Correct Binding Mode Research Articles

New thieno[2,3‐d]pyrimidine derivatives as EGFRWT and EGFRT790M inhibitors: Design, synthesis, antiproliferative activities, docking studies, ADMET, toxicity, MD simulation studies

AbstractA series of thieno[2,3‐d]pyrimidines were designed and synthesized as epidermal growth factor receptor (EGFR) inhibitors. These compounds were tested for their ability to inhibit MCF‐7 and A549 cancer cells. The most active compound, 12c, inhibited the growth of both cell lines, with IC50 values of 15.67 and 12.16 μM, respectively. It was found that 12c had inhibitory effects on both EGFRWT and EGFRT790M isoforms, with inhibitory partialities of 37.50 and 148.90 nM, respectively. Additionally, 12c was found to be safer than erlotinib against normal cell lines (IC50 = 38.61 μM). Compound 12c induced early and late apoptosis in A549 cells and arrested cell growth at G1 and G2/M phases. 12c was also found to increase caspases 3 and 8 ratios. Molecular docking indicated the correct binding modes of the synthesized compounds. MD simulations, MM‐GBSA, and PLIP studies confirmed the precise binding of 12c to the EGFR protein over 100 ns.

Discovery of new thieno[2,3-d]pyrimidines as EGFR tyrosine kinase inhibitors for cancer treatment.

Background: EGFR has been considered a vital molecular target in cancer management. Aim: The discovery of new thieno[2,3-d]pyrimidine derivatives as EGFR tyrosine kinase inhibitors. Methods: Nine derivatives were designed, synthesized and subjected to in vitro and in silico studies. Results: Compound 7a significantly inhibited the growth of HepG2 and PC3 cells for both EGFR wild-type and EGFRT790M. Compound 7a caused a significant apoptotic effect, arresting HepG2 cells' growth in the S and G2/M phases. Docking and molecular dynamics simulation studies confirmed the correct and stable binding modes of the synthesized compounds against the active sites. Conclusion: Compound 7a is a promising dual EGFR inhibitor for cancer treatment.

Design, synthesis, docking, MD simulations, and anti-proliferative evaluation of thieno[2,3-d]pyrimidine derivatives as new EGFR inhibitors

A group of EGFR inhibitors derived from thieno[2,3-d]pyrimidine nucleus was designed, synthesised, and examined as anti-proliferative lead compounds. MCF-7 and A549 cell lines were inhibited by 5b, the most active member. It had inhibitory partialities of 37.19 and 204.10 nM against EGFRWT and EGFRT790M, respectively. Compound 5b was 2.5 times safer against the WI-38 normal cell lines than erlotinib. Also, it demonstrated considerable potentialities for both early and late apoptosis induction in A549. Simultaneously, 5b arrested A549’s growth at G1 and G2/M phases. Harmoniously, 5b upregulated the BAX and downregulated the Bcl-2 genes by 3-fold and increased the BAX/Bcl-2 ratio by 8.3-fold comparing the untreated A549 cells. Molecular docking against EGFRWT and EGFRT790M indicated the correct binding modes. Furthermore, MD simulations confirmed the precise binding of 5b against the EGFR protein over 100 ns. Finally, various computational ADMET studies were carried out and indicated high degrees of drug-likeness and safety.

Computer aided drug discovery (CADD) of a thieno[2,3-d]pyrimidine derivative as a new EGFR inhibitor targeting the ribose pocket

Depending on the pharmacophoric characteristics of EGFR inhibitors, a new thieno[2,3-d]pyrimidine derivative has been developed. Firstly, the potential inhibitory effect of the designed compound against EGFR has been proven by docking experiments that showed correct binding modes and excellent binding energies of −98.44 and −88.00 kcal/mol, against EGFR wild-type and mutant type, respectively. Furthermore, MD simulations studies confirmed the precise energetic, conformational, and dynamic alterations that occurred after binding to EGFR. The correct binding was also confirmed by essential dynamics studies. To further investigate the general drug-like properties of the developed candidate, in silico ADME and toxicity studies have also been carried out. The thieno[2,3-d]pyrimidine derivative was synthesized following the earlier promising findings. Fascinatingly, the synthesized compound (4) showed promising inhibitory effects against EGFRWT and EGFRT790M with IC50 values of 25.8 and 182.3 nM, respectively. Also, it exhibited anticancer potentialities against A549 and MCF-7cell lines with IC50 values of 13.06 and 20.13 µM, respectively. Interestingly, these strong activities were combined with selectivity indices of 2.8 and 1.8 against the two cancer cell lines, respectively. Further investigations indicated the ability of compound 4 to arrest the cancer cells’ growth at the G2/M phase and to increase early and late apoptosis percentages from 2.52% and 2.80 to 17.99% and 16.72%, respectively. Additionally, it was observed that compound 4 markedly increased the levels of caspase-3 and caspase-9 by 4 and 3-fold compared to the control cells. Moreover, it up-regulated the level of BAX by 3-fold and down-regulated the level of Bcl-2 by 3-fold affording a BAX/Bcl-2 ratio of 9. Communicated by Ramaswamy H. Sarma

Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents.

Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. We examined the performance of five molecular docking programs (AutoDock 4, AutoDock Vina, DOCK 6, rDock, and RLDock) for their ability to model ribosomal-ligand interactions with oxazolidinones. Eleven ribosomal crystal structures with oxazolidinones as the ligands were docked. The accuracy was evaluated by calculating the docked complexes' root-mean-square deviation (RMSD) and the program's internal scoring function. The rankings for each program based on the median RMSD between the native and predicted were DOCK 6 > AD4 > Vina > RDOCK >> RLDOCK. Results demonstrate that the top-performing program, DOCK 6, could accurately replicate the ligand binding in only four of the eleven ribosomes due to the poor electron density of said ribosomal structures. In this study, we have further benchmarked the performance of the DOCK 6 docking algorithm and scoring in improving virtual screening (VS) enrichment using the dataset of 285 oxazolidinone derivatives against oxazolidinone binding sites in the S. aureus ribosome. However, there was no clear trend between the structure and activity of the oxazolidinones in VS. Overall, the docking performance indicates that the RNA pocket's high flexibility does not allow for accurate docking prediction, highlighting the need to validate VS. protocols for ligand-RNA before future use. Later, we developed a re-scoring method incorporating absolute docking scores and molecular descriptors, and the results indicate that the descriptors greatly improve the correlation of docking scores and pMIC values. Morgan fingerprint analysis was also used, suggesting that DOCK 6 underpredicted molecules with tail modifications with acetamide, n-methylacetamide, or n-ethylacetamide and over-predicted molecule derivatives with methylamino bits. Alternatively, a ligand-based approach similar to a field template was taken, indicating that each derivative's tail groups have strong positive and negative electrostatic potential contributing to microbial activity. These results indicate that one should perform VS. campaigns of ribosomal antibiotics with care and that more comprehensive strategies, including molecular dynamics simulations and relative free energy calculations, might be necessary in conjunction with VS. and docking.

CHARMM‐GUIhigh‐throughput simulator for efficient evaluation of protein–ligand interactions with different force fields

AbstractMolecular docking is one of the most popular computational tools for the hit discovery step in drug design. However, there is ample room for improvement of docking's ability to identify correct binding modes and discriminate active from decoy compounds. Molecular dynamics (MD) simulations of protein–ligand docking structures have been shown to be effective in improving docking results. Here, we present CHARMM‐GUI high‐throughput simulator (HTS) that prepares MD simulation systems and inputs for multiple protein–ligand complex structures in a high‐throughput manner. HTS supports commonly used MD programs (NAMD, GROMACS, AMBER, OpenMM, GENESIS, Desmond, LAMMPS, and Tinker) along with various force field combinations for protein and ligand, including CHARMM36m, Amber (ff19SB/ff14SB), OPLS‐AA/M, CGenFF, GAFF2, and OpenFF. Validation tests using Miller and the directory of useful decoys‐enhanced (DUD‐E) datasets demonstrate that short MD simulations using HTS‐generated systems and simple ligand RMSD calculations consistently outperform docking results. Specifically, MD simulations can better identify correct ligand‐binding modes among top 10 binding poses as compared to docking scores. In addition, MD simulations can better discriminate active from decoy compounds in the DUD‐E dataset than docking scores for both soluble and membrane proteins. We expect that HTS can be a useful tool to facilitate the hit discovery process in drug design by improving docking results.

The Inhibitory Potential of 2'-dihalo Ribonucleotides against HCV: Molecular Docking, Molecular Simulations, MM-BPSA, and DFT Studies.

Sofosbuvir is the first approved direct-acting antiviral (DAA) agent that inhibits the HCV NS5B polymerase, resulting in chain termination. The molecular models of the 2′-dihalo ribonucleotides used were based on experimental biological studies of HCV polymerase inhibitors. They were modeled within HCV GT1a and GT1b to understand the structure–activity relationship (SAR) and the binding interaction of the halogen atoms at the active site of NS5B polymerase using different computational approaches. The outputs of the molecular docking studies indicated the correct binding mode of the tested compounds against the active sites in target receptors, exhibiting good binding free energies. Interestingly, the change in the substitution at the ribose sugar was found to produce a mild effect on the binding mode. In detail, increasing the hydrophobicity of the substituted moieties resulted in a better binding affinity. Furthermore, in silico ADMET investigation implied the general drug likeness of the examined derivatives. Specifically, good oral absorptions, no BBB penetration, and no CYP4502D6 inhibitions were expected. Likely, the in silico toxicity studies against several animal models showed no carcinogenicity and high predicted TD50 values. The DFT studies exhibited a bioisosteric effect between the substituents at the 2′-position and the possible steric clash between 2′-substituted nucleoside analogs and the active site in the target enzyme. Finally, compound 6 was subjected to several molecular dynamics (MD) simulations and MM-PBSA studies to examine the protein-ligand dynamic and energetic stability.

Ligand and Structure-Based In Silico Determination of the Most Promising SARS-CoV-2 nsp16-nsp10 2'-o-Methyltransferase Complex Inhibitors among 3009 FDA Approved Drugs.

As a continuation of our earlier work against SARS-CoV-2, seven FDA-approved drugs were designated as the best SARS-CoV-2 nsp16-nsp10 2′-o-methyltransferase (2′OMTase) inhibitors through 3009 compounds. The in silico inhibitory potential of the examined compounds against SARS-CoV-2 nsp16-nsp10 2′-o-methyltransferase (PDB ID: (6W4H) was conducted through a multi-step screening approach. At the beginning, molecular fingerprints experiment with SAM (S-Adenosylmethionine), the co-crystallized ligand of the targeted enzyme, unveiled the resemblance of 147 drugs. Then, a structural similarity experiment recommended 26 compounds. Therefore, the 26 compounds were docked against 2′OMTase to reveal the potential inhibitory effect of seven promising compounds (Protirelin, (1187), Calcium folinate (1913), Raltegravir (1995), Regadenoson (2176), Ertapenem (2396), Methylergometrine (2532), and Thiamine pyrophosphate hydrochloride (2612)). Out of the docked ligands, Ertapenem (2396) showed an ideal binding mode like that of the co-crystallized ligand (SAM). It occupied all sub-pockets of the active site and bound the crucial amino acids. Accordingly, some MD simulation experiments (RMSD, RMSF, Rg, SASA, and H-bonding) have been conducted for the 2′OMTase—Ertapenem complex over 100 ns. The performed MD experiments verified the correct binding mode of Ertapenem against 2′OMTase exhibiting low energy and optimal dynamics. Finally, MM-PBSA studies indicated that Ertapenem bonded advantageously to the targeted protein with a free energy value of −43 KJ/mol. Furthermore, the binding free energy analysis revealed the essential amino acids of 2′OMTase that served positively to the binding. The achieved results bring hope to find a treatment for COVID-19 via in vitro and in vivo studies for the pointed compounds.

New Series of VEGFR-2 Inhibitors and Apoptosis Enhancers: Design, Synthesis and Biological Evaluation.

BackgroundCancer is still a major world health threat, causing a high rate of mortality. VEGFR-2 inhibitor anticancer agents are of great significance. However, they showed some serious side effects.PurposeTo discover new effective and safer anticancer agents, a new series of piperazinylquinoxaline-based derivatives was designed and synthesized on the basis of the pharmacophoric features of VEGFR-2 inhibitor drugs.MethodsThe new candidates were evaluated against A549 lung cancer cells, HepG-2 hepatoma cells, Caco-2 colon cancer cells, MDA breast cancer cells, and VEGFR-2 kinase. Moreover, cell cycle kinetics and apoptosis rates were studied in HepG-2 cells treated with compound 11, which was the most promising candidate.ResultsThe new derivatives revealed better antitumor results (IC50 from 6.48 to 38.58 µM) against the aforementioned cancer cell lines than sorafenib. Also, the new candidates showed VEGFR-2 inhibition with IC50 values ranging from 0.19 to 0.60 µM compared to 0.08 µM for sorafenib. Compound 11, meanwhile, showed IC50 values equal to 10.61, 9.52, 12.45, 11.52, and 0.19 µM against the cancer cell lines and VEGFR-2, respectively. Moreover, compound 11 raised the apoptosis rate in HepG-2 cells from 5% to 44% and caused 4, 2.3, and 3-fold increases in BAX/Bcl-2 ratio, caspase-3 level, and P53 expression, respectively, compared to control untreated cells. Finally, the new derivatives displayed the correct binding mode into VEGFR-2 kinase pocket, giving interactions with the essential residues.ConclusionThis work suggests that compound 11 is a very significant anticancer candidate, and piperazinylquinoxaline is an important scaffold in the development of new potential effective and safer VEGFR-2 inhibitor agents.

Docking Ligands into Flexible and Solvated Macromolecules. 8. Forming New Bonds─Challenges and Opportunities.

Over the years, structure-based design programs and specifically docking small molecules to proteins have become prominent in drug discovery. However, many of these computational tools have been developed to primarily dock enzyme inhibitors (and ligands to other protein classes) relying heavily on hydrogen bonds and electrostatic and hydrophobic interactions. In reality, many drug targets either feature metal ions, can be targeted covalently, or are simply not even proteins (e.g., nucleic acids). Herein, we describe several new features that we have implemented into Fitted to broaden its applicability to a wide range of covalent enzyme inhibitors and to metalloenzymes, where metal coordination is essential for drug binding. This updated version of our docking program was tested for its ability to predict the correct binding mode of drug-sized molecules in a large variety of proteins. We also report new datasets that were essential to demonstrate areas of success and those where additional efforts are required. This resource could be used by other program developers to assess their own software.

Induced fit docking workflow based on charmm-gui modules: LBS Finder & Refiner and High-Throughput Simulator

Molecular docking is a preferred method to predict ligand binding modes and their binding energy to target protein receptors, which is a critical first step in the early phase structure-based drug discovery. However, there is a persistent problem in docking that can be attributed to the induced fit effect, where the receptor binding site undergoes induced fit conformational changes upon ligand binding to achieve the correct binding mode. To solve this problem, we present a reliable stepwise docking workflow based on CHARMM-GUI LBS Finder & Refiner and High-Throughput Simulator.

Benchmarking the Ability of Common Docking Programs to Correctly Reproduce and Score Binding Modes in SARS-CoV-2 Protease Mpro.

The coronavirus SARS-CoV-2 main protease, Mpro, is conserved among coronaviruses with no human homolog and has therefore attracted significant attention as an enzyme drug target for COVID-19. The number of studies targeting Mpro for in silico screening has grown rapidly, and it would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. Clearly, current attempts at designing drugs targeting Mpro with the aid of computational docking would benefit from a priori knowledge of the ability of docking programs to predict correct binding modes and score these correctly. In the current work, we tested the ability of several leading docking programs, namely, Glide, DOCK, AutoDock, AutoDock Vina, FRED, and EnzyDock, to correctly identify and score the binding mode of Mpro ligands in 193 crystal structures. None of the codes were able to correctly identify the crystal structure binding mode (lowest energy pose with root-mean-square deviation < 2 Å) in more than 26% of the cases for noncovalently bound ligands (Glide: top performer), whereas for covalently bound ligands the top score was 45% (EnzyDock). These results suggest that one should perform in silico campaigns of Mpro with care and that more comprehensive strategies including ligand free energy perturbation might be necessary in conjunction with virtual screening and docking.

Testing automatic methods to predict free binding energy of host\u2013guest complexes in SAMPL7 challenge

The design of new host–guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host–guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host–guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges.

Targeting interface HSA through anti-cancer drugs: Molecular modeling and docking

Cancer is a serious disease that takes the lives of many people all around the world each year. For treating this disease, many different chemotherapeutic drugs were discovered and examined. However, most of these drugs have serious toxic side effects. In order to reduce these side effects and increase the overall efficiency of chemotherapeutic drugs, their interaction with human deoxyribonucleic acid (DNA) and blood proteins need to be analyzed. Human serum albumin (HSA) is a protein found in human blood and due to its binding capabilities, it is responsible for transporting many drugs to affected body parts. This work aims to study binding between HSA and different anticancer drugs. For this purpose, the molecular docking approach is used. Molecular docking is a method of predicting most probable orientations between two molecules when they are bound to each other. Firstly, each subdomain of HSA and anticancer drugs were prepared and optimized in special molecular dynamic (MD) software. Then, the docking approach was used to perform docking studies and estimating interactions between drugs and HSA. A group of chemotherapy drugs (gemcitabine, fluoxymesterone, vinblastine, daunorubicin and ergosterol peroxide) were selected for docking simulation. Results of the docking studies provided scores for possible conformations between each subdomain of HSA and each drug. Best scores represented the most possible conformations of drug-protein interactions with the lowest binding energy. The pose prediction has been made by identifying the correct binding modes. Affinity ranking for different drugs and subdomains were performed.

Prediction of ligand binding mode among multiple cross-docking poses by molecular dynamics simulations.

We propose a method to identify the correct binding mode of a ligand with a protein among multiple predicted docking poses. Our method consists of two steps. First, five independent MD simulations with different initial velocities are performed for each docking pose, in order to evaluate its stability. If the root-mean-square deviations (RMSDs) of heavy atoms from the docking pose are larger than a given threshold (2.0Å) in all five parallel runs, the pose is filtered out and discarded. Then, we perform accurate all-atom binding free energy calculations for the residual poses only. The pose with the lowest binding free energy is identified as the correct pose. As a test case, we applied our method to a previously built cross-docking test set, which included 104 complex systems. We found that the present method could successfully identify the correct ligand binding mode for 72% (75/104) of the complexes for current test set. The possible reasons for the failure of the method in the other cases were investigated in detail, to enable future improvements.

In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors.

Interleukin-2 (IL-2) is involved in the activation and differentiation of T-helper cells. Uncontrolled activated T cells play a key role in the pathophysiology by stimulating inflammation and autoimmune diseases like arthritis, psoriasis and Crohn's disease. T cells activation can be suppressed either by preventing IL-2 production or blocking the IL-2 interaction with its receptor. Hence, IL-2 is now emerging as a target for novel therapeutic approaches in several autoimmune disorders. This study was carried out to set up an effective virtual screening (VS) pipeline for IL-2. Four docking/scoring approaches (FRED, MOE, GOLD and Surflex-Dock) were compared in the re-docking process to test their performance in producing correct binding modes of IL-2 inhibitors. Surflex-Dock and FRED were the best in predicting the native pose in its top-ranking position. Shapegauss and CGO scoring functions identified the known inhibitors of IL-2 in top 1, 5 and 10 % of library and differentiated binders from non-binders efficiently with average AUC of > 0.9 and > 0.7, resp. The applied docking protocol served as a basis for the VS of a large database that will lead to the identification of more active compounds against IL-2.

Performance of human and server prediction in CAPRI rounds 38‐45

CAPRI challenges offer a variety of blind tests for protein-protein interaction prediction. In CAPRI Rounds 38-45, we generated a set of putative binding modes for each target with an FFT-based docking algorithm, and then scored and ranked these binding modes with a proprietary scoring function, ITScorePP. We have also developed a novel web server, Rebipp. The algorithm utilizes information retrieval to identify relevant biological information to significantly reduce the search space for a particular protein. In parallel, we have also constructed a GPU-based docking server, MDockPP, for protein-protein complex structure prediction. Here, the performance of our protocol in CAPRI rounds 38-45 is reported, which include 16 docking and scoring targets. Among them, three targets contain multiple interfaces: Targets 124, 125, and 136 have 2, 4, and 3 interfaces, respectively. In the predictor experiments, we predicted correct binding modes for nine targets, including one high-accuracy interface, six medium-accuracy binding modes, and six acceptable-accuracy binding modes. For the docking server prediction experiments, we predicted correct binding modes for eight targets, including one high-accuracy, three medium-accuracy, and five acceptable-accuracy binding modes.

An Improved Free Energy Perturbation FEP+ Sampling Protocol for Flexible Ligand-Binding Domains

Recent improvements to the free energy perturbation (FEP) calculations, especially FEP+ , established their utility for pharmaceutical lead optimization. Herein, we propose a modified version of the FEP/REST (i.e., replica exchange with solute tempering) sampling protocol, based on detail studies on several targets by probing a large number of perturbations with different sampling schemes. Improved FEP+ binding affinity predictions for regular flexible-loop motions and considerable structural changes can be obtained by extending the prior to REST (pre-REST) sampling time from 0.24 ns/λ to 5 ns/λ and 2 × 10 ns/λ, respectively. With this new protocol, much more precise ∆∆G values of the individual perturbations, including the sign of the transformations and decreased error were obtained. We extended the REST simulations from 5 ns to 8 ns to achieve reasonable free energy convergence. Implementing REST to the entire ligand as opposed to solely the perturbed region, and also some important flexible protein residues (pREST region) in the ligand binding domain (LBD) has considerably improved the FEP+ results in most of the studied cases. Preliminary molecular dynamics (MD) runs were useful for establishing the correct binding mode of the compounds and thus precise alignment for FEP+ . Our improved protocol may further increase the FEP+ accuracy.

The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations.

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site.

Fragment Binding Pose Predictions Using Unbiased Simulations and Markov-State Models.

Predicting the costructure of small-molecule ligands and their respective target proteins has been a long-standing problem in drug discovery. For weak binding compounds typically identified in fragment-based screening (FBS) campaigns, determination of the correct binding site and correct binding mode is usually done experimentally via X-ray crystallography. For many targets of pharmaceutical interest, however, establishing an X-ray system which allows for sufficient throughput to support a drug discovery project is not possible. In this case, exploration of fragment hits becomes a very laborious and consequently slow process with the generation of protein/ligand cocrystal structures as the bottleneck of the entire process. In this work, we introduce a computational method which is able to reliably predict binding sites and binding modes of fragment-like small molecules using solely the structure of the apoprotein and the ligand's chemical structure as input information. The method is based on molecular dynamics simulations and Markov-state models and can be run as a fully automated protocol requiring minimal human intervention. We describe the application of the method to a representative subset of different target classes and fragments from historical FBS efforts at Boehringer Ingelheim and discuss its potential integration into the overall fragment-based drug discovery workflow.