Rational approach to AIDS drug design through structural biology

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

Dynamozones are the most obvious sign of the evolution of conformational dynamics in HIV-1 protease

Proteins are not static but are flexible molecules that can adopt many different conformations. The HIV-1 protease is an important target for the development of therapies to treat AIDS, due to its critical role in the viral life cycle. We investigated several dynamics studies on the HIV-1 protease families to illustrate the significance of examining the dynamic behaviors and molecular motions for an entire understanding of their dynamics-structure-function relationships. Using computer simulations and principal component analysis approaches, the dynamics data obtained revealed that: (i) The flap regions are the most obvious sign of the evolution of conformational dynamics in HIV-1 protease; (ii) There are dynamic structural regions in some proteins that contribute to the biological function and allostery of proteins via appropriate flexibility. These regions are a clear sign of the evolution of conformational dynamics of proteins, which we call dynamozones. The flap regions are one of the most important dynamozones members that are critical for HIV-1 protease function. Due to the existence of other members of dynamozones in different proteins, we propose to consider dynamozones as a footprint of the evolution of the conformational dynamics of proteins.

Molecular Insights into the In Vivo Analgesic and Anti-Inflammatory Activity of Indomethacin Analogues

The primary objective of this research was to identify and explore the most potent and efficacious cyclooxygenase inhibitors, utilizing indole acetic acid drugs as a lead molecule. To achieve this objective, various derivatives (2a-2c and 2e-2g) of the selected lead molecule, indomethacin, were synthesized using a reflux condensation process, targeting the hydroxyl group. The synthesized analogues were subjected to different spectroscopic procedures to determine their structure and confirm their analogues. These derivatives were further screened for acute toxicity and anti-nociceptive and anti-inflammatory activity using established protocols. Docking analysis was performed to evaluate the possible protein-ligand interaction. The test compounds were found to be safe at doses of 50, 75, 100, and 200 mg/kg, i.p. The pharmacological screening revealed that test compounds 2a-2f had a superior peripheral analgesic effect at a dose of 10 mg/kg, in comparison to the parent drug indomethacin, while compound 2g exhibited slightly lower activity at the same dose. The hot plate results showed lower central analgesic activity of the test compounds compared to the standard Tramal, but it was still significant. Anti-inflammatory results were significant, comparable to Diclofenac sodium and indomethacin, except for compounds 2b, 2c, and 2e at a dose of 10 mg/kg body weight. Molecular docking analysis demonstrated that the derived compounds had augmented negative binding energies (-149.39, -146.72, -160.85, -159.34, -140.03, and -150.91 KJ/mol) compared to the parent drugs (-141.07), which supported the research's theme of producing stronger derivatives of standard drugs with significant anti-nociceptive and anti-inflammatory potential. The derived compounds exhibited significant analgesic and anti-inflammatory activities and, therefore, have the potential to be studied further as new drug candidates for pain and inflammation.

Accurate Host-Guest Binding Free Energies Using the AMOEBA Polarizable Force Field

A grand challenge of computational biophysics is accurate prediction of interactions between molecules. Molecular dynamics (MD) simulations have recently gained much interest as a tool to directly compute rigorous intermolecular binding affinities. The choice of a fixed point-charge or polarizable multipole force field used in MD is a topic of ongoing discussion. To compare alternative methods, we participated in the SAMPL7 and SAMPL8 Gibb octaacid host-guest challenges to assess the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) polarizable multipole force field. Advantages of AMOEBA over fixed charge models include improved representation of molecular electrostatic potentials and better description of water occupying the unligated host cavity. Prospective predictions for 26 host-guest systems exhibit a mean unsigned error vs experiment of 0.848 kcal/mol across all absolute binding free energies, demonstrating excellent agreement between computational and experimental results. In addition, we explore two topics related to the inclusion of ions in MD simulations: use of a neutral co-alchemical protocol and the effect of salt concentration on binding affinity. Use of the co-alchemical method minimally affects computed energies, but salt concentration significantly perturbs our binding results. Higher salt concentration strengthens binding through classical charge screening. In particular, added Na+ ions screen negatively charged carboxylate groups near the binding cavity, thereby diminishing repulsive coulomb interactions with negatively charged guests. Overall, the AMOEBA results demonstrate the accuracy available through a force field providing a detailed energetic description of the four octaacid hosts and 13 charged organic guests. Use of the AMOEBA polarizable atomic multipole force field in conjunction with an alchemical free energy protocol can achieve chemical accuracy in application to realistic molecular systems.

Evaluation of darunavir-derived HIV-1 protease inhibitors incorporating P2' amide-derivatives: Synthesis, biological evaluation and structural studies

We report here the synthesis and biological evaluation of darunavir derived HIV-1 protease inhibitors and their functional effect on enzyme inhibition and antiviral activity in MT-2 cell lines. The P2' 4-amino functionality was modified to make a number of amide derivatives to interact with residues in the S2' subsite of the HIV-1 protease active site. Several compounds exhibited picomolar enzyme inhibitory and low nanomolar antiviral activity. The X-ray crystal structure of the chloroacetate derivative bound to HIV-1 protease was determined. Interestingly, the active chloroacetate group converted to the acetate functionality during X-ray exposure. The structure revealed that the P2' carboxamide functionality makes enhanced hydrogen bonding interactions with the backbone atoms in the S2'-subsite.

The Effect of Treatment-Associated Mutations on HIV Replication and Transmission Cycles

HIV/AIDS mortality has been decreasing over the last decade. While promising, this decrease correlated directly with increased use of antiretroviral drugs. As a natural consequence of its high mutation rate, treatments provide selection pressure that promotes the natural selection of escape mutants. Individuals may acquire drug-naive strains, or those that have already mutated due to treatment. Even within a host, mutation affects HIV tropism, where initial infection begins with R5-tropic virus, but the clinical transition to AIDS correlates with mutations that lead to an X4-tropic switch. Furthermore, the high mutation rate of HIV has spelled failure for all attempts at an effective vaccine. Pre-exposure drugs are currently the most effective drug-based preventatives, but their effectiveness is also threatened by viral mutation. From attachment and entry to assembly and release, the steps in the replication cycle are also discussed to describe the drug mechanisms and mutations that arise due to those drugs. Revealing the patterns of HIV-1 mutations, their effects, and the coordinated attempt to understand and control them will lead to effective use of current preventative measures and treatment options, as well as the development of new ones.

Plant Metabolites as SARS-CoV-2 Inhibitors Candidates: In Silico and In Vitro Studies

Since it acquired pandemic status, SARS-CoV-2 has been causing all kinds of damage all over the world. More than 6.3 million people have died, and many cases of sequelae are in survivors. Currently, the only products available to most of the world’s population to fight the pandemic are vaccines, which still need improvement since the number of new cases, admissions into intensive care units, and deaths are again reaching worrying rates, which makes it essential to compounds that can be used during infection, reducing the impacts of the disease. Plant metabolites are recognized sources of diverse biological activities and are the safest way to research anti-SARS-CoV-2 compounds. The present study computationally evaluated 55 plant compounds in five SARS-CoV-2 targets such Main Protease (Mpro or 3CL or MainPro), RNA-dependent RNA polymerase (RdRp), Papain-Like Protease (PLpro), NSP15 Endoribonuclease, Spike Protein (Protein S or Spro) and human Angiotensin-converting enzyme 2 (ACE-2) followed by in vitro evaluation of their potential for the inhibition of the interaction of the SARS-CoV-2 Spro with human ACE-2. The in silico results indicated that, in general, amentoflavone, 7-O-galloylquercetin, kaempferitrin, and gallagic acid were the compounds with the strongest electronic interaction parameters with the selected targets. Through the data obtained, we can demonstrate that although the indication of individual interaction of plant metabolites with both Spro and ACE-2, the metabolites evaluated were not able to inhibit the interaction between these two structures in the in vitro test. Despite this, these molecules still must be considered in the research of therapeutic agents for treatment of patients affected by COVID-19 since the activity on other targets and influence on the dynamics of viral infection during the interaction Spro x ACE-2 should be investigated.

Repurposing antiviral phytochemicals from the leaf extracts of Spondias mombin (Linn) towards the identification of potential SARSCOV-2 inhibitors

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a pneumonia-like disease with a pattern of acute respiratory symptoms, currently remains a significant public health concern causing tremendous human suffering. Although several approved vaccines exist, vaccine hesitancy, limited vaccine availability, high rate of viral mutation, and the absence of approved drugs account for the persistence of SARS-CoV-2 infections. The investigation of possibly repurposing of phytochemical compounds as therapeutic alternatives has gained momentum due to their reported affordability and minimal toxicity. This study investigated anti-viral phytochemical compounds from ethanolic leaf extracts of Spondias mombin L as potential inhibitor candidates against SARS-CoV-2. We identified Geraniin and 2-O-Caffeoyl-(+)-allohydroxycitric acid as potential SARS-CoV-2 inhibitor candidates targeting the SARS-CoV-2 RNA-dependent polymerase receptor-binding domain (RBD) of SARS-CoV-2 viral S-protein and the 3C-like main protease (3CLpro). Geraniin exhibited binding free energy (ΔGbind) of - 25.87 kcal/mol and - 21.74 kcal/mol towards SARS-CoV-2 RNA-dependent polymerase and receptor-binding domain (RBD) of SARS-CoV-2 viral S-protein respectively, whereas 2-O-Caffeoyl-(+)-allohydroxycitric acid exhibited a ΔGbind of - 32 kcal/mol towards 3CLpro. Molecular Dynamics simulations indicated a possible interference to the functioning of SARS-CoV-2 targets by the two identified inhibitors. However, further in vitro and in vivo evaluation of these potential SARS-CoV-2 therapeutic inhibitor candidates is needed.

Using In Vitro and Machine Learning Approaches to Determine Species-Specific Dioxin-like Potency and Congener-Specific Relative Sensitivity among Birds for Brominated Dioxin Analogues

There is a paucity of experimental data regarding dioxin-like toxicity of polybrominated dibenzo-p-dioxins/dibenzofurans (PBDD/Fs) and non-ortho polybrominated biphenyls (PBBs). In this study, avian aryl hydrocarbon receptor 1 (AHR1)-luciferase reporter gene assays were used to determine their species-specific dioxin-like potencies (DLPs) and congener-specific interspecies relative sensitivities in birds. The results suggested that DLPs of the brominated congeners for chicken-like (Ile324_Ser380) species did not always follow World Health Organization toxicity equivalency factors of their chlorinated analogues. For ring-necked pheasant-like (Ile324_Ala380) and Japanese quail-like (Val324_Ala380) species, the difference in DLP for several congeners was 1 or even 2 orders of magnitude. Moreover, molecular docking and molecular dynamics simulation were performed to explore the interactions between the brominated congeners and AHR1-ligand-binding domain (LBD). The molecular mechanics energy (E_MM) between each congener and each individual amino acid (AA) residue in AHR1-LBD was calculated. These E_MM values could finely characterize the final conformation of species-specific AHR1-LBD for each brominated congener. Based on this, mechanism-driven generalized linear models were successfully built using machine learning algorithms and the spline approximation method, and these models could qualitatively predict the complex relationships between AHR1 conformations and DLPs or avian interspecies relative sensitivity to brominated dioxin-like compounds (DLCs). In addition, several AAs conserved among birds were found to potentially interact with species-specific AAs, thereby inducing species-specific interactions between AHR1 and brominated DLCs. The present study provides a novel strategy to facilitate the development of mechanism-driven computational prediction models for supporting safety assessment of DLCs, as well as a basis for the ecotoxicological risk assessment of brominated congeners in birds.

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Advancements in macromolecular crystallography: from past to present

Protein Crystallography or Macromolecular Crystallography (MX) started as a new discipline of science with the pioneering work on the determination of the protein crystal structures by John Kendrew in 1958 and Max Perutz in 1960. The incredible achievements in MX are attributed to the development of advanced tools, methodologies, and automation in every aspect of the structure determination process, which have reduced the time required for solving protein structures from years to a few days, as evident from the tens of thousands of crystal structures of macromolecules available in PDB. The advent of brilliant synchrotron sources, fast detectors, and novel sample delivery methods has shifted the paradigm from static structures to understanding the dynamic picture of macromolecules; further propelled by X-ray Free Electron Lasers (XFELs) that explore the femtosecond regime. The revival of the Laue diffraction has also enabled the understanding of macromolecules through time-resolved crystallography. In this review, we present some of the astonishing method-related and technological advancements that have contributed to the progress of MX. Even with the rapid evolution of several methods for structure determination, the developments in MX will keep this technique relevant and it will continue to play a pivotal role in gaining unprecedented atomic-level details as well as revealing the dynamics of biological macromolecules. With many exciting developments awaiting in the upcoming years, MX has the potential to contribute significantly to the growth of modern biology by unraveling the mechanisms of complex biological processes as well as impacting the area of drug designing.

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.

Rational Design of Peptide-Based Inhibitors Disrupting Protein-Protein Interactions

Protein-protein interactions (PPIs) are well-established as a class of promising drug targets for their implications in a wide range of biological processes. However, drug development toward PPIs is inevitably hampered by their flat and wide interfaces, which generally lack suitable pockets for ligand binding, rendering most PPI systems "undruggable." Here, we summarized drug design strategies for developing peptide-based PPI inhibitors. Importantly, several quintessential examples toward well-established PPI targets such as Bcl-2 family members, p53-MDM2, as well as APC-Asef are presented to illustrate the detailed schemes for peptide-based PPI inhibitor development and optimizations. This review supplies a comprehensive overview of recent progresses in drug discovery targeting PPIs through peptides or peptidomimetics, and will shed light on future therapeutic agent development toward the historically "intractable" PPI systems.

Structure-Based Drug Design for Tuberculosis: Challenges Still Ahead

Structure-based and computer-aided drug design approaches are commonly considered to have been successful in the fields of cancer and antiviral drug discovery but not as much for antibacterial drug development. The search for novel anti-tuberculosis agents is indeed an emblematic example of this trend. Although huge efforts, by consortiums and groups worldwide, dramatically increased the structural coverage of the Mycobacterium tuberculosis proteome, the vast majority of candidate drugs included in clinical trials during the last decade were issued from phenotypic screenings on whole mycobacterial cells. We developed here three selected case studies, i.e., the serine/threonine (Ser/Thr) kinases—protein kinase (Pkn) B and PknG, considered as very promising targets for a long time, and the DNA gyrase of M. tuberculosis, a well-known, pharmacologically validated target. We illustrated some of the challenges that rational, target-based drug discovery programs in tuberculosis (TB) still have to face, and, finally, discussed the perspectives opened by the recent, methodological developments in structural biology and integrative techniques.

Chemical Intuition in Drug Design and Discovery

The medicinal chemist plays the most important role in drug design, discovery and development. The primary goal is to discover leads and optimize them to develop clinically useful drug candidates. This process requires the medicinal chemist to deal with large sets of data containing chemical descriptors, pharmacological data, pharmacokinetics parameters, and in silico predictions. The modern medicinal chemist has a large number of tools and technologies to aid him in creating strategies and supporting decision-making. Alongside with these tools, human cognition, experience and creativity are fundamental to drug research and are important for the chemical intuition of medicinal chemists. Therefore, fine-tuning of data processing and in-house experience are essential to reach clinical trials. In this article, we will provide an expert opinion on how chemical intuition contributes to the discovery of drugs, discuss where it is involved in the modern drug discovery process, and demonstrate how multidisciplinary teams can create the optimal environment for drug design, discovery, and development.

Potent HIV-1 protease inhibitors incorporating squaramide-derived P2 ligands: Design, synthesis, and biological evaluation

We describe the design, synthesis, and biological evaluation of novel HIV-1 protease inhibitors containing a squaramide-derived scaffold as the P2 ligand in combination with a (R)-hydroxyethylamine sulfonamide isostere. Inhibitor 3h with an N-methyl-3-(R)-aminotetrahydrofuranyl squaramide P2-ligand displayed an HIV-1 protease inhibitory Ki value of 0.51 nM. An energy minimized model of 3h revealed the major molecular interactions between HIV-1 protease active site and the tetrahydrofuranyl squaramide scaffold that may be responsible for its potent activity.

Discovery of CCL18 antagonist blocking breast cancer metastasis

Our previous studies have proved that CCL18 is the most secreted chemokine in breast cancer microenvironment by tumor associated macrophages (TAMs). CCL18 promotes breast cancer invasiveness by binding to its cognate receptor PITPNM3 and activating the downstream signaling pathways. The high level of CCL18 in serum or tumor stroma is associated with tumor metastasis and poor patients overall survival. In this study, we identify an effective small molecular compound (SMC) to antagonize the effect of CCL18. We screen more than 1000 SMCs from Sun Yat-sen University SMC library and select 15 top scored SMCs by using computer-aided virtual screening based on the structure of CCL18. Then in vitro cell migration assay narrows down the selected 15 SMCs to the most effective SMC-21598. We find 10 µM SMC-21598 significantly inhibits CCL18-induced breast cancer cells adherence, invasiveness, and migration. Our further surface plasmon resonance (SPR), fluorescence spectroscopy and isothermal titration calorimetry (ITC) assays reveal that SMC-21598 binds tightly to CCL18, which blocks the binding of CCL18 with its receptor PITPNM3. The in vivo animal experiments show that SMC-21598 doesn't significantly affect xenografts growth, but inhibits lung metastasis. Our study provides a potential lead compound to antagonize CCL18 function. It would be of great significance to develop SMC drugs to ameliorate breast cancer metastasis and prolong patients' survival.

Detailed potential of mean force studies on host-guest systems from the SAMPL6 challenge

Accurately predicting receptor-ligand binding free energies is one of the holy grails of computational chemistry with many applications in chemistry and biology. Many successes have been reported, but issues relating to sampling and force field accuracy remain significant issues affecting our ability to reliably calculate binding free energies. In order to explore these issues in more detail we have examined a series of small host-guest complexes from the SAMPL6 blind challenge, namely octa-acids (OAs)-guest complexes and Curcurbit[8]uril (CB8)-guest complexes. Specifically, potential of mean force studies using umbrella sampling combined with the weighted histogram method were carried out on both systems with both known and unknown binding affinities. We find that using standard force fields and straightforward simulation protocols we are able to obtain satisfactory results, but that simply scaling our results allows us to significantly improve our predictive ability for the unknown test sets: the overall RMSD of the binding free energy versus experiment is reduced from 5.59 to 2.36 kcal/mol; for the CB8 test system, the RMSD goes from 8.04 to 3.51 kcal/mol, while for the OAs test system, the RSMD goes from 2.89 to 0.95 kcal/mol. The scaling approach was inspired by studies on structurally related known benchmark sets: by simply scaling, the RMSD was reduced from 6.23 to 1.19 kcal/mol and from 2.96 to 0.62 kcal/mol for the CB8 benchmark system and the OA benchmark system, respectively. We find this scaling procedure to correct absolute binding affinities to be highly effective especially when working across a "congeneric" series with similar charge states. It is less successful when applied to mixed ligands with varied charges and chemical characteristics, but improvement is still realized in the present case. This approach suggests that there are large systematic errors in absolute binding free energy calculations that can be straightforwardly accounted for using a scaling procedure. Random errors are still an issue, but near chemical accuracy can be obtained using the present strategy in select cases.

Anticipating innovations in structural biology

In this review, we describe how the interplay among science, technology and community interests contributed to the evolution of four structural biology data resources. We present the method by which data deposited by scientists are prepared for worldwide distribution, and argue that data archiving in a trusted repository must be an integral part of any scientific investigation.

Proteases and protease inhibitors in infectious diseases

There are numerous proteases of pathogenic organisms that are currently targeted for therapeutic intervention along with many that are seen as potential drug targets. This review discusses the chemical and biological makeup of some key druggable proteases expressed by the five major classes of disease causing agents, namely bacteria, viruses, fungi, eukaryotes, and prions. While a few of these enzymes including HIV protease and HCV NS3-4A protease have been targeted to a clinically useful level, a number are yet to yield any clinical outcomes in terms of antimicrobial therapy. A significant aspect of this review discusses the chemical and pharmacological characteristics of inhibitors of the various proteases discussed. A total of 25 inhibitors have been considered potent and safe enough to be trialed in humans and are at different levels of clinical application. We assess the mechanism of action and clinical performance of the protease inhibitors against infectious agents with their developmental strategies and look to the next frontiers in the use of protease inhibitors as anti-infective agents.

Carfilzomib resistance due to ABCB1/MDR1 overexpression is overcome by nelfinavir and lopinavir in multiple myeloma

Proteasome inhibitor (PI) carfilzomib (CFZ) has activity superior to bortezomib (BTZ) and is increasingly incorporated in multiple myeloma (MM) frontline therapy and relapsed settings. Most MM patients ultimately experience PI-refractory disease, an unmet medical need with poorly understood biology and dismal outcome. Pharmacologic targeting of ABCB1 improved patient outcomes, including MM, but suffered from adverse drug effects and insufficient plasma concentrations. Proteomics analysis identified ABCB1 overexpression as the most significant change in CFZ-resistant MM cells. We addressed the functional role of ABCB1 overexpression in MM and observed significantly upregulated ABCB1 in peripheral blood malignant plasma cells (PCs) vs untreated patients' bone marrow PC. ABCB1 overexpression reduces the proteasome-inhibiting activity of CFZ due to drug efflux, in contrast to BTZ. Likewise, the cytotoxicity of established anti-MM drugs was significantly reduced in ABCB1-expressing MM cells. In search for potential drugs targeting ABCB1 in clinical trials, we identified the HIV protease inhibitors nelfinavir (NFV) and lopinavir (LPV) as potent functional modulators of ABCB1-mediated drug export, most likely via modulation of mitochondria permeability transition pore. NFV and LPV restored CFZ activity at therapeutically relevant drug levels and thus represent ready-to-use drugs to be tested in clinical trials to target ABCB1 and to re-sensitize PC to established myeloma drugs, in particular CFZ.

Investigation of the binding free energies of FDA approved drugs against subtype B and C-SA HIV PR: ONIOM approach

Human immune virus subtype C is the most widely spread HIV subtype in Sub-Sahara Africa and South Africa. A profound structural insight on finding potential lead compounds is therefore necessary for drug discovery. The focus of this study is to rationalize the nine Food and Drugs Administration (FDA) HIV antiviral drugs complexed to subtype B and C-SA PR using ONIOM approach. To achieve this, an integrated two-layered ONIOM model was used to optimize the geometrics of the FDA approved HIV-1 PR inhibitors for subtype B. In our hybrid ONIOM model, the HIV-1 PR inhibitors as well as the ASP 25/25' catalytic active residues were treated at high level quantum mechanics (QM) theory using B3LYP/6-31G(d), and the remaining HIV PR residues were considered using the AMBER force field. The experimental binding energies of the PR inhibitors were compared to the ONIOM calculated results. The theoretical binding free energies (?G_bind) for subtype B follow a similar trend to the experimental results, with one exemption. The computational model was less suitable for C-SA PR. Analysis of the results provided valuable information about the shortcomings of this approach. Future studies will focus on the improvement of the computational model by considering explicit water molecules in the active pocket. We believe that this approach has the potential to provide much improved binding energies for complex enzyme drug interactions.

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.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

The Intersection of Structural and Chemical Biology - An Essential Synergy

The continual improvement in our ability to generate high resolution structural models of biological molecules has stimulated and supported innovative chemical biology projects that target increasingly challenging ligand interaction sites. In this review we outline some of the recent developments in chemical biology and rational ligand design and show selected examples that illustrate the synergy between these research areas.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Structure-Based Reverse Vaccinology Failed in the Case of HIV Because it Disregarded Accepted Immunological Theory

Two types of reverse vaccinology (RV) should be distinguished: genome-based RV for bacterial vaccines and structure-based RV for viral vaccines. Structure-based RV consists in trying to generate a vaccine by first determining the crystallographic structure of a complex between a viral epitope and a neutralizing monoclonal antibody (nMab) and then reconstructing the epitope by reverse molecular engineering outside the context of the native viral protein. It is based on the unwarranted assumption that the epitope designed to fit the nMab will have acquired the immunogenic capacity to elicit a polyclonal antibody response with the same protective capacity as the nMab. After more than a decade of intensive research using this type of RV, this approach has failed to deliver an effective, preventive HIV-1 vaccine. The structure and dynamics of different types of HIV-1 epitopes and of paratopes are described. The rational design of an anti-HIV-1 vaccine is shown to be a misnomer since investigators who claim that they design a vaccine are actually only improving the antigenic binding capacity of one epitope with respect to only one paratope and not the immunogenic capacity of an epitope to elicit neutralizing antibodies. Because of the degeneracy of the immune system and the polyspecificity of antibodies, each epitope studied by the structure-based RV procedure is only one of the many epitopes that the particular nMab is able to recognize and there is no reason to assume that this nMab must have been elicited by this one epitope of known structure. Recent evidence is presented that the trimeric Env spikes of the virus possess such an enormous plasticity and intrinsic structural flexibility that it is it extremely difficult to determine which Env regions are the best candidate vaccine immunogens most likely to elicit protective antibodies.

Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors

Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors (NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the highly active antiretroviral therapy in combination with other anti-HIV drugs. However, the rapid emergence of drug-resistant viral strains has limited the successful rate of the anti-HIV agents. Computational methods are a significant part of the drug design process and indispensable to study drug resistance. In this review, recent advances in computer-aided drug design for the rational design of new compounds against HIV-1 RT using methods such as molecular docking, molecular dynamics, free energy calculations, quantitative structure-activity relationships, pharmacophore modelling and absorption, distribution, metabolism, excretion and toxicity prediction are discussed. Successful applications of these methodologies are also highlighted.

X-ray crystallography over the past decade for novel drug discovery - where are we heading next?

INTRODUCTION

Macromolecular X-ray crystallography has been the primary methodology for determining the three-dimensional structures of proteins, nucleic acids and viruses. Structural information has paved the way for structure-guided drug discovery and laid the foundations for structural bioinformatics. However, X-ray crystallography still has a few fundamental limitations, some of which may be overcome and complemented using emerging methods and technologies in other areas of structural biology.

AREAS COVERED

This review describes how structural knowledge gained from X-ray crystallography has been used to advance other biophysical methods for structure determination (and vice versa). This article also covers current practices for integrating data generated by other biochemical and biophysical methods with those obtained from X-ray crystallography. Finally, the authors articulate their vision about how a combination of structural and biochemical/biophysical methods may improve our understanding of biological processes and interactions.

EXPERT OPINION

X-ray crystallography has been, and will continue to serve as, the central source of experimental structural biology data used in the discovery of new drugs. However, other structural biology techniques are useful not only to overcome the major limitation of X-ray crystallography, but also to provide complementary structural data that is useful in drug discovery. The use of recent advancements in biochemical, spectroscopy and bioinformatics methods may revolutionize drug discovery, albeit only when these data are combined and analyzed with effective data management systems. Accurate and complete data management is crucial for developing experimental procedures that are robust and reproducible.

Interactions of HIV-1 proteins as targets for developing anti-HIV-1 peptides

Protein–protein interactions (PPI) are essential in every step of the HIV replication cycle. Mapping the interactions between viral and host proteins is a fundamental target for the design and development of new therapeutics. In this review, we focus on rational development of anti-HIV-1 peptides based on mapping viral–host and viral–viral protein interactions all across the HIV-1 replication cycle. We also discuss the mechanism of action, specificity and stability of these peptides, which are designed to inhibit PPI. Some of these peptides are excellent tools to study the mechanisms of PPI in HIV-1 replication cycle and for the development of anti-HIV-1 drug leads that modulate PPI.

The cis conformation of proline leads to weaker binding of a p53 peptide to MDM2 compared to trans

The cis and trans conformations of the Xaa-Pro (Xaa: any amino acid) peptide bond are thermodynamically stable while other peptide bonds strongly prefer trans. The effect of proline cis-trans isomerization on protein binding has not been thoroughly investigated. In this study, computer simulations were used to calculate the absolute binding affinity for a p53 peptide (residues 17-29) to MDM2 for both cis and trans isomers of the p53 proline in position 27. Results show that the cis isomer of p53(17-29) binds more weakly to MDM2 than the trans isomer, and that this is primarily due to the difference in the free energy cost associated with the loss of conformational entropy of p53(17-29) when it binds to MDM2. The population of cis p53(17-29) was estimated to be 0.8% of the total population in the bound state. The stronger binding of trans p53(17-29) to MDM2 compared to cis may leave a minimal level of p53 available to respond to cellular stress. This study demonstrates that it is feasible to estimate the absolute binding affinity for an intrinsically disordered protein fragment binding to an ordered protein that are in good agreement with experimental results.

Emerging viral diseases: confronting threats with new technologies

Emerging viral diseases pose ongoing health threats, particularly in an era of globalization; however, new biomedical research technologies such as genome sequencing and structure-based vaccine and drug design have improved our ability to respond to viral threats.

The impact of active site mutations of South African HIV PR on drug resistance: Insight from molecular dynamics simulations, binding free energy and per-residue footprints

Molecular dynamics simulations and binding free energy calculations were used to provide an understanding of the impact of active site drug-resistant mutations of the South African HIV protease subtype C (C-SA HIV PR), V82A and V82F/I84V on drug resistance. Unique per-residue interaction energy 'footprints' were developed to map the overall drug-binding profiles for the wild type and mutants. Results confirmed that these mutations altered the overall binding landscape of the amino acid residues not only in the active site region but also in the flaps as well. Four FDA-approved drugs were investigated in this study; these include ritonavir (RTV), saquinavir (SQV), indinavir (IDV), and nelfinavir (NFV). Computational results compared against experimental findings were found to be complementary. Against the V82F/I84V variant, saquinavir, indinavir, and nelfinavir lose remarkable entropic contributions relative to both wild-type and V82A C-SA HIV PRs. The per-residue energy 'footprints' and the analysis of ligand-receptor interactions for the drug complexes with the wild type and mutants have also highlighted the nature of drug interactions. The data presented in this study will prove useful in the design of more potent inhibitors effective against drug-resistant HIV strains.

The role of mutations at codons 32, 47, 54, and 90 in HIV-1 protease flap dynamics

Treatment of Human Immunodeficiency Virus remains challenging due to the emergence of drug resistant strains under the selective pressure produced by standard anti-retroviral therapy. To explore the structural mechanisms of drug resistance, we performed 40 ns molecular dynamics simulations on three multi-drug resistant HIV-1 protease clinical isolates from patients attending an infectious diseases clinic in Detroit, MI. We identify a novel structural role for the I47V, V32I, I54M and L90M major resistance mutations in flap opening and closure of MDR-PR isolates. Our studies suggest I47V is involved in flap opening and the interaction between I47V and V32I tethers the flaps to the active site. Also, I54M and L90M may be responsible for asymmetric movement of the protease flaps. These findings can be utilized to improve drug design strategies against MDR HIV-1 PR variants.

Investigation on the mechanism for the binding and drug resistance of wild type and mutations of G86 residue in HIV-1 protease complexed with Darunavir by molecular dynamic simulation and free energy calculation

Residue Gly86 is considered as the highly conversed residue in the HIV-1 protease. In our work, the detailed binding free energies for the wild-type (WT) and mutated proteases binding to the TMC-114 are estimated to investigate the protein-inhibitor binding and drug resistance mechanism by molecule dynamic simulations and molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. The binding affinities between the mutants and inhibitor are different than that in the wild-type complex and the major resistance to Darunavir (DRV) of G86A and G86S originate from the electrostatic energy and entropy, respectively. Furthermore, free energy decomposition analysis for the WT and mutated complexes on the basis of per-residue indicates that the mutagenesis influences the energy contribution of the residue located at three regions: active site region (residue 24-32), the flap region, and the region around the mutated residue G86 (residue 79-88), especially the flap region. Finally, further hydrogen bonds and structure analysis are carried out to detect the relationship between the energy and conformation. In all, the G86 mutations change the flap region's conformation. The experimental results are in good agreement with available results.

The future of crystallography in drug discovery

INTRODUCTION

X-ray crystallography plays an important role in structure-based drug design (SBDD), and accurate analysis of crystal structures of target macromolecules and macromolecule-ligand complexes is critical at all stages. However, whereas there has been significant progress in improving methods of structural biology, particularly in X-ray crystallography, corresponding progress in the development of computational methods (such as in silico high-throughput screening) is still on the horizon. Crystal structures can be overinterpreted and thus bias hypotheses and follow-up experiments. As in any experimental science, the models of macromolecular structures derived from X-ray diffraction data have their limitations, which need to be critically evaluated and well understood for structure-based drug discovery.

AREAS COVERED

This review describes how the validity, accuracy and precision of a protein or nucleic acid structure determined by X-ray crystallography can be evaluated from three different perspectives: i) the nature of the diffraction experiment; ii) the interpretation of an electron density map; and iii) the interpretation of the structural model in terms of function and mechanism. The strategies to optimally exploit a macromolecular structure are also discussed in the context of 'Big Data' analysis, biochemical experimental design and structure-based drug discovery.

EXPERT OPINION

Although X-ray crystallography is one of the most detailed 'microscopes' available today for examining macromolecular structures, the authors would like to re-emphasize that such structures are only simplified models of the target macromolecules. The authors also wish to reinforce the idea that a structure should not be thought of as a set of precise coordinates but rather as a framework for generating hypotheses to be explored. Numerous biochemical and biophysical experiments, including new diffraction experiments, can and should be performed to verify or falsify these hypotheses. X-ray crystallography will find its future application in drug discovery by the development of specific tools that would allow realistic interpretation of the outcome coordinates and/or support testing of these hypotheses.

Automating crystallographic structure solution and refinement of protein-ligand complexes

High-throughput drug-discovery and mechanistic studies often require the determination of multiple related crystal structures that only differ in the bound ligands, point mutations in the protein sequence and minor conformational changes. If performed manually, solution and refinement requires extensive repetition of the same tasks for each structure. To accelerate this process and minimize manual effort, a pipeline encompassing all stages of ligand building and refinement, starting from integrated and scaled diffraction intensities, has been implemented in Phenix. The resulting system is able to successfully solve and refine large collections of structures in parallel without extensive user intervention prior to the final stages of model completion and validation.

CREDO: a structural interactomics database for drug discovery

CREDO is a unique relational database storing all pairwise atomic interactions of inter- as well as intra-molecular contacts between small molecules and macromolecules found in experimentally determined structures from the Protein Data Bank. These interactions are integrated with further chemical and biological data. The database implements useful data structures and algorithms such as cheminformatics routines to create a comprehensive analysis platform for drug discovery. The database can be accessed through a web-based interface, downloads of data sets and web services at http://www-cryst.bioc.cam.ac.uk/credo.

Database URL:http://www-cryst.bioc.cam.ac.uk/credo

Basic research in HIV vaccinology is hampered by reductionist thinking

This review describes the structure-based reverse vaccinology approach aimed at developing vaccine immunogens capable of inducing antibodies that broadly neutralize HIV-1. Some basic principles of protein immunochemistry are reviewed and the implications of the extensive polyspecificity of antibodies for vaccine development are underlined. Although it is natural for investigators to want to know the cause of an effective immunological intervention, the classic notion of causality is shown to have little explanatory value for a system as complex as the immune system, where any observed effect always results from many interactions between a large number of components. Causal explanations are reductive because a single factor is singled out for attention and given undue explanatory weight on its own. Other examples of the negative impact of reductionist thinking on HIV vaccine development are discussed. These include (1) the failure to distinguish between the chemical nature of antigenicity and the biological nature of immunogenicity, (2) the belief that when an HIV-1 epitope is reconstructed by rational design to better fit a neutralizing monoclonal antibody (nMab), this will produce an immunogen able to elicit Abs with the same neutralizing capacity as the Ab used as template for designing the antigen, and (3) the belief that protection against infection can be analyzed at the level of individual molecular interactions although it has meaning only at the level of an entire organism. The numerous unsuccessful strategies that have been used to design HIV-1 vaccine immunogens are described and it is suggested that the convergence of so many negative experimental results justifies the conclusion that reverse vaccinology is unlikely to lead to the development of a preventive HIV-1 vaccine. Immune correlates of protection in vaccines have not yet been identified because this will become feasible only retrospectively once an effective vaccine exists. The finding that extensive antibody affinity maturation is needed to obtain mature anti-HIV-1 Abs endowed with a broad neutralizing capacity explains why antigens designed to fit matured Mabs are not effective vaccine immunogens since these are administered to naive recipients who possess only B-cell receptors corresponding to the germline version of the matured Abs.

Experimental chemotherapy and approaches to drug discovery for Trypanosoma cruzi infection

In the 100 years since the discovery of Chagas disease, only two drugs have been developed and introduced into clinical practice, and these drugs were introduced over 40 years ago. The tools of drug discovery have improved dramatically in the interim; however, this has not translated into new drugs for Chagas disease. This has been largely because the main practitioners of drug discovery are pharmaceutical companies who are not financially motivated to invest in Chagas disease and other "orphan" diseases. As a result, it has largely been up to academic groups to bring drug candidates through the discovery pipeline and to clinical trials. The difficulty with drug discovery in academia has been the challenge of bringing together the diverse expertise in biology, chemistry, and pharmacology in concerted efforts towards a common goal of developing therapeutics. Funding is often inadequate, but lack of coordination amongst academic investigators with different expertise has also contributed to the slow progress. The purpose of this chapter is to provide an overview of approaches that can be accomplished in academic settings for preclinical drug discovery for Chagas disease. The chapter addresses methods of drug screening against Trypanosoma cruzi cultures and in animal models and includes general topics on compound selection, testing for drug-like properties (including oral bioavailability), investigating the pharmacokinetics and toxicity of compounds, and finally providing parameters to help with triaging compounds.

DESIGN OF HYBRID INHIBITORS TO HIV-1 PROTEASE

A series of HIV-1 protease (PR) inhibitors are designed to increase the binding affinity with PR subsites based on the quantum analysis of the contributions of molecular fragments in six FDA-approved PR drugs to the total binding interaction. The binding free energies were estimated by modified linear interaction energy approach [Zoete H, Michielin O, Karplus M, J Comput Aided Mol Des17:861, 2003], in which the binding free energy is written as a linear combination of the electrostatic interaction energy between PR and the ligand, Eelec, the van der Waals interaction energy between PR and the ligand, E vdW , and the difference of the solvation free energies of the complex, the receptor and the isolated ligand, ΔG solv . The parameters of these energy terms were fitted for a training set of 14 HIV-1 protease–inhibitor complexes of known 3D structure with a correlation coefficient of 0.91 and an unsigned mean error of 0.83 kcal/mol.

COMPUTATIONAL DESIGN OF NORBORNANE-BASED HIV-1 PROTEASE INHIBITORS

A series of norbornane-based HIV-1 protease (PR) inhibitors are designed theoretically to displace the tetrahedrally coordinated internal water molecule that bridges inhibitor to flaps via hydrogen bonds. These designed inhibitors use the norbornenone oxygen atom to mimic this structural water molecule and contain diols to interact with the carboxylate oxygens of catalytic aspartates. The binding free energies were estimated by modified linear interaction energy approach [Zoete H, Michielin O, Karplus M, J Comput Aided Mol Des17:861, 2003], in which the binding free energy is written as a linear combination of the electrostatic interaction energy between PR and the ligand, E elec , the van der Waals interaction energy between PR and the ligand, E vdW , and the difference of the solvation free energies of the complex, the receptor, and the isolated ligand, ΔG solv . The equation obtained in previous work [Da W. Zhang, Philip Lin Huang, Sylvia Lee-Huang, John Z. H. Zhang, J Theor Comput Chem7:485, 2008] is applied directly to calculate the binding free energy of designed norbornane-based HIV-1 PR inhibitors.