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Chen J, Shukla D. Effect of histidine covalent modification on strigolactone receptor activation and selectivity. Biophys J 2023; 122:1219-1228. [PMID: 36798027 PMCID: PMC10111262 DOI: 10.1016/j.bpj.2023.02.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 01/17/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
The parasitic weed Striga has led to billions of dollars' worth of agricultural productivity loss worldwide. Striga detects host plants using compounds of the strigolactone class of phytohormones. Early steps in the strigolactone signaling pathway involve substrate binding and hydrolysis followed by a conformational change to an "active" or "closed" state, after which it associates with a MAX2-family downstream signaling partner. The structures of the inactive and active states of strigolactone receptors are known through X-ray crystallography, and the transition pathway from the inactive to active state in apo receptors has previously been characterized using molecular dynamics simulations. However, it also has been suggested that a covalent butenolide modification of the receptor on the catalytic histidine through substrate hydrolysis promotes formation of the active state. Using molecular dynamics simulations, we show that the presence of the covalent butenolide enhances activation in both AtD14, a receptor found in Arabidopsis, and ShHTL7, a receptor found in Striga, but the enhancement is ∼50 times greater in ShHTL7. We also show that several conserved interactions with the covalent butenolide modification promote transition to the active state in both AtD14 (non-parasite) and ShHTL7 (parasite). Finally, we demonstrate that the enhanced activation of ShHTL7 likely results from disruption of ShHTL7-specific histidine interactions that inhibited activation in the apo case. These results provide a possible explanation for difference in strigolactone sensitivity seen between different strigolactone-sensitive proteins and can be used to aid the design of selective modulators to control Striga parasites.
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Bansal PD, Dutta S, Shukla D. Activation mechanism of the human Smoothened receptor. Biophys J 2023; 122:1400-1413. [PMID: 36883002 PMCID: PMC10111369 DOI: 10.1016/j.bpj.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 01/17/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Smoothened (SMO) is a membrane protein of the class F subfamily of G protein-coupled receptors (GPCRs) and maintains homeostasis of cellular differentiation. SMO undergoes conformational change during activation, transmitting the signal across the membrane, making it amenable to bind to its intracellular signaling partner. Receptor activation has been studied at length for class A receptors, but the mechanism of class F receptor activation remains unknown. Agonists and antagonists bound to SMO at sites in the transmembrane domain (TMD) and the cysteine-rich domain have been characterized, giving a static view of the various conformations SMO adopts. Although the structures of the inactive and active SMO outline the residue-level transitions, a kinetic view of the overall activation process remains unexplored for class F receptors. We describe SMO's activation process in atomistic detail by performing 300 μs of molecular dynamics simulations and combining it with Markov state model theory. A molecular switch, conserved across class F and analogous to the activation-mediating D-R-Y motif in class A receptors, is observed to break during activation. We also show that this transition occurs in a stage-wise movement of the transmembrane helices: TM6 first, followed by TM5. To see how modulators affect SMO activity, we simulated agonist and antagonist-bound SMO. We observed that agonist-bound SMO has an expanded hydrophobic tunnel in SMO's core TMD, whereas antagonist-bound SMO shrinks this tunnel, further supporting the hypothesis that cholesterol travels through a tunnel inside Smoothened to activate it. In summary, this study elucidates the distinct activation mechanism of class F GPCRs and shows that SMO's activation process rearranges the core TMD to open a hydrophobic conduit for cholesterol transport.
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Román Santiago A, Yin S, Elbert J, Lee J, Shukla D, Su X. Imparting Selective Fluorophilic Interactions in Redox Copolymers for the Electrochemically Mediated Capture of Short-Chain Perfluoroalkyl Substances. J Am Chem Soc 2023; 145:9508-9519. [PMID: 36944079 DOI: 10.1021/jacs.2c10963] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
With increasing regulations on per- and polyfluoroalkyl substances (PFAS) across the world, understanding the molecular level interactions that drive their binding by functional adsorbent materials is key to effective PFAS removal from water streams. With the phaseout of legacy long-chain PFAS, the emergence of short-chain PFAS has posed a significant challenge for material design due to their higher mobility and hydrophilicity and inefficient removal by conventional treatment methods. Here, we demonstrate how cooperative molecular interactions are essential to target short-chain PFAS (from C4 to C7) by tailoring structural units to enhance affinity while modulating the electrochemical control of capture and release of PFAS. We report a new class of fluorinated redox-active amine-functionalized copolymers to leverage both fluorophilic and electrostatic interactions for short-chain PFAS binding. We combine molecular dynamics (MD) simulations and electrosorption to elucidate the role of the designer functional groups in enabling affinity toward short-chain PFAS. Preferential interaction coefficients from MD simulations correlated closely with experimental trends: fluorination enhanced the overall PFAS uptake and promoted the capture of less hydrophobic short-chain PFAS (C ≤ 5), while electrostatic interactions provided by secondary amine groups were sufficient to capture PFAS with higher hydrophobicity (C ≥ 6). The addition of an induced electric field showed favorable kinetic enhancement for the shortest PFAS and increased the reversibility of release from the electrode. Integration of these copolymers with electrochemical separations showed potential for removing these contaminants at environmentally relevant conditions while eliminating the need for chemical regeneration.
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Chan MC, Chan KK, Procko E, Shukla D. Machine Learning Guided Design of High-Affinity ACE2 Decoys for SARS-CoV-2 Neutralization. J Phys Chem B 2023; 127:1995-2001. [PMID: 36827526 PMCID: PMC9999943 DOI: 10.1021/acs.jpcb.3c00469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/03/2023] [Indexed: 02/26/2023]
Abstract
A potential therapeutic strategy for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding to the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant variant, named sACE22.v.2.4, that exhibits subnanomolar affinity to the receptor-binding domain (RBD) of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identify other ACE2 variants that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified multiple ACE2 double mutants that bind SARS-CoV-2 S with tighter affinity as compared to the wild type, most notably L79V;N90D that binds RBD similarly to ACE22.v.2.4. The experimental validation of the double mutants successfully demonstrates the use of machine learning approaches for engineering protein-protein interactions and identifying high-affinity ACE2 peptides for targeting SARS-CoV-2.
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Mittal S, Dutta S, Shukla D. Reconciling membrane protein simulations with experimental DEER spectroscopy data. Phys Chem Chem Phys 2023; 25:6253-6262. [PMID: 36757376 DOI: 10.1039/d2cp02890e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Spectroscopy experiments are crucial to study membrane proteins for which traditional structure determination methods still prove challenging. Double electron-electron resonance (DEER) spectroscopy experiments provide protein residue-pair distance distributions that are indicative of their conformational heterogeneity. Atomistic molecular dynamics (MD) simulations are another tool that have been proven to be vital to study the structural dynamics of membrane proteins such as to identify inward-open, occluded, and outward-open conformations of transporter membrane proteins, among other partially open or closed states of the protein. Yet, studies have reported that there is no direct consensus between the distributional data from DEER experiments and MD simulations, which has challenged validation of structures obtained from long-timescale simulations and using simulations to design experiments. Current coping strategies for comparisons rely on heuristics, such as mapping the nearest matching peaks between two ensembles or biased simulations. Here we examine the differences in residue-pair distance distributions arising due to the choice of membranes around the protein and covalent modification of a pair of residues to nitroxide spin labels in DEER experiments. Through comparing MD simulations of two proteins, PepTSo and LeuT-both of which have been characterized using DEER experiments previously-we show that the proteins' dynamics are similar despite the choice of the detergent micelle as a membrane mimetic in DEER experiments. On the other hand, covalently modified residues show slight local differences in their dynamics and a huge divergence when the oxygen atom pair distances between spin labeled residues are measured rather than protein backbone distances. Given the computational expense associated with pairwise MTSSL labeled MD simulations, we examine the use of biased simulations to explore the conformational dynamics of the spin labels only to reveal that such simulations alter the underlying protein dynamics. Our study identifies the main cause for the mismatch between DEER experiments and MD simulations and will accelerate the development of potential mitigation strategies to improve the match.
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Bansal PD, Shukla D. Mechanism of the cholesterol transport in smoothened. Biophys J 2023; 122:507a. [PMID: 36784619 DOI: 10.1016/j.bpj.2022.11.2699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
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Sobecks BL, Chen J, Shukla D. Mechanistic Basis for Enhanced Strigolactone Sensitivity in KAI2 Triple Mutant. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524622. [PMID: 36712135 PMCID: PMC9882355 DOI: 10.1101/2023.01.18.524622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Striga hermonthica is a parasitic weed that destroys billions of dollars' worth of staple crops every year. Its rapid proliferation stems from an enhanced ability to metabolize strigolactones (SLs), plant hormones that direct root branching and shoot growth. Striga's SL receptor, ShHTL7, bears more similarity to the staple crop karrikin receptor KAI2 than to SL receptor D14, though KAI2 variants in plants like Arabidopsis thaliana show minimal SL sensitivity. Recently, studies have indicated that a small number of point mutations to HTL7 residues can confer SL sensitivity to AtKAI2. Here, we analyze both wild-type AtKAI2 and SL-sensitive mutant Var64 through all-atom, long-timescale molecular dynamics simulations to determine the effects of these mutations on receptor function at a molecular level. We demonstrate that the mutations stabilize SL binding by about 2 kcal/mol. They also result in a doubling of the average pocket volume, and eliminate the dependence of binding on certain pocket conformational arrangements. While the probability of certain non-binding SL-receptor interactions increases in the mutant compared with the wild-type, the rate of binding also increases by a factor of ten. All these changes account for the increased SL sensitivity in mutant KAI2, and suggest mechanisms for increasing functionality of host crop SL receptors.
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Weigle AT, Feng J, Shukla D. Thirty years of molecular dynamics simulations on posttranslational modifications of proteins. Phys Chem Chem Phys 2022; 24:26371-26397. [PMID: 36285789 PMCID: PMC9704509 DOI: 10.1039/d2cp02883b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.
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Jacobs M, Bansal P, Shukla D, Schroeder CM. Understanding Supramolecular Assembly of Supercharged Proteins. ACS CENTRAL SCIENCE 2022; 8:1350-1361. [PMID: 36188338 PMCID: PMC9523778 DOI: 10.1021/acscentsci.2c00730] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Indexed: 06/16/2023]
Abstract
Ordered supramolecular assemblies have recently been created using electrostatic interactions between oppositely charged proteins. Despite recent progress, the fundamental mechanisms governing the assembly of oppositely supercharged proteins are not fully understood. Here, we use a combination of experiments and computational modeling to systematically study the supramolecular assembly process for a series of oppositely supercharged green fluorescent protein variants. We show that net charge is a sufficient molecular descriptor to predict the interaction fate of oppositely charged proteins under a given set of solution conditions (e.g., ionic strength), but the assembled supramolecular structures critically depend on surface charge distributions. Interestingly, our results show that a large excess of charge is necessary to nucleate assembly and that charged residues not directly involved in interprotein interactions contribute to a substantial fraction (∼30%) of the interaction energy between oppositely charged proteins via long-range electrostatic interactions. Dynamic subunit exchange experiments further show that relatively small, 16-subunit assemblies of oppositely charged proteins have kinetic lifetimes on the order of ∼10-40 min, which is governed by protein composition and solution conditions. Broadly, our results inform how protein supercharging can be used to create different ordered supramolecular assemblies from a single parent protein building block.
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Kruse LH, Weigle AT, Irfan M, Martínez-Gómez J, Chobirko JD, Schaffer JE, Bennett AA, Specht CD, Jez JM, Shukla D, Moghe GD. Orthology-based analysis helps map evolutionary diversification and predict substrate class use of BAHD acyltransferases. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:1453-1468. [PMID: 35816116 DOI: 10.1111/tpj.15902] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/15/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
Large enzyme families catalyze metabolic diversification by virtue of their ability to use diverse chemical scaffolds. How enzyme families attain such functional diversity is not clear. Furthermore, duplication and promiscuity in such enzyme families limits their functional prediction, which has produced a burgeoning set of incompletely annotated genes in plant genomes. Here, we address these challenges using BAHD acyltransferases as a model. This fast-evolving family expanded drastically in land plants, increasing from one to five copies in algae to approximately 100 copies in diploid angiosperm genomes. Compilation of >160 published activities helped visualize the chemical space occupied by this family and define eight different classes based on structural similarities between acceptor substrates. Using orthologous groups (OGs) across 52 sequenced plant genomes, we developed a method to predict BAHD acceptor substrate class utilization as well as origins of individual BAHD OGs in plant evolution. This method was validated using six novel and 28 previously characterized enzymes and helped improve putative substrate class predictions for BAHDs in the tomato genome. Our results also revealed that while cuticular wax and lignin biosynthetic activities were more ancient, anthocyanin acylation activity was fixed in BAHDs later near the origin of angiosperms. The OG-based analysis enabled identification of signature motifs in anthocyanin-acylating BAHDs, whose importance was validated via molecular dynamic simulations, site-directed mutagenesis and kinetic assays. Our results not only describe how BAHDs contributed to evolution of multiple chemical phenotypes in the plant world but also propose a biocuration-enabled approach for improved functional annotation of plant enzyme families.
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Kleiman DE, Shukla D. Multiagent Reinforcement Learning-Based Adaptive Sampling for Conformational Dynamics of Proteins. J Chem Theory Comput 2022; 18:5422-5434. [PMID: 36044642 DOI: 10.1021/acs.jctc.2c00683] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Machine learning is increasingly applied to improve the efficiency and accuracy of molecular dynamics (MD) simulations. Although the growth of distributed computer clusters has allowed researchers to obtain higher amounts of data, unbiased MD simulations have difficulty sampling rare states, even under massively parallel adaptive sampling schemes. To address this issue, several algorithms inspired by reinforcement learning (RL) have arisen to promote exploration of the slow collective variables (CVs) of complex systems. Nonetheless, most of these algorithms are not well-suited to leverage the information gained by simultaneously sampling a system from different initial states (e.g., a protein in different conformations associated with distinct functional states). To fill this gap, we propose two algorithms inspired by multiagent RL that extend the functionality of closely related techniques (REAP and TSLC) to situations where the sampling can be accelerated by learning from different regions of the energy landscape through coordinated agents. Essentially, the algorithms work by remembering which agent discovered each conformation and sharing this information with others at the action-space discretization step. A stakes function is introduced to modulate how different agents sense rewards from discovered states of the system. The consequences are three-fold: (i) agents learn to prioritize CVs using only relevant data, (ii) redundant exploration is reduced, and (iii) agents that obtain higher stakes are assigned more actions. We compare our algorithm with other adaptive sampling techniques (least counts, REAP, TSLC, and AdaptiveBandit) to show and rationalize the gain in performance.
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Pandey KK, Shukla D. NDPD: an improved initial centroid method of partitional clustering for big data mining. JOURNAL OF ADVANCES IN MANAGEMENT RESEARCH 2022. [DOI: 10.1108/jamr-07-2021-0242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.Design/methodology/approachThis study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.FindingsThe performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.Originality/valueThe KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.
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Xue X, Wang J, Shukla D, Cheung LS, Chen LQ. When SWEETs Turn Tweens: Updates and Perspectives. ANNUAL REVIEW OF PLANT BIOLOGY 2022; 73:379-403. [PMID: 34910586 DOI: 10.1146/annurev-arplant-070621-093907] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Sugar translocation between cells and between subcellular compartments in plants requires either plasmodesmata or a diverse array of sugar transporters. Interactions between plants and associated microorganisms also depend on sugar transporters. The sugars will eventually be exported transporter (SWEET) family is made up of conserved and essential transporters involved in many critical biological processes. The functional significance and small size of these proteins have motivated crystallographers to successfully capture several structures of SWEETs and their bacterial homologs in different conformations. These studies together with molecular dynamics simulations have provided unprecedented insights into sugar transport mechanisms in general and into substrate recognition of glucose and sucrose in particular. This review summarizes our current understanding of the SWEET family, from the atomic to the whole-plant level. We cover methods used for their characterization, theories about their evolutionary origins, biochemical properties, physiological functions, and regulation. We also include perspectives on the future work needed to translate basic research into higher crop yields.
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Horne J, Shukla D. Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering. Ind Eng Chem Res 2022; 61:6235-6245. [PMID: 36051311 PMCID: PMC9432854 DOI: 10.1021/acs.iecr.1c04943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Proteins are Nature's molecular machinery and comprise diverse roles while consisting of chemically similar building blocks. In recent years, protein engineering and design have become important research areas, with many applications in the pharmaceutical, energy, and biocatalysis fields, among others-where the aim is to ultimately create a protein given desired structural and functional properties. It is often critical to model the relationship between a protein's sequence, folded structure, and biological function to assist in such protein engineering pursuits. However, significant challenges remain in concretely mapping an amino acid sequence to specific protein properties and biological activities. Mutations may enhance or diminish molecular protein function, and the epistatic interactions between mutations result in an inherently complex mapping between genetic modifications and protein function. Therefore, estimating the quantitative effects of mutations on protein function(s) remains a grand challenge of biology, bioinformatics, and many related fields and would rapidly accelerate protein engineering tasks when successful. Such estimation is often known as variant effect prediction (VEP). However, progress has been demonstrated in recent years with the development of machine learning (ML) methods in modeling the relationship between mutations and protein function. In this Review, recent advances in variant effect prediction (VEP) are discussed as tools for protein engineering, focusing on techniques incorporating gains from the broader ML community and challenges in estimating biomolecular functional differences. Primary developments highlighted include convolutional neural networks, graph neural networks, and natural language embeddings for protein sequences.
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Park KS, Xue Z, Patel BB, An H, Kwok JJ, Kafle P, Chen Q, Shukla D, Diao Y. Chiral emergence in multistep hierarchical assembly of achiral conjugated polymers. Nat Commun 2022; 13:2738. [PMID: 35585050 PMCID: PMC9117306 DOI: 10.1038/s41467-022-30420-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 04/27/2022] [Indexed: 11/09/2022] Open
Abstract
Intimately connected to the rule of life, chirality remains a long-time fascination in biology, chemistry, physics and materials science. Chiral structures, e.g., nucleic acid and cholesteric phase developed from chiral molecules are common in nature and synthetic soft materials. While it was recently discovered that achiral but bent-core mesogens can also form chiral helices, the assembly of chiral microstructures from achiral polymers has rarely been explored. Here, we reveal chiral emergence from achiral conjugated polymers, in which hierarchical helical structures are developed through a multistep assembly pathway. Upon increasing concentration beyond a threshold volume fraction, dispersed polymer nanofibers form lyotropic liquid crystalline (LC) mesophases with complex, chiral morphologies. Combining imaging, X-ray and spectroscopy techniques with molecular simulations, we demonstrate that this structural evolution arises from torsional polymer molecules which induce multiscale helical assembly, progressing from nano- to micron scale helical structures as the solution concentration increases. This study unveils a previously unknown complex state of matter for conjugated polymers that can pave way to a field of chiral (opto)electronics. We anticipate that hierarchical chiral helical structures can profoundly impact how conjugated polymers interact with light, transport charges, and transduce signals from biomolecular interactions and even give rise to properties unimagined before.
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Chan MC, Procko E, Shukla D. Structural Rearrangement of the Serotonin Transporter Intracellular Gate Induced by Thr276 Phosphorylation. ACS Chem Neurosci 2022; 13:933-945. [PMID: 35258286 DOI: 10.1021/acschemneuro.1c00714] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The reuptake of the neurotransmitter serotonin from the synaptic cleft by the serotonin transporter, SERT, is essential for proper neurological signaling. Biochemical studies have shown that Thr276 of transmembrane helix 5 is a site of PKG-mediated SERT phosphorylation, which has been proposed to shift the SERT conformational equilibria to promote inward-facing states, thus enhancing 5-HT transport. Recent structural and simulation studies have provided insights into the conformation transitions during substrate transport but have not shed light on SERT regulation via post-translational modifications. Using molecular dynamics simulations and Markov state models, we investigate how Thr276 phosphorylation impacts the SERT mechanism and its role in enhancing transporter stability and function. Our simulations show that Thr276 phosphorylation alters the hydrogen-bonding network involving residues on transmembrane helix 5. This in turn decreases the free energy barriers for SERT to transition to the inward-facing state, thus facilitating 5-HT import. The results provide atomistic insights into in vivo SERT regulation and can be extended to other pharmacologically important transporters in the solute carrier family.
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Sobecks BL, Chen J, Shukla D. Dual Role of Strigolactone Receptor Signaling Partner in Inhibiting Substrate Hydrolysis. J Phys Chem B 2022; 126:2188-2195. [PMID: 35275626 DOI: 10.1021/acs.jpcb.1c10663] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Plant branch and root growth relies on metabolism of the strigolactone (SL) hormone. The interaction between the SL molecule, Oryza sativa DWARF14 (D14) SL receptor, and D3 F-box protein has been shown to play a critical role in SL perception. Previously, it was believed that D3 only interacts with the closed form of D14 to induce downstream signaling, but recent experiments indicate that D3, as well as its C-terminal helix (CTH), can interact with the open form as well to inhibit strigolactone signaling. Two hypotheses for the CTH induced inhibition are that either the CTH affects the conformational ensemble of D14 by stabilizing catalytically inactive states or the CTH interacts with SLs in a way that prevents them from entering the binding pocket. In this study, we have performed molecular dynamics (MD) simulations to assess the validity of these hypotheses. We used an apo system with only D14 and the CTH to test the active site conformational stability and a holo system with D14, the CTH, and an SL molecule to test the interaction between the SL and CTH. Our simulations show that the CTH affects both active site conformation and the ability of SLs to move into the binding pocket. In the apo system, the CTH allosterically stabilized catalytic residues into their inactive conformation. In the holo system, significant interactions between SLs and the CTH hindered the ability of SLs to enter the D14 binding pocket. These two mechanisms account for the observed decrease in SL binding to D14 and subsequent ligand hydrolysis in the presence of the CTH.
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Zhang L, Dutta S, Xiong S, Chan M, Chan KK, Fan TM, Bailey KL, Lindeblad M, Cooper LM, Rong L, Gugliuzza AF, Shukla D, Procko E, Rehman J, Malik AB. Engineered ACE2 decoy mitigates lung injury and death induced by SARS-CoV-2 variants. Nat Chem Biol 2022; 18:342-351. [PMID: 35046611 PMCID: PMC8885411 DOI: 10.1038/s41589-021-00965-6] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 12/16/2021] [Indexed: 12/15/2022]
Abstract
Vaccine hesitancy and emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) escaping vaccine-induced immune responses highlight the urgency for new COVID-19 therapeutics. Engineered angiotensin-converting enzyme 2 (ACE2) proteins with augmented binding affinities for SARS-CoV-2 spike (S) protein may prove to be especially efficacious against multiple variants. Using molecular dynamics simulations and functional assays, we show that three amino acid substitutions in an engineered soluble ACE2 protein markedly augmented the affinity for the S protein of the SARS-CoV-2 WA-1/2020 isolate and multiple VOCs: B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma) and B.1.617.2 (Delta). In humanized K18-hACE2 mice infected with the SARS-CoV-2 WA-1/2020 or P.1 variant, prophylactic and therapeutic injections of soluble ACE22.v2.4-IgG1 prevented lung vascular injury and edema formation, essential features of CoV-2-induced SARS, and above all improved survival. These studies demonstrate broad efficacy in vivo of an engineered ACE2 decoy against SARS-CoV-2 variants in mice and point to its therapeutic potential.
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Chan MC, Selvam B, Young HJ, Procko E, Shukla D. The substrate import mechanism of the human serotonin transporter. Biophys J 2022; 121:715-730. [PMID: 35114149 PMCID: PMC8943754 DOI: 10.1016/j.bpj.2022.01.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/18/2021] [Accepted: 01/25/2022] [Indexed: 11/17/2022] Open
Abstract
The serotonin transporter (SERT) initiates the reuptake of extracellular serotonin in the synapse to terminate neurotransmission. The cryogenic electron microscopy structures of SERT bound to ibogaine and the physiological substrate serotonin resolved in different states have provided a glimpse of the functional conformations at atomistic resolution. However, the conformational dynamics and structural transitions to intermediate states are not fully understood. Furthermore, the molecular basis of how serotonin is recognized and transported remains unclear. In this study, we performed unbiased microsecond-long simulations of the human SERT to investigate the structural dynamics to various intermediate states and elucidated the complete substrate import pathway. Using Markov state models, we characterized a sequential order of conformational-driven ion-coupled substrate binding and transport events and calculated the free energy barriers of conformation transitions associated with the import mechanism. We find that the transition from the occluded to inward-facing state is the rate-limiting step for substrate import and that the substrate decreases the free energy barriers to achieve the inward-facing state. Our study provides insights on the molecular basis of dynamics-driven ion-substrate recognition and transport of SERT that can serve as a model for other closely related neurotransmitter transporters.
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45
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Chen J, Nelson DC, Shukla D. Activation Mechanism of Strigolactone Receptors and Its Impact on Ligand Selectivity between Host and Parasitic Plants. J Chem Inf Model 2022; 62:1712-1722. [PMID: 35192364 DOI: 10.1021/acs.jcim.1c01258] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Parasitic weeds such as Striga have led to significant losses in agricultural productivity worldwide. These weeds use the plant hormone strigolactone as a germination stimulant. Strigolactone signaling involves substrate hydrolysis followed by a conformational change of the receptor to a "closed" or "active" state that associates with a signaling partner, MAX2/D3. Crystal structures of active and inactive AtD14 receptors have helped elucidate the structural changes involved in activation. However, the mechanism by which the receptor activates remains unknown. The ligand dependence of AtD14 activation has been disputed by mutagenesis studies showing that enzymatically inactive receptors are able to associate with MAX2 proteins. Furthermore, activation differences between strigolactone receptor in Striga, ShHTL7, and AtD14 could contribute to the high sensitivity to strigolactones exhibited by parasitic plants. Using molecular dynamics simulations, we demonstrate that both AtD14 and ShHTL7 could adopt an active conformation in the absence of ligand. However, ShHTL7 exhibits a higher population in the inactive apo state as compared to the AtD14 receptor. We demonstrate that this difference in inactive state population is caused by sequence differences between their D-loops and interactions with the catalytic histidine that prevent full binding pocket closure in ShHTL7. These results indicate that ligand hydrolysis would enhance the active state population by destabilizing the inactive state in ShHTL7 as compared to AtD14. We also show that the mechanism of activation is more concerted in AtD14 than in ShHTL7 and that the main barrier to activation in ShHTL7 is closing of the binding pocket.
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46
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Mi X, Shukla D. Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning. J Phys Chem B 2022; 126:1492-1503. [PMID: 35142529 DOI: 10.1021/acs.jpcb.1c10574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Excipients are major components of drugs and are used to improve drug attributes such as stability and appearance. Excipients approved by the U.S. Food and Drug Administration (FDA) are regarded as safe for humans in allowed concentrations, but their potential interactions with drug targets have not been investigated systematically, which might influence a drug's efficacy. Deep learning models have been used for the identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand-protein interaction. One-shot learning techniques provide a potential approach to address this low data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models to data sets that include ligands binding to G-protein-coupled receptors (GPCRs) and kinases. The predicted results suggest that one-shot learning could be used for predicting ligand-protein interactions, and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning can be used to make accurate predictions for excipient-protein interactions, and these methods could be used for selecting excipients with limited drug-protein interactions.
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Dutta S, Selvam B, Shukla D. Distinct Binding Mechanisms for Allosteric Sodium Ion in Cannabinoid Receptors. ACS Chem Neurosci 2022; 13:379-389. [PMID: 35019279 DOI: 10.1021/acschemneuro.1c00760] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The therapeutic potential of cannabinoid receptors is not fully explored due to psychoactive side effects and lack of selectivity associated with orthosteric ligands. Allosteric modulators have the potential to become selective therapeutics for cannabinoid receptors. Biochemical experiments have shown the effects of the allosteric Na+ binding on cannabinoid receptor activity. However, the Na+ coordination site and binding pathway are still unknown. Here, we perform molecular dynamic simulations to explore Na+ binding in the cannabinoid receptors, CB1 and CB2. Simulations reveal that Na+ binds to the primary binding site from different extracellular sites for CB1 and CB2. A distinct secondary Na+ coordination site is identified in CB1 that is not present in CB2. Furthermore, simulations also show that intracellular Na+ could bind to the Na+ binding site in CB1. Constructed Markov state models show that the standard free energy of Na+ binding is similar to the previously calculated free energy for other class A GPCRs.
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Chan MC, Young HJ, Selvam B, Szymanski SK, Procko E, Shukla D. Combining simulations and deep mutagenesis to elucidate structural dynamics of monoamine transporters. Biophys J 2022. [DOI: 10.1016/j.bpj.2021.11.449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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49
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Zhao C, Kleiman DE, Shukla D. Optimization of hydration sites in plant hormone receptors for agrochemical design. Biophys J 2022. [DOI: 10.1016/j.bpj.2021.11.1784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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50
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Zhao C, Shukla D. Molecular basis of the activation and dissociation of dimeric PYL2 receptor in abscisic acid signaling. Phys Chem Chem Phys 2022; 24:724-734. [PMID: 34935010 DOI: 10.1039/d1cp03307g] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Phytohormone abscisic acid (ABA) is essential for plant responses to biotic and abiotic stresses. Dimeric receptors are a class of PYR1/PYL/RCAR (pyrabactin resistance 1/PYR1-like/regulatory component of ABA receptors) ABA receptors that are important for various ABA responses. While extensive experimental and computational studies have investigated these receptors, it remains not fully understood how ABA leads to their activation and dissociation for interaction with downstream protein phosphatase 2C (PP2C). Here, we study the activation and the homodimeric association processes of the PYL2 receptor as well as its heterodimeric association with protein phosphatase 2C 16 (HAB1) using molecular dynamics simulations. Free energy landscapes from ∼223 μs simulations show that dimerization substantially constrains PYL2 conformational plasticity and stabilizes the inactive state, resulting in lower ABA affinity. Also, we establish the thermodynamic model for competitive binding between homodimeric PYL2 association and heterodimeric PYL2-HAB1 association in the absence and presence of ABA. Our results suggest that the binding of ABA destabilizes the PYL2 complex and further stabilizes PYL2-HAB1 association, thereby promoting PYL2 dissociation. Overall, this study explains several key aspects on the activation of dimeric ABA receptors, which provide new avenues for selective regulation of these receptors.
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