Benchmarking AlphaFold ‐enabled molecular docking predictions for antibiotic discovery
2022; Springer Nature; Volume: 18; Issue: 9 Linguagem: Inglês
10.15252/msb.202211081
ISSN1744-4292
AutoresFelix Wong, Aarti Krishnan, Erica J. Zheng, H. Stärk, Abigail L. Manson, Ashlee M. Earl, Tommi Jaakkola, James J. Collins,
Tópico(s)Click Chemistry and Applications
ResumoArticle6 September 2022Open Access Transparent process Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery Felix Wong Felix Wong Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Aarti Krishnan Aarti Krishnan orcid.org/0000-0002-7936-4442 Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Erica J Zheng Erica J Zheng Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Program in Chemical Biology, Harvard University, Cambridge, MA, USA Contribution: Data curation, Investigation, Writing - original draft, Writing - review & editing Search for more papers by this author Hannes Stärk Hannes Stärk orcid.org/0000-0002-4463-326X Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA Contribution: Formal analysis, Writing - review & editing Search for more papers by this author Abigail L Manson Abigail L Manson orcid.org/0000-0002-3800-0714 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Software, Formal analysis, Writing - original draft, Writing - review & editing Search for more papers by this author Ashlee M Earl Ashlee M Earl Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Software, Formal analysis, Writing - original draft, Writing - review & editing Search for more papers by this author Tommi Jaakkola Tommi Jaakkola Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA Contribution: Formal analysis, Writing - review & editing Search for more papers by this author James J Collins Corresponding Author James J Collins [email protected] orcid.org/0000-0002-5560-8246 Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Contribution: Conceptualization, Supervision, Funding acquisition, Writing - original draft, Writing - review & editing Search for more papers by this author Felix Wong Felix Wong Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Aarti Krishnan Aarti Krishnan orcid.org/0000-0002-7936-4442 Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Erica J Zheng Erica J Zheng Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Program in Chemical Biology, Harvard University, Cambridge, MA, USA Contribution: Data curation, Investigation, Writing - original draft, Writing - review & editing Search for more papers by this author Hannes Stärk Hannes Stärk orcid.org/0000-0002-4463-326X Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA Contribution: Formal analysis, Writing - review & editing Search for more papers by this author Abigail L Manson Abigail L Manson orcid.org/0000-0002-3800-0714 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Software, Formal analysis, Writing - original draft, Writing - review & editing Search for more papers by this author Ashlee M Earl Ashlee M Earl Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Software, Formal analysis, Writing - original draft, Writing - review & editing Search for more papers by this author Tommi Jaakkola Tommi Jaakkola Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA Contribution: Formal analysis, Writing - review & editing Search for more papers by this author James J Collins Corresponding Author James J Collins [email protected] orcid.org/0000-0002-5560-8246 Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Contribution: Conceptualization, Supervision, Funding acquisition, Writing - original draft, Writing - review & editing Search for more papers by this author Author Information Felix Wong1,2,3,†, Aarti Krishnan1,2,3,†, Erica J Zheng3,4, Hannes Stärk5, Abigail L Manson3, Ashlee M Earl3, Tommi Jaakkola5 and James J Collins *,1,2,3,6 1Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA 2Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA 3Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA 4Program in Chemical Biology, Harvard University, Cambridge, MA, USA 5Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA 6Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA † These authors contributed equally to this work *Corresponding author. Tel: +1 617 324 6607; E-mail: [email protected] Molecular Systems Biology (2022)18:e11081https://doi.org/10.15252/msb.202211081 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein-ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning-based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. This work indicates that advances in modeling protein-ligand interactions, particularly using machine learning-based approaches, are needed to better harness AlphaFold2 for drug discovery. Synopsis Assessing molecular docking simulations based on AlphaFold2-predicted structures with high-throughput measurements of protein-ligand interactions reveals weak model performance. Machine learning-based approaches improve performance and better harness AlphaFold2 for drug discovery. AlphaFold2-based molecular docking predictions for 296 Escherichia coli proteins, 218 active antibacterial compounds and 100 inactive compounds predict widespread promiscuity and similar distributions of binding affinities between active and inactive compounds. Quantitative enzymatic inhibition assays for 12 essential E. coli proteins treated with each of the 218 antibacterial compounds confirm extensive promiscuity. The enzymatic inhibition dataset reveals that the performance of the molecular docking model is weak. Rescoring of docking poses using machine learning-based scoring functions improves model performance. Introduction A major challenge in drug discovery is the identification of drug-target interactions. Various approaches to identifying molecular drug targets have been developed, including those based on biochemical assays, genetic interactions, and molecular docking (Kitchen et al, 2004; Schenone et al, 2013). Molecular docking, in particular, has proven versatile for identifying protein-ligand interactions and drug mechanisms of action. In molecular docking, ligand binding poses within a targeted binding site of a protein are computationally modeled using scoring functions, and poses are optimized to provide structural information and activity predictions in the form of thermodynamic binding affinities. While docking has been used to enrich for potential hit compounds that bind pre-specified proteins in “one target, many compounds” approaches, the process of “reverse docking,” in which a small molecule is docked across different potential protein targets, leverages docking to discover binding partners and drug mechanisms of action (Kharkar et al, 2014; Lee et al, 2016). Although versatile, reverse docking requires a priori knowledge of the protein structures of interest, and its application to drug-target identification has been limited by the number and quality of target protein structures (Chen & Zhi, 2001; Kharkar et al, 2014; Lee et al, 2016). Here, we reasoned that the recent release of the AlphaFold2 database of protein structure predictions (Jumper et al, 2021; Varadi et al, 2022) could enable reverse docking approaches that span Escherichia coli's essential proteome, allowing for the extensive prediction of binding targets of antibacterial compounds (Fig 1A). We hypothesized that such an approach could enrich for true protein-ligand interactions from the large, combinatorial space of all possible interactions between antibacterial compounds and essential proteins. As computational docking approaches are known to predict many false positives (Adeshina et al, 2020), the predicted protein-ligand interactions could be experimentally interrogated, in part, using biochemical assays that measure enzymatic activity, with binding interactions supported by enzymatic inhibition. In addition to inspiring further studies that expand on the interactions discovered in this way, these experiments could be used to benchmark the performance of our modeling platform and reveal the prediction accuracy possible with AlphaFold2-enabled molecular docking simulations. Figure 1. Growth inhibition screens in Escherichia coli reveal 218 active compounds, whose interactions with essential proteins are predicted by combining AlphaFold2 with molecular docking A. Schematic of the approach. To define our chemical space of interest, we performed high-throughput screens of growth inhibition against wild-type E. coli. Compounds that inhibited growth were taken as active, and each active compound was computationally docked with each of 296 AlphaFold2-predicted E. coli essential protein structures. For comparison, a subset of the inactive compounds was docked in the same way. An interaction matrix showing the thermodynamic binding affinities predicted by the docking simulations was then constructed. A protein-ligand interaction was predicted to occur if its predicted binding affinity was smaller than a threshold value. All possible interactions for a subset of essential proteins, including those not predicted to occur, were empirically tested to benchmark model performance. B. Growth inhibition measurements for 39,128 compounds, from which 218 compounds (including known antibiotics) were identified as active against E. coli BW25113. Data are shown from two biological replicates. Compounds with mean relative growth less than 0.2 were classified as active (red points), and all other compounds were classified as inactive (blue points). C. Distribution of the compound classes represented in the 218 active compounds. Download figure Download PowerPoint To this end, we assembled a set of antibacterial compounds arising from a high-throughput growth inhibition screen against Escherichia coli. We then deployed computational docking simulations using AutoDock Vina (Eberhardt et al, 2021) and AlphaFold2-predicted protein structures to identify protein-ligand interactions between these antibacterial compounds and all proteins from E. coli's essential proteome. These simulations predicted both specific protein-ligand interactions and widespread compound and protein promiscuity. By assembling a set of known or inferred antibiotic binding interactions from the literature, we found that our predictions only partially recapitulate these interactions. To further test our predictions, we measured enzymatic activity for diverse essential E. coli proteins involved in DNA replication, transcription, metabolism, and cell wall synthesis. Treatment of each protein with each antibacterial compound revealed that multiple compounds inhibit enzymatic activity, confirming extensive promiscuity and enabling statistical benchmarking of model performance. Detailed comparisons of our in silico predictions with experimental data showed that our approach predicted empirical protein-ligand interactions with an average accuracy between 41 and 73%, depending on the binding affinity threshold used. Independent of the binding affinity threshold, the area under the receiver operating characteristic curve (auROC) across the essential proteins tested ranged from 0.18 to 0.71 (average 0.48). Furthermore, model performance was similar using experimentally determined protein structures. In view of the observation that a random model corresponds to an auROC of 0.5, these findings indicate that molecular docking simulations exhibit weak performance. Computational docking platforms based on different scoring functions are widely available. Notably, machine learning-based scoring functions have previously been shown to improve docking performance, as measured by the auROC (Ballester & Mitchell, 2010; Durrant & McCammon, 2010; Pereira et al, 2016; Wójcikowski et al, 2017, 2019). To assess the robustness of our results to variation in the docking methods used, we considered alternative docking approaches involving another docking platform (DOCK6.9; Allen et al, 2015) and machine learning-based scoring functions. By rescoring our predictions with four machine learning-based scoring functions—RF-Score (Ballester & Mitchell, 2010), RF-Score-VS (Wójcikowski et al, 2017), PLEC score (Wójcikowski et al, 2019), and NNScore (Durrant & McCammon, 2010)—we found improvements in performance, as measured by the auROC, with three of the four scoring functions (RF-Score, RF-Score-VS, and NNScore). In contrast, employing DOCK6.9 and rescoring with the PLEC score did not improve model performance. Lastly, we show that consensus models comprising several machine learning-based scoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. Taken together, these results demonstrate the need to further develop methods of more accurately modeling protein-ligand interactions and suggest the potential of machine learning to improve modeling predictions. By providing a comprehensive dataset for benchmarking protein-ligand interaction predictions and demonstrating how machine learning can better harness AlphaFold2-predicted protein structures for molecular docking, our work informs the application of AlphaFold2 to drug discovery. Results A screen of 39,128 compounds reveals 218 antibacterial compounds active against Escherichia coli We first defined our chemical space of interest by screening a library of 39,128 unique compounds comprising the most clinically used antibiotics, natural products, and structurally diverse molecules with molecular weights between 40 Da and 4,200 Da—a range which includes those of most known antibiotics—for growth inhibition against wild-type E. coli K-12 BW25113 (Dataset EV1). Compounds were screened at 50 μM with cells grown in LB medium, and optical density values after overnight incubation were measured. Defining active compounds as those that inhibit relative growth by 80%, we found 218 structurally diverse compounds with activity (Fig 1B). Most (∼ 80%) of the 218 active compounds could be classified into known antibiotic structural classes, including the β-lactam, aminoglycoside, tetracycline, quinolone, and polyketide classes (Fig 1C). The remaining active compounds comprised of known antibacterial compounds—including toxins and antineoplastic compounds—and additional compounds whose antibacterial activities against E. coli have not previously been reported (Dataset EV1). Molecular docking of compounds with AlphaFold2-predicted Escherichia coli essential protein structures We next investigated the potential binding targets of all active compounds, as predicted by molecular docking with AlphaFold2-predicted protein structures. We reasoned that many active compounds exert their antibacterial activities largely by interacting with essential proteins in E. coli. Previous studies have identified essential genes in E. coli using transposon-directed insertion site sequencing (Goodall et al, 2018) and CRISPR interference screening (Rousset et al, 2018, 2021). Building on these studies, we shortlisted genes identified as essential in at least two of the three studies, resulting in a total of 296 out of ∼ 4,000 total genes in E. coli (Blattner et al, 1997; Materials and Methods and Dataset EV2). As positive controls for our docking simulations, we additionally included experimentally determined structures in complex with various ligands from the Protein Data Bank (Berman et al, 2000; Dataset EV2). We proceeded to dock all 218 active compounds against the 296 AlphaFold2-predicted essential protein structures using AutoDock Vina, a widely used and benchmarked open-source program for docking (Pereira et al, 2016; Vieira & Sousa, 2019; Eberhardt et al, 2021; Fig EV1). We describe and compare our approach with different docking methods and introduce relevant concepts, in Box 1. In total, our approach resulted in binding pose and binding affinity predictions for 64,528 protein-ligand pairs (Fig 2A and Dataset EV2). For comparison, we performed analogous docking simulations for 100 randomly selected inactive compounds, which resulted in binding pose and affinity predictions for 29,600 protein-ligand pairs (Fig 2A and Dataset EV2). Box 1. Integrating AlphaFold2 with molecular docking. Different software for performing molecular docking are widely available and commonly used platforms include AutoDock Vina (Eberhardt et al, 2021) and DOCK (Allen et al, 2015). Docking aims to estimate the binding pose of a ligand interacting with a macromolecule, such as a protein, and associated quantities such as the binding affinity. How this is done depends on the software used: some platforms, such as AutoDock Vina, rely on empirical free energy scoring functions that aim to directly estimate the free energy of binding for a pose, while others such as DOCK use force field-based scoring functions that account for intermolecular van der Waals and electrostatic interactions between the protein and ligand. Recent advances in integrating machine learning with docking have resulted in machine learning-based scoring functions, and their use to rescore poses generated by other docking platforms (Ballester & Mitchell, 2010; Durrant & McCammon, 2010; Pereira et al, 2016; Wójcikowski et al, 2017, 2019). As shown in the workflow here, in order to leverage AlphaFold2 for docking, we first downloaded all 296 AlphaFold2-predicted E. coli essential protein structures from the AlphaFold Protein Structure Database (Jumper et al, 2021; Varadi et al, 2022). We assembled a list of simplified molecular-input line-entry system (SMILES) strings describing the chemical structures of our 218 antibacterial compounds of interest and prepared the compounds and proteins for docking as required for the program used. As a key input to docking, the active site of each protein must be specified. Blind docking approaches computationally estimate active sites; alternatively, active sites can be specified based on those empirically evidenced in the Protein Data Bank. As the active sites for all protein structures were not known, we used blind docking to identify potential active sites and supplemented the active site selection with information from the Protein Data Bank (when available) for our assessments of model performance. We used AutoDock Vina to predict binding poses and binding affinities for all protein-ligand pairs of interest. The resulting binding affinities (kcal/mol) can be interpreted as the free energy of ligand binding, with lower energies indicating stronger binding. Analogous binding affinities from DOCK6.9 are represented by grid scores (kcal/mol), which measure binding energy but should not be directly compared with the free energies predicted by AutoDock Vina. Binding affinities predicted by the machine learning-based rescoring functions considered in this work are represented by pKd values—equal to the negative logarithm of the dissociation constant—and higher pKd values indicate stronger binding. Figure 2. Binding affinity predictions for 218 active compounds, 100 inactive compounds, and 296 AlphaFold2-predicted Escherichia coli essential protein structures A. Interaction matrix showing the predicted binding affinities (kcal/mol) between all pairs of active or inactive compounds and essential proteins modeled, discretized into bins of < −7 kcal/mol (strong predicted binding), < −5 kcal/mol (moderate predicted binding), and > −5 kcal/mol (no predicted binding). Predictions for active compounds are shown at top, and inactive compounds are shown at bottom. B, C. Rank-ordered binding affinities for the protein-ligand pairs modeled by our approach. Vertical lines indicate binding affinity thresholds of −5 kcal/mol and −7 kcal/mol. Plots are for protein-ligand interactions involving all 218 active compounds (B) or 100 inactive compounds (C). D. Histograms of numbers of predicted essential protein targets with binding affinity < −5 kcal/mol (left) or < −7 kcal/mol (right), for all 218 active compounds. E. Histograms of numbers of predicted binding compounds with binding affinity < −5 kcal/mol (left) or < −7 kcal/mol (right), for all 296 essential proteins. F, G. Similar to (D–E), but for all 100 inactive compounds modeled. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Schematic of the computational docking approach A. (Left) Schematic of the computational docking approach using AutoDock Vina. 296 essential proteins in E. coli were identified, and their AlphaFold2-predicted structures were curated. The 218 active compounds and 100 inactive compounds were represented in three dimensions in SDF files. All compounds and proteins were prepared for docking as shown and then docked using AutoDock Vina run on a high-performance computing server. The resulting binding pose and thermodynamic binding affinity predictions for all 64,528 (active compounds) and 29,600 (inactive compounds) pairwise protein-ligand interactions were analyzed and ranked. (Right) Superimposed predicted and experimental structures for methotrexate binding to E. coli FolA (PDB 1DRE), which was used as a positive docking control from the Protein Data Bank (Dataset EV2). B. Similar to (A), but for the docking of 218 active compounds and 100 inactive compounds, and a subset of 12 essential proteins, respectively, using DOCK6.9. The 12 selected essential proteins correspond to all proteins empirically tested in this study. Download figure Download PowerPoint Upon analyzing the predicted binding affinities, we found that our approach predicted widespread compound and protein promiscuity for both active and inactive compounds. For a stringent binding affinity threshold of −7 (−5) kcal/mol—corresponding to the highest-ranked 9.6% (31%) of the predicted binding affinities (Fig 2B)—we found that, of the 218 active compounds screened, 187 (207) were predicted to bind to at least three proteins (Fig 2D). Additionally, of the 296 essential proteins screened, 178 (216) were predicted to bind to at least three compounds (Fig 2E). Similar binding affinity thresholds apply to the 100 inactive compounds screened (Fig 2C), of which 86 (99) were predicted to bind at least three proteins (Fig 2F), and 137 (204) essential proteins were predicted to bind to at least three compounds (Fig 2G). These findings suggest that docking does not distinguish between active and inactive compounds and point to potential limitations in docking performance. Nevertheless, as molecular docking is known to produce many false positives (Adeshina et al, 2020; Bender et al, 2021), we further investigated the performance of our approach by (i) comparing its predictions with known antibiotic binding targets and (ii) experimentally interrogating the predicted protein-ligand interactions involving active compounds, as described below. Comparing model predictions with known antibiotic binding targets We first assessed the performance of our approach by comparing its predictions to known interactions involving commonly used classes of antibiotics. We searched the literature for previously studied antibiotic-protein target pairs (as described in detail in Materials and Methods) and assembled a dataset comprising 142 experimentally evidenced or inferred interactions in E. coli (Dataset EV3). The compounds in this dataset represent diverse antibiotic classes and target various proteins, such as the 30S ribosomal subunit and the enoyl-acyl carrier protein reductase FabI. Of the 142 curated antibiotic-protein interactions, we found that the model correctly predicted only 3 interactions with a binding affinity threshold of −7 kcal/mol and 43 interactions with a binding affinity threshold of −5 kcal/mol, resulting in true-positive rates of 2.1 and 30.3%, respectively. While an assessment of the false-positive rate with this data may have limitations—the lack of evidence of an antibiotic-protein interaction does not necessarily imply that there is no such interaction—the same binding affinity thresholds encompass 9.6% (−7 kcal/mol) and 31% (−5 kcal/mol) of the modeled protein-ligand interactions involving active compounds, as described above. If true protein-ligand interactions were rare, this would suggest that the false-positive rates predicted by our model are comparable to its true-positive rates, even for a stringent binding affinity threshold of −7 kcal/mol. Consistent with this reasoning, the same binding affinity thresholds encompass 10% (−7 kcal/mol) and 30% (−5 kcal/mol) of the modeled protein-ligand interactions involving inactive compounds (Fig 2C), which are likely to not bind any essential protein given that they do not inhibit bacterial growth. This comparison, therefore, suggests that the performance of our modeling platform is weak. Although various thresholds may be chosen to reflect one's desired stringency, based on these results we assumed −7 kcal/mol to be a stringent binding affinity threshold, and −5 kcal/mol to be an inclusive binding affinity threshold. We further compare the results with both thresholds for our assessments of model performance below. Enzymatic inhibition measurements for 12 essential proteins reveal widespread promiscuity Given that our approach generated essential proteome-wide predictions of protein-ligand binding, we aimed to further test a subset of these predictions experimentally. We reasoned that many predictions could be validated or refuted using i
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