HotLig: A Molecular Surface-Directed Approach to Scoring Protein–Ligand Interactions

Conformational alteration in glycan induces phospholipase Cβ1 activation and angiogenesis

BACKGROUND

In endothelial cells, phospholipase C (PLC) β1-activated Ca²⁺ is a crucial second messenger for the signaling pathways governing angiogenesis. PLCβ1 is inactivated by complexing with an intracellular protein called translin-associated factor X (TRAX). This study demonstrates specific interactions between Globo H ceramide (GHCer) and TRAX, which highlight a new angiogenic control through PLCβ1 activation.

METHODS

Globo-series glycosphingolipids (GSLs), including GHCer and stage-specific embryonic antigen-3 ceramide (SSEA3Cer), were analyzed using enzyme-linked immunosorbent assay (ELISA) and Biacore for their binding with TRAX. Angiogenic activities of GSLs in human umbilical vein endothelial cells (HUVECs) were evaluated. Molecular dynamics (MD) simulation was used to study conformations of GSLs and their molecular interactions with TRAX. Fluorescence resonance energy transfer (FRET) analysis of HUVECs by confocal microscopy was used to validate the release of PLCβ1 from TRAX. Furthermore, the in vivo angiogenic activity of extracellular vesicles (EVs) containing GHCer was confirmed using subcutaneous Matrigel plug assay in mice.

RESULTS

The results of ELISA and Biacore analysis showed a stable complex between recombinant TRAX and synthetic GHCer with KD of 40.9 nM. In contrast, SSEA3Cer lacking a fucose residue of GHCer at the terminal showed ~ 1000-fold decrease in the binding affinity. These results were consistent with their angiogenic activities in HUVECs. The MD simulation indicated that TRAX interacted with the glycan moiety of GHCer at amino acid Q223, Q219, L142, S141, and E216. At equilibrium the stable complex maintained 4.6 ± 1.3 H-bonds. TRAX containing double mutations with Q223A and Q219A lost its ability to interact with GHCer in both MD simulation and Biacore assays. Removal of the terminal fucose from GHCer to become SSEA3Cer resulted in decreased H-bonding to 1.2 ± 1.0 by the MD simulation. Such specific H-bonding was due to the conformational alteration in the whole glycan which was affected by the presence or absence of the fucose moiety. In addition, ELISA, Biacore, and in-cell FRET assays confirmed the competition between GHCer and PLCβ1 for binding to TRAX. Furthermore, the Matrigel plug assay showed robust vessel formation in the plug containing tumor-secreted EVs or synthetic GHCer, but not in the plug with SSEA3Cer. The FRET analysis also indicated the disruption of colocalization of TRAX and PLCβ1 in cells by GHCer derived from EVs.

CONCLUSIONS

Overall, the fucose residue in GHCer dictated the glycan conformation for its complexing with TRAX to release TRAX-sequestered PLCβ1, leading to Ca²⁺ mobilization in endothelial cells and enhancing angiogenesis in tumor microenvironments.

KORP-PL: a coarse-grained knowledge-based scoring function for protein-ligand interactions

MOTIVATION

Despite the progress made in studying protein-ligand interactions and the widespread application of docking and affinity prediction tools, improving their precision and efficiency still remains a challenge. Computational approaches based on the scoring of docking conformations with statistical potentials constitute a popular alternative to more accurate but costly physics-based thermodynamic sampling methods. In this context, a minimalist and fast sidechain-free knowledge-based potential with a high docking and screening power can be very useful when screening a big number of putative docking conformations.

RESULTS

Here, we present a novel coarse-grained potential defined by a 3D joint probability distribution function that only depends on the pairwise orientation and position between protein backbone and ligand atoms. Despite its extreme simplicity, our approach yields very competitive results with the state-of-the-art scoring functions, especially in docking and screening tasks. For example, we observed a twofold improvement in the median 5% enrichment factor on the DUD-E benchmark compared to Autodock Vina results. Moreover, our results prove that a coarse sidechain-free potential is sufficient for a very successful docking pose prediction.

AVAILABILITYAND IMPLEMENTATION

The standalone version of KORP-PL with the corresponding tests and benchmarks are available at https://team.inria.fr/nano-d/korp-pl/ and https://chaconlab.org/modeling/korp-pl.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dissecting the conformation of glycans and their interactions with proteins

The use of in silico strategies to develop the structural basis for a rational optimization of glycan-protein interactions remains a great challenge. This problem derives, in part, from the lack of technologies to quantitatively and qualitatively assess the complex assembling between a glycan and the targeted protein molecule. Since there is an unmet need for developing new sugar-targeted therapeutics, many investigators are searching for technology platforms to elucidate various types of molecular interactions within glycan-protein complexes and aid in the development of glycan-targeted therapies. Here we discuss three important technology platforms commonly used in the assessment of the complex assembly of glycosylated biomolecules, such as glycoproteins or glycosphingolipids: Biacore analysis, molecular docking, and molecular dynamics simulations. We will also discuss the structural investigation of glycosylated biomolecules, including conformational changes of glycans and their impact on molecular interactions within the glycan-protein complex. For glycoproteins, secreted protein acidic and rich in cysteine (SPARC), which is associated with various lung disorders, such as chronic obstructive pulmonary disease (COPD) and lung cancer, will be taken as an example showing that the core fucosylation of N-glycan in SPARC regulates protein-binding affinity with extracellular matrix collagen. For glycosphingolipids (GSLs), Globo H ceramide, an important tumor-associated GSL which is being actively investigated as a target for new cancer immunotherapies, will be used to demonstrate how glycan structure plays a significant role in enhancing angiogenesis in tumor microenvironments.

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

Mechanisms Inspired Targeting Peptides

Peptides, as a large group of molecules, are composed of amino acid residues and can be divided into linear or cyclic peptides according to the structure. Over 13,000 molecules of natural peptides have been found and many of them have been well studied. In artificial peptide libraries, the number of peptide diversity could be up to 1 × 10¹³. Peptides have more complex structures and higher affinity to target proteins comparing with small molecular compounds. Recently, the development of targeting cancer immune checkpoint (CIP) inhibitors is having a very important role in tumor therapy. Peptides targeting ligands or receptors in CIP have been designed based on three-dimensional structures of target proteins or directly selected by random peptide libraries in biological display systems. Most of these targeting peptides work as inhibitors of protein-protein interaction and improve CD8+ cytotoxic T-lymphocyte (CTL) activation in the tumor microenvironment, for example, PKHB1, Ar5Y4 and TPP1. Peptides could be designed to regulate CIP protein degradation in vivo, such as PD-LYSO and PD-PALM. Besides its use in developing therapeutic drugs for targeting CIP, targeting peptides could be used in drug's targeted delivery and diagnosis in tumor immune therapy.

FAM129B, an antioxidative protein, reduces chemosensitivity by competing with Nrf2 for Keap1 binding

BACKGROUND

The transcription factor Nrf2 is a master regulator of antioxidant response. While Nrf2 activation may counter increasing oxidative stress in aging, its activation in cancer can promote cancer progression and metastasis, and confer resistance to chemotherapy and radiotherapy. Thus, Nrf2 has been considered as a key pharmacological target. Unfortunately, there are no specific Nrf2 inhibitors for therapeutic application. Moreover, high Nrf2 activity in many tumors without Keap1 or Nrf2 mutations suggests that alternative mechanisms of Nrf2 regulation exist.

METHODS

Interaction of FAM129B with Keap1 is demonstrated by immunofluorescence, colocalization, co-immunoprecipitation and mammalian two-hybrid assay. Antioxidative function of FAM129B is analyzed by measuring ROS levels with DCF/flow cytometry, Nrf2 activation using luciferase reporter assay and determination of downstream gene expression by qPCR and wester blotting. Impact of FAM129B on in vivo chemosensitivity is examined in mice bearing breast and colon cancer xenografts. The clinical relevance of FAM129B is assessed by qPCR in breast cancer samples and data mining of publicly available databases.

FINDINGS

We have demonstrated that FAM129B in cancer promotes Nrf2 activity by reducing its ubiquitination through competition with Nrf2 for Keap1 binding via its DLG and ETGE motifs. In addition, FAM129B reduces chemosensitivity by augmenting Nrf2 antioxidative signaling and confers poor prognosis in breast and lung cancer.

INTERPRETATION

These findings demonstrate the important role of FAM129B in Nrf2 activation and antioxidative response, and identify FMA129B as a potential therapeutic target. FUND: The Chang Gung Medical Foundation (Taiwan) and the Ministry of Science and Technology (Taiwan).

Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database

Background

Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.

Results

In this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. The method is based on a functional site and physicochemical interaction-annotated domain profile database, called fiDPD, which was built using protein domains found in the Protein Data Bank. This method was applied to 13 target proteins from the very recent Critical Assessment of Structure Prediction (CASP10/11), and our calculations gave a Matthews correlation coefficient (MCC) value of 0.66 for PFS prediction and an 80% recall in the prediction of the associated physicochemical interactions.

Conclusions

Our results show that, in addition to the PFSs, the physical interactions at these sites are also conserved in the evolution of proteins. This work provides a valuable sequence-based tool for rational drug design and side-effect assessment. The method is freely available and can be accessed at http://202.119.249.49.

Structure-based design for binding peptides in anti-cancer therapy

The conventional anticancer therapeutics usually lack cancer specificity, leading to damage of normal tissues that patients find hard to tolerate. Ideally, anticancer therapeutics carrying payloads of drugs equipped with cancer targeting peptides can act like "guided missiles" with the capacity of targeted delivery toward many types of cancers. Peptides are amenable for conjugation to nano drugs for functionalization, thereby improving drug delivery and cellular uptake in cancer-targeting therapies. Peptide drugs are often more difficult to design through molecular docking and in silico analysis than small molecules, because peptide structures are more flexible, possess intricate molecular conformations, and undergo complex interactions. In this review, the development and application of strategies for structure-based design of cancer-targeting peptides against GRP78 are discussed. This Review also covers topics related to peptide pharmacokinetics and targeting delivery, including molecular docking studies, features that provide advantages for in vivo use, and properties that influence the cancer-targeting ability. Some advanced technologies and special peptides that can overcome the pharmacokinetic challenges have also been included.

Human Leukocyte Antigen C*12:02:02 and Killer Immunoglobulin-Like Receptor 2DL5 are Distinctly Associated with Ankylosing Spondylitis in the Taiwanese

Human leukocyte antigen (HLA) class I ligands and Killer immunoglobulin-like receptors (KIRs) regulate the cytolytic activity of natural killer (NK) cells and certain T cells. We examined their genetic predisposition to disease susceptibility and clinical phenotypes in Taiwanese ankylosing spondylitis (AS) patients. KIR genotyping and Human Leucocyte Antigen C (HLA-C) sequencing were performed in 653 Taiwanese AS patients and 952 healthy controls. KIR genotype distributions and HLA-C allele frequencies were compared in patients and controls and among patients with and without HLA-B27 positivity, early age onset and spinal syndesmophytes. HLA-C alleles were functionally characterized using 3D structural modelling with peptide simulation. This study discovered that the HLA-C*12:02:02 allele (43.42% vs. 3.31%; p < 0.00001 odds ratio (OR), 16.88; 95% confidence intervals (CI): 11.27-25.28) confers a strong risk for Taiwanese AS development. The 3D modelling results identified four unique amino acid polymorphisms, Ala73, Trp156, Arg219 and Met304, that may affect the function of the HLA-C*12:02:02 allele. KIR2DL5 (p = 0.0047; pFDR = 0.0423) and the KIR Bx haplotype (p = 0.0000275) were protective against Taiwanese AS, while KIR 2DS4/1D (22 base pair truncated deletion; p = 0.0044; pFDR = 0.1998) appeared to be a risk factor for it. KIR2DL5 combined with the HLA-C1/C2 heterozygous genotype showed a protective effect (AS 5.97% vs. normal 11.66%; p = 0.002; pFDR = 0.0127, OR, 0.48 95% CI: 0.33-0.70); in contrast, KIR 2DS4/1D combined with the HLA-C1C1 homozygous genotype (AS 45.33% vs. normal 35.92%; p = 0.002; pFDR = 0.0127, OR, 1.48 95% CI: 1.21-1.81) represented a risk factor for AS development. Our data suggested that interactions between KIRs and their cognate HLA-C ligands may contribute to the pathogenesis of AS.

Data for peptide-binding assay with oriented immobilization of GRP78 in Biacore

To develop peptide-conjugated liposomes for cancer imaging and therapy, the label-free surface plasmon resonance (SPR) biosensor (Biacore™) is a practical and also preferred strategy to examine protein-peptide interaction. A new Biacore protocol with “oriented immobilization” for peptide-binding assay, which overcomes the drawbacks of conventional protocols, was presented in this data article. These results were complementary to the research article Wang at al., [1], which reported a series of new cancer-targeting peptides found with HotLig software (Wang et al., 2013) [2], and this newly developed Biacore protocol.

Structure-based optimization of GRP78-binding peptides that enhances efficacy in cancer imaging and therapy

It is more challenging to design peptide drugs than small molecules through molecular docking and in silico analysis. Here, we developed a structure-based approach with various computational and analytical techniques to optimize cancer-targeting peptides for molecular imaging and therapy. We first utilized a peptide-binding protein database to identify GRP78, a specific cancer cell-surface marker, as a target protein for the lead, L-peptide. Subsequently, we used homologous modeling and molecular docking to identify a peptide-binding domain within GRP78 and optimized a series of peptides with a new protein-ligand scoring program, HotLig. Binding of these peptides to GRP78 was confirmed using an oriented immobilization technique for the Biacore system. We further examined the ability of the peptides to target cancer cells through in vitro binding studies with cell lines and clinical cancer specimens, and in vivo tumor imaging and targeted chemotherapeutic studies. MicroSPECT/CT imaging revealed significantly greater uptake of (188)Re-liposomes linked to these peptides as compared with non-targeting (188)Re-liposomes. Conjugation with these peptides also significantly increased the therapeutic efficacy of Lipo-Dox. Notably, peptide-conjugated Lipo-Dox significantly reduced stem-cell subpopulation in xenografts of breast cancer. The structure-based optimization strategy for peptides described here may be useful for developing peptide drugs for cancer imaging and therapy.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets

There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on how accurate their predicted ranking is. However, despite their proven advantages, machine-learning scoring functions are still not widely applied. This seems to be due to insufficient understanding of their properties and the lack of user-friendly software implementing them. Here we present a study where the accuracy of AutoDock Vina, arguably the most commonly-used docking software, is strongly improved by following a machine learning approach. We also analyse the factors that are responsible for this improvement and their generality. Most importantly, with the help of a proposed benchmark, we demonstrate that this improvement will be larger as more data becomes available for training Random Forest models, as regression models implying additive functional forms do not improve with more training data. We discuss how the latter opens the door to new opportunities in scoring function development. In order to facilitate the translation of this advance to enhance structure-based molecular design, we provide software to directly re-score Vina-generated poses and thus strongly improve their predicted binding affinity. The software is available at http://istar.cse.cuhk.edu.hk/rf-score-3.tgz and http://crcm. marseille.inserm.fr/fileadmin/rf-score-3.tgz.

Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results

Our comparative assessment of scoring functions (CASF) benchmark is created to provide an objective evaluation of current scoring functions. The key idea of CASF is to compare the general performance of scoring functions on a diverse set of protein-ligand complexes. In order to avoid testing scoring functions in the context of molecular docking, the scoring process is separated from the docking (or sampling) process by using ensembles of ligand binding poses that are generated in prior. Here, we describe the technical methods and evaluation results of the latest CASF-2013 study. The PDBbind core set (version 2013) was employed as the primary test set in this study, which consists of 195 protein-ligand complexes with high-quality three-dimensional structures and reliable binding constants. A panel of 20 scoring functions, most of which are implemented in main-stream commercial software, were evaluated in terms of "scoring power" (binding affinity prediction), "ranking power" (relative ranking prediction), "docking power" (binding pose prediction), and "screening power" (discrimination of true binders from random molecules). Our results reveal that the performance of these scoring functions is generally more promising in the docking/screening power tests than in the scoring/ranking power tests. Top-ranked scoring functions in the scoring power test, such as X-Score(HM), ChemScore@SYBYL, ChemPLP@GOLD, and PLP@DS, are also top-ranked in the ranking power test. Top-ranked scoring functions in the docking power test, such as ChemPLP@GOLD, Chemscore@GOLD, GlidScore-SP, LigScore@DS, and PLP@DS, are also top-ranked in the screening power test. Our results obtained on the entire test set and its subsets suggest that the real challenge in protein-ligand binding affinity prediction lies in polar interactions and associated desolvation effect. Nonadditive features observed among high-affinity protein-ligand complexes also need attention.

Zhankuic acid A isolated from Taiwanofungus camphoratus is a novel selective TLR4/MD-2 antagonist with anti-inflammatory properties

TLR4, a membrane receptor that functions in complex with its accessory protein myeloid differentiation factor-2 (MD-2), is a therapeutic target for bacterial infections. Taiwanofungus camphoratus is highly valued as a medicinal mushroom for cancer, hypertension, and inflammation in traditional medicine. Zhankuic acid A (ZAA) is the major pharmacologically active compound of T. camphoratus. The mechanism of action of T. camphoratus or ZAA has not been fully elucidated. We analyzed the structure of human TLR4/MD-2 complex with ZAA by X-score and HotLig modeling approaches. Two Abs against MD-2 were used to verify the MD-2/ZAA interaction. The inflammation and survival of the mice pretreated with ZAA and injected with LPS were monitored. The modeling structure shows that ZAA binds the MD-2 hydrophobic pocket exclusively via specific molecular recognition; the contact interface is dominated by hydrophobic interactions. Binding of ZAA to MD-2 reduced Ab recognition to native MD-2, similar to the effect of LPS binding. Furthermore, ZAA significantly ameliorated LPS-induced endotoxemia and Salmonella-induced diarrhea in mice. Our results suggest that ZAA, which can compete with LPS for binding to MD-2 as a TLR4/MD-2 antagonist, may be a potential therapeutic agent for gram-negative bacterial infections.