Structure Based Drug Design

Keywords: HIV-1 protease inhibitors; structure-based drug design; pyrrolidine-based inhibitors; crystallography; deviating binding modes Structure-based drug design is an integral part of industrial and academic drug discovery projects. Initial lead structures are, in general, optimized in terms of affinity using iterative cycles comprising synthesis, biological evaluation, computational methods, and structural analysis. X-ray crystallography commonly suggests the existence of a single well-defined state, termed binding mode, which is generally assumed to be consistent in a series of similar ligands and therefore used for the following optimization process. During the further development of symmetrically disubstituted 3,4-amino-pyrrolidines as human immunodeficiency virus type 1 protease inhibitors, we discovered that, by modification of the P1/P1′ moieties of our lead structure, the activity of the inhibitors towards the active-site mutation Ile84Val was altered, however, not being explainable with the initial underlying structure-activity relationship. The cocrystallization of the most potent derivative in complex with the human immunodeficiency virus type 1 protease surprisingly led to two different crystal forms (P2 1 2 1 2 1 and P6 1 22). Structural analysis revealed two completely different binding modes; the interaction of the pyrrolidine nitrogen atom with the catalytic aspartates remains as the only similarity. The study presented clearly demonstrates that structural biology has to escort the entire lead optimization process not to fail by an initially observed binding orientation.

Estrogens are structurally related steroids that regulate important physiological processes. 17beta-estradiol (E2) is reversibly oxidized to estrone (E1) and both E2 and E1 can be irreversibly converted to estriol (E3), which also originates directly from androstenedione. The action of E2 has been traditionally explained by the binding to the estrogen receptor (ER) alpha and ER beta, however the G protein-coupled receptor (GPR) 30 has been recently involved in the rapid signaling triggered by estrogens. Although the role of E2 in the development of breast cancer has been largely documented, the contribution of E3 still remains to be completely evaluated. Here, we demonstrate for the first time that E3 acts as a GPR30 antagonist since it was able to inhibit the GPR30-mediated responses such as the rapid ERK activation, the up-regulation of target genes like c-fos and connective tissue growth factor, the proliferative effects observed in ER-negative SkBr3 cells.

Retroviral protease (PR) from the human immunodeficiency virus type 1 (HIV-1) was identified over a decade ago as a potential target for structure-based drug design. This effort was very successful. Four drugs are already approved, and others are undergoing clinical trials. The techniques utilized in this remarkable example of structure-assisted drug design included crystallography, NMR, computational studies, and advanced chemical synthesis. The development of these drugs is discussed in detail. Other approaches to designing HIV-1 PR inhibitors, based on the concepts of symmetry and on the replacement of a water molecule that had been found tetrahedrally coordinated between the enzyme and the inhibitors, are also discussed. The emergence of drug-induced mutations of HIV-1 PR leads to rapid loss of potency of the existing drugs and to the need to continue the development process. The structural basis of drug resistance and the ways of overcoming this phenomenon are mentioned.

Background:  Thioredoxin  reductase  is  an  important  enzyme  in
antioxidant  defense and regulation of cell function, its inhibition has
cytotoxic effects.
Aim:  To predict if; there  is correlation  between the already  in vitro
cytotoxic studied Au (III) complexes & their docking studies
Material  &  Method:  The  enzyme  in  complex  with  a  potential
inhibitor will be downloaded from the Protein Data Bank (PDB). The
inhibitory  binding  site  of  the  enzyme  will  be  defined  as  the  8  A°-sphere of residues surrounding the inhibitor.
Four gold-based compounds along with cisplatin and XAN were then
docked into the defined site and their poses inside the active site were
analyzed.
Results:  These  compounds  have  already  shown  in  vitro  activity
against  the  Hep-2  cell  line  and  this  cytotoxicity  compared  with  the
results generated by the computer
Conclusion: Computer aided drug design is excellent & modern tool
to correlate between the structures & inhibition properties of Au  (III)
complexes.

Herein we will focus on the use of quantum mechanics (QM) in drug design (DD) to solve disparate problems from scoring protein-ligand poses to building QM QSAR models. Through the variational principle of QM we know that we can obtain a more accurate representation of molecular systems than classical models, and while this is not a matter of debate, it still has not been shown that the expense of QM approaches is offset by improved accuracy in DD applications. Objectively validating the improved applicability and performance of QM over classical-based models in DD will be the focus of research in the coming years along with research on the conformational sampling problem as it relates to proteinligand complexes.

This study was performed to asses and establishes the prevalence of Polypharmacy in geriatric population in the Medicine ward of Rajah Muthaiya Medical College and Hospital, Annamalai University, during one year from January 2013 to January 2014. Demographic analyses of this prospective study revealed that out of 520 patients, 342 (65.76%) were males and 178 (34.23%) were females. All the collected prescriptions were scrutinized for Polypharmacy and were categorized as minor Polypharmacy -concurrent use of ≤ 5 drugs; and major Polypharmacy -concurrent use of > 5 drugs. Out of 502 Prescriptions 61(11.73%) prescriptions were minor Polypharmacy and 457(88.26%) prescriptions were major Polypharmacy. The maximum patients were in the age group of 60-64 (38.84%) range lead to a significant increase in the number of medications. The most common diseases associated systems were Cardiovascular system 147 (28.26 %) patients, and followed by Respiratory system 103(19.80%). Our results show that there is a higher prevalence of Polypharmacy among the males than females. The length of hospital stay of geriatric patients is increase in major Polypharmacy compare with minor Polypharmacy. The prevalence of cardiovascular drugs and respiratory drugs were often involved in Polypharmacy among geriatric patients. Polypharmacy is very common among geriatric patient and health care professional’s interventions to improve the optimal use of medication in geriatric could lead to reduction in the drug related problems associated with Polypharmacy.

Quantum mechanical (QM) methods are becoming popular in computational drug design and development mainly because high accuracy is required to estimate (relative) binding affinities. For low-to medium-throughput in silico screening, (e.g., scoring and prioritizing a series of inhibitors sharing the same molecular scaffold) efficient approximations have been developed in the past decade, like linear scaling QM in which the computation time scales almost linearly with the number of basis functions. Furthermore, QM-based procedures have been used recently for determining protonation states of ionizable groups, evaluating energies, and optimizing molecular structures. For high-throughput in silico screening QM approaches have been employed to derive robust quantitative structure-activity relationship models. It is expected that the use of QM methods will keep growing in all phases of computer-aided drug design and development. However, extensive sampling of conformational space and treatment of solution of macromolecules are still limiting factors for the broad application of QM in drug design.

Virtual screening (VS) overcomes the limitations of traditional high-throughput screening (HTS) by applying computer-based methods in drug discovery. VS takes advantage of fast algorithms to filter chemical space and successfully select potential drug candidates. A key aspect in structure-based VS is the sampling of ligand-receptor conformations and the evaluation of these poses to predict near-native binding modes. The development of fast and accurate algorithms during the last few years has allowed VS to become an important tool in drug discovery and design. Herein, an overview of widely used ligand-based (e.g., similarity, pharmacophore searches) and structure-based (protein-ligand docking) VS methods is discussed. Their strengths and limitations are described, along with many successful stories. This review not only serves as an introductory guide for inexperienced VS users but also presents a general overview of the current state and scope of these powerful tools.

We have developed an evolutionary approach for flexible ligand docking. This approval, GEMDOCK, uses a Generic Evolutionary Method for molecular DOCKing and an empirical scoring function. The former combines both discrete and continuous global search strategies with local search strategies to speed up convergence, whereas the latter results in rapid recognition of potential ligands. GEMDOCK was tested on a diverse data set of 100 protein-ligand complexes from the Protein Data Bank. In 79% of these complexes, the docked lowest energy ligand structures had root-mean-square derivations (RMSDs) below 2.0 Å with respect to the corresponding crystal structures. The success rate increased to 85% if the structure water molecules were retained. We evaluated GEMDOCK on two cross-docking experiments in which each ligand of a protein ensemble was docked into each protein of the ensemble. Seventysix percent of the docked structures had RMSDs below 2.0 Å when the ligands were docked into foreign structures. We analyzed and validated GEM-DOCK with respect to various search spaces and scoring functions, and found that if the scoring function was perfect, then the predicted accuracy was also essentially perfect. This study suggests that GEMDOCK is a useful tool for molecular recognition and may be used to systematically evaluate and thus improve scoring functions. Proteins 2004; 55:288 -304.

The SARS-CoV-2 virus is the pathogenic agent that caused the COVID-19 disease. The epicenter of this disease is the city of Wuhan, China. It is already categorized as “pandemic” by WHO, as many countries already affected with the infections, including recently Indonesia. Although the standard RT-PCR and DNA sequencing protocols has already developed for diagnostic, no drugs are available to cure this disease until today. The anti-malaria drug of chloroquine phosphate was repurposed, as well as other anti-viral drugs. In this regard, a structural bioinformatics pipeline was utilized to validate the claim in the computational realm. Within the sphere of the online molecular docking method, it was found that all the tested repurposed drugs attached accordingly with the SARS-CoV-2 protease enzyme that plays a role in viral replication. The repurposed drugs could be proposed as drug candidates for COVID-19, after clinical trials or further laboratory testing

A series of N-, S-, and COOH-blocked glutathione derivatives were evaluated against the pathogenic parasites Trypanosoma brucei, Trypanosoma cruzi, and Leishmania donovani in vitro, to identify the determinants necessary for activity and for further development into an active lead structure. The results show that N,S-blocked glutathione diesters are the most effective inhibitors of T. brucei with structures 14-16 being the most active, 14 having an IC 50 ∼ 1.9 µM. The toxicity effects observed for glutathione derivatives 12, 14, and 16 have been correlated to the K562 antileukemic activity of these compounds and their inhibitory effects on the glyoxalase system of the host. Diester compounds based on S-2,4-dinitrophenylglutathione (17-22) were found to be significantly better inhibitors of T. brucei with ED 50 's in the range 16-0.19 µM. Compounds 19 and 20 were the two best inhibitors, with an ED 50 of ∼1.07 and 0.19 µM, respectively; however 20 displayed toxicity in parasitic assays. Monoesters, monoamides, and diamides tested generally exhibited low in vitro activity. The compounds did not inhibit glutathionylspermidine synthetase and trypanothione reductase enzyme targets in the unique trypanothione pathway of these parasites. Diester compounds per se were considered to be ineffective inhibitors of trypanothione metabolism suggesting that these compounds might act as prodrugs, being hydrolyzed in situ into a variety of glutathione derivatives which include combinations of monoesters, free acids, and amines, some of which are inhibitors of trypanothione metabolism.

A novel series of non-covalent, benzimidazole-based inhibitors of DPP-4 has been developed from a small fragment hit using structure-based drug design. A highly versatile synthetic route was created for the development of SAR, which led to the discovery of potent and selective inhibitors with excellent pharmaceutical properties.

In today's research environment, a wealth of experimental/theoretical structural data is available and the number of therapeutically relevant macromolecular structures is growing rapidly. This, coupled with the huge number of small non-peptide potential drug candidates easily available (over 7 million compounds), highlight the need of using computeraided techniques for the efficient identification and optimization of novel hit compounds. Virtual (or in silico) ligand screening based on the three-dimensional structure of macromolecular targets (SB-VLS) is firmly established as an important approach to identify chemical entities that have a high likelihood of binding to a target molecule to elicit desired biological responses. A myriad of free applications and services facilitating the drug discovery process have been posted on the Web. In this review, we cite over 350 URLs that are useful for SB-VLS projects and essentially free for academic groups. We attempt to provide links for in silico ADME/tox prediction tools, compound collections, some ligand-based methods, characterization/simulation of 3D targets and homology modeling tools, druggable pocket predictions, active site comparisons, analysis of macromolecular interfaces, protein docking tools to help identify binding pockets and protein-ligand docking/scoring methods. As such, we aim at providing both, methods pertaining to the field of Structural Bioinformatics (defined here as tools to study macromolecules) and methods pertaining to the field of Chemoinformatics (defined here as tools to make better decisions faster in the arena of drug/lead identification and optimization). We also report several recent success stories using these free computer methods. This review should help readers finding free computer tools useful for their projects. Overall, we are confident that these tools will facilitate rapid and cost-effective identification of new hit compounds. The URLs presented in this review will be updated regularly at www.vls3d.com in the coming months, "Links" section.

Assessing whether a protein structure is a good target or not before actually doing structure-based drug design on it is an important step to speed up the ligand discovery process. This is known as the " druggability " or " ligandability " assessment problem that has attracted increasing interest in recent years. The assessment typically includes the detection of ligand-binding sites on the protein surface and the prediction of their abilities to bind drug-like small molecules. A brief summary of the established methods of binding sites detection and druggability(ligandability) prediction, as well as a detailed description of the CAVITY approach developed in the authors' group was given. CAVITY showed good performance on ligand-binding site detection, and was successfully used to predict both the ligandabil-ities and druggabilities of the detected binding sites.

GTP-bound mutant form H-Ras (Harvey-Ras) proteins are found in 30% of human tumors. Activation of H-Ras is due to point mutation at positions 12, 13, 59 and/or 61 codon. Mutant form of H-Ras proteins is continuously involved in signal transduction for cell growth and proliferation through interaction of downstream-regulated protein Raf. In this paper, we have reported the virtual screening of lead compounds for H-Ras P21 mutant protein from ChemBank and DrugBank databases using LigandFit and DrugBank-BLAST. The analysis resulted in 13 hits which were docked and scored to identify structurally active leads that make similar interaction to those of bound complex of H-Ras P21 mutant-Raf. This approach produced two different leads, 3-Aminopropanesulphonic acid (docked energy -3.014 kcal/mol) and Hydroxyurea (docked energy -0.009 kcal/mol) with finest Lipinski’s rule-of-five. Their docked energy scores were better than the complex structure of H-Ras P21 mutant protein bound with Raf (1.18 kcal/mol). All the leads were docked into effector region forming interaction with ILE36, GLU37, ASP38 and SER39.

Purine nucleoside phosphorylase (PNP) catalyzes the phosphorolysis of the N-ribosidic bonds of purine nucleosides and deoxynucleosides. PNP is a target for inhibitor development aiming at T-cell immune response modulation and has been submitted to extensive structure-based drug design. More recently, the 3-D structure of human PNP has been refined to 2.3 A A resolution, which allowed a redefinition of the residues involved in the substrate-binding sites and provided a more reliable model for structure-based design of inhibitors. This work reports crystallographic study of the complex of Human PNP:guanine (HsPNP:Gua) solved at 2.7 A A resolution using synchrotron radiation. Analysis of the structural differences among the HsPNP:Gua complex, PNP apoenzyme, and HsPNP:immucillin-H provides explanation for inhibitor binding, refines the purine-binding site, and can be used for future inhibitor design.

This paper deals with a combination of work in the fields of artificial intelligence and computer security. It describes a decision model based on a new genetic algorithm approach for intrusion response system (NGAA-IRS). A brief survey of intrusion detection and response system (IDRS), genetic algorithm (GA), and its application to IDRS are presented. Then, the proposed model, parameters and evolution process for GA are discussed in details. The model is characterized by a new implementation of individual structure based on a matrix of response-resource entries and a fitness function based on costbenefit approach for selecting the appropriate solution. These features are specific to NGAA-IRS model and do not be used in other implementations beforehand.

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5202379

Mycobacterium tuberculosis dethiobiotin synthase (MtDTBS) is a crucial enzyme involved in the biosynthesis of biotin in the causative agent of tuberculosis, M. tuberculosis. Here, we report a binder of MtDTBS, cyclopentylacetic acid 2 (K D = 3.4 ± 0.4 mM), identified via in silico screening. X-ray crystallography showed that 2 binds in the 7,8-diaminopelargonic acid (DAPA) pocket of MtDTBS. Appending an acidic group to the para-position of the aromatic ring of the scaffold revealed compounds 4c and 4d as more potent binders, with K D = 19 ± 5 and 17 ± 1 μM, respectively. Further optimization identified tetrazole 7a as a particularly potent binder (K D = 57 ± 5 nM) and inhibitor (K i = 5 ± 1 μM) of MtDTBS. Our findings highlight the first reported inhibitors of MtDTBS and serve as a platform for the further development of potent inhibitors and novel therapeutics for the treatment of tuberculosis.

Calculating free energies of binding (ΔGbind) between ligands and their target protein is of major interest to drug discovery and safety, yet it is still associated with several challenges and difficulties. Linear interaction energy (LIE) is an efficient in silico method for ΔGbind computation. LIE models can be trained and used to directly calculate binding affinities from interaction energies involving ligands in the bound and unbound states only, and LIE can be combined with statistical weighting to calculate ΔGbind for flexible proteins that may bind their ligands in multiple orientations. Here, we investigate if LIE predictions can be effectively improved by explicitly including the entropy of (de)solvation into our free-energy calculations. For that purpose, we combine LIE calculations for the protein−ligand-bound state with explicit free-energy perturbation to rigorously compute the unbound ligand's solvation free energy. We show that for 28 Cytochrome P450 2A6 (CYP2A6) ligands, coupling LIE with alchemical solvation free-energy calculation helps to improve obtained correlation between computed and reference (experimental) binding data.

The envelope (E) protein from coronaviruses is a small polypeptide that contains at least one a-helical transmembrane domain. Absence, or inactivation, of E protein results in attenuated viruses, due to alterations in either virion morphology or tropism. Apart from its morphogenetic properties, protein E has been reported to have membrane permeabilizing activity. Further, the drug hexamethylene amiloride (HMA), but not amiloride, inhibited in vitro ion channel activity of some synthetic coronavirus E proteins, and also viral replication. We have previously shown for the coronavirus species responsible for severe acute respiratory syndrome (SARS-CoV) that the transmembrane domain of E protein (ETM) forms pentameric a-helical bundles that are likely responsible for the observed channel activity. Herein, using solution NMR in dodecylphosphatidylcholine micelles and energy minimization, we have obtained a model of this channel which features regular a-helices that form a pentameric left-handed parallel bundle. The drug HMA was found to bind inside the lumen of the channel, at both the C-terminal and the N-terminal openings, and, in contrast to amiloride, induced additional chemical shifts in ETM. Full length SARS-CoV E displayed channel activity when transiently expressed in human embryonic kidney 293 (HEK-293) cells in a whole-cell patch clamp set-up. This activity was significantly reduced by hexamethylene amiloride (HMA), but not by amiloride. The channel structure presented herein provides a possible rationale for inhibition, and a platform for future structure-based drug design of this potential pharmacological target.

Better treatment of protein flexibility is essential in structure-based drug design projects such as virtual screening and protein-ligand docking. Diversity in ligand-binding mechanisms and receptor conformational changes makes it difficult to treat dynamic features of the receptor during the docking simulation. Thus, the use of pregenerated multiple receptor conformations is applied today in virtual screening studies. However, generation of a small relevant set of receptor conformations remains challenging. To address this problem, we propose a new protocol for the generation of multiple receptor conformations via normal mode analysis and for the selection of several receptor conformations suitable for docking/virtual screening. We validated this protocol on cyclin-dependent kinase 2, which possesses a binding site located at the interface between two subdomains and is known to undergo significant conformational changes in the active site region upon ligand binding. We believe that the suggested rules for the choice of suitable receptor conformations can be applied to other targets when dealing with in silico screening on flexible receptors.

The impact of invasive fungal infections on human health is a serious, but largely overlooked, public health issue. Commonly affecting the immunocompromised community, fungal infections are predominantly caused by species of Candida, Cryptococcus, and Aspergillus. Treatments are reliant on the aggressive use of pre-existing antifungal drug classes that target the fungal cell wall and membrane. Despite their frequent use, these drugs are subject to unfavorable drug-drug interactions, can cause undesirable side-effects and have compromised efficacy due to the emergence of antifungal resistance. Hence, there is a clear need to develop novel classes of antifungal drugs. A promising approach involves exploiting the metabolic needs of fungi by targeted interruption of essential metabolic pathways. This review highlights potential antifungal targets including enolase, a component of the enolase-plasminogen complex, and enzymes from the mannitol biosynthesis and purine nucleotide biosynthes...

Fragment-based ligand design approaches, such as the multi-copy simultaneous search (MCSS) methodology, have proven to be useful tools in the search for novel therapeutic compounds that bind pre-specified targets of known structure. MCSS offers a variety of advantages over more traditional high-throughput screening methods, and has been applied successfully to challenging targets. The methodology is quite general and can be used to construct functionality maps for proteins, DNA, and RNA. In this review, we describe the main aspects of the MCSS method and outline the general use of the methodology as a fundamental tool to guide the design of de novo lead compounds. We focus our discussion on the evaluation of MCSS results and the incorporation of protein flexibility into the methodology. In addition, we demonstrate on several specific examples how the information arising from the MCSS functionality maps has been successfully used to predict ligand binding to protein targets and RNA.

Computer methods can now be used on almost every stage of drug development, but the most common areas of computers application are virtual screening and lead generation/optimization stages. Accurate prediction of the protein-ligand binding affinities is a crucial step in the structure-based drug design approach. Current algorithms and tools for binding energy calculation that are used upon the development of new drug candidates with an emphasize on underlying principles, advantages and limitations, software and general considerations in the selection of specific methods are discussed in the paper. Four main classes of currently available physics-based computer methods (molecular docking, end point / approximate free energy, relative binding free energy, and absolute binding free energy) are reviewed in details. Molecular docking approaches are the method of choice to filter out compounds-nonbinders, but they are not accurate enough to predict binding affinity. The end point methods are more physically rigorous and closer to real free energy calculations, but they are more computationally-intensive and not predictive for some types of proteins. Relative binding free energy methods take into account conformational and entropic contributions, thus offering more accurate predictions. However, they have high computational requirements and can be used only to compare related ligands or receptors. The extremely computational-dependent method of absolute binding free energy calculation is the most powerful approach, giving predictions with good correlations to experimental binding affinities. 1. INTRODUCTION Rational structure-based computer-aided modeling of protein-ligand interactions is now a key component in modern drug discovery paradigm (Charifson,1997). It is widely accepted that computational methods have played an extremely important role in the design process for a growing number of marketed drugs, and in the development of new drug candidates (Mobley & Dill 2010). Moreover, by the aid of computer-aided drug design (CADD), the cost of drug development could be reduced by up to 50% (Tan, 2010). Computer methods can now be used on almost every stage of drug development, but the most common areas of computers application are virtual screening and lead generation/optimization stages (Xiang, 2012). Virtual screening methods, which are designed for searching large libraries of compounds in silico, are widely used within the drug R&D industry and play an indispensable role in modern CADD efforts. These methods usually give a much higher hit rate than the traditional high throughput screening (HTS) (Tang, 2006) and the hits from VS appear more drug-like than the ones from HTS (Shekhar, 2008). At the same time, there is concern that VS methods may have reached a limit in effectiveness (Schneider, 2010). Current virtual screening methods are not very effective in selecting molecules that are actually active against the selected target molecule, although they are undoubtedly useful in eliminating some inactive compounds (Chodera, 2012). Limitations of the VS methods come from a variety of approximations used to allow large numbers of compounds to be screened quickly, often neglecting statistical mechanical and chemical effects for computational efficiency (Chodera, 2012), thus leading to the inaccuracies in the estimation of protein-ligand binding energy. Lead optimization is another crucially important step (Keseru & Makara, 2006) among all of the stages of drug discovery process. From the computational side, the key step in lead optimization process is an accurate prediction of the protein-ligand binding affinities (Jorgensen, 2009), since it is currently accepted that the biological activity of a compound is closely related to the affinity of the compound to macromolecular receptor (Gohlke & Klebe, 2002). Unfortunately, available methods for binding affinity estimation do not possess enough balance between calculation efficiency and reliability, and in a typical situation the most accurate methods are the most time consuming, while the fastest algorithms usually are not very rigorous and accurate (Xiang, 2012). In this review we are going to discuss current approaches and tools for binding energy calculation that are used upon the development of new drug candidates with an emphasize on underlying principles, advantages and limitations, software and general considerations on the selection of specific methods for different users. Computational Approaches to Binding Energy Prediction: Currently available physics-based computer methods can be grouped in at least four different classes. Below are listed from the fastest to slowest, and from the least

Knowledge of the 3D structure of proteins can play a key role in both understanding the biochemical function of protein targets, and developing small-molecule drugs that interact with these targets. This review will discuss recent advances in automation and miniaturization, which are making the determination of protein structures faster, more reliable, and more economical than has been possible historically. genomics supplement | reviews

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. Through structurebased virtual screening, novel pyrazole hit 8a was identified as Aurora kinase A inhibitor (IC 50 ) 15.1 µM). X-ray cocrystal structure of 8a in complex with Aurora A protein revealed the C-4 position ethyl carboxylate side chain as a possible modification site for improving the potency. On the basis of this insight, bioisosteric replacement of the ester with amide linkage and changing the ethyl substituent to hydrophobic 3-acetamidophenyl ring led to the identification of 12w with a ∼450-fold improved Aurora kinase A inhibition potency (IC 50 ) 33 nM), compared to 8a. Compound 12w showed selective inhibition of Aurora A kinase over Aurora B/C, which might be due to the presence of a unique H-bond interaction between the 3-acetamido group and the Aurora A nonconserved Thr217 residue, which in Aurora B/C is Glu and found to sterically clash with the 3-acetamido group in modeling studies.

The apicomplexan family of pathogens, which includes Plasmodium spp. and Toxoplasma gondii, are primarily obligate intracellular parasites and invade multiple cell types. These parasites express extracellular membrane protein receptors, adhesins, to form specific pathogen–host cell interaction complexes. Various adhesins are used to invade a variety of cell types. The receptors are linked to an acto- myosin motor, which is part of a complex comprised of many proteins known as the invasion machinery or glideosome. To date, reviews on invasion have focused primarily on the molecular pathways and sig- nals of invasion, with little or no structural information presented. Over 75 structures of parasite recep- tors and glideosome proteins have been deposited with the Protein Data Bank. These structures include adhesins, motor proteins, bridging proteins, inner membrane complex and cytoskeletal proteins, as well as co-crystal structures with peptides and antibodies. These structures provide information regarding key interactions necessary for target receptor engagement, machinery complex formation, how force is trans- mitted, and the basis of inhibitory antibodies. Additionally, these structures can provide starting points for the development of antibodies and inhibitory molecules targeting protein–protein interactions, with the aim to inhibit invasion. This review provides an overview of the parasite adhesin protein families, the glideosome components, glideosome architecture, and discuss recent work regarding alternative models.

A group of ligand molecules that possess high in silico affinity for
Chitinase enzyme were screened to obtain the best ligand which have the
highest fitting score and the relative energy. The conformational analysis of
filariasis causing drugs and its pharmacophore studies against chitinase are
successfully analyzed using the various software tools and databases such
as Accelrys discovery studio,(PS)2, SAVS, swissprot, drug bank, etc. These
tools and software were used to first obtain the target sequence and then
predict its secondary and tertiary structure. Accelrys Discovery Studio helped
to run various protocols which included molecular properties prediction,
conformation analysis, toxicity prediction, ADMET descriptor prediction.
Docking of the ligands having appropriate properties was done with target
protein. Thus ligands with best fitting score were obtained as most efficient
ligand which could act as drug for the filariasis against chitinase enzyme.

A series of (E)-2-(2-substituted benzylidene)-and 2-(2-substituted benzyl)-6-methoxy-tetralones were prepared, using an efficient synthetic scheme, and evaluated for their inhibitory activity against cytochrome P450C24A1 (CYP24A1) hydroxylase. In general the reduced benzyl tetralones were more active than the parent benzylidene tetralones. The 2-ethyl and 2-trifluoromethyl benzyl tetralone derivatives (4c and 4b) showed optimal activity in this series with IC 50 values of 1.92 mM and 2.08 mM, respectively compared with the standard ketoconazole IC 50 0.52 mM. The 2-bromobenzyl tetralone (4d) showed a preference for CYP27A1 (IC 50 59 nM) over CYP24A1 (IC50 16.3 mM) and may be a useful lead in CYP27A1 inhibition studies. The 2-ethylphenyl benzyl derivative (9c), which showed weak activity against the wild type CYP24A1 (IC 50 25.57 mM), exhibited enhanced inhibitory activity towards L148F and M416T mutants, this difference in activity for the L148F mutant has been explained using molecular modelling.

HIV infected patients often take at least three anti-HIV drugs together in Highly Active Antiretroviral Therapy (HAART) and/or Ritonavir-Boosted Protease Inhibitor Therapy (PI/r) to suppress the viral replications. The potential drug–drug interactions affect efficacy of anti-HIV treatment and major source of such interaction is competition for the drug metabolizing enzyme, cytochrome P450 (CYP). CYP3A4 isoform is the enzyme responsible for metabolism of currently available HIV-1 protease drugs. Hence administration of these drugs in HARRT or PI/r leads to increased toxicity and reduced efficacy in HIV treatment. We used computational molecular docking method to predict such interactions by which to compare experimentally measured metabolism of each HIV-1 protease drug. AutoDock 4.0 was used to carry out molecular docking of 10 HIV-protease drugs into CYP3A4 to explore sites of reaction and interaction energies (i.e., binding affinity) of the complexes. Arg105, Arg106, Ser119, Arg212, Ala370, Arg372, and Glu374 are identified as major drug binding residues, and consistent with previous data of site-directed mutagenesis, crystallography structure, modeling, and docking studies. In addition, our docking results suggested that phenylalanine clusters and heme are also participated in the binding to mediate drug oxidative metabolism. We have shown that HIV-1 protease drugs such as tipranavir, nelfinavir, lopinavir, and atazanavir differ in their binding modes on each other for metabolic clearance in CYP3A4, whereas ritonavir, amprenavir, indinavir, saquinavir, fosamprenavir, and darunavir share the same binding mode.

In search for specific drugs against steroid-dependent cancers we have developed a novel set of potent inhibitors of steroidogenic human 17b-hydroxysteroid dehydrogenase type 1 (17b-HSD 1). The X-ray structure of 17b-HSD 1 in complex with estradiol served as basis for the design of the inhibitors. 2-Substituted estrone and D-homo-estrone derivatives were synthesized and tested for 17b-HSD 1 inhibition. The best 17b-HSD 1 inhibitor, 2-phenethyl-D-homo-estrone, revealed an IC 50 of 15 nM in vitro. The inhibitory potency of compounds is comparable or better to that of previously described inhibitors. An interaction within the cofactor binding site is not necessary to obtain this high binding affinity for substances developed.

Advances in bioinformatics and protein modeling algorithms, in addition to the enormous increase in experimental protein structure information, have aided in the generation of databases that comprise homology models of a significant portion of known genomic protein sequences. Currently, 3D structure information can be generated for up to 56% of all known proteins. However, there is considerable controversy concerning the real value of homology models for drug design. This review provides an overview of the latest developments in this area and includes selected examples of successful applications of the homology modeling technique to pharmaceutically relevant questions. In addition, the strengths and limitations of the application of homology models during all phases of the drug discovery process are discussed.

In the last twenty years the efforts to design and optimize new drugs have been based on the three dimensional structure of the selected target proteins. In this regard, useful information has been achieved mainly by protein crystallography, which has recently turned from a low into a high-throughput process thanks to the improvement in robot technologies, automation procedure and the use of synchrotron radiation facilities . This review examines the impact of Structure Based Drug Design (SBDD) on the discovery of ligands as the selective estrogen receptor modulators (SERMs) of the Estrogen Receptor (ER) , which is involved in the regulation of several physiological and pathological processes.

In this Letter, we provide the structure–activity relationships, optimization of design, testing criteria, and human half-life data for a series of selective COX-2 inhibitors. During the course of our structure-based drug design efforts, we discovered two distinct binding modes within the COX-2 active site for differently substituted members of this class. The challenge of a undesirably long human half-life for the first clinical candidate 1t1/2 = 360 h was addressed by multiple strategies, leading to the discovery of 29b-(S) (SC-75416) with t1/2 = 34 h.We describe our strategy to discover a selective inhibitor of COX-2 with a shorter human half-life compared with the previous clinical candidate SD-8381 (5c-(S), t1/2 ∼160 h). In this paper, we disclose a series of selective COX-2 inhibitors based on the benzopyran template that display potency against COX-2 in animal models of pain and inflammation. We also discussed the discovery of two COX-2 binding modes and utilizing the microsomal data as a filter leading to the discovery of clinical candidate 29b-(S) (SC-75416), successfully advanced through a phase II efficacy trial.

As part of our effort to inhibit bacterial fatty acid biosynthesis through the recently validated target biotin carboxylase, we employed a unique combination of two emergent lead discovery strategies. We used both de novo fragment-based drug discovery and virtual screening, which employs 3D shape and electrostatic property similarity searching. We screened a collection of unbiased low-molecular-weight molecules and identified a structurally diverse collection of weak-binding but ligand-efficient fragments as potential building blocks for biotin carboxylase ATP-competitive inhibitors. Through iterative cycles of structurebased drug design relying on successive fragment costructures, we improved the potency of the initial hits by up to 3000-fold while maintaining their ligandefficiency and desirable physicochemical properties. In one example, hitexpansion efforts resulted in a series of amino-oxazoles with antibacterial activity. These results successfully demonstrate that virtual screening approaches can substantially augment fragment-based screening approaches to identify novel antibacterial agents. ARTICLE www.acschemicalbiology.org VOL.4 NO.6 • ACS CHEMICAL BIOLOGY ARTICLE

Purine nucleoside phosphorylase (PNP) catalyzes the phosphorolysis of the N-ribosidic bonds of purine nucleosides and deoxynucleosides. PNP is a target for inhibitor development aiming at T-cell immune response modulation and has been submitted to extensive structure-based drug design. More recently, the 3-D structure of human PNP has been refined to 2.3 Å resolution, which allowed a redefinition of the residues involved in the substrate-binding sites and provided a more reliable model for structure-based design of inhibitors. This work reports crystallographic study of the complex of Human PNP:guanine (HsPNP:Gua) solved at 2.7 Å resolution using synchrotron radiation. Analysis of the structural differences among the HsPNP:Gua complex, PNP apoenzyme, and HsPNP:immucillin-H provides explanation for inhibitor binding, refines the purine-binding site, and can be used for future inhibitor design.