A hierarchical method for molecular docking using cloud computing

Discovering small molecules that interact with protein targets will be a key part of future drug discovery efforts. Molecular docking of drug-like molecules is likely to be valuable in this field; however, the great number of such molecules makes the potential size of this task enormous. In this paper, a method to screen small molecular databases using cloud computing is proposed. This method is called the hierarchical method for molecular docking and can be completed in a relatively short period of time. In this method, the optimization of molecular docking is divided into two subproblems based on the different effects on the protein–ligand interaction energy. An adaptive genetic algorithm is developed to solve the optimization problem and a new docking program (FlexGAsDock) based on the hierarchical docking method has been developed. The implementation of docking on a cloud computing platform is then discussed. The docking results show that this method can be conveniently used for the efficient molecular design of drugs.     

Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery–a structure-based approach

Cancer progression is a global burden. The incidence and mortality now reach 30 million deaths per year. Several pathways of cancer are under investigation for the discovery of effective therapeutics. The present study highlights the structural details of the ubiquitin protein ‘Ubiquitin-conjugating enzyme E2D4’ (UBE2D4) for the novel lead structure identification in cancer drug discovery process. The evaluation of 3D structure of UBE2D4 was carried out using homology modelling techniques. The optimized structure was validated by standard computational protocols. The active site region of the UBE2D4 was identified using computational tools like CASTp, Q-site Finder and SiteMap. The hydrophobic pocket which is responsible for binding with its natural receptor ubiquitin ligase CHIP (C-terminal of Hsp 70 interacting protein) was identified through protein-protein docking study. Corroborating the results obtained from active site prediction tools and protein-protein docking study, the domain of UBE2D4 which is responsible for cancer cell progression is sorted out for further docking study. Virtual screening with large structural database like CB_Div Set and Asinex BioDesign small molecular structural database was carried out. The obtained new ligand molecules that have shown affinity towards UBE2D4 were considered for ADME prediction studies. The identified new ligand molecules with acceptable parameters of docking, ADME are considered as potent UBE2D4 enzyme inhibitors for cancer therapy.     

Exploring docking methods for virtual screening: application to the identification of neuraminidase and Ftsz potential inhibitors

Virtual screenings based on molecular docking play a major role in medicinal chemistry for the identification of new bioactive molecules. For this purpose, several docking methods can be used. Here, using Arguslab as software and a Gold Platinum subset library of commercially available compounds from Asinex, two docking methods associated to the scoring function Ascore were employed to investigate virtual screenings. One method is based on a genetic algorithm and the other based on a shape-based method. As case studies, both docking techniques were explored by targeting the PC190723 binding site of FtsZ protein from Staphylococcus aureus and the active site of N8 neuraminidase from Influenza virus. Following four docking sequences for each docking engine, the genetic algorithm led to multiple docking results, whereas the shape-based method gave reproducible results. The present study shows that the stochastic nature of the genetic algorithm will require the biological evaluation of more compounds than the shape-based method. This study showed that both methods are complementary and also led to the identification of neuraminidase and FtsZ potential inhibitors.     

Molecular docking and enzymatic evaluation to identify selective inhibitors of aspartate semialdehyde dehydrogenase

Microbes that have gained resistance against antibiotics pose a major emerging threat to human health. New targets must be identified that will guide the development of new classes of antibiotics. The selective inhibition of key microbial enzymes that are responsible for the biosynthesis of essential metabolites can be an effective way to counter this growing threat. Aspartate semialdehyde dehydrogenases (ASADHs) produce an early branch point metabolite in a microbial biosynthetic pathway for essential amino acids and for quorum sensing molecules. In this study, molecular modeling and docking studies were performed to achieve two key objectives that are important for the identification of new selective inhibitors of ASADH. First, virtual screening of a small library of compounds was used to identify new core structures that could serve as potential inhibitors of the ASADHs. Compounds have been identified from diverse chemical classes that are predicted to bind to ASADH with high affinity. Next, molecular docking studies were used to prioritize analogs within each class for synthesis and testing against representative bacterial forms of ASADH from Streptococcus pneumoniae and Vibrio cholerae. These studies have led to new micromolar inhibitors of ASADH, demonstrating the utility of this molecular modeling and docking approach for the identification of new classes of potential enzyme inhibitors.     

FGO: a novel ontology for identification of ligand functional group

Small molecules play crucial role in the modulation of biological functions by interacting with specific macromolecules. Hence small molecule interactions are captured by a variety of experimental methods to estimate and propose correlations between molecular structures to their biological activities. The tremendous expanse in publicly available small molecules is also driving new efforts to better understand interactions involving small molecules particularly in area of drug docking and pharmacogenomics. We have studied and designed a functional group identification system with the associated ontology for it. The functional group identification system can detect the functional group components from given ligand structure with specific coordinate information. Functional group ontology (FGO) proposed by us is a structured classification of chemical functional group which acts as an important source of prior knowledge that may be automatically integrated to support identification, categorization and predictive data analysis tasks. We have used a new annotation method which can be used to construct the original structure from given ontological expression using exact coordinate information. Here, we also discuss about ontology-driven similarity measure of functional groups and uses of such novel ontology for pharmacophore searching and de-novo ligand designing.     

Proposed structural models of the prothrombinase (FXa-FVa) complex

Activated coagulation factor V (FVa) functions as a cofactor to factor Xa (FXa) in the conversion of prothrombin (PT) to thrombin. This essential procoagulant reaction, despite being the subject of extensive investigation, is not fully understood structurally and functionally. To elucidate the structure of the FXa-FVa complex, we have performed protein:protein (Pr:Pr) docking simulation with the pseudo-Brownian Pr:Pr docking ICM package and with the shape-complementarity Pr:Pr docking program PPD. The docking runs were carried out using a new model of full-length human FVa and the X-ray structure of human FXa. Five representative models of the FXa-FVa complex were in overall agreement with some of the available experimental data, but only one model was found to be consistent with almost all of the reported experimental results. The use of hybrid docking approach (theoretical plus experimental) is definitively important to study such large macromolecular complexes. The FXa-FVa model we have created will be instrumental for further investigation of this macromolecular system and will guide future site directed mutagenesis experiments.     

Response surface methodology in docking study of small molecule BACE-1 inhibitors

Computational evaluation of ligand-receptor binding via docking strategy is a well established approach in structure-based drug design. This technique has been applied frequently in developing molecules of biological interest. However, any procedure would require an optimization set up to be more efficient, economic and time-saving. Advantages of modern statistical optimization methods over conventional one-factor-at-a-time studies have been well revealed. The optimization by experimental design provides a combination of factor levels simultaneously satisfying the requirements considered for each of the responses and factors. In this study, response surface method was applied to optimize the prominent factors (number of genetic algorithm runs, population size, maximum number of evaluations, torsion degrees for ligand and number of rotatable bonds in ligand) in AutoDock4.2-based binding study of small molecule β-secretase inhibitors as anti-alzheimer agents. Results revealed that a number of rotatable bonds in ligand and maximum number of docking evaluations were determinant variables affecting docking outputs. The interference between torsion degrees for ligand and number of genetic algorithm runs for docking procedure was found to be the significant interaction term in our model. Optimized docking outputs exhibited a high correlation with experimental fluorescence resonance energy transfer-based IC(50)s for β-secretase inhibitors (R(2)?=?0.9133).     

Use of natural molecules as anti-angiogenic inhibitors for vascular endothelial growth factor receptor

Angiogenesis refers to the formation of new blood vessels, controlled by certain chemicals, which on stimulation repairs damaged cells or form new ones. Other chemicals, called angiogenesis inhibitors, signal the process to stop, having only mild side effects and are non toxic to most healthy cells. In our study, attempt was made to find potent anti-angiogenic inhibitor (pazopanib was considered as a reference drug) for vascular endothelial growth factor receptor (VEGFR-1/FLT-1), which served as a molecular target, using natural agents targeting biological processes important in cancer. Hundreds of natural molecules were initially screened based on lipinski''s rule of five and the satisfying ones were taken for receptor-ligand interaction study using docking tools like HEX and quantum. Around fifteen molecules were taken as lead molecule and their binding pocket on VEGF was analyzed using SwissPDBviewer and Q-site finder. The investigational drug pazopanib was found to be interacting with leucine 32 and glutamine 30 in terms of hydrogen bond with the distance of 1.86 and 2.49 A0 respectively. Ames test for the molecules was predicted for probability of mutagenicity on molecular systems such as blood, cardiovascular system, gastrointestinal system; kidney, liver and lung were considered for further screening of the molecules. The natural molecules curcumin, epigallocatechin gallate (EGCG), barrigtozenol and finasteride were showing reliable interaction with VEGFR and their pharmacokinetics parameters were comparatively good than the pazopanib. The dietary product curcumin and EGCG can be cancer chemopreventive agents and the natural molecules barringtozenol and finasteride can be effective inhibitors for VEGFR.     

Structure-based design of selective phosphodiesterase 4B inhibitors based on ginger phenolic compounds

Phosphodiesterase 4 (PDE4) has been established as a drug target for inflammatory diseases of respiratory tract like asthma and chronic obstructive pulmonary disease. The selective inhibitors of PDE4B, a subtype of PDE4, are devoid of adverse effects like nausea and vomiting commonly associated with non-selective PDE4B inhibitors. This makes the development of PDE4B subtype selective inhibitors a desirable research goal. Thus, in the present study, molecular docking, molecular dynamic simulations and binding free energy were performed to explore potential selective PDE4B inhibitors based on ginger phenolic compounds. The results of docking studies indicate that some of the ginger phenolic compounds demonstrate higher selective PDE4B inhibition than existing selective PDE4B inhibitors. Additionally, 6-gingerol showed the highest PDE4B inhibitory activity as well as selectivity. The comparison of binding mode of PDE4B/6-gingerol and PDE4D/6-gingerol complexes revealed that 6-gingerol formed additional hydrogen bond and hydrophobic interactions with active site and control region 3 (CR3) residues in PDE4B, which were primarily responsible for its PDE4B selectivity. The results of binding free energy demonstrated that electrostatic energy is the primary factor in elucidating the mechanism of PDE4B inhibition by 6-gingerol. Dynamic cross-correlation studies also supported the results of docking and molecular dynamics simulation. Finally, a small library of molecules were designed based on the identified structural features, majority of designed molecules showed higher PDE4B selectivity than 6-gingerol. These results provide important structural features for designing new selective PDE4B inhibitors as anti-inflammatory drugs and promising candidates for synthesis and pre-clinical pharmacological investigations.     

3-D clustering: a tool for high throughput docking

This report describes a computer program for clustering docking poses based on their 3-dimensional (3D) coordinates as well as on their chemical structures. This is chiefly intended for reducing a set of hits coming from high throughput docking, since the capacity to prepare and biologically test such molecules is generally far more limited than the capacity to generate such hits. The advantage of clustering molecules based on 3D, rather than 2D, criteria is that small variations on a scaffold may bring about different binding modes for molecules that would not be predicted by 2D similarity alone. The program does a pose-by-pose/atom-by-atom comparison of a set of docking hits (poses), scoring both spatial and chemical similarity. Using these pair-wise similarities, the whole set is clustered based on a user-supplied similarity threshold. An output coordinate file is created that mirrors the input coordinate file, but contains two new properties: a cluster number and similarity to the cluster center. Poses in this output file can easily be sorted by cluster and displayed together for visual inspection with any standard molecular viewing program, and decisions made about which molecule should be selected for biological testing as the best representative of this group of similar molecules with similar binding modes.     

A method for biomolecular structural recognition and docking allowing conformational flexibility.

In this work, we present an algorithm developed to handle biomolecular structural recognition problems, as part of an interdisciplinary research endeavor of the Computer Vision and Molecular Biology fields. A key problem in rational drug design and in biomolecular structural recognition is the generation of binding modes between two molecules, also known as molecular docking. Geometrical fitness is a necessary condition for molecular interaction. Hence, docking a ligand (e.g., a drug molecule or a protein molecule), to a protein receptor (e.g., enzyme), involves recognition of molecular surfaces. Conformational transitions by "hinge-bending" involves rotational movements of relatively rigid parts with respect to each other. The generation of docked binding modes between two associating molecules depends on their three dimensional structures (3-D) and their conformational flexibility. In comparison to the particular case of rigid-body docking, the computational difficulty grows considerably when taking into account the additional degrees of freedom intrinsic to the flexible molecular docking problem. Previous docking techniques have enabled hinge movements only within small ligands. Partial flexibility in the receptor molecule is enabled by a few techniques. Hinge-bending motions of protein receptors domains are not addressed by these methods, although these types of transitions are significant, e.g., in enzymes activity. Our approach allows hinge induced motions to exist in either the receptor or the ligand molecules of diverse sizes. We allow domains/subdomains/group of atoms movements in either of the associating molecules. We achieve this by adapting a technique developed in Computer Vision and Robotics for the efficient recognition of partially occluded articulated objects. These types of objects consist of rigid parts which are connected by rotary joints (hinges). Our method is based on an extension and generalization of the Hough transform and the Geometric Hashing paradigms for rigid object recognition. We show experimental results obtained by the successful application of the algorithm to cases of bound and unbound molecular complexes, yielding fast matching times. While the "correct" molecular conformations of the known complexes are obtained with small RMS distances, additional, predictive good-fitting binding modes are generated as well. We conclude by discussing the algorithm's implications and extensions, as well as its application to investigations of protein structures in Molecular Biology and recognition problems in Computer Vision.     

Homology modeling and virtual screening studies of FGF-7 protein—a structure-based approach to design new molecules against tumor angiogenesis

Keratinocyte growth factor (KGF) protein is a member of the fibroblast growth factor (FGF) family, which is also known as FGF-7. The FGF-7 plays an important role in tumor angiogenesis. In the present work, FGF-7 is treated as a potential therapeutic target to prevent angiogenesis in cancerous tissue. Computational techniques are applied to evaluate and validate the 3D structure of FGF-7 protein. The active site region of the FGF-7 protein is identified based on hydrophobicity calculations using CASTp and Q-site Finder active site prediction tools. The protein–protein docking study of FGF-7 with its natural receptor FGFR2b is carried out to confirm the active site region in FGF-7. The amino acid residues Asp34, Arg67, Glu116, and Thr194 in FGF-7 interact with the receptor protein (FGFR2b). A grid is generated at the active site region of FGF-7 using Glide module of Schrödinger suite. Subsequently, a virtual screening study is carried out at the active site using small molecular structural databases to identify the ligand molecules. The binding interactions of the ligand molecules, with piperazine moiety as a pharmacophore, are observed at Arg67 and Glu149 residues of the FGF-7 protein. The identified ligand molecules against the FGF-7 protein show permissible pharmacokinetic properties (ADME). The ligand molecules with good docking scores and satisfactory pharmacokinetic properties are prioritized and identified as novel ligands for the FGF-7 protein in cancer therapy.     

Recent Efforts in Identification of Privileged Scaffolds as Antiviral Agents

Viral infections are the most important health concern nowadays to mankind, which is unexpectedly increasing the health complications and fatality rate worldwide. The recent viral infection outbreak developed a pressing need for small molecules that can be quickly deployed for the control/treatment of re-emerging or new emerging viral infections. Numerous viruses, including the human immunodeficiency virus (HIV), hepatitis, influenza, SARS-CoV-1, SARS-CoV-2, and others, are still challenging due to emerging resistance to known drugs. Therefore, there is always a need to search for new antiviral small molecules that can combat viral infection with new modes of action. This review highlighted recent progress in developing new antiviral molecules based on natural product-inspired scaffolds. Herein, the structure-activity relationship of the FDA-approved drugs along with the molecular docking studies of selected compounds have been discussed against several target proteins. The findings of new small molecules as neuraminidase inhibitors, other than known drug scaffolds, Anti-HIV and SARS-CoV are incorporated in this review paper.