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DNA-encoded chemical libraries yield non-covalent and non-peptidic SARS-CoV-2 main protease inhibitors. Commun Chem 2023; 6:164. [PMID: 37542196 PMCID: PMC10403511 DOI: 10.1038/s42004-023-00961-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/19/2023] [Indexed: 08/06/2023] Open
Abstract
The development of SARS-CoV-2 main protease (Mpro) inhibitors for the treatment of COVID-19 has mostly benefitted from X-ray structures and preexisting knowledge of inhibitors; however, an efficient method to generate Mpro inhibitors, which circumvents such information would be advantageous. As an alternative approach, we show here that DNA-encoded chemistry technology (DEC-Tec) can be used to discover inhibitors of Mpro. An affinity selection of a 4-billion-membered DNA-encoded chemical library (DECL) using Mpro as bait produces novel non-covalent and non-peptide-based small molecule inhibitors of Mpro with low nanomolar Ki values. Furthermore, these compounds demonstrate efficacy against mutant forms of Mpro that have shown resistance to the standard-of-care drug nirmatrelvir. Overall, this work demonstrates that DEC-Tec can efficiently generate novel and potent inhibitors without preliminary chemical or structural information.
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Abstract
Small molecules that selectively bind to the pseudokinase JH2 domain over the JH1 kinase domain of JAK2 kinase are sought. Virtual screening led to the purchase of 17 compounds among which 9 were found to bind to V617F JAK2 JH2 with affinities of 40 - 300 μM in a fluorogenic assay. Ten analogues were then purchased yielding 9 additional active compounds. Aminoanilinyltriazine 22 was particularly notable as it shows no detectable binding to JAK2 JH1, and it has a 65-μM dissociation constant K d with V617F JAK2 JH2. A crystal structure for 22 in complex with wild-type JAK2 JH2 was obtained to elucidate the binding mode. Additional de novo design led to the synthesis of 19 analogues of 22 with the most potent being 33n with K d values of 2-3 μM for WT and V617F JAK2 JH2, and with 16-fold selectivity relative to binding with WT JAK2 JH1.
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3
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Structure-Guided Identification of DNMT3B Inhibitors. ACS Med Chem Lett 2020; 11:971-976. [PMID: 32435413 DOI: 10.1021/acsmedchemlett.0c00011] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 02/07/2020] [Indexed: 02/07/2023] Open
Abstract
Methyltransferase 3 beta (DNMT3B) inhibitors that interfere with cancer growth are emerging possibilities for treatment of melanoma. Herein we identify small molecule inhibitors of DNMT3B starting from a homology model based on a DNMT3A crystal structure. Virtual screening by docking led to purchase of 15 compounds, among which 5 were found to inhibit the activity of DNMT3B with IC50 values of 13-72 μM in a fluorogenic assay. Eight analogues of 7, 10, and 12 were purchased to provide 2 more active compounds. Compound 11 is particularly notable as it shows good selectivity with no inhibition of DNMT1 and 22 μM potency toward DNMT3B. Following additional de novo design, exploratory synthesis of 17 analogues of 11 delivered 5 additional inhibitors of DNMT3B with the most potent being 33h with an IC50 of 8.0 μM. This result was well confirmed in an ultrahigh-performance liquid chromatography (UHPLC)-based analytical assay, which yielded an IC50 of 4.8 μM. Structure-activity data are rationalized based on computed structures for DNMT3B complexes.
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Abstract
Bacterial resistance to β-lactam antibiotics is largely mediated by β-lactamases, which catalyze the hydrolysis of these drugs and continue to emerge in response to antibiotic use. β-Lactamases that hydrolyze the last resort carbapenem class of β-lactam antibiotics (carbapenemases) are a growing global health threat. Inhibitors have been developed to prevent β-lactamase-mediated hydrolysis and restore the efficacy of these antibiotics. However, there are few inhibitors available for problematic carbapenemases such as oxacillinase-48 (OXA-48). A DNA-encoded chemical library approach was used to rapidly screen for compounds that bind and potentially inhibit OXA-48. Using this approach, a hit compound, CDD-97, was identified with submicromolar potency (Ki = 0.53 ± 0.08 μM) against OXA-48. X-ray crystallography showed that CDD-97 binds noncovalently in the active site of OXA-48. Synthesis and testing of derivatives of CDD-97 revealed structure-activity relationships and informed the design of a compound with a 2-fold increase in potency. CDD-97, however, synergizes poorly with β-lactam antibiotics to inhibit the growth of bacteria expressing OXA-48 due to poor accumulation into E. coli. Despite the low in vivo activity, CDD-97 provides new insights into OXA-48 inhibition and demonstrates the potential of using DNA-encoded chemistry technology to rapidly identify β-lactamase binders and to study β-lactamase inhibition, leading to clinically useful inhibitors.
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C-N Coupling of DNA-Conjugated (Hetero)aryl Bromides and Chlorides for DNA-Encoded Chemical Library Synthesis. Bioconjug Chem 2020; 31:770-780. [PMID: 32019312 PMCID: PMC7086399 DOI: 10.1021/acs.bioconjchem.9b00863] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
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DNA-encoded
chemical library (DECL) screens are a rapid and economical
tool to identify chemical starting points for drug discovery. As a
robust transformation for drug discovery, palladium-catalyzed C–N
coupling is a valuable synthetic method for the construction of DECL
chemical matter; however, currently disclosed methods have only been
demonstrated on DNA-attached (hetero)aromatic iodide and bromide electrophiles.
We developed conditions utilizing an N-heterocyclic
carbene–palladium catalyst that extends this reaction to the
coupling of DNA-conjugated (hetero)aromatic chlorides with (hetero)aromatic
and select aliphatic amine nucleophiles. In addition, we evaluated
steric and electronic effects within this catalyst series, carried
out a large substrate scope study on two representative (hetero)aryl
bromides, and applied this newly developed method within the construction
of a 63 million-membered DECL.
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Palladium-Catalyzed Hydroxycarbonylation of (Hetero)aryl Halides for DNA-Encoded Chemical Library Synthesis. Bioconjug Chem 2019; 30:2209-2215. [PMID: 31329429 PMCID: PMC6706801 DOI: 10.1021/acs.bioconjchem.9b00447] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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A strategy
for DNA-compatible, palladium-catalyzed hydroxycarbonylation
of (hetero)aryl halides on DNA–chemical conjugates has been
developed. This method generally provided the corresponding carboxylic
acids in moderate to very good conversions for (hetero)aryl iodides
and bromides, and in poor to moderate conversions for (hetero)aryl
chlorides. These conditions were further validated by application
within a DNA-encoded chemical library synthesis and subsequent discovery
of enriched features from the library in selection experiments against
two protein targets.
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Abstract
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A hypodiboric
acid system for the reduction of nitro groups on
DNA–chemical conjugates has been developed. This transformation
provided good to excellent yields of the reduced amine product for
a variety of functionalized aromatic, heterocyclic, and aliphatic
nitro compounds. DNA tolerance to reaction conditions, extension to
decigram scale reductions, successful use in a DNA-encoded chemical
library synthesis, and subsequent target selection are also described.
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8
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Quantitative Comparison of Enrichment from DNA-Encoded Chemical Library Selections. ACS COMBINATORIAL SCIENCE 2019; 21:75-82. [PMID: 30672692 PMCID: PMC6372980 DOI: 10.1021/acscombsci.8b00116] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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DNA-encoded
chemical libraries (DELs) provide a high-throughput
and cost-effective route for screening billions of unique molecules
for binding affinity for diverse protein targets. Identifying candidate
compounds from these libraries involves affinity selection, DNA sequencing,
and measuring enrichment in a sample pool of DNA barcodes. Successful
detection of potent binders is affected by many factors, including
selection parameters, chemical yields, library amplification, sequencing
depth, sequencing errors, library sizes, and the chosen enrichment
metric. To date, there has not been a clear consensus about how enrichment
from DEL selections should be measured or reported. We propose a normalized z-score enrichment metric using a binomial distribution
model that satisfies important criteria that are relevant for analysis
of DEL selection data. The introduced metric is robust with respect
to library diversity and sampling and allows for quantitative comparisons
of enrichment of n-synthons from parallel DEL selections.
These features enable a comparative enrichment analysis strategy that can
provide valuable information about hit compounds in early stage drug
discovery.
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9
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The BioFragment Database (BFDb): An open-data platform for computational chemistry analysis of noncovalent interactions. J Chem Phys 2018; 147:161727. [PMID: 29096505 DOI: 10.1063/1.5001028] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Accurate potential energy models are necessary for reliable atomistic simulations of chemical phenomena. In the realm of biomolecular modeling, large systems like proteins comprise very many noncovalent interactions (NCIs) that can contribute to the protein's stability and structure. This work presents two high-quality chemical databases of common fragment interactions in biomolecular systems as extracted from high-resolution Protein DataBank crystal structures: 3380 sidechain-sidechain interactions and 100 backbone-backbone interactions that inaugurate the BioFragment Database (BFDb). Absolute interaction energies are generated with a computationally tractable explicitly correlated coupled cluster with perturbative triples [CCSD(T)-F12] "silver standard" (0.05 kcal/mol average error) for NCI that demands only a fraction of the cost of the conventional "gold standard," CCSD(T) at the complete basis set limit. By sampling extensively from biological environments, BFDb spans the natural diversity of protein NCI motifs and orientations. In addition to supplying a thorough assessment for lower scaling force-field (2), semi-empirical (3), density functional (244), and wavefunction (45) methods (comprising >1M interaction energies), BFDb provides interactive tools for running and manipulating the resulting large datasets and offers a valuable resource for potential energy model development and validation.
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Computationally-guided optimization of small-molecule inhibitors of the Aurora A kinase-TPX2 protein-protein interaction. Chem Commun (Camb) 2017; 53:9372-9375. [PMID: 28787041 PMCID: PMC5591577 DOI: 10.1039/c7cc05379g] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Free energy perturbation theory, in combination with enhanced sampling of protein-ligand binding modes, is evaluated in the context of fragment-based drug design, and used to design two new small-molecule inhibitors of the Aurora A kinase-TPX2 protein-protein interaction.
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Bringing Clarity to the Prediction of Protein-Ligand Binding Free Energies via "Blurring". J Chem Theory Comput 2014; 10:1314-1325. [PMID: 24803861 PMCID: PMC4006398 DOI: 10.1021/ct400995c] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Indexed: 02/03/2023]
Abstract
We present a method to evaluate the free energies of ligand binding utilizing a Monte Carlo estimation of the configuration integrals concomitant with uncertainty quantification. Ensembles for integration are built through systematically perturbing an initial ligand conformation in a rigid binding pocket, which is optimized separately prior to incorporation of the ligand. We call the procedure producing the ensembles "blurring", and it is carried out using an in-house developed code. The Boltzmann factor contribution of each pose to the configuration integral is computed and from there the free energy is obtained. Potential function uncertainties are estimated using a fragment-based error propagation method. This method has been applied to a set of small aromatic ligands complexed with T4 Lysozyme L99A mutant. Microstate energies have been determined with the force fields ff99SB and ff94, and the semiempirical method PM6DH2 in conjunction with continuum solvation models including Generalized Born (GB), the Conductor-like Screening Model (COSMO), and SMD. Of the methods studied, PM6DH2-based scoring gave binding free energy estimates, which yielded a good correlation to the experimental binding affinities (R2 = 0.7). All methods overestimated the calculated binding affinities. We trace this to insufficient sampling, the single static protein structure, and inaccuracies in the solvent models we have used in this study.
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Abstract
Computer-aided drug design could benefit from a greater understanding of how errors arise and propagate in biomolecular modeling. With such knowledge, model predictions could be associated with quantitative estimates of their uncertainty. In addition, novel algorithms could be designed to proactively reduce prediction errors. We investigated how errors propagate in statistical mechanical ensembles and found that free energy evaluations based on single molecular configurations yield maximum uncertainties in free energy. Furthermore, increasing the size of the ensemble by sampling and averaging over additional independent configurations reduces uncertainties in free energy dramatically. This finding suggests a general strategy that could be utilized as a post-hoc correction for improved precision in virtual screening and free energy estimation.
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13
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Fragment-based error estimation in biomolecular modeling. Drug Discov Today 2013; 19:45-50. [PMID: 23993915 DOI: 10.1016/j.drudis.2013.08.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Revised: 08/15/2013] [Accepted: 08/20/2013] [Indexed: 10/26/2022]
Abstract
Computer simulations are becoming an increasingly more important component of drug discovery. Computational models are now often able to reproduce and sometimes even predict outcomes of experiments. Still, potential energy models such as force fields contain significant amounts of bias and imprecision. We have shown how even small uncertainties in potential energy models can propagate to yield large errors, and have devised some general error-handling protocols for biomolecular modeling with imprecise energy functions. Herein we discuss those protocols within the contexts of protein-ligand binding and protein folding.
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14
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The Effects of Computational Modeling Errors on the Estimation of Statistical Mechanical Variables. J Chem Theory Comput 2012; 8:3769-3776. [PMID: 23413365 DOI: 10.1021/ct300024z] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Computational models used in the estimation of thermodynamic quantities of large chemical systems often require approximate energy models that rely on parameterization and cancellation of errors to yield agreement with experimental measurements. In this work, we show how energy function errors propagate when computing statistical mechanics-derived thermodynamic quantities. Assuming that each microstate included in a statistical ensemble has a measurable amount of error in its calculated energy, we derive low-order expressions for the propagation of these errors in free energy, average energy, and entropy. Through gedanken experiments we show the expected behavior of these error propagation formulas on hypothetical energy surfaces. For very large microstate energy errors, these low-order formulas disagree with estimates from Monte Carlo simulations of error propagation. Hence, such simulations of error propagation may be required when using poor potential energy functions. Propagated systematic errors predicted by these methods can be removed from computed quantities, while propagated random errors yield uncertainty estimates. Importantly, we find that end-point free energy methods maximize random errors and that local sampling of potential energy wells decreases random error significantly. Hence, end-point methods should be avoided in energy computations and should be replaced by methods that incorporate local sampling. The techniques described herein will be used in future work involving the calculation of free energies of biomolecular processes, where error corrections are expected to yield improved agreement with experiment.
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15
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Erratum: “Model for the fast estimation of basis set superposition error in biomolecular systems” [J. Chem. Phys. 135, 144110 (2011)]. J Chem Phys 2012. [DOI: 10.1063/1.3693327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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16
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Statistics-based model for basis set superposition error correction in large biomolecules. Phys Chem Chem Phys 2012; 14:7795-9. [DOI: 10.1039/c2cp23715f] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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17
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Pairwise additivity of energy components in protein-ligand binding: the HIV II protease-Indinavir case. J Chem Phys 2011; 135:085101. [PMID: 21895219 DOI: 10.1063/1.3624750] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
An energy expansion (binding energy decomposition into n-body interaction terms for n ≥ 2) to express the receptor-ligand binding energy for the fragmented HIV II protease-Indinavir system is described to address the role of cooperativity in ligand binding. The outcome of this energy expansion is compared to the total receptor-ligand binding energy at the Hartree-Fock, density functional theory, and semiempirical levels of theory. We find that the sum of the pairwise interaction energies approximates the total binding energy to ∼82% for HF and to >95% for both the M06-L density functional and PM6-DH2 semiempirical method. The contribution of the three-body interactions amounts to 18.7%, 3.8%, and 1.4% for HF, M06-L, and PM6-DH2, respectively. We find that the expansion can be safely truncated after n=3. That is, the contribution of the interactions involving more than three parties to the total binding energy of Indinavir to the HIV II protease receptor is negligible. Overall, we find that the two-body terms represent a good approximation to the total binding energy of the system, which points to pairwise additivity in the present case. This basic principle of pairwise additivity is utilized in fragment-based drug design approaches and our results support its continued use. The present results can also aid in the validation of non-bonded terms contained within common force fields and in the correction of systematic errors in physics-based score functions.
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Model for the fast estimation of basis set superposition error in biomolecular systems. J Chem Phys 2011; 135:144110. [PMID: 22010701 PMCID: PMC3212865 DOI: 10.1063/1.3641894] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2011] [Accepted: 09/01/2011] [Indexed: 11/14/2022] Open
Abstract
Basis set superposition error (BSSE) is a significant contributor to errors in quantum-based energy functions, especially for large chemical systems with many molecular contacts such as folded proteins and protein-ligand complexes. While the counterpoise method has become a standard procedure for correcting intermolecular BSSE, most current approaches to correcting intramolecular BSSE are simply fragment-based analogues of the counterpoise method which require many (two times the number of fragments) additional quantum calculations in their application. We propose that magnitudes of both forms of BSSE can be quickly estimated by dividing a system into interacting fragments, estimating each fragment's contribution to the overall BSSE with a simple statistical model, and then propagating these errors throughout the entire system. Such a method requires no additional quantum calculations, but rather only an analysis of the system's interacting fragments. The method is described herein and is applied to a protein-ligand system, a small helical protein, and a set of native and decoy protein folds.
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Abstract
The routine prediction of three-dimensional protein structure from sequence remains a challenge in computational biochemistry. It has been intuited that calculated energies from physics-based scoring functions are able to distinguish native from nonnative folds based on previous performance with small proteins and that conformational sampling is the fundamental bottleneck to successful folding. We demonstrate that as protein size increases, errors in the computed energies become a significant problem. We show, by using error probability density functions, that physics-based scores contain significant systematic and random errors relative to accurate reference energies. These errors propagate throughout an entire protein and distort its energy landscape to such an extent that modern scoring functions should have little chance of success in finding the free energy minima of large proteins. Nonetheless, by understanding errors in physics-based score functions, they can be reduced in a post-hoc manner, improving accuracy in energy computation and fold discrimination.
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Formal Estimation of Errors in Computed Absolute Interaction Energies of Protein-ligand Complexes. J Chem Theory Comput 2011; 7:790-797. [PMID: 21666841 PMCID: PMC3110077 DOI: 10.1021/ct100563b] [Citation(s) in RCA: 114] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A largely unsolved problem in computational biochemistry is the accurate prediction of binding affinities of small ligands to protein receptors. We present a detailed analysis of the systematic and random errors present in computational methods through the use of error probability density functions, specifically for computed interaction energies between chemical fragments comprising a protein-ligand complex. An HIV-II protease crystal structure with a bound ligand (indinavir) was chosen as a model protein-ligand complex. The complex was decomposed into twenty-one (21) interacting fragment pairs, which were studied using a number of computational methods. The chemically accurate complete basis set coupled cluster theory (CCSD(T)/CBS) interaction energies were used as reference values to generate our error estimates. In our analysis we observed significant systematic and random errors in most methods, which was surprising especially for parameterized classical and semiempirical quantum mechanical calculations. After propagating these fragment-based error estimates over the entire protein-ligand complex, our total error estimates for many methods are large compared to the experimentally determined free energy of binding. Thus, we conclude that statistical error analysis is a necessary addition to any scoring function attempting to produce reliable binding affinity predictions.
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