Structure Based Drug Design

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.

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.

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.

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.

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

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.

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.

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...

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

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.

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.

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 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.

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.