Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease

Preparing for the next pandemic

New lead drugs to treat COVID-19 are beginning to emerge.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.

Uncovering New Drug Properties in Target-Based Drug-Drug Similarity Networks

Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.

Protein-Ligand Empirical Interaction Components for Virtual Screening

A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives. Experimentally derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.

Rational Drug Design Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools

BACKGROUND

While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs).

METHODS

We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader.

CONCLUSION

This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities.

Docking Methodologies and Recent Advances

Docking, a molecular modelling method, has wide applications in identification and optimization in modern drug discovery. This chapter addresses the recent advances in the docking methodologies like fragment docking, covalent docking, inverse docking, post processing, hybrid techniques, homology modeling etc. and its protocol like searching and scoring functions. Advances in scoring functions for e.g. consensus scoring, quantum mechanics methods, clustering and entropy based methods, fingerprinting, etc. are used to overcome the limitations of the commonly used force-field, empirical and knowledge based scoring functions. It will cover crucial necessities and different algorithms of docking and scoring. Further different aspects like protein flexibility, ligand sampling and flexibility, and the performance of scoring function will be discussed.

Cellular aspartyl proteases promote the unconventional secretion of biologically active HIV-1 matrix protein p17

The human immune deficiency virus type 1 (HIV-1) matrix protein p17 (p17), although devoid of a signal sequence, is released by infected cells and detected in blood and in different organs and tissues even in HIV-1-infected patients undergoing successful combined antiretroviral therapy (cART). Extracellularly, p17 deregulates the function of different cells involved in AIDS pathogenesis. The mechanism of p17 secretion, particularly during HIV-1 latency, still remains to be elucidated. A recent study showed that HIV-1-infected cells can produce Gag without spreading infection in a model of viral latency. Here we show that in Gag-expressing cells, secretion of biologically active p17 takes place at the plasma membrane and occurs following its interaction with phosphatidylinositol-(4,5)-bisphosphate and its subsequent cleavage from the precursor Gag (Pr55^Gag) operated by cellular aspartyl proteases. These enzymes operate a more complex Gag polypeptide proteolysis than the HIV-1 protease, thus hypothetically generating slightly truncated or elongated p17s in their C-terminus. A 17 C-terminal residues excised p17 was found to be structurally and functionally identical to the full-length p17 demonstrating that the final C-terminal region of p17 is irrelevant for the protein’s biological activity. These findings offer new opportunities to identify treatment strategies for inhibiting p17 release in the extracellular microenvironment.

Strategies for Antiviral Drug Discovery

The need for antiviral drugs is growing rapidly as more viral diseases are recognized. The methods used to discover these drugs have evolved considerably over the past 40 years and the overall process of discovery can be broken down into sub-processes which include lead generation, lead optimization and lead development. Various methods are now employed to ensure these processes are carried out efficiently. For lead generation, screening methodologies have developed to the extent where hundreds of thousands of compounds can be screened against a particular target. An alternative approach is to use the structures of enzyme substrates as a starting point for drug discovery. Much use is now made of X-ray crystallographic data of target–inhibitor complexes for the optimization of lead structures, and methods for preparing libraries of compounds to assist both generation and optimization of leads are welldeveloped. The methods used to predict and improve the pharmacokinetic properties of compounds are also changing rapidly. Finally, novel approaches to antiviral therapy using oligonucleotide-based compounds or modulating the host immune response are also being explored. This review discusses these approaches, provides examples of where their application has been successful and sets them against a historical background.

The X-ray Crystal Structure of the Sulfonated Azo Dye Congo Red, a Non-Peptidic Inhibitor of HIV-1 Protease which also Binds to Reverse Transcriptase and Amyloid Proteins

Congo Red is a sulfonated azo dye and widely used biological stain that has recently been the focus of intense interest because it has been shown to bind to proteins involved in viral recognition and replication. Congo Red also finds wide use as a histological stain for amyloid proteins of the type found in neurodegenerative conditions such as Alzheimer's disease, transmissible spongiform encephalopathies in cattle and mink, and scrapie in sheep. Congo Red has been demonstrated to protect normal prion protein from being converted to the protease-resistant form, an important step in the pathology of the so-called ‘slow viral’ diseases. The range of biological molecules to which Congo Red binds makes it an important lead compound in drug development, for example in the development of new anti-HIV and anti-Alzheimer's therapeutic agents. In this report we present the first high-resolution structure of Congo Red: the low-temperature (173 K) X-ray crystal structure determination of its calcium salt. Two conformations of the molecule are found in the same crystal structure, one in which the central biphenyl group assumes a twisted (25°) conformation, and one in which the biphenyl group is planar and is located on a crystallo-graphic inversion centre. In both conformations the sulfonate groups are oriented anti with respect to the long molecular axis and assume eclipsed conformations with respect to the naphthalene rings. A comparison is made with a published structure [Turned, W.G., and Finch, J.T. (1992) J Mol Biol 227: 1205-1223] in which Congo Red is bound to porcine insulin, this complex serving as a model for amyloid binding. The results illustrate the conformational flexibility possessed by the biphenyl spacer, which allows the hydrophobic portion of the molecule to assume an optimum fit in the hydrophobic binding pockets of target proteins. A model is presented for the binding of Congo Red to the HIV protease in which the sulfonate groups interact with the side-chains of arginine residues. This proposed binding mode is consistent with the observed binding for other sulfonated aromatic inhibitors such as Evans Blue.

Anti-Human Immunodeficiency Virus Activity, Bioavailability and Drug Resistance Profile of the Novel Proteinase Inhibitor MDL 74,695

MDL 74,695, a novel dipeptide-like compound containing the ‘difluorostatone type’ transition state mimic and a potent inhibitor of the human immunodeficiency virus (HIV) proteinase, was investigated for anti-HIV activity in vitro. The compound showed selective inhibition of both HIV-1 and HIV-2 in MT-4 cells. A potent antiviral effect against a range of clinical isolates of HIV-1 cultured in human peripheral blood mononuclear cells and primary monocytes was also demonstrated. The antiviral activity of MDL 74,695 against viruses resistant to a range of reverse transcriptase inhibitors was equivalent to the wild-type. In rats MDL 74,695 (30 mg kg−1) was 4.9% orally bioavailable and maintained levels above the in vitro 50% inhibitory concentration (IC50) for approximately 3 h. Viruses with reduced sensitivity to MDL 74,695 and saquinavir were selected in cell culture by continuous passage in increasing drug concentrations, and first appeared after 20 and 17 passages, respectively. Amino acid changes were identified at positions 48 (glycine to valine), 50 (isoleucine to valine) and 82 (valine to either isoleucine or alanine) in various combinations for MDL 74,695-resistant viruses. For saquinavir-resistant viruses changes were identified at positions 48 (glycine to valine) and 90 (leucine to methionine). Studies using MDL 74,695, saquinavir and a third proteinase inhibitor indinavir, indicated that virus selected in the presence of MDL 74,695, with amino acid exchanges at positions 48 and 82 showed cross-resistance to saquinavir. However, viruses selected in the presence of MDL 74,695 with amino acid exchanges at positions 50 and 82 showed no significant change in sensitivity to saquinavir. Likewise, viruses selected in the presence of saquinavir with amino acid exchanges at positions 48 and 90 remained sensitive to MDL 74,695. All viruses selected after growth in the presence of either MDL 74,695 or saquinavir showed little or no resistance to indinavir.

Identification of Small-Molecule Inhibitors against Meso-2, 6-Diaminopimelate Dehydrogenase from Porphyromonas gingivalis

Species-specific antimicrobial therapy has the potential to combat the increasing threat of antibiotic resistance and alteration of the human microbiome. We therefore set out to demonstrate the beginning of a pathogen-selective drug discovery method using the periodontal pathogen Porphyromonas gingivalis as a model. Through our knowledge of metabolic networks and essential genes we identified a “druggable” essential target, meso-diaminopimelate dehydrogenase, which is found in a limited number of species. We adopted a high-throughput virtual screen method on the ZINC chemical library to select a group of potential small-molecule inhibitors. Meso-diaminopimelate dehydrogenase from P. gingivalis was first expressed and purified in Escherichia coli then characterized for enzymatic inhibitor screening studies. Several inhibitors with similar structural scaffolds containing a sulfonamide core and aromatic substituents showed dose-dependent inhibition. These compounds were further assayed showing reasonable whole-cell activity and the inhibition mechanism was determined. We conclude that the establishment of this target and screening strategy provides a model for the future development of new antimicrobials.

Multistage virtual screening and identification of novel HIV-1 protease inhibitors by integrating SVM, shape, pharmacophore and docking methods

The HIV-1 protease has proven to be a crucial component of the HIV replication machinery and a reliable target for anti-HIV drug discovery. In this study, we applied an optimized hierarchical multistage virtual screening method targeting HIV-1 protease. The method sequentially applied SVM (Support Vector Machine), shape similarity, pharmacophore modeling and molecular docking. Using a validation set (270 positives, 155,996 negatives), the multistage virtual screening method showed a high hit rate and high enrichment factor of 80.47% and 465.75, respectively. Furthermore, this approach was applied to screen the National Cancer Institute database (NCI), which contains 260,000 molecules. From the final hit list, 6 molecules were selected for further testing in an in vitro HIV-1 protease inhibitory assay, and 2 molecules (NSC111887 and NSC121217) showed inhibitory potency against HIV-1 protease, with IC50 values of 62 μM and 162 μM, respectively. With further chemical development, these 2 molecules could potentially serve as HIV-1 protease inhibitors.

Beware of machine learning-based scoring functions-on the danger of developing black boxes

Training machine learning algorithms with protein-ligand descriptors has recently gained considerable attention to predict binding constants from atomic coordinates. Starting from a series of recent reports stating the advantages of this approach over empirical scoring functions, we could indeed reproduce the claimed superiority of Random Forest and Support Vector Machine-based scoring functions to predict experimental binding constants from protein-ligand X-ray structures of the PDBBind dataset. Strikingly, these scoring functions, trained on simple protein-ligand element-element distance counts, were almost unable to enrich virtual screening hit lists in true actives upon docking experiments of 10 reference DUD-E datasets; this is a a feature that, however, has been verified for an a priori less-accurate empirical scoring function (Surflex-Dock). By systematically varying ligand poses from true X-ray coordinates, we show that the Surflex-Dock scoring function is logically sensitive to the quality of docking poses. Conversely, our machine-learning based scoring functions are totally insensitive to docking poses (up to 10 Å root-mean square deviations) and just describe atomic element counts. This report does not disqualify using machine learning algorithms to design scoring functions. Protein-ligand element-element distance counts should however be used with extreme caution and only applied in a meaningful way. To avoid developing novel but meaningless scoring functions, we propose that two additional benchmarking tests must be systematically done when developing novel scoring functions: (i) sensitivity to docking pose accuracy, and (ii) ability to enrich hit lists in true actives upon structure-based (docking, receptor-ligand pharmacophore) virtual screening of reference datasets.

Renin modulates HIV replication in T cells

HIV is known to subvert cellular machinery to enhance its replication. Recently, HIV has been reported to enhance TC renin expression. We hypothesized that HIV induces and maintains high renin expression to promote its own replication in TCs. Renin enhanced HIV replication in TCs in a dose-dependent manner. (P)RR-deficient TCs, as well as those lacking renin, displayed attenuated NF-κB activity and HIV replication. TCs treated with renin and Hpr displayed activation of the (P)RR-PLZF protein signaling cascade. Renin, HIV, and Hpr activated the PI3K pathway. Both renin and Hpr cleaved Agt (a renin substrate) to Ang I and also cleaved Gag polyproteins (protease substrate) to p24. Furthermore, aliskiren, a renin inhibitor, reduced renin- and Hpr-induced cleavage of Agt and Gag polyproteins. These findings indicate that renin contributes to HIV replication in TCs via the (P)RR-PLZF signaling cascade and through cleavage of the Gag polyproteins.

Stereoselective synthesis of O-tosyl azabicyclic derivatives via aza Prins reaction of endocyclic N-acyliminium ions: application to the total synthesis of (±)-epi-indolizidine 167B and 209D

A diastereoselective synthesis of 4-O-tosyl piperidine containing azabicyclic derivatives has been established via Prins cyclization reaction. This protocol has been applied for the total synthesis of (±)-epi-indolizidine 167B and 209D.

Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Homology modeling and virtual screening approaches to identify potent inhibitors of VEB-1 β-lactamase

BACKGROUND

blaVEB-1 is an integron-located extended-spectrum β-lactamase gene initially detected in Escherichia coli and Pseudomonas aeruginosa strains from south-east Asia. Several recent studies have reported that VEB-1-positive strains are highly resistant to ceftazidime, cefotaxime and aztreonam antibiotics. One strategy to overcome resistance involves administering antibiotics together with β-lactamase inhibitors during the treatment of infectious diseases. During this study, four VEB-1 β-lactamase inhibitors were identified using computer-aided drug design.

METHODS

The SWISS-MODEL tool was utilized to generate three dimensional structures of VEB-1 β-lactamase, and the 3D model VEB-1 was verified using PROCHECK, ERRAT and VERIFY 3D programs. Virtual screening was performed by docking inhibitors obtained from the ZINC Database to the active site of the VEB-1 protein using AutoDock Vina software.

RESULTS AND CONCLUSION

Homology modeling studies were performed to obtain a three-dimensional structure of VEB-1 β-lactamase. The generated model was validated, and virtual screening of a large chemical ligand library with docking simulations was performed using AutoDock software with the ZINC database. On the basis of the dock-score, four molecules were subjected to ADME/TOX analysis, with ZINC4085364 emerging as the most potent inhibitor of the VEB-1 β-lactamase.

Computational drug repositioning: from data to therapeutics

Traditionally, most drugs have been discovered using phenotypic or target-based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome-wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods.

A model of interaction between nicotinamide adenine dinucleotide phosphate (NADPH) oxidase and apocynin analogues by docking method

Some apocynin analogues have exhibited outstanding inhibition to NADPH oxidase. In this study, the key interactions between apocynin analogues and NADPH oxidase were analyzed by the docking method. The potential active site was first identified by the SiteID program combining with the key residue CYS378. Afterwards, the compounds in the training set were docked into NADPH oxidase (1K4U) under specific docking constraints to discuss the key interactions between ligands and the receptor. These key interactions were then validated by the consistence between the docking result and the experimental result of the test set. The result reveals that the Pi interaction between apocynin analogues and NADPH oxidase has a direct contribution to inhibition activities, except for H-bond formation and docking score. The key interactions might be valuable to discover and screen apocynin analogues as potent inhibitors of NADPH oxidase.

Evaluation of DOCK 6 as a pose generation and database enrichment tool

In conjunction with the recent American Chemical Society symposium titled "Docking and Scoring: A Review of Docking Programs" the performance of the DOCK6 program was evaluated through (1) pose reproduction and (2) database enrichment calculations on a common set of organizer-specified systems and datasets (ASTEX, DUD, WOMBAT). Representative baseline grid score results averaged over five docking runs yield a relatively high pose identification success rate of 72.5 % (symmetry corrected rmsd) and sampling rate of 91.9 % for the multi site ASTEX set (N = 147) using organizer-supplied structures. Numerous additional docking experiments showed that ligand starting conditions, symmetry, multiple binding sites, clustering, and receptor preparation protocols all affect success. Encouragingly, in some cases, use of more sophisticated scoring and sampling methods yielded results which were comparable (Amber score ligand movable protocol) or exceeded (LMOD score) analogous baseline grid-score results. The analysis highlights the potential benefit and challenges associated with including receptor flexibility and indicates that different scoring functions have system dependent strengths and weaknesses. Enrichment studies with the DUD database prepared using the SB2010 preparation protocol and native ligand pairings yielded individual area under the curve (AUC) values derived from receiver operating characteristic curve analysis ranging from 0.29 (bad enrichment) to 0.96 (good enrichment) with an average value of 0.60 (27/38 have AUC ≥ 0.5). Strong early enrichment was also observed in the critically important 1.0-2.0 % region. Somewhat surprisingly, an alternative receptor preparation protocol yielded comparable results. As expected, semi-random pairings yielded poorer enrichments, in particular, for unrelated receptors. Overall, the breadth and number of experiments performed provide a useful snapshot of current capabilities of DOCK6 as well as starting points to guide future development efforts to further improve sampling and scoring.

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .

Identification of inhibitors against Mycobacterium tuberculosis thiamin phosphate synthase, an important target for the development of anti-TB drugs

Tuberculosis (TB) continues to pose a serious challenge to human health afflicting a large number of people throughout the world. In spite of the availability of drugs for the treatment of TB, the non-compliance to 6–9 months long chemotherapeutic regimens often results in the emergence of multidrug resistant strains of Mycobacterium tuberculosis adding to the precariousness of the situation. This has necessitated the development of more effective drugs. Thiamin biosynthesis, an important metabolic pathway of M.tuberculosis, is shown to be essential for the intracellular growth of this pathogen and hence, it is believed that inhibition of this pathway would severely affect the growth of M.tuberculosis. In this study, a comparative homology model of M.tuberculosis thiamin phosphate synthase (MtTPS) was generated and employed for virtual screening of NCI diversity set II to select potential inhibitors. The best 39 compounds based on the docking results were evaluated for their potential to inhibit the MtTPS activity. Seven compounds inhibited MtTPS activity with IC₅₀ values ranging from 20 – 100 µg/ml and two of these exhibited weak inhibition of M.tuberculosis growth with MIC₉₉ values being 125 µg/ml and 162.5 µg/ml while one compound was identified as a very potent inhibitor of M.tuberculosis growth with an MIC₉₉ value of 6 µg/ml. This study establishes MtTPS as a novel drug target against M.tuberculosis leading to the identification of new lead molecules for the development of antitubercular drugs. Further optimization of these lead compounds could result in more potent therapeutic molecules against Tuberculosis.

Drug repositioning for orphan diseases

The need and opportunity to discover therapeutics for rare or orphan diseases are enormous. Due to limited prevalence and/or commercial potential, of the approximately 6000 orphan diseases (defined by the FDA Orphan Drug Act as <200 000 US prevalence), only a small fraction (5%) is of interest to the biopharmaceutical industry. The fact that drug development is complicated, time-consuming and expensive with extremely low success rates only adds to the low rate of therapeutics available for orphan diseases. An alternative and efficient strategy to boost the discovery of orphan disease therapeutics is to find connections between an existing drug product and orphan disease. Drug Repositioning or Drug Repurposing--finding a new indication for a drug--is one way to maximize the potential of a drug. The advantages of this approach are manifold, but rational drug repositioning for orphan diseases is not trivial and poses several formidable challenges--pharmacologically and computationally. Most of the repositioned drugs currently in the market are the result of serendipity. One reason the connection between drug candidates and their potential new applications are not identified in an earlier or more systematic fashion is that the underlying mechanism 'connecting' them is either very intricate and unknown or indirect or dispersed and buried in an ever-increasing sea of information, much of which is emerging only recently and therefore is not well organized. In this study, we will review some of these issues and the current methodologies adopted or proposed to overcome them and translate chemical and biological discoveries into safe and effective orphan disease therapeutics.

Chemical-protein interactome and its application in off-target identification

Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency.

Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing

BACKGROUND

Weak intermolecular interactions such as hydrogen bonding and hydrophobic interactions are key players in stabilizing energetically-favored ligands, in an open conformational environment of protein structures. However, it is still poorly understood how the binding parameters associated with these interactions facilitate a drug-lead to recognize a specific target and improve drugs efficacy. To understand this, comprehensive analysis of hydrophobic interactions, hydrogen bonding and binding affinity have been analyzed at the interface of c-Src and c-Abl kinases and 4-amino substituted 1H-pyrazolo [3, 4-d] pyrimidine compounds.

METHODOLOGY

In-silico docking studies were performed, using Discovery Studio software modules LigandFit, CDOCKER and ZDOCK, to investigate the role of ligand binding affinity at the hydrophobic pocket of c-Src and c-Abl kinase. Hydrophobic and hydrogen bonding interactions of docked molecules were compared using LigPlot program. Furthermore, 3D-QSAR and MFA calculations were scrutinized to quantify the role of weak interactions in binding affinity and drug efficacy.

CONCLUSIONS

The in-silico method has enabled us to reveal that a multi-targeted small molecule binds with low affinity to its respective targets. But its binding affinity can be altered by integrating the conformationally favored functional groups at the active site of the ligand-target interface. Docking studies of 4-amino-substituted molecules at the bioactive cascade of the c-Src and c-Abl have concluded that 3D structural folding at the protein-ligand groove is also a hallmark for molecular recognition of multi-targeted compounds and for predicting their biological activity. The results presented here demonstrate that hydrogen bonding and optimized hydrophobic interactions both stabilize the ligands at the target site, and help alter binding affinity and drug efficacy.

Structure-Based Approaches to Target Fishing and Ligand Profiling

Chemogenomics is an emerging interdisciplinary field aiming at identifying all possible ligands of all possible targets. If one groups targets in columns and ligands in rows, chemogenomic approaches to drug discovery just fill the interaction matrix. Since experimental data do not suffice, several computational methods are currently actively developed to supplement time-consuming and costly experiments. They are either designed to fill rows and thus profile a ligand towards a heterogeneous set of targets (target profiling) or to fill columns and thus identify novel ligands for an existing target (standard virtual screening). At the interface of both strategies are now true chemogenomic computational methods filling well defined areas in the matrix. The present review will focus on (protein) structure-based approaches and illustrates major advances in this novel exciting field which is supposed to massively impact rational drug design in the next decade.

The early years of retroviral protease crystal structures

Soon after its discovery, the attempts to develop anti-AIDS therapeutics focused on the retroviral protease (PR)-an enzyme used by lentiviruses to process the precursor polypeptide into mature viral proteins. An urgent need for the three-dimensional structure of PR to guide rational drug design prompted efforts to produce milligram quantities of this enzyme. However, only minute amounts of PR were present in the HIV-1 and HIV-2 viruses, and initial attempts to express this protein in bacteria were not successful. This review describes X-ray crystallographic studies of the retroviral proteases carried out at NCI-Frederick in the late 1980s and early 1990s and puts into perspective the crucial role that the total protein chemical synthesis played in unraveling the structure, mechanism of action, and inhibition of HIV-1 PR. Notably, the first fully correct structure of HIV-1 PR and the first cocrystal structure of its complex with an inhibitor (a substrate-derived, reduced isostere hexapeptide MVT-101) were determined using chemically synthesized protein. Most importantly, these sets of coordinates were made freely available to the research community and were used worldwide to solve X-ray structures of HIV-1 PR complexes with an array of inhibitors and set in motion a variety of theoretical studies. Publication of the structure of chemically synthesized HIV-1 PR complexed with MVT-101 preceded only by six years the approval of the first PR inhibitor as an anti-AIDS drug.

Harvesting candidate genes responsible for serious adverse drug reactions from a chemical-protein interactome

Identifying genetic factors responsible for serious adverse drug reaction (SADR) is of critical importance to personalized medicine. However, genome-wide association studies are hampered due to the lack of case-control samples, and the selection of candidate genes is limited by the lack of understanding of the underlying mechanisms of SADRs. We hypothesize that drugs causing the same type of SADR might share a common mechanism by targeting unexpectedly the same SADR-mediating protein. Hence we propose an approach of identifying the common SADR-targets through constructing and mining an in silico chemical-protein interactome (CPI), a matrix of binding strengths among 162 drug molecules known to cause at least one type of SADR and 845 proteins. Drugs sharing the same SADR outcome were also found to possess similarities in their CPI profiles towards this 845 protein set. This methodology identified the candidate gene of sulfonamide-induced toxic epidermal necrolysis (TEN): all nine sulfonamides that cause TEN were found to bind strongly to MHC I (Cw*4), whereas none of the 17 control drugs that do not cause TEN were found to bind to it. Through an insight into the CPI, we found the Y116S substitution of MHC I (B*5703) enhances the unexpected binding of abacavir to its antigen presentation groove, which explains why B*5701, not B*5703, is the risk allele of abacavir-induced hypersensitivity. In conclusion, SADR targets and the patient-specific off-targets could be identified through a systematic investigation of the CPI, generating important hypotheses for prospective experimental validation of the candidate genes.

Why do tragedies caused by Vioxx or Avandia only happen to certain individuals? The unexpected bindings among drugs and human proteins might play important roles in such serious adverse drug reactions (SADRs). To mine these unexpected chemical-protein interactions, 162 drug molecules known to cause SADRs are ‘hybridized’ onto 845 proteins to construct a chemical-protein interaction matrix, from which two aspects of the information, the binding strength and the binding conformation, are disclosed. Followed by the data-mining strategies, the unexpected bindings that mediate SADRs are identified. For example, abacavir is found to bind to the antigen presentation groove of MHC I molecule in patients carrying the B*5701 allele but not B*5703, which explains why HLA-B*5701, not B*5703, is the risk allele of abacavir hypersensitivity. This research could explain to the public that SADR happens when some of the innocent proteins are attacked by drugs unexpectedly, and variances in certain people's genome make their proteins more sensitive to the drug. By pre-therapy screening, the susceptible people could be protected. Furthermore, new drugs or modified drugs will be designed to avoid these patient-specific unintended bindings, in a step toward realizing personalized medicine.

Conformationally homogeneous heterocyclic pseudotetrapeptides as three-dimensional scaffolds for rational drug design: receptor-selective somatostatin analogues

A would-be amide: A 1,4-disubstituted 1,2,3-triazole was used as a surrogate for a trans amide bond to create a library of 16 diastereomeric pseudotetrapeptides as beta-turn mimetics. High-resolution structural analysis indicated that these scaffolds adopt distinct, rigid, conformationally homogeneous beta-turn-like structures (see example), some of which bind somatostatin receptor subtypes selectively, and some of which show broad-spectrum activity.

Computational design of novel cyclic urea as HIV-1 protease inhibitor

A series of novel cyclic urea molecules 5,6-dihydroxy-1,3-diazepane-2,4,7-trione as HIV-1 protease inhibitors were designed using computational techniques. The designed molecules were compared with the known cyclic urea molecules by performing docking studies, calculating their ADME (Absorption, Distribution, Metabolism, and Excretion) properties and protein ligand interaction energy. These novel molecules were designed by substituting the P 1/P′ 1 positions (4th and 7th position of 1, 3-diazepan-2-one) with double bonded oxygens. This reduces the molecular weight and increases the bioavailability, indicating better ADME properties. The docking studies showed good binding affinity towards HIV-1 protease. The biological activity of these inhibitors were predicted by a model equation generated by the regression analysis between biological activity (log 1/K i ) of known inhibitors and their protein ligand interaction energy. The synthetic studies are in progress.

Virtual ligand screening: strategies, perspectives and limitations

In contrast to high-throughput screening, in virtual ligand screening (VS), compounds are selected using computer programs to predict their binding to a target receptor. A key prerequisite is knowledge about the spatial and energetic criteria responsible for protein–ligand binding. The concepts and prerequisites to perform VS are summarized here, and explanations are sought for the enduring limitations of the technology. Target selection, analysis and preparation are discussed, as well as considerations about the compilation of candidate ligand libraries. The tools and strategies of a VS campaign, and the accuracy of scoring and ranking of the results, are also considered.

Accurate reproduction of 161 small-molecule complex crystal structures using the EUDOC program: expanding the use of EUDOC to supramolecular chemistry

EUDOC is a docking program that has successfully predicted small-molecule-bound protein complexes and identified drug leads from chemical databases. To expand the application of the EUDOC program to supramolecular chemistry, we tested its ability to reproduce crystal structures of small-molecule complexes. Of 161 selected crystal structures of small-molecule guest-host complexes, EUDOC reproduced all these crystal structures with guest structure mass-weighted root mean square deviations (mwRMSDs) of <1.0 A relative to the corresponding crystal structures. In addition, the average interaction energy of these 161 guest-host complexes (-50.1 kcal/mol) was found to be nearly half of that of 153 previously tested small-molecule-bound protein complexes (-108.5 kcal/mol), according to the interaction energies calculated by EUDOC. 31 of the 161 complexes could not be reproduced with mwRMSDs of <1.0 A if neighboring hosts in the crystal structure of a guest-host complex were not included as part of the multimeric host system, whereas two of the 161 complexes could not be reproduced with mwRMSDs of <1.0 A if water molecules were excluded from the host system. These results demonstrate the significant influence of crystal packing on small molecule complexation and suggest that EUDOC is able to predict small-molecule complexes and that it is useful for the design of new materials, molecular sensors, and multimeric inhibitors of protein-protein interactions.

Benchmarking sets for molecular docking

Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40x40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.