Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

DATABASE ENRICHMENTS OF MAO-B THROUGH ENSEMBLE DOCKING

Objective: The recent growth of highly resoluted crystallographic structures, together with the continuous improvements of the computing power, has established molecular docking as a leading drug design technique. However, the problems concerning the receptor flexibility and the lowered ability of docking software to correctly score the occurred interactions in some receptors are still relevant. Methods: Recently, several research groups have reported an enhancement in enrichment values when ensemble docking has been applied. Therefore, we utilized the latest technique for a dataset of Monoamine Oxidase–B (MAO-B) inhibitors. The docking program GOLD 5.3 was used in our study. Several docking parameters (grid space, scoring functions and ligand flexibility) were altered in order to achieve the optimal docking protocol. Results: The results of 200 000+docking simulations are represented in a modest table. The ensembled simulations demonstrated low ability of the docking software to correctly score the actives seeded in the dataset. However, the superimposed complex-1S3B-1OJA-1OJC, achieved a moderate enrichment value equaled to 9. No significant improvements were noted when five complexed receptors were employed. Conclusion: As a conclusion, it should be noted that in some cases the ensemble docking enhanced the database enrichments, however overall the value is not suitable for future virtual screening. Further investigations in that area should be considered

Targeting Acetylcholinesterase: Identification of Chemical Leads by High Throughput Screening, Structure Determination and Molecular Modeling

Acetylcholinesterase (AChE) is an essential enzyme that terminates cholinergic transmission by rapid hydrolysis of the neurotransmitter acetylcholine. Compounds inhibiting this enzyme can be used (inter alia) to treat cholinergic deficiencies (e.g. in Alzheimer's disease), but may also act as dangerous toxins (e.g. nerve agents such as sarin). Treatment of nerve agent poisoning involves use of antidotes, small molecules capable of reactivating AChE. We have screened a collection of organic molecules to assess their ability to inhibit the enzymatic activity of AChE, aiming to find lead compounds for further optimization leading to drugs with increased efficacy and/or decreased side effects. 124 inhibitors were discovered, with considerable chemical diversity regarding size, polarity, flexibility and charge distribution. An extensive structure determination campaign resulted in a set of crystal structures of protein-ligand complexes. Overall, the ligands have substantial interactions with the peripheral anionic site of AChE, and the majority form additional interactions with the catalytic site (CAS). Reproduction of the bioactive conformation of six of the ligands using molecular docking simulations required modification of the default parameter settings of the docking software. The results show that docking-assisted structure-based design of AChE inhibitors is challenging and requires crystallographic support to obtain reliable results, at least with currently available software. The complex formed between C5685 and Mus musculus AChE (C5685•mAChE) is a representative structure for the general binding mode of the determined structures. The CAS binding part of C5685 could not be structurally determined due to a disordered electron density map and the developed docking protocol was used to predict the binding modes of this part of the molecule. We believe that chemical modifications of our discovered inhibitors, biochemical and biophysical characterization, crystallography and computational chemistry provide a route to novel AChE inhibitors and reactivators

Machine Learning Scoring Functions for Drug Discoveries from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions

In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.Comment: 22 pages, 8 figure

Structure- and Ligand-Based Design of Novel Antimicrobial Agents

The use of computer based techniques in the design of novel therapeutic agents is a rapidly emerging field. Although the drug-design techniques utilized by Computational Medicinal Chemists vary greatly, they can roughly be classified into structure-based and ligand-based approaches. Structure-based methods utilize a solved structure of the design target, protein or DNA, usually obtained by X-ray or NMR methods to design or improve compounds with activity against the target. Ligand-based methods use active compounds with known affinity for a target that may yet be unresolved. These methods include Pharmacophore-based searching for novel active compounds or Quantitative Structure-Activity Relationship (QSAR) studies. The research presented here utilized both structure and ligand-based methods against two bacterial targets: Bacillus anthracis and Mycobacterium tuberculosis. The first part of this thesis details our efforts to design novel inhibitors of the enzyme dihydropteroate synthase from B. anthracis using crystal structures with known inhibitors bound. The second part describes a QSAR study that was performed using a series of novel nitrofuranyl compounds with known, whole-cell, inhibitory activity against M. tuberculosis. Dihydropteroate synthase (DHPS) catalyzes the addition of p-amino benzoic acid (pABA) to dihydropterin pyrophosphate (DHPP) to form pteroic acid as a key step in bacterial folate biosynthesis. It is the traditional target of the sulfonamide class of antibiotics. Unfortunately, bacterial resistance and adverse effects have limited the clinical utility of the sulfonamide antibiotics. Although six bacterial crystal structures are available, the flexible loop regions that enclose pABA during binding and contain key sulfonamide resistance sites have yet to be visualized in their functional conformation. To gain a new understanding of the structural basis of sulfonamide resistance, the molecular mechanism of DHPS action, and to generate a screening structure for high-throughput virtual screening, molecular dynamics simulations were applied to model the conformations of the unresolved loops in the active site. Several series of molecular dynamics simulations were designed and performed utilizing enzyme substrates and inhibitors, a transition state analog, and a pterin-sulfamethoxazole adduct. The positions of key mutation sites conserved across several bacterial species were closely monitored during these analyses. These residues were shown to interact closely with the sulfonamide binding site. The simulations helped us gain new understanding of the positions of the flexible loops during inhibitor binding that has allowed the development of a DHPS structural model that could be used for high-through put virtual screening (HTVS). Additionally, insights gained on the location and possible function of key mutation sites on the flexible loops will facilitate the design of new, potent inhibitors of DHPS that can bypass resistance mutations that render sulfonamides inactive. Prior to performing high-throughput virtual screening, the docking and scoring functions to be used were validated using established techniques against the B. anthracis DHPS target. In this validation study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, as well as nine scoring functions, were evaluated for their utility in virtual screening against the novel pterin binding site. Their performance in ligand docking and virtual screening against this target was examined by their ability to reproduce a known inhibitor conformation and to correctly detect known active compounds seeded into three separate decoy sets. Enrichment was demonstrated by calculated enrichment factors at 1% and Receiver Operating Characteristic (ROC) curves. The effectiveness of post-docking relaxation prior to rescoring and consensus scoring were also evaluated. Of the docking and scoring functions evaluated, Surflex with SurflexScore and Glide with GlideScore performed best overall for virtual screening against the DHPS target. The next phase of the DHPS structure-based drug design project involved high-throughput virtual screening against the DHPS structural model previously developed and docking methodology validated against this target. Two general virtual screening methods were employed. First, large, virtual libraries were pre-filtered by 3D pharmacophore and modified Rule-of-Three fragment constraints. Nearly 5 million compounds from the ZINC databases were screened generating 3,104 unique, fragment-like hits that were subsequently docked and ranked by score. Second, fragment docking without pharmacophore filtering was performed on almost 285,000 fragment-like compounds obtained from databases of commercial vendors. Hits from both virtual screens with high predicted affinity for the pterin binding pocket, as determined by docking score, were selected for in vitro testing. Activity and structure-activity relationship of the active fragment compounds have been developed. Several compounds with micromolar activity were identified and taken to crystallographic trials. Finally, in our ligand-based research into M. tuberculosis active agents, a series of nitrofuranylamide and related aromatic compounds displaying potent activity was investigated utilizing 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) techniques. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the Minimum Inhibitory Concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (cLogP, LogD), Polar Surface Area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation and high internal validity (cross validated r2 \u3e .5) were developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents

Application of Binding Free Energy Calculations to Prediction of Binding Modes and Affinities of MDM2 and MDMX Inhibitors

Molecular docking is widely used to obtain binding modes and binding affinities of a molecule to a given target protein. Despite considerable efforts, however, prediction of both properties by docking remains challenging mainly due to protein’s structural flexibility and inaccuracy of scoring functions. Here, an integrated approach has been developed to improve the accuracy of binding mode and affinity prediction, and tested for small molecule MDM2 and MDMX antagonists. In this approach, initial candidate models selected from docking are subjected to equilibration MD simulations to further filter the models. Free energy perturbation molecular dynamics (FEP/MD) simulations are then applied to the filtered ligand models to enhance the ability in predicting the near-native ligand conformation. The calculated binding free energies for MDM2 complexes are overestimated compared to experimental measurements mainly due to the difficulties in sampling highly flexible apo-MDM2. Nonetheless, the FEP/MD binding free energy calculations are more promising for discriminating binders from nonbinders than docking scores. In particular, the comparison between the MDM2 and MDMX results suggests that apo-MDMX has lower flexibility than apo-MDM2. In addition, the FEP/MD calculations provide detailed information on the different energetic contributions to ligand binding, leading to a better understanding of the sensitivity and specificity of protein-ligand interactions