Mechanistic Basis for Enhanced Strigolactone Sensitivity in KAI2 Triple Mutant

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.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

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.

Understanding Supramolecular Assembly of Supercharged Proteins

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.

Orthology-based analysis helps map evolutionary diversification and predict substrate class use of BAHD acyltransferases

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.

Multiagent Reinforcement Learning-Based Adaptive Sampling for Conformational Dynamics of Proteins

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.

NDPD: an improved initial centroid method of partitional clustering for big data mining

Purpose

The 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/approach

This 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.

Findings

The 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/value

The 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.

When SWEETs Turn Tweens: Updates and Perspectives

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.

Chiral emergence in multistep hierarchical assembly of achiral conjugated polymers

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.

Structural Rearrangement of the Serotonin Transporter Intracellular Gate Induced by Thr276 Phosphorylation

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.

Dual Role of Strigolactone Receptor Signaling Partner in Inhibiting Substrate Hydrolysis

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.

Engineered ACE2 decoy mitigates lung injury and death induced by SARS-CoV-2 variants

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.

The substrate import mechanism of the human serotonin transporter

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.

Activation Mechanism of Strigolactone Receptors and Its Impact on Ligand Selectivity between Host and Parasitic Plants

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.

Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning

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.

Distinct Binding Mechanisms for Allosteric Sodium Ion in Cannabinoid Receptors

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.

Molecular basis of the activation and dissociation of dimeric PYL2 receptor in abscisic acid signaling

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.

Maxmin Data Range Heuristic-Based Initial Centroid Method of Partitional Clustering for Big Data Mining

The centroid-based clustering algorithm depends on the number of clusters, initial centroid, distance measures, and statistical approach of central tendencies. The initial centroid initialization algorithm defines convergence speed, computing efficiency, execution time, scalability, memory utilization, and performance issues for big data clustering. Nowadays various researchers have proposed the cluster initialization techniques, where some initialization techniques reduce the number of iterations with the lowest cluster quality, and some initialization techniques increase the cluster quality with high iterations. For these reasons, this study proposed the initial centroid initialization based Maxmin Data Range Heuristic (MDRH) method for K-Means (KM) clustering that reduces the execution times, iterations, and improves quality for big data clustering. The proposed MDRH method has compared against the classical KM and KM++ algorithms with four real datasets. The MDRH method has achieved better effectiveness and efficiency over RS, DB, CH, SC, IS, and CT quantitative measurements.

Machine learning guided design of high affinity ACE2 decoys for SARS-CoV-2 neutralization

A potential therapeutic candidate for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding of the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant ACE2 variant, named ACE2 ₂ .v.2.4, that exhibits nanomolar affinity binding to the RBD domain of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identified other ACE2 variants, namely double mutants, that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified several ACE2 double mutants that bind to RBD with tighter affinity as compared to the wild type, most notably, L79V;N90D that binds RBD with similar affinity to ACE2 ₂ .v.2.4. The successful experimental validation of the double mutants demonstrated the use transfer and supervised learning approaches for engineering protein-protein interactions and identifying high affinity ACE2 peptides for targeting SARS-CoV-2.

Engineered High-Affinity ACE2 Peptide Mitigates ARDS and Death Induced by Multiple SARS-CoV-2 Variants

Vaccine hesitancy and continuing emergence of SARS-CoV-2 variants of concern that may escape vaccine-induced immune responses highlight the urgent need for effective COVID-19 therapeutics. Monoclonal antibodies used in the clinic have varying efficacies against distinct SARS-CoV-2 variants; thus, there is considerable interest in engineered ACE2 peptides with augmented binding affinities for SARS-CoV-2 Spike protein. These could have therapeutic benefit against multiple viral variants. Using molecular dynamics simulations, we show how three amino acid substitutions in an engineered soluble ACE2 peptide (sACE2 2 .v2.4-IgG1) markedly increase affinity for the SARS-CoV-2 Spike (S) protein. We demonstrate high binding affinity to S protein of the early SARS-CoV-2 WA-1/2020 isolate and also to multiple variants of concern: B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), and B.1.617.2 (Delta) SARS-CoV-2 variants. In humanized K18-hACE2 mice, prophylactic and therapeutic administration of sACE2 2 .v2.4-IgG1 peptide prevented acute lung vascular endothelial injury and lung edema (essential features of ARDS) and significantly improved survival after infection by SARS-CoV-2 WA-1/2020 as well as P.1 variant of concern. These studies demonstrate for the first time broad efficacy in vivo of an ACE2 decoy peptide against multiple SARS-CoV-2 variants and point to its therapeutic potential.

Role of substrate recognition in modulating strigolactone receptor selectivity in witchweed

Witchweed, or Striga hermonthica, is a parasitic weed that destroys billions of dollars' worth of crops globally every year. Its germination is stimulated by strigolactones exuded by its host plants. Despite high sequence, structure, and ligand-binding site conservation across different plant species, one strigolactone receptor in witchweed, ShHTL7, uniquely exhibits a picomolar EC50 for downstream signaling. Previous biochemical and structural analyses have hypothesized that this unique ligand sensitivity can be attributed to a large binding pocket volume in ShHTL7 resulting in enhanced ability to bind substrates, but additional structural details of the substrate-binding process would help explain its role in modulating the ligand selectivity. Using long-timescale molecular dynamics simulations, we demonstrate that mutations at the entrance of the binding pocket facilitate a more direct ligand-binding pathway to ShHTL7, whereas hydrophobicity at the binding pocket entrance results in a stable “anchored” state. We also demonstrate that several residues on the D-loop of AtD14 stabilize catalytically inactive conformations. Finally, we show that strigolactone selectivity is not modulated by binding pocket volume. Our results indicate that while ligand binding is not the sole modulator of strigolactone receptor selectivity, it is a significant contributing factor. These results can be used to inform the design of selective antagonists for strigolactone receptors in witchweed.

Identification and analysis of sugar transporters capable of co-transporting glucose and xylose simultaneously

Simultaneous co-fermentation of glucose and xylose is a key desired trait of engineered Saccharomyces cerevisiae for efficient and rapid production of biofuels and chemicals. However, glucose strongly inhibits xylose transport by endogenous hexose transporters of S. cerevisiae. We identified structurally distant sugar transporters (Lipomyces starkeyi LST1_205437 and Arabidopsis thaliana AtSWEET7) capable of co-transporting glucose and xylose from previously unexplored oleaginous yeasts and plants. Kinetic analysis showed that LST1_205437 had lenient glucose inhibition on xylose transport and AtSWEET7 transported glucose and xylose simultaneously with no inhibition. Modelling studies of LST1_205437 revealed that Ala335 residue at sugar binding site can accommodates both glucose and xylose. Docking studies with AtSWEET7 revealed that Trp59, Trp183, Asn145, and Asn179 residues stabilized the interactions with sugars, allowing both xylose and glucose to be co-transported. In addition, we altered sugar preference of LST1_205437 by single amino acid mutation at Asn365. Our findings provide a new mechanistic insight on glucose and xylose transport mechanism of sugar transporters and the identified sugar transporters can be employed to develop engineered yeast strains for producing cellulosic biofuels and chemicals.

Impact of Increased Membrane Realism on Conformational Sampling of Proteins

The realism and accuracy of lipid bilayer simulations through molecular dynamics (MD) are heavily dependent on the lipid composition. While the field is pushing toward implementing more heterogeneous and realistic membrane compositions, a lack of high-resolution lipidomic data prevents some membrane protein systems from being modeled with the highest level of realism. Given the additional diversity of real-world cellular membranes and protein-lipid interactions, it is still not fully understood how altering membrane complexity affects modeled membrane protein functions or if it matters over long-timescale simulations. This is especially true for organisms whose membrane environments have little to no computational study, such as the plant plasma membrane. Tackling these issues in tandem, a generalized, realistic, and asymmetric plant plasma membrane with more than 10 different lipid species is constructed herein. Classical MD simulations of pure membrane constructs were performed to evaluate how altering the compositional complexity of the membrane impacted the plant membrane properties. The apo form of a plant sugar transporter, OsSWEET2b, was inserted into membrane models where lipid diversity was calculated in either a size-dependent or size-independent manner. An adaptive sampling simulation regime validated by Markov-state models was performed to capture the gating dynamics of OsSWEET2b in each of these membrane constructs. In comparison to previous OsSWEET2b simulations performed in a pure POPC bilayer, we confirm that simulations performed within a native-like membrane composition alter the stabilization of apo OsSWEET2b conformational states by ∼1 kcal/mol. The free-energy barriers of intermediate conformational states decrease when realistic membrane complexity is simplified, albeit roughly within sampling error, suggesting that protein-specific responses to membranes differ due to altered packing caused by compositional fluctuations. This work serves as a case study where a more realistic bilayer composition makes unbiased conformational sampling easier to achieve than with simplified bilayers.