Automatic and efficient decomposition of two-dimensional structures of small molecules for fragment-based high-throughput docking

A pH-dependent cluster of charges in a conserved cryptic pocket on flaviviral envelopes

Flaviviruses are enveloped viruses which include human pathogens that are predominantly transmitted by mosquitoes and ticks. Some, such as dengue virus, exhibit the phenomenon of antibody-dependent enhancement (ADE) of disease, making vaccine-based routes of fighting infections problematic. The pH-dependent conformational change of the envelope (E) protein required for fusion between the viral and endosomal membranes is an attractive point of inhibition by antivirals as it has the potential to diminish the effects of ADE. We examined six flaviviruses by employing large-scale molecular dynamics (MD) simulations of raft systems that represent a substantial portion of the flaviviral envelope. We utilised a benzene-mapping approach that led to a discovery of shared hotspots and conserved cryptic sites. A cryptic pocket previously shown to bind a detergent molecule exhibited strain-specific characteristics. An alternative conserved cryptic site at the E protein domain interfaces showed a consistent dynamic behaviour across flaviviruses and contained a conserved cluster of ionisable residues. Constant-pH simulations revealed cluster and domain-interface disruption under low pH conditions. Based on this, we propose a cluster-dependent mechanism that addresses inconsistencies in the histidine-switch hypothesis and highlights the role of cluster protonation in orchestrating the domain dissociation pivotal for the formation of the fusogenic trimer.

Fragment-based inhibitor design for SARS-CoV2 main protease

COVID-19 disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) has resulted in tremendous loss of lives across the world and is continuing to do so. Extensive work is under progress to develop inhibitors which can prevent the disease by arresting the virus in its life cycle. One such way is by targeting the main protease of the virus which is crucial for the cleavage and conversion of polyproteins into functional units of polypeptides. In this endeavor, our effort was to identify hit molecule inhibitors for SARS-CoV2 main protease using fragment-based drug discovery (FBDD), based on the available crystal structure of chromene-based inhibitor (PDB_ID: 6M2N). The designed molecules were validated by molecular docking and molecular dynamics simulations. The stability of the complexes was further assessed by calculating their binding free energies, normal mode analysis, mechanical stiffness, and principal component analysis.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

Identifying novel putative ERK1/2 inhibitors via hybrid scaffold hopping -FBDD approach

ERK inhibitors are continuously explored by the researchers due to their clinical significance in resistant tumor cell lines. Though many ERK1/2 inhibitors are reported, there is still need to identify novel hits to increase the number of molecules in clinical trials. Therefore, an urgent need is to examine the existing chemical space for ERK inhibitory potential with an aim to develop novel scaffolds which can act as potent ERKs inhibitors. In this study, Ulixertinib, a known ERK2 inhibitor was selected to perform scaffold hopping to discover new scaffolds with similar binding mode followed by molecular docking analysis of the hits with highest similarity score to determine, both the binding mode and affinity in the catalytic domain of ERK2. The top hit was then subjected to FBDD to identify side chains which could enhance the binding affinity in the catalytic domain of ERK2. Again, docking analysis was performed to validate and determine their binding affinity. Further the top hit identified after docking analysis was subjected to molecular dynamic simulations. Overall, 3 hits (ligand 6, 8 and 10) were found to possess optimum pharmacodynamic and pharmacokinetic profile, in-silico, to be claimed as putative ERK2 inhibitors. This study disclosed new lead molecules with putative ERK2 inhibitory potential which may be further validated via biological evaluation.

Update and Potential Opportunities in CBP [Cyclic Adenosine Monophosphate (cAMP) Response Element-Binding Protein (CREB)-Binding Protein] Research Using Computational Techniques

CBP [cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB)-binding protein] is one of the most researched proteins for its therapeutic function. Several studies have identified its vast functions and interactions with other transcription factors to initiate cellular signals of survival. In cancer and other diseases such as Alzheimer's, Rubinstein-taybi syndrome, and inflammatory diseases, CBP has been implicated and hence an attractive target in drug design and development. In this review, we explore the various computational techniques that have been used in CBP research, furthermore we identified computational gaps that could be explored to facilitate the development of highly therapeutic CBP inhibitors.

AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4.

Click Inspired Synthesis of Novel Cinchonidine Glycoconjugates as Promising Plasmepsin Inhibitors

Among all the malaria parasites, P. falciparum is the most predominant species which has developed drug resistance against most of the commercial anti-malarial drugs. Thus, finding a new molecule for the inhibition of enzymes of P. falciparum is the pharmacological challenge in present era. Herein, ten novel molecules have been designed with an amalgamation of cinchonidine, carbohydrate moiety and triazole ring by utilizing copper-catalyzed click reaction of cinchonidine-derived azide and clickable glycosyl alkynes. The molecular docking of developed molecules showed promising results for plasmepsin inhibition in the form of effective binding with target proteins.

Computational Screening of Potential Inhibitors of β-N-Acetyl-D-Hesosaminidases Using Combined Core-Fragment Growth and Pharmacophore Restraints

As a type of β-N-acetyl-D-hexosaminidase enzyme purified from the Ostriniafurnacalis (Asian corn borer), OfHex1 has been previously reported to participate in chitin degradation, indicating that it may be an ideal target for designing new environmentally friendly pesticides. Besides, a natural product, TMG-chitotriomycin, has been found to be an effective inhibitor of OfHex1, and some studies have shown that the interactions between TMG unit and residues in - 1 subsite of OfHex1 are very conservative and important, inspiring us to design new inhibitors of β-N-acetyl-D-hexosaminidase with a new strategy. In the present study, the virtual screening of TMG unit as the core fragment was conducted using the combined computational methods, such as docking, molecular dynamics, pharmacophore model, and pesticide-likeness rule. Nine compounds with the binding free energy lower than TMG-β-(GlcNAc)₂ were obtained. According to the decomposition energy and the interactions analysis, compounds 2, 3, 6 and 8, forming the hydrogen bonds with Val327 and Trp490, were considered as the possible lead structures for the further study. Our findings indicated that fragment-based lead discovery strategy might provide valuable insights into designing novel potential OfHex1 inhibitors.

Developing Hispolon-based novel anticancer therapeutics against human (NF-κβ) using in silico approach of modelling, docking and protein dynamics

Hispolon is a polyphenolic compound derived from black hoof mushroom (Phellinus linteus) or shaggy bracket mushroom (Inonotus hispidus) which induces the inhibition of cancer-promoting nuclear factor-kappa beta (NF-κβ) complex. To develop more potent lead molecules with enhanced anticancer efficiency, the mechanism of hispolon-mediated nuclear factor-κβ inhibition has been investigated by molecular modelling and docking. Ten derivatives of hispolon (DRG1-10) have been developed by pharmacophore-based design with a view to enhance the anticancer efficacy. Hispolon and its derivatives were further screened for different pharmacological parameters like binding free energy, drug likeliness, absorption-digestion-metabolism-excretion (ADME), permeability, mutagenicity, toxicity and inhibitory concentration 50 (IC50) to find a potent lead molecule. Based on pharmacological validation, comparative molecular dynamics (MD) simulations have been performed for three lead molecules: Hispolon, DRG2 and DRG7 complexed with human NF-κβ up to 50 ns. By analysing different factors like root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA) and principal component analysis (PCA), Gibb's free energy plots DRG2 have more binding efficiency compared to hispolon and DRG7. In RMSD plot, hispolon-bound NF-κβ has the most deviation within a range between 0.125 and 0.45 nm, and DRG2-bound complex showed the range between 0.125 and 0.25 nm. The residues of NF-κβ responsible for hydrophobic interactions with ligand, e.g. Met469, Leu522 and Cys533, have the lowest fluctuation values in DRG2-bound complex. The average Rg fluctuation for DRG2-bound NF-κβ has been recorded under 2.025 nm for most of the simulation time which is much less compared to hispolon and DRG7. Gibb's free energy plots also define the highest stability of DRG2-bound NF-κβ. Communicated by Ramaswamy H. Sarma.

Discovery of Novel Adenosine Receptor Antagonists through a Combined Structure- and Ligand-Based Approach Followed by Molecular Dynamics Investigation of Ligand Binding Mode

An intense effort is made by pharmaceutical and academic research laboratories to identify and develop selective antagonists for each adenosine receptor (AR) subtype as potential clinical candidates for "soft" treatment of various diseases. Crystal structures of subtypes A_2A and A₁ARs offer exciting opportunities for structure-based drug design. In the first part of the present work, Maybridge HitFinder library of 14400 compounds was utilized to apply a combination of structure-based against the crystal structure of A_2AAR and ligand-based methodologies. The docking poses were rescored by CHARMM energy minimization and calculation of the desolvation energy using Poisson-Boltzmann equation electrostatics. Out of the eight selected and tested compounds, five were found positive hits (63% success). Although the project was initially focused on targeting A_2AAR, the identified antagonists exhibited low micromolar or micromolar affinity against A_2A/A₃, ARs, or A₃AR, respectively. Based on these results, 19 compounds characterized by novel chemotypes were purchased and tested. Sixteen of them were identified as AR antagonists with affinity toward combinations of the AR family isoforms (A_2A/A₃, A₁/A₃, A₁/A_2A/A₃, and A₃). The second part of this work involves the performance of hundreds of molecular dynamics (MD) simulations of complexes between the ARs and a total of 27 ligands to resolve the binding interactions of the active compounds, which were not achieved by docking calculations alone. This computational work allowed the prediction of stable and unstable complexes which agree with the experimental results of potent and inactive compounds, respectively. Of particular interest is that the 2-amino-thiophene-3-carboxamides, 3-acylamino-5-aryl-thiophene-2-carboxamides, and carbonyloxycarboximidamide derivatives were found to be selective and possess a micromolar to low micromolar affinity for the A₃ receptor.

Fragment-based virtual screening approach and molecular dynamics simulation studies for identification of BACE1 inhibitor leads

Traditional structure-based virtual screening method to identify drug-like small molecules for BACE1 is so far unsuccessful. Location of BACE1, poor Blood Brain Barrier permeability and P-glycoprotein (Pgp) susceptibility of the inhibitors make it even more difficult. Fragment-based drug design method is suitable for efficient optimization of initial hit molecules for target like BACE1. We have developed a fragment-based virtual screening approach to identify/optimize the fragment molecules as a starting point. This method combines the shape, electrostatic, and pharmacophoric features of known fragment molecules, bound to protein conjugate crystal structure, and aims to identify both chemically and energetically feasible small fragment ligands that bind to BACE1 active site. The two top-ranked fragment hits were subjected for a 53 ns MD simulation. Principle component analysis and free energy landscape analysis reveal that the new ligands show the characteristic features of established BACE1 inhibitors. The potent method employed in this study may serve for the development of potential lead molecules for BACE1-directed Alzheimer's disease therapeutics.

Fragment-based in silico screening of bromodomain ligands

We review the results of fragment-based high-throughput docking to the N-terminal bromodomain of BRD4 and the CREBBP bromodomain. In both docking campaigns the ALTA (anchor-based library tailoring) procedure was used to reduce the size of the initial library by selecting for flexible docking only the molecules that contain a fragment with favorable predicted binding energy. Ranking by a force field-based energy with solvation has resulted in small-molecule hits with low-micromolar affinity and favorable ligand efficiency. Importantly, the binding modes predicted by docking have been validated by X-ray crystallography. One of the hits for the CREBBP bromodomain has been optimized by medicinal chemistry into a series of potent and selective ligands.

Fragment-Based De Novo Design of Cyclin-Dependent Kinase 2 Inhibitors

Cyclin-dependent kinases (CDKs) are core components of the cell cycle machinery that govern the transition between phases during cell cycle progression. Abnormalities in CDKs activity and regulation are common features of cancer, making CDK family members attractive targets for the development of anticancer drugs. One of the main bottlenecks hampering the development of drugs for kinase is the difficulty to attain selectivity. A huge variety of small molecules have been reported as CDK inhibitors, as potential anticancer agents, but none of these has been approved for commercial use. Computer-based molecular design supports drug discovery by suggesting novel new chemotypes and compound modifications for lead candidate optimization. One of the methods known as de novo ligand design technique has emerged as a complementary approach to high-throughput screening. Several automated de novo software programs have been written, which automatically design novel structures to perfectly fit in known binding site. The de novo design supports drug discovery assignments by generating novel pharmaceutically active agents with desired properties in a cost as well as time efficient approach. This chapter describes procedure and an overview of computer-based molecular de novo design methods on a conceptual level with successful examples of CDKs inhibitors.

ACFIS: a web server for fragment-based drug discovery

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown ‘chemical space’ to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for ‘chemical space’, which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/.

Discovery of CREBBP Bromodomain Inhibitors by High-Throughput Docking and Hit Optimization Guided by Molecular Dynamics

We have identified two chemotypes of CREBBP bromodomain ligands by fragment-based high-throughput docking. Only 17 molecules from the original library of two-million compounds were tested in vitro. Optimization of the two low-micromolar hits, the 4-acylpyrrole 1 and acylbenzene 9, was driven by molecular dynamics results which suggested improvement of the polar interactions with the Arg1173 side chain at the rim of the binding site. The synthesis of only two derivatives of 1 yielded the 4-acylpyrrole 6 which shows a single-digit micromolar affinity for the CREBBP bromodomain and a ligand efficiency of 0.34 kcal/mol per non-hydrogen atom. Optimization of the acylbenzene hit 9 resulted in a series of derivatives with nanomolar potencies, good ligand efficiency and selectivity (see Unzue, A.; Xu, M.; Dong, J.; Wiedmer, L.; Spiliotopoulos, D.; Caflisch, A.; Nevado, C.Fragment-Based Design of Selective Nanomolar Ligands of the CREBBP Bromodomain. J. Med. Chem. 2015, DOI: 10.1021/acs.jmedchem.5b00172). The in silico predicted binding mode of the acylbenzene derivative 10 was validated by solving the structure of the complex with the CREBBP bromodomain.

Fragment-based similarity searching with infinite color space

Fragment-based searching and abstract representation of molecular features through reduced graphs have separately been used for virtual screening. Here, we combine these two approaches and apply the algorithm RedFrag to virtual screens retrospectively and prospectively. It uses a new type of reduced graph that does not suffer from information loss during its construction and bypasses the necessity of feature definitions. Built upon chemical epitopes resulting from molecule fragmentation, the reduced graph embodies physico-chemical and 2D-structural properties of a molecule. Reduced graphs are compared with a continuous-similarity-distance-driven maximal common subgraph algorithm, which calculates similarity at the fragmental and topological levels. The performance of the algorithm is evaluated by retrieval experiments utilizing precompiled validation sets. By predicting and experimentally testing ligands for endothiapepsin, a challenging model protease, the method is assessed in a prospective setting. Here, we identified five novel ligands with affinities as low as 2.08 μM.

Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing's capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of "re-dockings" with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing's docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening.

LIBSA--a method for the determination of ligand-binding preference to allosteric sites on receptor ensembles

Incorporation of receptor flexibility into computational drug discovery through the relaxed complex scheme is well suited for screening against a single binding site. In the absence of a known pocket or if there are multiple potential binding sites, it may be necessary to do docking against the entire surface of the target (global docking). However no suitable and easy-to-use tool is currently available to rank global docking results based on the preference of a ligand for a given binding site. We have developed a protocol, termed LIBSA for LIgand Binding Specificity Analysis, that analyzes multiple docked poses against a single or ensemble of receptor conformations and returns a metric for the relative binding to a specific region of interest. By using novel filtering algorithms and the signal-to-noise ratio (SNR), the relative ligand-binding frequency at different pockets can be calculated and compared quantitatively. Ligands can then be triaged by their tendency to bind to a site instead of ranking by affinity alone. The method thus facilitates screening libraries of ligand cores against a large library of receptor conformations without prior knowledge of specific pockets, which is especially useful to search for hits that selectively target a particular site. We demonstrate the utility of LIBSA by showing that it correctly identifies known ligand binding sites and predicts the relative preference of a set of related ligands for different pockets on the same receptor.

Advances in the identification of β-secretase inhibitors

INTRODUCTION

Alzheimer's disease (AD), which is characterized by progressive intellectual deterioration, is the most common cause of dementia. β-Secretase (or BACE1) expression is a trigger for amyloid β peptide formation, a cause of AD, and thus is a molecular target for the development of drugs against AD. Many BACE1 inhibitors have been identified by academic and pharmaceutical research groups and a number of advanced technologies in drug discovery have been applied to the drug discovery.

AREAS COVERED

The purpose of this review is to present and discuss the methodologies used for BACE1 inhibitor drug discovery via substrate- and structure-based design, high-throughput screening and fragment-based drug design. The authors also review the advantages and disadvantages of these methodologies.

EXPERT OPINION

Many BACE1 inhibitors have been designed using X-ray crystal structure-based drug design as well as through in silico screening. Nevertheless, there are serious problems with regards to deciding the best X-ray crystal structure for designing BACE1 inhibitors through computational approaches. There are two prominent configurations of BACE1 but there is still room for improvement. Future developments may make it possible to identify BACE1 inhibitors as potential drug candidates.

Methylations of tryptophan-modified naphthoquinone affect its inhibitory potential toward Aβ aggregation

Aggregation of amyloid beta (Aβ) is the hallmark of Alzheimer's disease (AD). Small molecules inhibiting Aβ can be valuable therapeutics for AD. We have previously reported that 1,4-naphthoquinon-2-yl-l-tryptophan (NQTrp), reduces aggregation and oligomerization of Aβ in vitro and in vivo. In silico analysis further showed that certain functional groups of NQTrp, not in the aromatic rings, are also involved in binding and inhibiting Aβ. To better understand the exact mode of action and identify the groups crucial for NQTrp inhibitory activity, we conducted structure-activity analysis. Four derivatives of NQTrp were studied in silico: a D-isomer, two single-methylated and one double-methylated derivative. In silico results showed that the NQTrp groups involved in hydrogen bonds are the anilinic NH (i.e., the NH linker between the quinone and tryptophan moieties), the quinonic carbonyls, and the carboxylic acid. These predictions were supported by in vitro results. Our results should aid in designing improved small-molecule inhibitors of Aβ aggregation for treating AD.

Sanjeevini: a freely accessible web-server for target directed lead molecule discovery

Background

Computational methods utilizing the structural and functional information help to understand specific molecular recognition events between the target biomolecule and candidate hits and make it possible to design improved lead molecules for the target.

Results

Sanjeevini represents a massive on-going scientific endeavor to provide to the user, a freely accessible state of the art software suite for protein and DNA targeted lead molecule discovery. It builds in several features, including automated detection of active sites, scanning against a million compound library for identifying hit molecules, all atom based docking and scoring and various other utilities to design molecules with desired affinity and specificity against biomolecular targets. Each of the modules is thoroughly validated on a large dataset of protein/DNA drug targets.

Conclusions

The article presents Sanjeevini, a freely accessible user friendly web-server, to aid in drug discovery. It is implemented on a tera flop cluster and made accessible via a web-interface at http://www.scfbio-iitd.res.in/sanjeevini/sanjeevini.jsp. A brief description of various modules, their scientific basis, validation, and how to use the server to develop in silico suggestions of lead molecules is provided.

Discovery of a novel chemotype of tyrosine kinase inhibitors by fragment-based docking and molecular dynamics

We have discovered a novel chemical class of inhibitors of the EphB4 tyrosine kinase by fragment-based high-throughput docking followed by explicit solvent molecular dynamics simulations for assessment of the binding mode. The synthesis of a single derivative (compound 7) of the hit identified in silico has resulted in an improvement of the inhibitory potency in an enzymatic assay from 8.4 μM to 160 nM and a ligand efficiency of 0.39 kcal/mol per non-hydrogen atom. Such remarkable improvement in affinity is due to an additional hydroxyl group involved in two favorable (buried) hydrogen bonds as predicted by molecular dynamics and validated by the crystal structure of the complex with EphA3 solved at 1.7 Å resolution.

High-throughput virtual screening lead to discovery of non-peptidic inhibitors of West Nile virus NS3 protease

The non-structural 3 protease is an essential flaviviral enzyme and therefore one of the most promising targets for drug development against West Nile virus infections. In this chapter, we discuss in detail the computational methods used in the previous two docking campaigns which lead to the discovery of non-peptidic low micromolar inhibitors. Not only an X-ray structure but also an alternative conformation generated from molecular dynamic simulations is used in the in silico screening. Moreover, unique scoring schemes are developed based on the properties of the binding site of the protein.

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

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

In silico identification and crystal structure validation of caspase-3 inhibitors without a P1 aspartic acid moiety

Using a fragment-based docking procedure, several small-molecule inhibitors of caspase-3 were identified and tested and the crystal structures of three inhibitor complexes were determined. The crystal structures revealed that one inhibitor (NSC 18508) occupies only the S1 subsite, while two other inhibitors (NSC 89167 and NSC 251810) bind only to the prime part of the substrate-binding site. One of the major conformational changes observed in all three caspase-3-inhibitor complexes is a rotation of the Tyr204 side chain, which blocks the S2 subsite. In addition, the structural variability of the residues shaping the S1-S4 as well as the S1' subsites supports an induced-fit mechanism for the binding of the inhibitors in the active site. The high-resolution crystal structures reported here provide novel insights into the architecture of the substrate-binding site, which might be useful for the design of more potent caspase inhibitors.

Reproducing crystal binding modes of ligand functional groups using Site-Identification by Ligand Competitive Saturation (SILCS) simulations

The applicability of a computational method, Site Identification by Ligand Competitive Saturation (SILCS), to identify regions on a protein surface with which different types of functional groups on low-molecular weight inhibitors interact is demonstrated. The method involves molecular dynamics (MD) simulations of a protein in an aqueous solution of chemically diverse small molecules from which probability distributions of fragments types, termed FragMaps, are obtained. In the present application, SILCS simulations are performed with an aqueous solution of 1 M benzene and propane to map the affinity pattern of the protein for aromatic and aliphatic functional groups. In addition, water hydrogen and oxygen atoms serve as probes for hydrogen-bond donor and acceptor affinity, respectively. The method is tested using a set of 7 proteins for which crystal structures of complexes with several high affinity inhibitors are known. Good agreement is obtained between FragMaps and the positions of chemically similar functional groups in inhibitors as observed in the X-ray crystallographic structures. Quantitative capabilities of the SILCS approach are demonstrated by converting FragMaps to free energies, termed Grid Free Energies (GFE), and showing correlation between the GFE values and experimental binding affinities. For proteins for which ligand decoy sets are available, GFE values are shown to typically score the crystal conformation and conformations similar to it more favorable than decoys. Additionally, SILCS is tested for its ability to capture the subtle differences in ligand affinity across homologous proteins, information which may be of utility toward specificity-guided drug design. Taken together, our results show that SILCS can recapitulate the known location of functional groups of bound inhibitors for a number of proteins, suggesting that the method may be of utility for rational drug design.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

Structure-based optimization of potent and selective inhibitors of the tyrosine kinase erythropoietin producing human hepatocellular carcinoma receptor B4 (EphB4)

The tyrosine kinase EphB4 is an attractive target for drug design because of its recognized role in cancer-related angiogenesis. Recently, a series of commercially available xanthine derivatives were identified as micromolar inhibitors of EphB4 by high-throughput fragment-based docking into the ATP-binding site of the kinase domain. Here, we have exploited the binding mode obtained by automatic docking for the optimization of these EphB4 inhibitors by chemical synthesis. Addition of only two heavy atoms, methyl and hydroxyl groups, to compound 4 has yielded the single-digit nanomolar inhibitor 66, with a remarkable improvement of the ligand efficiency from 0.26 to 0.37 kcal/(mol per non-hydrogen atom). Compound 66 shows very high affinity for a few other tyrosine kinases with threonine as gatekeeper residue (Abl, Lck, and Src). On the other hand, it is selective against kinases with a larger gatekeeper. A 45 ns molecular dynamics (MD) simulation of the complex of EphB4 and compound 66 provides further validation of the binding mode obtained by fragment-based docking.

Flaviviral protease inhibitors identified by fragment-based library docking into a structure generated by molecular dynamics

Fragment-based docking was used to select a conformation for virtual screening from a molecular dynamics trajectory of the West Nile virus nonstructural 3 protease. This conformation was chosen from an ensemble of 100 molecular dynamics snapshots because it optimally accommodates benzene, the most common ring in known drugs, and two positively charged fragments (methylguanidinium and 2-phenylimidazoline). The latter fragments were used as probes because of the large number of hydrogen bond acceptors in the substrate binding site of the protease. Upon high-throughput docking of a diversity set of 18,694 molecules and pose filtering, only five compounds were chosen for experimental validation, and two of them are active in the low micromolar range in an enzymatic assay and a tryptophan fluorescence quenching assay. Evidence for specific binding to the protease active site is provided by nuclear magnetic resonance spectroscopy. The two inhibitors have different scaffolds (diphenylurea and diphenyl ester) and are promising lead candidates because they have a molecular weight of about 300 Da.

The use of MoStBioDat for rapid screening of molecular diversity

MoStBioDat is a uniform data storage and extraction system with an extensive array of tools for structural similarity measures and pattern matching which is essential to facilitate the drug discovery process. Structure-based database screening has recently become a common and efficient technique in early stages of the drug development, shifting the emphasis from rational drug design into the probability domain of more or less random discovery. The virtual ligand screening (VLS), an approach based on high-throughput flexible docking, samples a virtually infinite molecular diversity of chemical libraries increasing the concentration of molecules with high binding affinity. The rapid process of subsequent examination of a large number of molecules in order to optimize the molecular diversity is an attractive alternative to the traditional methods of lead discovery. This paper presents the application of the MoStBioDat package not only as a data management platform but mainly in substructure searching. In particular, examples of the applications of MoStBioDat are discussed and analyzed.

Computational fragment-based binding site identification by ligand competitive saturation

Fragment-based drug discovery using NMR and x-ray crystallographic methods has proven utility but also non-trivial time, materials, and labor costs. Current computational fragment-based approaches circumvent these issues but suffer from limited representations of protein flexibility and solvation effects, leading to difficulties with rigorous ranking of fragment affinities. To overcome these limitations we describe an explicit solvent all-atom molecular dynamics methodology (SILCS: Site Identification by Ligand Competitive Saturation) that uses small aliphatic and aromatic molecules plus water molecules to map the affinity pattern of a protein for hydrophobic groups, aromatic groups, hydrogen bond donors, and hydrogen bond acceptors. By simultaneously incorporating ligands representative of all these functionalities, the method is an in silico free energy-based competition assay that generates three-dimensional probability maps of fragment binding (FragMaps) indicating favorable fragment∶protein interactions. Applied to the two-fold symmetric oncoprotein BCL-6, the SILCS method yields two-fold symmetric FragMaps that recapitulate the crystallographic binding modes of the SMRT and BCOR peptides. These FragMaps account both for important sequence and structure differences in the C-terminal halves of the two peptides and also the high mobility of the BCL-6 His116 sidechain in the peptide-binding groove. Such SILCS FragMaps can be used to qualitatively inform the design of small-molecule inhibitors or as scoring grids for high-throughput in silico docking that incorporate both an atomic-level description of solvation and protein flexibility.

Fragment-based drug discovery is based on a simple yet powerful principle: instead of trying to screen through the vast number of possible drug-like compounds during the drug discovery process, screen representative drug-like fragments, which are far fewer in number. Once a suitable fragment is discovered, it can then be built up or linked with other fragments to give a drug-like molecule. Because such fragments are small, even “good” fragments bind weakly to their targets, therefore requiring significant time, labor, and materials costs for experimental detection and characterization of binding. In the present work, we describe a computational approach to the problem of detecting and characterizing fragment binding. Importantly, the method provides atomic-resolution results and also explicitly takes into account the effect that molecular water has on binding and the inherent flexibility of protein targets. The methodology is demonstrated by application to the BCL-6 protein, which is implicated in a variety of cancers, is conceptually easy to understand, and can yield results in a matter of days using present-day commodity computers.