Proteochemometrics analysis of substrate interactions with dengue virus NS3 proteases

Proteomic investigations of dengue virus infection: key discoveries over the last 10 years

INTRODUCTION

Dengue virus (DENV) infection remains one of the most significant infectious diseases in humans. Several efforts have been made to address its molecular mechanisms. Over the last 10 years, proteomics has been widely applied to investigate various aspects of DENV infection.

AREAS COVERED

In this review, we briefly introduce common proteomics approaches using various mass spectrometric modalities followed by summarizing all the discoveries obtained from proteomic investigations of DENV infection over the last 10 years. These include the data on DENV-vector interactions and host responses to address the DENV biology and disease mechanisms. Moreover, applications of proteomics to disease prevention, diagnosis, vaccine design, development of anti-DENV agents and other new treatment strategies are discussed.

EXPERT OPINION

Despite efforts on disease prevention, DENV infection is still a significant global healthcare burden that affects the general population. As summarized herein, proteomic technologies with high-throughput capabilities have provided more in-depth details of protein dynamics during DENV infection. More extensive applications of proteomics and other powerful research tools would provide a promise to better cope and prevent this mosquito-borne infectious disease.

Prediction of viral protease inhibitors using proteochemometrics approach

Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsin-like proteases and their ligands. We have developed an original protocol allowing us to collect a comprehensive set of 15 protein sequences and more than 9000 ligands from the ChEMBL database. The N-grams were derived from the 3D-based alignment, accurately superposing ligand-binding regions. In testing the ligand set in SAR mode with MNA descriptors, an accuracy above 0.95 was determined that shows the perspective of the antiviral drug search in virtual chemical libraries. The effective PCM models were built with the TLMNA descriptor. The strong validation procedure with pair exclusion simulated the prediction of interactions between the new ligands and new targets, resulting in accuracy estimation up to 0.89. The PCM approach shows slightly lower accuracy caused by more uncertainty compared with SAR, but it overcomes the problem of data incompleteness.

The Inhibition of NS2B/NS3 Protease: A New Therapeutic Opportunity to Treat Dengue and Zika Virus Infection

In the global pandemic scenario, dengue and zika viruses (DENV and ZIKV, respectively), both mosquito-borne members of the flaviviridae family, represent a serious health problem, and considering the absence of specific antiviral drugs and available vaccines, there is a dire need to identify new targets to treat these types of viral infections. Within this drug discovery process, the protease NS2B/NS3 is considered the primary target for the development of novel anti-flavivirus drugs. The NS2B/NS3 is a serine protease that has a dual function both in the viral replication process and in the elusion of the innate immunity. To date, two main classes of NS2B/NS3 of DENV and ZIKV protease inhibitors have been discovered: those that bind to the orthosteric site and those that act at the allosteric site. Therefore, this perspective article aims to discuss the main features of the use of the most potent NS2B/NS3 inhibitors and their impact at the social level.

In vitro and in vivo characterization of erythrosin B and derivatives against Zika virus

Zika virus (ZIKV) causes significant human diseases without specific therapy. Previously we found erythrosin B, an FDA-approved food additive, inhibited viral NS2B−NS3 interactions, leading to inhibition of ZIKV infection in cell culture. In this study, we performed pharmacokinetic and in vivo studies to demonstrate the efficacy of erythrosin B against ZIKV in 3D mini-brain organoid and mouse models. Our results showed that erythrosin B is very effective in abolishing ZIKV replication in the 3D organoid model. Although pharmacokinetics studies indicated that erythrosin B had a low absorption profile, mice challenged by a lethal dose of ZIKV showed a significantly improved survival rate upon oral administration of erythrosin B, compared to vehicle control. Limited structure−activity relationship studies indicated that most analogs of erythrosin B with modifications on the xanthene ring led to loss or reduction of inhibitory activities towards viral NS2B−NS3 interactions, protease activity and antiviral efficacy. In contrast, introducing chlorine substitutions on the isobenzofuran ring led to slightly increased activities, suggesting that the isobenzofuran ring is well tolerated for modifications. Cytotoxicity studies indicated that all derivatives are nontoxic to human cells. Overall, our studies demonstrated erythrosin B is an effective antiviral against ZIKV both in vitro and in vivo.

A Critical Observation on the Design and Development of Reported Peptide Inhibitors of DENV NS2B-NS3 Protease in the Last Two Decades

Dengue is one of the neglected tropical diseases, which remains a reason for concern as cases seem to rise every year. The failure of the only dengue vaccine, Dengvaxia®, has made the problem more severe and humanity has no immediate respite from this global burden. Dengue virus (DENV) NS2B-NS3 protease is an attractive target partly due to its role in polyprotein processing. Also, since it is among the most conserved domains in the viral genome, it could produce a broad scope of opportunities toward antiviral drug discovery in general. This review has made a detailed analysis of each case of the design and development of peptide inhibitors against DENV NS2B-NS3 protease in the last two decades. Also, we have discussed the reasons attributed to their inhibitory activity, and wherever possible, we have highlighted the concerns raised, challenges met, and suggestions to improve the inhibitory activity. Thus, we attempt to take the readers through the designing and development of reported peptide inhibitors and gain insight from these developments, which could further contribute toward strategizing the designing and development of peptide inhibitors of DENV protease with improved properties in the coming future.

Methylene blue is a potent and broad-spectrum inhibitor against Zika virus in vitro and in vivo

Many flaviviruses including the Dengue virus (DENV), Zika virus (ZIKV), West Nile virus, Yellow Fever virus, and Japanese encephalitis virus are significant human pathogens, unfortunately without any specific therapy. Here, we demonstrate that methylene blue, an FDA-approved drug, is a broad-spectrum and potent antiviral against Zika virus and Dengue virus both in vitro and in vivo. We found that methylene blue can considerably inhibit the interactions between viral protease NS3 and its NS2B co-factor, inhibit viral protease activity, inhibit viral growth, protect 3D mini-brain organoids from ZIKV infection, and reduce viremia in a mouse model. Mechanistic studies confirmed that methylene blue works in both entry and post entry steps, reduces virus production in replicon cells and inhibited production of processed NS3 protein. Overall, we have shown that methylene blue is a potent antiviral for management of flavivirus infections, particularly for Zika virus. As an FDA-approved drug, methylene blue is well-tolerated for human use. Therefore, methylene blue represents a promising and easily developed therapy for management of infections by ZIKV and other flaviviruses.

Discovery of Dengue Virus Inhibitors

To date, there is still no approved anti-dengue agent to treat dengue infection in the market. Although the only licensed dengue vaccine, Dengvaxia is available, its protective efficacy against serotypes 1 and 2 of dengue virus was reported to be lower than serotypes 3 and 4. Moreover, according to WHO, the risk of being hospitalized and having severe dengue increased in seronegative individuals after they received Dengvaxia vaccination. Nevertheless, various studies had been carried out in search of dengue virus inhibitors. These studies focused on the structural (C, prM, E) and non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5) of dengue virus as well as host factors as drug targets. Hence, this article provides an overall up-to-date review of the discovery of dengue virus inhibitors that are only targeting the structural and non-structural viral proteins as drug targets.

New insights into the selective inhibition of the β-carbonic anhydrases of pathogenic bacteria Burkholderia pseudomallei and Francisella tularensis: a proteochemometrics study

Nowadays, antibiotic resistance has turned into one of the most important worldwide health problems. Biological end point of critical enzymes induced by potent inhibitors is recently being considered as a highly effective and popular strategy to defeat antibiotic-resistant pathogens. For instance, the simple but critical β-carbonic anhydrase has recently been in the center of attention for anti-pathogen drug discoveries. However, no β-carbonic anhydrase selective inhibitor has yet been developed. Available β-carbonic anhydrase inhibitors are also highly potent with regard to human carbonic anhydrases, leading to severe inevitable side effects in case of usage. Therefore, developing novel inhibitors with high selectivity against pathogenic β-carbonic anhydrases is of great essence. Herein, for the first time, we have conducted a proteochemometric study to explore the structural and the chemical aspects of the interactions governed by bacterial β-carbonic anhydrases and their inhibitors. We have found valuable information which can lead to designing novel inhibitors with better selectivity for bacterial β-carbonic anhydrases.

Probing the chemical interaction space governed by 4-aminosubstituted benzenesulfonamides and carbonic anhydrase isoforms

Isoform diversity, critical physiological roles and involvement in major diseases/disorders such as glaucoma, epilepsy, Alzheimer's disease, obesity, and cancers have made carbonic anhydrase (CA), one of the most interesting case studies in the field of computer aided drug design. Since applying non-selective inhibitors can result in major side effects, there have been considerable efforts so far to achieve selective inhibitors for different isoforms of CA. Using proteochemometrics approach, the chemical interaction space governed by a group of 4-amino-substituted benzenesulfonamides and human CAs has been explored in the present study. Several validation methods have been utilized to assess the validity, robustness and predictivity power of the proposed proteochemometric model. Our model has offered major structural information that can be applied to design new selective inhibitors for distinct isoforms of CA. To prove the applicability of the proposed model, new compounds have been designed based on the offered discriminative structural features.

Finding the molecular scaffold of nuclear receptor inhibitors through high-throughput screening based on proteochemometric modelling

Nuclear receptors (NR) are a class of proteins that are responsible for sensing steroid and thyroid hormones and certain other molecules. In that case, NR have the ability to regulate the expression of specific genes and associated with various diseases, which make it essential drug targets. Approaches which can predict the inhibition ability of compounds for different NR target should be particularly helpful for drug development. In this study, proteochemometric modelling was introduced to analysis the bioactivity between chemical compounds and NR targets. Results illustrated the ability of our PCM model for high-throughput NR-inhibitor screening after evaluated on both internal (AUC > 0.870) and external (AUC > 0.746) validation set. Moreover, in-silico predicted bioactive compounds were clustered according to structure similarity and a series of representative molecular scaffolds can be derived for five major NR targets. Through scaffolds analysis, those essential bioactive scaffolds of different NR target can be detected and compared. Generally, the methods and molecular scaffolds proposed in this article can not only help the screening of potential therapeutic NR-inhibitors but also able to guide the future NR-related drug discovery.

A conformational switch high-throughput screening assay and allosteric inhibition of the flavivirus NS2B-NS3 protease

The flavivirus genome encodes a single polyprotein precursor requiring multiple cleavages by host and viral proteases in order to produce the individual proteins that constitute an infectious virion. Previous studies have revealed that the NS2B cofactor of the viral NS2B-NS3 heterocomplex protease displays a conformational dynamic between active and inactive states. Here, we developed a conformational switch assay based on split luciferase complementation (SLC) to monitor the conformational change of NS2B and to characterize candidate allosteric inhibitors. Binding of an active-site inhibitor to the protease resulted in a conformational change of NS2B and led to significant SLC enhancement. Mutagenesis of key residues at an allosteric site abolished this induced conformational change and SLC enhancement. We also performed a virtual screen of NCI library compounds to identify allosteric inhibitors, followed by in vitro biochemical screening of the resultant candidates. Only three of these compounds, NSC135618, 260594, and 146771, significantly inhibited the protease of Dengue virus 2 (DENV2) in vitro, with IC₅₀ values of 1.8 μM, 11.4 μM, and 4.8 μM, respectively. Among the three compounds, only NSC135618 significantly suppressed the SLC enhancement triggered by binding of active-site inhibitor in a dose-dependent manner, indicating that it inhibits the conformational change of NS2B. Results from virus titer reduction assays revealed that NSC135618 is a broad spectrum flavivirus protease inhibitor, and can significantly reduce titers of DENV2, Zika virus (ZIKV), West Nile virus (WNV), and Yellow fever virus (YFV) on A549 cells in vivo, with EC₅₀ values in low micromolar range. In contrast, the cytotoxicity of NSC135618 is only moderate with CC₅₀ of 48.8 μM on A549 cells. Moreover, NSC135618 inhibited ZIKV in human placental and neural progenitor cells relevant to ZIKV pathogenesis. Results from binding, kinetics, Western blot, mass spectrometry and mutagenesis experiments unambiguously demonstrated an allosteric mechanism for inhibition of the viral protease by NSC135618.

Exploring the origin of phosphodiesterase inhibition via proteochemometric modeling

A proteochemometric study of a set of phosphodiesterase 4B and 4D inhibitors sheds light on the origin of their inhibition and selectivities.

Proteochemometric Modeling of the Interaction Space of Carbonic Anhydrase and its Inhibitors: An Assessment of Structure-based and Sequence-based Descriptors

Due to its physiological and clinical roles, carbonic anhydrase (CA) is one of the most interesting case studies. There are different classes of CAinhibitors including sulfonamides, polyamines, coumarins and dithiocarbamates (DTCs). However, many of them hardly act as a selective inhibitor against a specific isoform. Therefore, finding highly selective inhibitors for different isoforms of CA is still an ongoing project. Proteochemometrics modeling (PCM) is able to model the bioactivity of multiple compounds against different isoforms of a protein. Therefore, it would be extremely applicable when investigating the selectivity of different ligands towards different receptors. Given the facts, we applied PCM to investigate the interaction space and structural properties that lead to the selective inhibition of CA isoforms by some dithiocarbamates. Our models have provided interesting structural information that can be considered to design compounds capable of inhibiting different isoforms of CA in an improved selective manner. Validity and predictivity of the models were confirmed by both internal and external validation methods; while Y-scrambling approach was applied to assess the robustness of the models. To prove the reliability and the applicability of our findings, we showed how ligands-receptors selectivity can be affected by removing any of these critical findings from the modeling process.

Use of machine learning approaches for novel drug discovery

INTRODUCTION

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.

AREAS COVERED

This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.

EXPERT OPINION

Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?

Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand-target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope

As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.

Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features

Serine proteases, implicated in important physiological functions, have a high intra-family similarity, which leads to unwanted off-target effects of inhibitors with insufficient selectivity. However, the availability of sequence and structure data has now made it possible to develop approaches to design pharmacological agents that can discriminate successfully between their related binding sites. In this study, we have quantified the relationship between 12,625 distinct protease inhibitors and their bioactivity against 67 targets of the serine protease family (20,213 data points) in an integrative manner, using proteochemometric modelling (PCM). The benchmarking of 21 different target descriptors motivated the usage of specific binding pocket amino acid descriptors, which helped in the identification of active site residues and selective compound chemotypes affecting compound affinity and selectivity. PCM models performed better than alternative approaches (models trained using exclusively compound descriptors on all available data, QSAR) employed for comparison with R(2)/RMSE values of 0.64 ± 0.23/0.66 ± 0.20 vs. 0.35 ± 0.27/1.05 ± 0.27 log units, respectively. Moreover, the interpretation of the PCM model singled out various chemical substructures responsible for bioactivity and selectivity towards particular proteases (thrombin, trypsin and coagulation factor 10) in agreement with the literature. For instance, absence of a tertiary sulphonamide was identified to be responsible for decreased selective activity (by on average 0.27 ± 0.65 pChEMBL units) on FA10. Among the binding pocket residues, the amino acids (arginine, leucine and tyrosine) at positions 35, 39, 60, 93, 140 and 207 were observed as key contributing residues for selective affinity on these three targets.

Peptide Binding Prediction to Five Most Frequent HLA-DQ Proteins - a Proteochemometric Approach

Major histocompatibility complex (MHC) proteins class II, are glycoproteins binding within the cell to short peptides with foreign origin, called epitopes, and present them at the cell surface for inspection by T-cells. Apart from presenting foreign antigens, they are able to present also common self-antigens and trigger autoimmune diseases as coeliac disease and diabetes mellitus type 1. The MHC proteins are extremely polymorphic. The polymorphism is located mainly in the peptide binding site. In the present study, we apply a proteochemometric approach to derive a model for prediction of peptide binding to human MHC class II proteins from locus HLA-DQ. Proteochemometrics was applied on 2624 peptides binding to five most frequent HLA-DQ proteins. The sequences of peptides and proteins were described by three z-descriptors relating to hydrophobicity, steric effects and polarity of amino acids. Cross-terms accounting for the protein-peptide interactions also were included. The derived model was validated by external test set of 660 peptides and showed rpred (2) =0.808, AUC=0.965, 92.5 % accuracy at threshold of pIC50 =5.3 and average sensitivity of 83 % among the top 10 % best predicted nonamers. The model is implemented in the server for MHC binding prediction EpiTOP and is freely available at http://www.ddg-pharmfac.net/epitop.

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.

Proteochemometric modeling in a Bayesian framework

Proteochemometrics (PCM) is an approach for bioactivity predictive modeling which models the relationship between protein and chemical information. Gaussian Processes (GP), based on Bayesian inference, provide the most objective estimation of the uncertainty of the predictions, thus permitting the evaluation of the applicability domain (AD) of the model. Furthermore, the experimental error on bioactivity measurements can be used as input for this probabilistic model.

In this study, we apply GP implemented with a panel of kernels on three various (and multispecies) PCM datasets. The first dataset consisted of information from 8 human and rat adenosine receptors with 10,999 small molecule ligands and their binding affinity. The second consisted of the catalytic activity of four dengue virus NS3 proteases on 56 small peptides. Finally, we have gathered bioactivity information of small molecule ligands on 91 aminergic GPCRs from 9 different species, leading to a dataset of 24,593 datapoints with a matrix completeness of only 2.43%.

GP models trained on these datasets are statistically sound, at the same level of statistical significance as Support Vector Machines (SVM), with R02 values on the external dataset ranging from 0.68 to 0.92, and RMSEP values close to the experimental error. Furthermore, the best GP models obtained with the normalized polynomial and radial kernels provide intervals of confidence for the predictions in agreement with the cumulative Gaussian distribution. GP models were also interpreted on the basis of individual targets and of ligand descriptors. In the dengue dataset, the model interpretation in terms of the amino-acid positions in the tetra-peptide ligands gave biologically meaningful results.

Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling

Resistance to pesticides is an increasing problem in agriculture. Despite practices such as phased use and cycling of ‘orthogonally resistant’ agents, resistance remains a major risk to national and global food security. To combat this problem, there is a need for both new approaches for pesticide design, as well as for novel chemical entities themselves. As summarized in this opinion article, a technique termed ‘proteochemometric modelling’ (PCM), from the field of chemoinformatics, could aid in the quantification and prediction of resistance that acts via point mutations in the target proteins of an agent. The technique combines information from both the chemical and biological domain to generate bioactivity models across large numbers of ligands as well as protein targets. PCM has previously been validated in prospective, experimental work in the medicinal chemistry area, and it draws on the growing amount of bioactivity information available in the public domain. Here, two potential applications of proteochemometric modelling to agrochemical data are described, based on previously published examples from the medicinal chemistry literature.

A straightforward experimental approach to expression, purification, refolding, and enzymatic analysis of recombinant dengue virus NS2B(H)-NS3pro protease

Dengue virus threatens around 2.5 billion people worldwide; about 50 million become infected every year, and yet no vaccine or drug is available for prevention and/or treatment. The flaviviral NS2B-NS3pro complex is indispensable for flaviviral replication and is considered to be an important drug target. The aim of this study was to develop a simple and generally applicable experimental strategy to construct, purify, and assay a highly active recombinant NS2B(H)-NS3pro complex that would be useful for high-throughput screening of potential inhibitors. The sequence of NS2B(H)-NS3pro was generated by overlap extension PCR (SOE-PCR) and cloned into the pTrcHisA vector. Hexahistidine-tagged NS2B(H)-NS3pro complex was expressed in E. coli predominantly as insoluble protein and purified to >95% purity by single-step immobilized metal affinity chromatography. SDS-PAGE followed by immunoblotting of the purified enzyme demonstrated the presence of the NS2B(H)-NS3pro precursor and its autocleavage products, NS3pro and NS2B(H), as 37, 21, and 10 kDa bands, respectively. Kinetic parameters, Km, kcat, and kcat/Km for the fluorophore-linked protease model substrate Ac-nKRR-amc were obtained using inner-filter effect correction. The kinetic parameters Km, kcat, and kcat/Km for Ac-nKRR-amc substrate were 100 µM, 0.112 s(-1), and 1120 M(-1)·s(-1), respectively. A simplified procedure for the cloning, overexpression, and purification of the NS2B(H)-NS3pro complex was applied, and a highly active recombinant NS2B(H)-NS3pro complex was obtained that could be useful for the design of high-throughput assays aimed at flaviviral inhibitor discovery.

Visually interpretable models of kinase selectivity related features derived from field-based proteochemometrics

Achieving selectivity for small organic molecules toward biological targets is a main focus of drug discovery but has been proven difficult, for example, for kinases because of the high similarity of their ATP binding pockets. To support the design of more selective inhibitors with fewer side effects or with altered target profiles for improved efficacy, we developed a method combining ligand- and receptor-based information. Conventional QSAR models enable one to study the interactions of multiple ligands toward a single protein target, but in order to understand the interactions between multiple ligands and multiple proteins, we have used proteochemometrics, a multivariate statistics method that aims to combine and correlate both ligand and protein descriptions with affinity to receptors. The superimposed binding sites of 50 unique kinases were described by molecular interaction fields derived from knowledge-based potentials and Schrödinger's WaterMap software. Eighty ligands were described by Mold(2), Open Babel, and Volsurf descriptors. Partial least-squares regression including cross-terms, which describe the selectivity, was used for model building. This combination of methods allows interpretation and easy visualization of the models within the context of ligand binding pockets, which can be translated readily into the design of novel inhibitors.

Design and evaluation of substrate-based octapeptide and non substrate-based tetrapeptide inhibitors of dengue virus NS2B-NS3 proteases

A series of 45 peptide inhibitors was designed, synthesized, and evaluated against the NS2B-NS3 proteases of the four subtypes of dengue virus, DEN-1-4. The design was based on proteochemometric models for Michaelis (Km) and cleavage rate constants (kcat) of protease substrates. This led first to octapeptides showing submicromolar or low micromolar inhibitory activities on the four proteases. Stepwise removal of cationic substrate non-prime side residues and variations in the prime side sequence resulted finally in an uncharged tetrapeptide, WYCW-NH2, with inhibitory Ki values of 4.2, 4.8, 24.4, and 11.2 μM for the DEN-1-4 proteases, respectively. Analysis of the inhibition data by proteochemometric modeling suggested the possibility for different binding poses of the shortened peptides compared to the octapeptides, which was supported by results of docking of WYCW-NH2 into the X-ray structure of DEN-3 protease.

Identification of Candidate Vaccine Antigens In Silico

The identification of immunogenic whole-protein antigens is fundamental to the successful discovery of candidate subunit vaccines and their rapid, effective, and efficient transformation into clinically useful, commercially successful vaccine formulations. In the wider context of the experimental discovery of vaccine antigens, with particular reference to reverse vaccinology, this chapter adumbrates the principal computational approaches currently deployed in the hunt for novel antigens: genome-level prediction of antigens, antigen identification through the use of protein sequence alignment-based approaches, antigen detection through the use of subcellular location prediction, and the use of alignment-independent approaches to antigen discovery. Reference is also made to the recent emergence of various expert systems for protein antigen identification.

Fluorimetric and HPLC-based dengue virus protease assays using a FRET substrate

The number of dengue virus infections is increasing and the dengue NS2B-NS3 protease is considered a promising target for the development of antiviral therapies. Therefore, reliable and fast screening systems are needed for the discovery of new lead structures. In this chapter, we describe two dengue virus protease assays based on an internally quenched, high-affinity Förster resonance energy transfer (FRET) substrate (Km = 105 μM). A fluorimetric assay using a microtiter fluorescence plate reader can be used for high-throughput screening of a large number of compounds. Alternatively, an HPLC-based assay with fluorescence detection can be applied to confirm the compound hits and to avoid false-positive results that may arise due to the inner filter effect of some compounds.

Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

Proteochemometric modeling is founded on the principles of QSAR but is able to benefit from additional information in model training due to the inclusion of target information.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

Peptide binding to the HLA-DRB1 supertype: a proteochemometrics analysis

A proteochemometrics approach was applied to a set of 2666 peptides binding to 12 HLA-DRB1 proteins. Sequences of both peptide and protein were described using three z-descriptors. Cross terms accounting for adjacent positions and for every second position in the peptides were included in the models, as well as cross terms for peptide/protein interactions. Models were derived based on combinations of different blocks of variables. These models had moderate goodness of fit, as expressed by r2, which ranged from 0.685 to 0.732; and good cross-validated predictive ability, as expressed by q2, which varied from 0.678 to 0.719. The external predictive ability was tested using a set of 356 HLA-DRB1 binders, which showed an r2(pred) in the range 0.364-0.530. Peptide and protein positions involved in the interactions were analyzed in terms of hydrophobicity, steric bulk and polarity.