For each compound, various entries such as physical properties and vendor information can be added for convenient use in subsequent analyses. Constant pH MD simulation (109) where protonation state of titratable residue can change during the simulation may also be useful. Cohen ML. Yu W, He X, Vanommeslaeghe K, MacKerell AD. Following are the steps required to perform a standard MD simulation (see Note 2 for additional MD techniques). In: Klon AE, editor. The process starts with the biological identification of a putative target to which ligand binding should lead to antimicrobial activity. 2MD simulation is an efficient way to generate conformational ensembles. Bethesda, MD 20894, Web Policies VS against a database containing commercially available compounds, is an efficient way to find potential low-molecular weight binders to the target protein (59). Molecular Mechanics. Changing patterns of infectious disease. MacKerell AD, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WE, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wirkiewicz-Kuczera J, Yin D, Karplus M. All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. SILCS is a novel CADD protocol developed in our lab to facilitate ligand design (65). Raman EP, Yu W, Lakkaraju SK, MacKerell AD. The excess chemical potential (. Sugita Y, Okamoto Y. Replica-exchange molecular dynamics method for protein folding. The database, if extremely large, can be divided into several pieces for more efficient use. Ewing TA, Makino S, Skillman AG, Kuntz I. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases. Of these, the similarity search method is the most straightforward and rapid approach (87). When multiple hits for a specific bacterial target with activity data are available, structure-activity relationship (SAR) models can be developed and used to predict new compounds with improved activity (93). For the chemical modification of the hit compound build in the modification onto the compounds with all other coordinates in the ligand and the remainder of the system identical to those from the original MD simulation. This work was supported by NIH grant CA107331, University of Maryland Center for Biomolecular Therapeutics, Samuel Waxman Cancer Research Foundation, and the Computer-Aided Drug Design (CADD) Center at the University of Maryland, Baltimore. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. When developing SAR using pharmacophore descriptors, the appropriate conformations of the compounds that are responsible for the biological activity must be used. Jo S, Kim T, Iyer VG, Im W. CHARMM-GUI: A web-based graphical user interface for CHARMM. These databases are most often in 2D SDF format and need further refinement. Eastman P, Friedrichs MS, Chodera JD, Radmer RJ, Bruns CM, Ku JP, Beauchamp KA, Lane TJ, Wang L-P, Shukla D, Tye T, Houston M, Stich T, Klein C, Shirts MR, Pande VS. OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation. Database screening methods are often used for hit identification (59) while a number of methods may be used for hit optimization (4, 24, 60). These are converted to GFE FragMaps based on a Boltzmann transformation, which allow for quantitative evaluation of ligand affinities, including the contribution of individual atoms. Prepare the target structure in the required DOCK input format. Bernstein FC, Koetzle TF, Williams GJB, Meyer EF, Jr, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M. The protein data bank: A computer-based archival file for macromolecular structures. These force fields are used by the respective programs to estimate the energy and forces associated with, for example, a drug-protein complex. Desmethyl Macrolides: Synthesis and Evaluation of 4,8,10-Tridesmethyl Cethromycin. Balancing target flexibility and target denaturation in computational fragment-based inhibitor discovery. The GCMC/MD approach allows for the application of the SILCS method to target systems with deep or occluded pockets such as nuclear receptors and GPCRs (70). Sheridan RP, Kearsley SK. To save computational time, the single step FEP (SSFEP) may be applied (103). The High-Temperature Equation of State by a Perturbation Method. Raman EP, Yu W, Guvench O, MacKerell AD. An alternative to docking based VS is target-based pharmacophore VS (84). is Co-founder and CSO of SilcsBio LLC. Zhong S, Chen X, Zhu X, Dziegielewska B, Bachman KE, Ellenberger T, Ballin JD, Wilson GM, Tomkinson AE, MacKerell AD. For example, different protonation states of histidine residues can offer different hydrogen bonding types to potential ligands. Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. 4For VS, consensus scoring can be used instead of a single scoring scheme to rank hit compounds to allow more diversity of the identified compounds (86). Khandogin J, Brooks CL. The final generated pharmacophore models or hypotheses are ranked by the sum of all the feature GFEs in the model for a given number of features. Notably, CADD methods are evolving with researchers continually updating and implementing new CADD techniques with higher levels of accuracy and speed (2426). Toward the design of new antibiotics, computer-aided drug design (CADD) can be combined with wet-lab techniques to elucidate the mechanism of drug resistance, to search for new antibiotic targets and to design novel antibiotics for both known and new targets. The user is advised to check that the event of interest (e.g. Tanimoto T. An elementary mathematical theory of classification and prediction. It uses all-atom explicit-solvent MD simulations that include small organic solutes, such as propane, methanol and others, to identify 3D functional-group binding patterns on the target. The 1D or 2D distributions are recorded for each hit compound. Brylinski M, Skolnick J. Liu H, Mark AE, van Gunsteren WF. Prepare the system in a similar way as described in section 3.1 for MD simulations. Evaluate the interaction energy of the hit compound with the full environment for both the initial, unmodified and modified states for the simulations in the presence of the target and hit compound alone in solution. 50,000 compounds are selected from this round based on the vdW attractive energy normalized for the compound molecular weight (. Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure. Additional scoring metrics can include the DOCK or AUTODOCK scores (49, 50), or the average interaction energies from MD simulations, with many other variations available. will also be available for a limited time. Using in silico database screening, Chang et al. Furci LM, Lopes P, Eakanunkul S, Zhong S, MacKerell AD, Wilks A. Inhibition of the Bacterial Heme Oxygenases from Pseudomonas aeruginosa and Neisseria meningitidis: Novel Antimicrobial Targets. Gedeck P, Kramer C, Ertl P. 4 - Computational Analysis of Structure-Activity Relationships. Ten independent 100 cycle GCMC-MD runs are recommended. Such findings may help to overcome the resistance of this bacterium to common antibiotics such as methicillin, fluoroquinolones and oxazolidinones. Constant pH Molecular Dynamics with Proton Tautomerism. Choose a sampling method and scoring scheme for docking. Towards a new age of virtual ADME/TOX and multidimensional drug discovery. Design and Evaluation of a Molecular Fingerprint Involving the Transformation of Property Descriptor Values into a Binary Classification Scheme. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. This is in contrast to the need for many simulations in which the chemical modification is introduced in standard FEP methods (102). In addition structural clustering algorithms can be used to extract representative conformations from MD trajectories (. Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV. Using ligand-based drug design (LBDD), our lab with Andrade and coworkers investigated analogs of the third-generation ketolide antibiotic telithromycin as a possible means to address the bacterial resistance problem associated with that class of antibiotics (1618). Shirts MR, Chodera JD. Hossain M, Chowdhury DUS, Farhana J, Akbar MT, Chakraborty A, Islam S, Mannan A. Here we present a docking protocol using the DOCK program (49) to illustrate the typical docking VS workflow. Wang J, Wang W, Kollman PA, Case DA. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Information from the CADD methods is then used to design compounds that are subjected to chemical synthesis and biological assay, with the information from those experiments used to further develop the SAR, yielding further improvements in the compounds with respect to activity as well as absorption, disposition, metabolism and excretion (ADME) considerations (23). Wet-lab, SBDD and LBDD CADD techniques are outlined in solid lines, dashed lines or dotted lines, respectively. For larger system, more advanced MD techniques can be employed to enhance the sampling efficiency such as replica exchange methods. Reoptimization of MDL Keys for Use in Drug Discovery. SAR) can be used by the medicinal chemist to qualitatively design new, synthetically-accessible compounds that can be quantitatively evaluated. Pharmacophore Modeling Using Site-Identification by Ligand Competitive Saturation (SILCS) with Multiple Probe Molecules. The last conformation from the production MD is used as the starting conformation of the next GCMC cycle. Finally, the database needs to be saved in the format required by the software to be used in following studies, for example, MOE (. Oashi T, Ringer AL, Raman EP, MacKerell AD., Jr Automated Selection of Compounds with Physicochemical Properties To Maximize Bioavailability and Druglikeness. Run the MD simulation in the NPT ensemble for the time scale corresponding to the phenomena being studied. Hom K, Heinzl GA, Eakanunkul S, Lopes PEM, Xue F, MacKerell AD, Wilks A. Download the commercial database(s) from chemical vendors such as Chembridge, Chemdiv, Maybridge, Specs, etc. Raman EP, Vanommeslaeghe K, MacKerell AD. Lead compound derivatives were subsequently identified again using CADD in combination with medicinal chemistry (20) and the accumulated SAR information will facilitate the development of next generation antibiotics targeting gram positive pathogenic bacteria. Automation of the CHARMM General Force Field (CGenFF) I: Bond Perception and Atom Typing. An example, is the binding response program (, Virtual database screening (VS) techniques are generally used to screen large, Commercially available CADD software packages include Discovery Studio (. SSFEP has the ability to give rapid predictions of binding affinity changes related to modifications and, thus, is quite useful for lead optimization (104). Figure 1 illustrates the basic CADD workflow that can be interactively used with experimental techniques to identify novel lead compounds as well as direct iterative ligand optimization (3, 4, 21, 22). The 4-dimensional bioavailability (4D-BA) descriptor (83) is a scalar term derived from the four criteria in RO5 and thus facilitates the selection of potential bioavailable compounds in an automatic fashion. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries. Chayan A, Andrew C, James EP, Alexander DM. However, with all MD based methods the user must perform careful analysis to assure that the conformational ensemble is adequately converged for effective use in CADD. The GFE FragMaps can be used to guide ligand docking using the MC-SILCS approach (. In addition to water, add solute molecules such as benzene, propane, methanol, formamide, acetaldehyde, imidazole, methylammonium and acetate at a concentration of about 0.25 M. Place weak restraints only on the backbone C carbon atoms with a force constant (k in 1/2 kx, This system is minimized for 5000 steps with the steepest descent (SD) algorithm (, During GCMC, solutes and water are exchanged between their gas-phase reservoirs and the simulation system. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). Silico Drug Discovery and Design: Theory, Methods, Challenges, and Applications. Enhanced Conformational Sampling Using Replica Exchange with Concurrent Solute Scaling and Hamiltonian Biasing Realized in One Dimension. Scoring the binding poses uses a physical force field based scoring function that includes both van der Waals (vdW) and electrostatic terms (see, Dock the entire compound database using a single crystal structure of the target or multiple conformations from MD mentioned above. Healy JR, Bezawada P, Shim J, Jones JW, Kane MA, MacKerell AD, Coop A, Matsumoto RR. The method may be applied using the following protocol with most simulations packages. Once lead compounds are identified from experiments, LBDD methods can be utilized to start to develop an SAR or find more hit compounds. 2D distributions can be between all possible distance or angle pairs. Quantitative Conformationally Sampled Pharmacophore for Opioid Ligands: Reevaluation of Hydrophobic Moieties Essential for Biological Activity. Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations. An example of a recently identified novel antibiotic target is the protein heme oxygenase, involved in the metabolism of heme by bacteria as required to access iron (1012). Run five 10 ns MD simulations of the hit compound-target complex and of the hit compound alone in solution. The CADD methods presented in the chapter such as SILCS for SBDD or CSP for LBDD take this issue into account and thus have advantages over other CADD methods that only rely on single crystal structure or limited ligand conformations. The program MOE (, Choose the types of fingerprint used to define the compounds in the database. Nguyen AT, Jones JW, Ruge MA, Kane MA, Oglesby-Sherrouse AG. Identification of potential targets in Staphylococcus aureus N315 using computer aided protein data analysis. The pharmacophore is then used in VS against a compound database (iv) that contains multiple conformations of each compound from which hit compounds are identified (v) and further tested in bioassays (vi). Structure based drug design (SBDD) and ligand based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. In collaborative studies with the Wilks lab, we have successfully applied CADD techniques to identify inhibitors of the bacterial heme oxygenases from Pseudomonas aeruginosa and Neisseria meningitides, thereby confirming the potential role of heme oxygenases as a novel antimicrobial targets (13, 14). Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone , and Side-Chain 1 and 2 Dihedral Angles. Similar to docking VS, the desired binding site needs to be defined. A pharmacophore model is defined as spatially distributed chemical features that are essential for specific ligand-target binding. Comparative protein modelling by satisfaction of spatial restraints. Computational Identification of Inhibitors of Protein-Protein Interactions. Journal of Chemical Information and Modeling. Despite the fact that numerous antibiotic drugs are available and have been routinely used for a much longer time than most other drugs, the fight between humans and the surrounding bacteria responsible for infections are ongoing and will be so for the foreseeable future. Baell JB, Holloway GA. New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays. Yu W, Lakkaraju SK, Raman EP, Fang L, MacKerell AD. pharmacophore features. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Why do we need so many chemical similarity search methods? 3D database searching in drug design. In SDBB, the 3D structure of the target can be identified by X-ray crystallography or NMR or using homology modeling. Zwanzig RW. Aqueous solvation effects of the simulated compounds can be included using explicit solvent or are treated using an implicit solvation model such as the generalized Born continuum solvent model (. Convert 2D SDF files into 3D structure files such as MOL2 format files using a chemical data tool such as Open Babel (, All 3D structures can be further optimized using a force field based minimization to obtain more chemically-accurate structures and assign atomic charges for subsequent screening studies if required. 2D Conformationally Sampled Pharmacophore: A Ligand-Based Pharmacophore To Differentiate Opioid Agonists from Antagonists. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Here we illustrate the development of SAR using our in-house developed conformationally sampled pharmacophore (CSP) protocol (94, 95). The 50,000 compounds selected from the first round of VS are subject to a second round of docking using a more rigorous optimization that includes more steps of minimization and multiple protein conformations (~10) are used to take target flexibility into account. Deng Y, Roux B. Computation of binding free energy with molecular dynamics and grand canonical Monte Carlo simulations. Efficient Drug Lead Discovery and Optimization. Zhong S, Oashi T, Yu W, Shapiro P, MacKerell AD., Jr . Calculate the free energy difference, G, in the presence of the protein and in aqueous solution based on the free energy perturbation formula (, Computer-aided drug design, molecular dynamics, virtual screening, docking, Site Identification by Ligand Competitive Saturation, SILCS, structure-activity relationship, pharmacophore, force field. Double headed arrows indicate the two techniques can be used interactively in several iterative rounds of ligand design. Guvench O, MacKerell AD., Jr Computational Fragment-Based Binding Site Identification by Ligand Competitive Saturation. I. Desmethyl Macrolides: Synthesis and Evaluation of 4,8,10-Tridesmethyl Telithromycin. Before Martin YC. This approach may also be used as lead validation, as a compound that has multiple analogs with biological activity from which SAR can be developed is appropriate for further studies (88). Bernard D, Coop A, MacKerell AD. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. While multiple methods can be used to generate pharmacophores (84), we will present a method based on information from SILCS as described in section 3.2. RDKit: Cheminformatics and Machine Learning Software. The developed regression model can then be used to quantitatively predict the activity of the modified compounds (93). Compounds are then ranked based on their interactions energies and selected for further analyses. Lead Validation and SAR Development via Chemical Similarity Searching: Application to Compounds Targeting the pY+3 Site of the SH2 Domain of p56lck. For example, in our SILCS-Pharm protocol, LGFE and RMSD are used together to rank compounds that pass our pharmacophore model filtering. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA. A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules. Thus conformational sampling of a protein or ligand that produces an ensemble of biological meaningful conformations is necessary either for SBDD or for LBDD. Sali A, Blundell TL. Zhong S, MacKerell AD. Rais R, Acharya C, Tririya G, MacKerell AD, Polli JE. Ekins S, Boulanger B, Swaan P, Hupcey MZ. In this scenario, each compound in the database is docked to each target conformation and the most favorable score for that compound is used for ranking as described below. 1Conformational flexibility of molecules is a very important feature no matter if it is a small ligand or a large protein. Todeschini R, Consonni V, Xiang H, Holliday J, Buscema M, Willett P. Similarity Coefficients for Binary Chemoinformatics Data: Overview and Extended Comparison Using Simulated and Real Data Sets. Merck molecular force field. The approach uses a pre-computed MD simulation of the hit compound-target complex from which the free energy difference due to small, single non-hydrogen atom modifications (e.g. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. Accessibility With respect to ligands, many computational tools for prediction of ionization state are available, though common sense by the user is often adequate to deal with the most common ionizable groups such as carboxylates. Walsh C. Where will new antibiotics come from? Conformational Sampling of Oligosaccharides Using Hamiltonian Replica Exchange with Two-Dimensional Dihedral Biasing Potentials and the Weighted Histogram Analysis Method (WHAM). Basic CADD workflow in drug discovery. While the ZINC database is available, researchers may want to prepare an in-house database for specific use. Word JM, Lovell SC, Richardson JS, Richardson DC. Site Identification by Ligand Competitive Saturation (SILCS) Simulations for Fragment-Based Drug Design. The utility of the SSFEP approach is that the G values for many modifications may be rapidly evaluated as the same trajectories from the original MD simulations of the hit compound are used in each case. Knowledge of the relationship of these properties to activity (i.e. Capra JA, Laskowski RA, Thornton JM, Singh M, Funkhouser TA. Nguyen AT, O'Neill MJ, Watts AM, Robson CL, Lamont IL, Wilks A, Oglesby-Sherrouse AG. Computational approaches for the design of proteinprotein interaction inhibitors. Yang M, MacKerell AD. LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. Define the desired binding pocket on the protein surface either using experimental information or by using a binding pocket prediction program as described in the Materials section. Automatic atom type and bond type perception in molecular mechanical calculations. Various docking programs are available that differ based on the scoring function used to describe the interaction between small molecule and the target and the conformational sampling method used to generate the binding poses of the ligand on the protein.
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