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Publication Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket(American Chemical Society, 2023-01-27) Wang, Jinan; Miao, YinglongLigand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.Publication Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network(American Chemical Society, 2023-05-23) Do, Hung N.; Miao, YinglongWe have developed a new deep boosted molecular dynamics (DBMD) method. Probabilistic Bayesian neural network models were implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD was demonstrated on model systems of alanine dipeptide and the fast-folding protein and RNA structures. For alanine dipeptide, 30 ns DBMD simulations captured up to 83–125 times more backbone dihedral transitions than 1 μs conventional molecular dynamics (cMD) simulations and were able to accurately reproduce the original free energy profiles. Moreover, DBMD sampled multiple folding and unfolding events within 300 ns simulations of the chignolin model protein and identified low-energy conformational states comparable to previous simulation findings. Finally, DBMD captured a general folding pathway of three hairpin RNAs with the GCAA, GAAA, and UUCG tetraloops. Based on a deep learning neural network, DBMD provides a powerful and generally applicable approach to boosting biomolecular simulations. DBMD is available with open source in OpenMM at https://github.com/MiaoLab20/DBMD/.Publication Critical Non-Covalent Binding Intermediate for an Allosteric Covalent Inhibitor of SUMO E1(American Chemical Society, 2023-03-10) Pawnikar, Shristi; Bhattarai, Apurba; Ouyang, S. Xiaohu; Vega, Ramir; Chen, Yuan; Miao, YinglongPost-translational modifications by small ubiquitin-like modifiers (SUMOs) are dysregulated in many types of cancers. The SUMO E1 enzyme has recently been suggested as a new immuno-oncology target. COH000 was recently identified as a highly specific allosteric covalent inhibitor of SUMO E1. However, a marked discrepancy was found between the X-ray structure of the covalent COH000-bound SUMO E1 complex and the available structure–activity relationship (SAR) data of inhibitor analogues due to unresolved noncovalent protein–ligand interactions. Here, we have investigated noncovalent interactions between COH000 and SUMO E1 during inhibitor dissociation through novel Ligand Gaussian accelerated molecular dynamics (LiGaMD) simulations. Our simulations have identified a critical low-energy non-covalent binding intermediate conformation of COH000 that agreed excellently with published and new SAR data of the COH000 analogues, which were otherwise inconsistent with the X-ray structure. Altogether, our biochemical experiments and LiGaMD simulations have uncovered a critical non-covalent binding intermediate during allosteric inhibition of the SUMO E1 complex.Publication Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes(Oxford University Press, 2023-07-05) Zhu, Wensi; Shenoy, Aditi; Kundrotas, Petras; Elofsson, ArneMotivation Despite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established. Results We analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g. TM-score) but poorly on another (e.g. DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures. Availability and implementation All scripts, models, and data used to perform the analysis in this study are freely available at https://gitlab.com/ElofssonLab/afm-benchmark.Publication Identification of a novel transport system in Borrelia burgdorferi that links the inner and outer membranes(Pathogens and Disease, 2023-06-29) Bowen, Hannah G.; Kenedy, Melisha R.; Johnson, David K.; MacKerell, Alexander D., Jr.; Akins, Darrin R.Borrelia burgdorferi, the spirochete that causes Lyme disease, is a diderm organism that is similar to Gram-negative organisms in that it contains both an inner and outer membrane. Unlike typical Gram-negative organisms, however, B. burgdorferi lacks lipopolysaccharide (LPS). Using computational genome analyses and structural modeling, we identified a transport system containing six proteins in B. burgdorferi that are all orthologs to proteins found in the lipopolysaccharide transport (LPT) system that links the inner and outer membranes of Gram-negative organisms and is responsible for placing LPS on the surface of these organisms. While B. burgdorferi does not contain LPS, it does encode over 100 different surface-exposed lipoproteins and several major glycolipids, which like LPS are also highly amphiphilic molecules, though no system to transport these molecules to the borrelial surface is known. Accordingly, experiments supplemented by molecular modeling were undertaken to determine whether the orthologous LPT system identified in B. burgdorferi could transport lipoproteins and/or glycolipids to the borrelial outer membrane. Our combined observations strongly suggest that the LPT transport system does not transport lipoproteins to the surface. Molecular dynamic modeling, however, suggests that the borrelial LPT system could transport borrelial glycolipids to the outer membrane.Publication Challenges and frontiers of computational modelling of biomolecular recognition(Cambridge University Press, 2022-08-19) Wang, Jinan; Bhattarai, Apurba; Do, Hung Nguyen; Miao, YinglongBiomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterize biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.Publication Editorial: Protein recognition and associated diseases(Frontiers Media, 2023-05-22) Gromiha, M. Michael; Kundrotas, Petras; Marti, Marcelo Adrian; Venclovas, Česlovas; Li, MinghuiPublication Effects of presenilin-1 familial Alzheimer’s disease mutations on γ-secretase activation for cleavage of amyloid precursor protein(Nature Research, 2023-02-14) Do, Hung N.; Devkota, Sujan; Bhattarai, Apurba; Wolfe, Michael S.; Miao, YinglongPresenilin-1 (PS1) is the catalytic subunit of γ-secretase which cleaves within the transmembrane domain of over 150 peptide substrates. Dominant missense mutations in PS1 cause early-onset familial Alzheimer’s disease (FAD); however, the exact pathogenic mechanism remains unknown. Here we combined Gaussian accelerated molecular dynamics (GaMD) simulations and biochemical experiments to determine the effects of six representative PS1 FAD mutations (P117L, I143T, L166P, G384A, L435F, and L286V) on the enzyme-substrate interactions between γ-secretase and amyloid precursor protein (APP). Biochemical experiments showed that all six PS1 FAD mutations rendered γ-secretase less active for the endoproteolytic (ε) cleavage of APP. Distinct low-energy conformational states were identified from the free energy profiles of wildtype and PS1 FAD-mutant γ-secretase. The P117L and L286V FAD mutants could still sample the “Active” state for substrate cleavage, but with noticeably reduced conformational space compared with the wildtype. The other mutants hardly visited the “Active” state. The PS1 FAD mutants were found to reduce γ-secretase proteolytic activity by hindering APP residue L49 from proper orientation in the active site and/or disrupting the distance between the catalytic aspartates. Therefore, our findings provide mechanistic insights into how PS1 FAD mutations affect structural dynamics and enzyme-substrate interactions of γ-secretase and APP.Publication Towards a structurally resolved human protein interaction network(Nature Research, 2023-01-23) Burke, David F.; Bryant, Patrick; Barrio-Hernandez, Inigo; Memon, Danish; Pozzati, Gabriele; Shenoy, Aditi; Zhu, Wensi; Dunham, Alistair S.; Albanese, Pascal; Keller, Andrew; Scheltema, Richard A.; Bruce, James E.; Leitner, Alexander; Kundrotas, Petras; Beltrao, Pedro; Elofsson, ArneCellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.Publication Pharmacological hallmarks of allostery at the M4 muscarinic receptor elucidated through structure and dynamics(eLife Sciences Publications, 2023-05-20) Vuckovic, Ziva; Wang, Jinan; Pham, Vi; Mobbs, Jesse I.; Belousoff, Matthew J.; Bhattarai, Apurba; Burger, Wessel A.C.; Thompson, Geoff; Yeasmin, Mahmuda; Nawaratne, Vindhya; Leach, Katie; van der Westhuizen, Emma T.; Khajehali, Elham; Liang, Yi-Lynn; Glukhova, Alisa; Wootten, Denise; Lindsley, Craig W.; Tobin, Andrew; Sexton, Patrick; Danev, Radostin; Valant, Celine; Miao, Yinglong; Christopoulos, Arthur; Thal, David M.Allosteric modulation of G protein-coupled receptors (GPCRs) is a major paradigm in drug discovery. Despite decades of research, a molecular-level understanding of the general principles that govern the myriad pharmacological effects exerted by GPCR allosteric modulators remains limited. The M4 muscarinic acetylcholine receptor (M4 mAChR) is a validated and clinically relevant allosteric drug target for several major psychiatric and cognitive disorders. In this study, we rigorously quantified the affinity, efficacy, and magnitude of modulation of two different positive allosteric modulators, LY2033298 (LY298) and VU0467154 (VU154), combined with the endogenous agonist acetylcholine (ACh) or the high-affinity agonist iperoxo (Ipx), at the human M4 mAChR. By determining the cryo-electron microscopy structures of the M4 mAChR, bound to a cognate Gi1 protein and in complex with ACh, Ipx, LY298-Ipx, and VU154-Ipx, and applying molecular dynamics simulations, we determine key molecular mechanisms underlying allosteric pharmacology. In addition to delineating the contribution of spatially distinct binding sites on observed pharmacology, our findings also revealed a vital role for orthosteric and allosteric ligand–receptor–transducer complex stability, mediated by conformational dynamics between these sites, in the ultimate determination of affinity, efficacy, cooperativity, probe dependence, and species variability. There results provide a holistic framework for further GPCR mechanistic studies and can aid in the discovery and design of future allosteric drugs.Publication Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search(Nature Research, 2022-10-12) Bryant, Patrick; Pozzati, Gabriele; Zhu, Wensi; Shenoy, Aditi; Kundrotas, Petras; Elofsson, ArneAlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb.Publication GWYRE: A Resource for Mapping Variants onto Experimental and Modeled Structures of Human Protein Complexes(2022-06-15) Malladi, Sukhaswami; Powell, Harold R.; David, Alessia; Islam, Suhail A.; Copeland, Matthew M.; Kundrotas, Petras J.; Sternberg, Michael J. E.; Vakser, Ilya A.Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein–protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein–protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at http://www.gwyre.org.Publication DOCKGROUND membrane protein-protein set(Public Library of Science, 2022-05-17) Kotthoff, Ian; Kundrotas, Petras J.; Vakser, Ilya A.Membrane proteins are significantly underrepresented in Protein Data Bank despite their essential role in cellular mechanisms and the major progress in experimental protein structure determination. Thus, computational approaches are especially valuable in the case of membrane proteins and their assemblies. The main focus in developing structure prediction techniques has been on soluble proteins, in part due to much greater availability of the structural data. Currently, structure prediction of protein complexes (protein docking) is a well-developed field of study. However, the generic protein docking approaches are not optimal for the membrane proteins because of the differences in physicochemical environment and the spatial constraints imposed by the membranes. Thus, docking of the membrane proteins requires specialized computational methods. Development and benchmarking of the membrane protein docking approaches has to be based on high-quality sets of membrane protein complexes. In this study we present a new dataset of 456 non-redundant alpha helical binary interfaces. The set is significantly larger and more representative than the previously developed sets. In the future, it will become the basis for the development of docking and scoring benchmarks, similar to the ones for soluble proteins in the Dockground resource http://dockground.compbio.ku.edu.Publication Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives(Dove Medical Press, 2021-02-20) Pawnikar, Shristi; Bhattarai, Apurba; Wang, Jinan; Miao, YinglongBiomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design.Publication Efficient purification and assembly of ribonucleoprotein complex for interaction analysis by MST assay coupled with GaMD simulations(Cell Press, 2021-03-19) Gao, Yunrong; Cao, Dongdong; Pawnikar, Shristi; Akhter, Sana; Miao, Yinglong; Liang, BoHere, we describe a generic protocol for monitoring protein-RNA interaction using a cleavable GFP fusion of a recombinant RNA-binding protein. We detail each expression and purification step, including high salt and heparin column for contaminant RNA removal. After the assembly of RNA into the ribonucleoprotein complex, the MicroScale Thermophoresis assay enables the binding affinity to be obtained quickly with a small amount of sample. Further Gaussian accelerated molecular dynamics simulations allow us to analyze protein:RNA interactions in detail.Publication A clinically relevant polymorphism in the Na+/taurocholate cotransporting polypeptide (NTCP) occurs at a rheostat position(Elsevier, 2020-11-09) Ruggiero, Melissa J.; Malhotra, Shipra; Fenton, Aron W.; Swint-Kruse, Liskin; Karanicolas, John; Hagenbuch, BrunoConventionally, most amino acid substitutions at “important” protein positions are expected to abolish function. However, in several soluble-globular proteins, we identified a class of nonconserved positions for which various substitutions produced progressive functional changes; we consider these evolutionary “rheostats”. Here, we report a strong rheostat position in the integral membrane protein, Na+/taurocholate (TCA) cotransporting polypeptide, at the site of a pharmacologically relevant polymorphism (S267F). Functional studies were performed for all 20 substitutions (S267X) with three substrates (TCA, estrone-3-sulfate, and rosuvastatin). The S267X set showed strong rheostatic effects on overall transport, and individual substitutions showed varied effects on transport kinetics (Km and Vmax) and substrate specificity. To assess protein stability, we measured surface expression and used the Rosetta software (https://www.rosettacommons.org) suite to model structure and stability changes of S267X. Although buried near the substrate-binding site, S267X substitutions were easily accommodated in the Na+/TCA cotransporting polypeptide structure model. Across the modest range of changes, calculated stabilities correlated with surface-expression differences, but neither parameter correlated with altered transport. Thus, substitutions at rheostat position 267 had wide-ranging effects on the phenotype of this integral membrane protein. We further propose that polymorphic positions in other proteins might be locations of rheostat positions.Publication Costameric integrin and sarcoglycan protein levels are altered in a Drosophila model for Limb-girdle muscular dystrophy type 2H(Springer Nature, 2021-01-28) Bawa, Simranjot; Gameros, Samantha; Baumann, Kenny; Brooks, David S.; Kollhoff, Joseph A.; Zolkiewski, Michal; Cecconi, Andrea David Re; Panini, Nicolò; Russo, Massimo; Piccirillo, Rosanna; Johnson, David K.; Kashipathy, Maithri M.; Battaile, Kevin P.; Lovell, Scott; Bouyain, Samuel E. A.; Kawakami, Jessica; Geisbrecht, Erika R.Mutations in two different domains of the ubiquitously expressed TRIM32 protein give rise to two clinically separate diseases, one of which is Limb-girdle muscular dystrophy type 2H (LGMD2H). Uncovering the muscle-specific role of TRIM32 in LGMD2H pathogenesis has proven difficult, as neurogenic phenotypes, independent of LGMD2H pathology, are present in TRIM32 KO mice. We previously established a platform to study LGMD2H pathogenesis using Drosophila melanogaster as a model. Here we show that LGMD2H disease-causing mutations in the NHL domain are molecularly and structurally conserved between fly and human TRIM32. Furthermore, transgenic expression of a subset of myopathic alleles (R394H, D487N, and 520fs) induce myofibril abnormalities, altered nuclear morphology, and reduced TRIM32 protein levels, mimicking phenotypes in patients afflicted with LGMD2H. Intriguingly, we also report for the first time that the protein levels of βPS integrin and sarcoglycan δ, both core components of costameres, are elevated in TRIM32 disease-causing alleles. Similarly, murine myoblasts overexpressing a catalytically inactive TRIM32 mutant aberrantly accumulate α- and β-dystroglycan and α-sarcoglycan. We speculate that the stoichiometric loss of costamere components disrupts costamere complexes to promote muscle degeneration.Publication Pathways and Mechanism of Caffeine Binding to Human Adenosine A2A Receptor(Frontiers Media, 2021-04-27) Do, Hung N.; Akhter, Sana; Miao, YinglongCaffeine (CFF) is a common antagonist to the four subtypes of adenosine G-protein-coupled receptors (GPCRs), which are critical drug targets for treating heart failure, cancer, and neurological diseases. However, the pathways and mechanism of CFF binding to the target receptors remain unclear. In this study, we have performed all-atom-enhanced sampling simulations using a robust Gaussian-accelerated molecular dynamics (GaMD) method to elucidate the binding mechanism of CFF to human adenosine A2A receptor (A2AAR). Multiple 500–1,000 ns GaMD simulations captured both binding and dissociation of CFF in the A2AAR. The GaMD-predicted binding poses of CFF were highly consistent with the x-ray crystal conformations with a characteristic hydrogen bond formed between CFF and residue N6.55 in the receptor. In addition, a low-energy intermediate binding conformation was revealed for CFF at the receptor extracellular mouth between ECL2 and TM1. While the ligand-binding pathways of the A2AAR were found similar to those of other class A GPCRs identified from previous studies, the ECL2 with high sequence divergence serves as an attractive target site for designing allosteric modulators as selective drugs of the A2AAR.Publication Machine learning differentiates enzymatic and non-enzymatic metals in proteins(Nature Research, 2021-06-17) Feehan, Ryan; Franklin, Meghan W.; Slusky, Joanna S. G.Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model’s ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.Publication Diversity in lac Operon Regulation among Diverse Escherichia coli Isolates Depends on the Broader Genetic Background but Is Not Explained by Genetic Relatedness(American Society for Microbiology, 2019-11-12) Phillips, Kelly N.; Widmann, Scott; Lai, Huei-Yi; Nguyen, Jennifer; Ray, J. Christian J.; Balázsi, Gábor; Cooper, Tim F.Transcription of bacterial genes is controlled by the coordinated action of cis- and trans-acting regulators. The activity and mode of action of these regulators can reflect different requirements for gene products in different environments. A well-studied example is the regulatory function that integrates the environmental availability of glucose and lactose to control the Escherichia coli lac operon. Most studies of lac operon regulation have focused on a few closely related strains. To determine the range of natural variation in lac regulatory function, we introduced a reporter construct into 23 diverse E. coli strains and measured expression with combinations of inducer concentrations. We found a wide range of regulatory functions. Several functions were similar to the one observed in a reference lab strain, whereas others depended weakly on the presence of cAMP. Some characteristics of the regulatory function were explained by the genetic relatedness of strains, indicating that differences varied on relatively short time scales. The regulatory characteristics explained by genetic relatedness were among those that best predicted the initial growth of strains following transition to a lactose environment, suggesting a role for selection. Finally, we transferred the lac operon, with the lacI regulatory gene, from five natural isolate strains into a reference lab strain. The regulatory function of these hybrid strains revealed the effect of local and global regulatory elements in controlling expression. Together, this work demonstrates that regulatory functions can be varied within a species and that there is variation within a species to best match a function to particular environments.