam early development and innate immunity are presented for the first time.

Subangstrom accuracy in pHLA-I modeling by Rosetta FlexPepDock refinement protocol

Flexible peptides binding to human leukocyte antigen (HLA) play a key role in mediating human immune responses and are also involved in idiosyncratic adverse drug reactions according to recent research. However, the structural determinations of pHLA complexes remain challenging under the present conditions. In this paper, the performance of a new peptide docking method, namely FlexPepDock, was systematically investigated by a benchmark of 30 crystallized structures of peptide-HLA class I complexes. The docking results showed that the near-native pHLA-I models with peptide bb-RMSD less than 2 Å were ranked in the top 1 model for 100% (70/70) docking cases, and the subangstrom models with peptide bb-RMSD less than 1 Å were ranked in the top 5 lowest-energy models for 65.7% (46/70) docking cases. Furthermore, 10 out of 70 docking cases ranked the subangstrom all-atom models in the top 5 lowest-energy models. The results showed that the FlexPepDock can generate high-quality models of pHLA-I complexes and can be widely applied to pHLA-I modeling and mechanism research of peptide-mediated immune responses.

Predicting affinity- and specificity-enhancing mutations at protein-protein interfaces

Manipulations of PPIs (protein-protein interactions) are important for many biological applications such as synthetic biology and drug design. Combinatorial methods have been traditionally used for such manipulations, failing, however, to explain the effects achieved. We developed a computational method for prediction of changes in free energy of binding due to mutation that bring about deeper understanding of the molecular forces underlying binding interactions. Our method could be used for computational scanning of binding interfaces and subsequent analysis of the interfacial sequence optimality. The computational method was validated in two biological systems. Computational saturated mutagenesis of a high-affinity complex between an enzyme AChE (acetylcholinesterase) and a snake toxin Fas (fasciculin) revealed the optimal nature of this interface with only a few predicted affinity-enhancing mutations. Binding measurements confirmed high optimality of this interface and identified a few mutations that could further improve interaction fitness. Computational interface scanning of a medium-affinity complex between TIMP-2 (tissue inhibitor of metalloproteinases-2) and MMP (matrix metalloproteinase) 14 revealed a non-optimal nature of the binding interface with multiple mutations predicted to stabilize the complex. Experimental results corroborated our computational predictions, identifying a large number of mutations that improve the binding affinity for this interaction and some mutations that enhance binding specificity. Overall, our computational protocol greatly facilitates the discovery of affinity- and specificity-enhancing mutations and thus could be applied for design of potent and highly specific inhibitors of any PPI.

Mapping of the binding landscape for a picomolar protein-protein complex through computation and experiment

Our understanding of protein evolution would greatly benefit from mapping of binding landscapes, i.e., changes in protein-protein binding affinity due to all single mutations. However, experimental generation of such landscapes is a tedious task due to a large number of possible mutations. Here, we use a simple computational protocol to map the binding landscape for two homologous high-affinity complexes, involving a snake toxin fasciculin and acetylcholinesterase from two different species. To verify our computational predictions, we experimentally measure binding between 25 Fas mutants and the 2 enzymes. Both computational and experimental results demonstrate that the Fas sequence is close to the optimum when interacting with its targets, yet a few mutations could further improve Kd, kon, and koff. Our computational predictions agree well with experimental results and generate distributions similar to those observed in other high-affinity PPIs, demonstrating the potential of simple computational protocols in capturing realistic binding landscapes.

Combining peptide and DNA for protein assay: CRIP1 detection for breast cancer staging

In this work, a novel method for a protein assay is proposed which uses the specific protein-binding peptide of the target protein and sequence-specific DNA to interact with the target as the capture and detection probe, respectively. Meanwhile, since the DNA sequence can be coupled with gold nanoparticles to amplify the signal readout, a sensitive and easily operated method for protein assay is developed. We have also employed a transcription factor named as cysteine-rich intestinal protein 1 (CRIP1), which has been identified as an ideal biomarker for staging of breast cancer, as the model protein for this study. With the proposed method, CRIP1 can be determined in a linear range from 1.25 to 10.13 ng/mL, with a detection limit of 1.25 ng/mL. Furthermore, the proposed method can be directly used to assay CRIP1 in tissue samples. Owing to its desirable sensitivity, excellent reproducibility, and high selectivity, the proposed method may hold great potential in clinical practice in the future.

Protein folding and de novo protein design for biotechnological applications

In the postgenomic era, the medical/biological fields are advancing faster than ever. However, before the power of full-genome sequencing can be fully realized, the connection between amino acid sequence and protein structure, known as the protein folding problem, needs to be elucidated. The protein folding problem remains elusive, with significant difficulties still arising when modeling amino acid sequences lacking an identifiable template. Understanding protein folding will allow for unforeseen advances in protein design; often referred to as the inverse protein folding problem. Despite challenges in protein folding, de novo protein design has recently demonstrated significant success via computational techniques. We review advances and challenges in protein structure prediction and de novo protein design, and highlight their interplay in successful biotechnological applications.

The impact of cysteine-rich intestinal protein 1 (CRIP1) in human breast cancer

Background

CRIP1 (cysteine-rich intestinal protein 1) has been found in several tumor types, its prognostic impact and its role in cellular processes, particularly in breast cancer, are still unclear.

Methods

To elucidate the prognostic impact of CRIP1, we analyzed tissues from 113 primary invasive ductal breast carcinomas using immunohistochemistry. For the functional characterization of CRIP1, its endogenous expression was transiently downregulated in T47D and BT474 breast cancer cells and the effects analyzed by immunoblotting, WST-1 proliferation assay and invasion assay.

Results

We found a significant correlation between CRIP1 and HER2 (human epidermal growth factor receptor 2) expression levels (p = 0.016) in tumor tissues. In Kaplan Meier analyses, CRIP1 expression was significantly associated with the distant metastases-free survival of patients, revealing a better prognosis for high CRIP1 expression (p = 0.039). Moreover, in multivariate survival analyses, the expression of CRIP1 was an independent negative prognostic factor, along with the positive prognosticators nodal status and tumor size (p = 0.029). CRIP1 knockdown in the T47D and BT474 breast cancer cell lines led to the increased phosphorylation of MAPK and Akt, to the reduced phosphorylation of cdc2, and to a significantly elevated cell proliferation in vitro (p < 0.001). These results indicate that reduced CRIP1 levels may increase cell proliferation and activate cell growth. In addition, CRIP1 knockdown increased cell invasion in vitro.

Conclusions

Because the lack of CRIP1 expression in breast cancer tissue is significantly associated with a worse prognosis for patients and low endogenous CRIP1 levels in vitro increased the malignant potential of breast cancer cells, we hypothesize that CRIP1 may act as a tumor suppressor in proliferation and invasion processes. Therefore, CRIP1 may be an independent prognostic marker with significant predictive power for use in breast cancer therapy.

Keratinocyte growth factor phage model peptides can promote human oral mucosal epithelial cell proliferation

OBJECTIVE

The objective of this study was to find keratinocyte growth factor (KGF) mimic peptides by a phage display library screening and to analyze their effects on proliferation of human oral mucosal epithelial cells (HOMECs).

STUDY DESIGN

A phage display library was screened by anti-KGF antibody. ELISA was performed to select monoclonal phages with higher binding activity. The promotion of the phage model peptides on HOMEC proliferation were analyzed by MTT and their cell affinities were confirmed by immunofluorescence assay. Their effect on KGFR, human beta-defensin 3, c-Fos, and c-Jun in HOMEC were analyzed by quantitative real-time PCR.

RESULTS

Two model peptides with higher affinity with HOMEC were found to have promotive activity on cell proliferation, similar to that of KGF. These 2 model peptides have no KGF-like promotion effect on the expression of c-Fos and c-Jun.

CONCLUSIONS

The 2 phage model peptides can promote the proliferation of HOMEC in vitro without tumorigenic effects, which suggests their possible usages in oral mucosal wound healing.

An optical probe for noninvasive molecular imaging of orthotopic brain tumors overexpressing epidermal growth factor receptor

We have developed a near-infrared (NIR) probe that targets cells overexpressing the EGF receptor (EGFR) for imaging glioblastoma brain tumors in live subjects. A peptide specific for the EGFR was modified with various lengths of monodiscrete polyethylene glycol (PEG) units and a NIR Cy5.5 fluorescence dye. The lead compound, compound 2, with one unit of PEG displayed good binding (8.9 μmol/L) and cellular uptake in glioblastoma cells overexpressing EGFR in vitro. The in vivo studies have shown that the probe was able to selectively label glioblastoma-derived orthotopic brain tumors. In vivo image analyses of peptide binding to the tumors using fluorescence-mediated molecular tomography revealed that the compound could distinguish between tumors expressing different levels of EGFR. The data presented here represent the first demonstration of differential quantitation of tumors expressing EGFR in live animals by a targeted NIR fluorescence probe using a molecular imaging device.

Structural and dynamic determinants of protein-peptide recognition

Protein-peptide interactions play important roles in many cellular processes, including signal transduction, trafficking, and immune recognition. Protein conformational changes upon binding, an ill-defined peptide binding surface, and the large number of peptide degrees of freedom make the prediction of protein-peptide interactions particularly challenging. To address these challenges, we perform rapid molecular dynamics simulations in order to examine the energetic and dynamic aspects of protein-peptide binding. We find that, in most cases, we recapitulate the native binding sites and native-like poses of protein-peptide complexes. Inclusion of electrostatic interactions in simulations significantly improves the prediction accuracy. Our results also highlight the importance of protein conformational flexibility, especially side-chain movement, which allows the peptide to optimize its conformation. Our findings not only demonstrate the importance of sufficient sampling of the protein and peptide conformations, but also reveal the possible effects of electrostatics and conformational flexibility on peptide recognition.

Arlm1 is a male-specific modifier of astrocytoma resistance on mouse Chr 12

While many cancers show a sex bias, the genetic basis and molecular mechanisms underlying sex bias are not always clear. Astrocytoma and glioblastoma show male predominance in humans. We have shown previously that glial tumors forming in the Nf1-/+; Trp53-/+cis (NPcis) mouse model also show a sex bias in some genetic contexts. Using cross-species comparisons we have identified candidate male-specific modifiers of astrocytoma/glioblastoma. Linkage analysis of B6X(B6X129)-NPcis mice identifies a modifier of astrocytoma resistance specific to males, named Arlm1, on distal mouse Chr 12. Arlm1 is syntenic to human Chr 7p15, 7p21, 7q36, and 14q32 regions that are altered in human glioblastoma. A subset of these genes shows male-specific correlations to glioblastoma patient survival time and represents strong candidates for the Arlm1 modifier gene. Identification of male-specific modifier genes will lead to a better understanding of the molecular basis of male predominance in astrocytoma and glioblastoma.

Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans

One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies. So far, few cis expression quantitative trait loci (eQTLs) have been reliably related to disease susceptibility. Trans-regulating mechanisms may play a more prominent role in disease susceptibility. We analyzed 12,808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1,490 European unrelated subjects. We applied a method of extraction of expression patterns-independent component analysis-to identify sets of co-regulated genes. These patterns were then related to 675,350 SNPs to identify major trans-acting regulators. We detected three genomic regions significantly associated with co-regulated gene modules. Association of these loci with multiple expression traits was replicated in Cardiogenics, an independent study in which expression profiles of monocytes were available in 758 subjects. The locus 12q13 (lead SNP rs11171739), previously identified as a type 1 diabetes locus, was associated with a pattern including two cis eQTLs, RPS26 and SUOX, and 5 trans eQTLs, one of which (MADCAM1) is a potential candidate for mediating T1D susceptibility. The locus 12q24 (lead SNP rs653178), which has demonstrated extensive disease pleiotropy, including type 1 diabetes, hypertension, and celiac disease, was associated to a pattern strongly correlating to blood pressure level. The strongest trans eQTL in this pattern was CRIP1, a known marker of cellular proliferation in cancer. The locus 12q15 (lead SNP rs11177644) was associated with a pattern driven by two cis eQTLs, LYZ and YEATS4, and including 34 trans eQTLs, several of them tumor-related genes. This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease.

MALDI imaging identifies prognostic seven-protein signature of novel tissue markers in intestinal-type gastric cancer

Proteomics-based approaches allow us to investigate the biology of cancer beyond genomic initiatives. We used histology-based matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry to identify proteins that predict disease outcome in gastric cancer after surgical resection. A total of 181 intestinal-type primary resected gastric cancer tissues from two independent patient cohorts were analyzed. Protein profiles of the discovery cohort (n = 63) were directly obtained from tumor tissue sections by MALDI imaging. A seven-protein signature was associated with an unfavorable overall survival independent of major clinical covariates. The prognostic significance of three individual proteins identified (CRIP1, HNP-1, and S100-A6) was validated immunohistochemically on tissue microarrays of an independent validation cohort (n = 118). Whereas HNP-1 and S100-A6 were found to further subdivide early-stage (Union Internationale Contre le Cancer [UICC]-I) and late-stage (UICC II and III) cancer patients into different prognostic groups, CRIP1, a protein previously unknown in gastric cancer, was confirmed as a novel and independent prognostic factor for all patients in the validation cohort. The protein pattern described here serves as a new independent indicator of patient survival complementing the previously known clinical parameters in terms of prognostic relevance. These results show that this tissue-based proteomic approach may provide clinically relevant information that might be beneficial in improving risk stratification for gastric cancer patients.

Transgenic overexpression of Tcfap2c/AP-2gamma results in liver failure and intestinal dysplasia

Background

The transcription factor Tcfap2c has been demonstrated to be essential for various processes during mammalian development. It has been found to be upregulated in various undifferentiated tumors and is implicated with poor prognosis. Tcfap2c is reported to impinge on cellular proliferation, differentiation and apoptosis. However, the physiological consequences of Tcfap2c-expression remain largely unknown.

Methodology/Principal Findings

Therefore we established a gain of function model to analyze the role of Tcfap2c in development and disease. Induction of the transgene led to robust expression in all tissues (except brain and testis) and lead to rapid mortality within 3–7 days. In the liver cellular proliferation and apoptosis was detected. Accumulation of microvesicular lipid droplets and breakdown of major hepatic metabolism pathways resulted in steatosis. Serum analysis showed a dramatic increase of enzymes indicative for hepatic failure. After induction of Tcfap2c we identified a set of 447 common genes, which are deregulated in both liver and primary hepatocyte culture. Further analysis showed a prominent repression of the cytochrome p450 system, PPARA, Lipin1 and Lipin2. These data indicate that in the liver Tcfap2c represses pathways, which are responsible for fatty acid metabolism. In the intestine, Tcfap2c expression resulted in expansion of Sox9 positive and proliferative active epithelial progenitor cells resulting in dysplastic growth of mucosal crypt cells and loss of differentiated mucosa.

Conclusions

The transgenic mice show that ectopic expression of Tcfap2c is not tolerated. Due to the phenotype observed, iTcfap2c-mice represent a model system to study liver failure. In intestine, Tcfap2c induced cellular hyperplasia and suppressed terminal differentiation indicating that Tcfap2c serves as a repressor of differentiation and inducer of proliferation. This might be achieved by the Tcfap2c mediated activation of Sox9 known to be expressed in intestinal and hepatic stem/progenitor cell populations.

Phage display and molecular imaging: expanding fields of vision in living subjects

In vivo molecular imaging enables non-invasive visualization of biological processes within living subjects, and holds great promise for diagnosis and monitoring of disease. The ability to create new agents that bind to molecular targets and deliver imaging probes to desired locations in the body is critically important to further advance this field. To address this need, phage display, an established technology for the discovery and development of novel binding agents, is increasingly becoming a key component of many molecular imaging research programs. This review discusses the expanding role played by phage display in the field of molecular imaging with a focus on in vivo applications. Furthermore, new methodological advances in phage display that can be directly applied to the discovery and development of molecular imaging agents are described. Various phage library selection strategies are summarized and compared, including selections against purified target, intact cells, and ex vivo tissue, plus in vivo homing strategies. An outline of the process for converting polypeptides obtained from phage display library selections into successful in vivo imaging agents is provided, including strategies to optimize in vivo performance. Additionally, the use of phage particles as imaging agents is also described. In the latter part of the review, a survey of phage-derived in vivo imaging agents is presented, and important recent examples are highlighted. Other imaging applications are also discussed, such as the development of peptide tags for site-specific protein labeling and the use of phage as delivery agents for reporter genes. The review concludes with a discussion of how phage display technology will continue to impact both basic science and clinical applications in the field of molecular imaging.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Bioassembled layered silicate-metal nanoparticle hybrids

Here we report on the bioenabled assembly of layered nanohybrids using peptides identified with regard to their affinity to the nanoparticle surface. A dodecamer peptide termed M1, determined from a phage peptide display library, was found to bind to the surface of a layered aluminosilicate (montmorillonite, MMT). Fusion of a metal binding domain to the M1 peptide or the M1 peptide by itself was able to direct the growth of metal nanoparticles, such as gold and cobalt-platinum, respectively, on the MMT. This method of producing hybrid nanoclay materials will have utility in catalytic, optical, biomedical, and composite materials applications.

Identification of structural mechanisms of HIV-1 protease specificity using computational peptide docking: implications for drug resistance

Drug-resistant mutations (DRMs) in HIV-1 protease are a major challenge to antiretroviral therapy. Protease-substrate interactions that are determined to be critical for native selectivity could serve as robust targets for drug design that are immune to DRMs. In order to identify the structural mechanisms of selectivity, we developed a peptide-docking algorithm to predict the atomic structure of protease-substrate complexes and applied it to a large and diverse set of cleavable and noncleavable peptides. Cleavable peptides showed significantly lower energies of interaction than noncleavable peptides with six protease active-site residues playing the most significant role in discrimination. Surprisingly, all six residues correspond to sequence positions associated with drug resistance mutations, demonstrating that the very residues that are responsible for native substrate specificity in HIV-1 protease are altered during its evolution to drug resistance, suggesting that drug resistance and substrate selectivity may share common mechanisms.

Computer-aided design of functional protein interactions

Predictive methods for the computational design of proteins search for amino acid sequences adopting desired structures that perform specific functions. Typically, design of 'function' is formulated as engineering new and altered binding activities into proteins. Progress in the design of functional protein-protein interactions is directed toward engineering proteins to precisely control biological processes by specifically recognizing desired interaction partners while avoiding competitors. The field is aiming for strategies to harness recent advances in high-resolution computational modeling-particularly those exploiting protein conformational variability-to engineer new functions and incorporate many functional requirements simultaneously.

Peptidic modulators of protein-protein interactions: progress and challenges in computational design

With the decline in productivity of drug-development efforts, novel approaches to rational drug design are being introduced and developed. Naturally occurring and synthetic peptides are emerging as novel promising compounds that can specifically and efficiently modulate signaling pathways in vitro and in vivo. We describe sequence-based approaches that use peptides to mimic proteins in order to inhibit the interaction of the mimicked protein with its partners. We then discuss a structure-based approach, in which protein-peptide complex structures are used to rationally design and optimize peptidic inhibitors. We survey flexible peptide docking techniques and discuss current challenges and future directions in the rational design of peptidic inhibitors.

Targeted delivery of nucleic-acid-based therapeutics to the pulmonary circulation

Targeted delivery of functional nucleic acids (genes and oligonucleotides) to pulmonary endothelium may become a novel therapy for the treatment of various types of lung diseases. It may also provide a new research tool to study the functions and regulation of novel genes in pulmonary endothelium. Its success is largely dependent on the development of a vehicle that is capable of efficient pulmonary delivery with minimal toxicity. This review summarizes the recent progress that has been made in our laboratory along these research directions. Factors that affect pulmonary nucleic acids delivery are also discussed.

MedusaScore: an accurate force field-based scoring function for virtual drug screening

Virtual screening is becoming an important tool for drug discovery. However, the application of virtual screening has been limited by the lack of accurate scoring functions. Here, we present a novel scoring function, MedusaScore, for evaluating protein-ligand binding. MedusaScore is based on models of physical interactions that include van der Waals, solvation, and hydrogen bonding energies. To ensure the best transferability of the scoring function, we do not use any protein-ligand experimental data for parameter training. We then test the MedusaScore for docking decoy recognition and binding affinity prediction and find superior performance compared to other widely used scoring functions. Statistical analysis indicates that one source of inaccuracy of MedusaScore may arise from the unaccounted entropic loss upon ligand binding, which suggests avenues of approach for further MedusaScore improvement.