Stem Rot of Pearl Millet Prevalence, Symptomatology, Disease Cycle, Disease Rating Scale and Pathogen Characterization in Pearl Millet-Klebsiella Pathosystem.

The oldest and most extensively cultivated form of millet, known as pearl millet (Pennisetum glaucum (L.) R. Br. Syn. Pennisetum americanum (L.) Leeke), is raised over 312.00 lakh hectares in Asian and African countries. India is regarded as the significant hotspot for pearl millet diversity. In the Indian state of Haryana, where pearl millet is grown, a new and catastrophic bacterial disease known as stem rot of pearl millet spurred by the bacterium Klebsiella aerogenes (formerly Enterobacter) was first observed during fall 2018. The disease appears in form of small to long streaks on leaves, lesions on stem, and slimy rot appearance of stem. The associated bacterium showed close resemblance to Klebsiella aerogenes that was confirmed by a molecular evaluation based on 16S rDNA and gyrA gene nucleotide sequences. The isolates were also identified to be Klebsiella aerogenes based on biochemical assays, where Klebsiella isolates differed in D-trehalose and succinate alkalisation tests. During fall 2021-2023, the disease has spread all the pearl millet-growing districts of the state, extending up to 70% disease incidence in the affected fields. The disease is causing considering grain as well as fodder losses. The proposed scale, consisting of six levels (0-5), is developed where scores 0, 1, 2, 3, 4, and 5 have been categorized as highly resistant, resistant, moderately resistant, moderately susceptible, susceptible, and highly susceptible disease reaction, respectively. The disease cycle, survival of pathogen, and possible losses have also been studied to understand other features of the disease.

Molecular dynamics simulation of RAC1 protein and its de novo variants related to developmental disorders.

Neurodevelopmental disorders (NDDs) are conceptualized as childhood disability, but it has increasingly been recognized as lifelong neurological conditions that could notably impact adult functioning and quality of life. About 1%-3% of the general population suffers from NDDs including ADHD, ASD, IDD, communication disorders, motor disorders, etc. Studies suggest that Rho GTPases are key in neuronal development, highlighting the importance of altered GTPase signaling in NDDs. RAC1, a member of the Rho GTPase family, plays a critical role in neurogenesis, migration, synapse formation, axon growth, and regulation of actin cytoskeleton dynamics. We performed 6µs all-atom molecular dynamics simulation of native RAC1 (PDB: 3TH5) and three-point mutations (C18Y, N39S, and Y64D) related to developmental disorders to understand the impact of mutations on protein stability and functional dynamics. Our analysis, which included root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), radius of gyration (Rg), free energy landscape (FEL), and principal component analysis (PCA), revealed that the N39S and Y64D mutations induced significant structural changes in RAC1. These alterations primarily occurred in the functional region adjacent to switch II, a region crucial for complex conformational rearrangements during the GDP and GTP exchange cycle.Communicated by Ramaswamy H. Sarma.

Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event.

Ras GTPase is an enzyme that catalyzes the hydrolysis of guanosine triphosphate (GTP) and plays an important role in controlling crucial cellular signaling pathways. However, this enzyme has always been believed to be undruggable due to its strong binding affinity with its native substrate GTP. To understand the potential origin of high GTPase/GTP recognition, here we reconstruct the complete process of GTP binding to Ras GTPase via building Markov state models (MSMs) using a 0.1 ms long all-atom molecular dynamics (MD) simulation. The kinetic network model, derived from the MSM, identifies multiple pathways of GTP en route to its binding pocket. While the substrate stalls onto a set of non-native metastable GTPase/GTP encounter complexes, the MSM accurately discovers the native pose of GTP at its designated catalytic site in crystallographic precision. However, the series of events exhibit signatures of conformational plasticity in which the protein remains trapped in multiple non-native conformations even when GTP has already located itself in its native binding site. The investigation demonstrates mechanistic relays pertaining to simultaneous fluctuations of switch 1 and switch 2 residues which remain most instrumental in maneuvering the GTP-binding process. Scanning of the crystallographic database reveals close resemblance between observed non-native GTP binding poses and precedent crystal structures of substrate-bound GTPase, suggesting potential roles of these binding-competent intermediates in allosteric regulation of the recognition process.

Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning.

A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.

First report of Klebsiella Leaf Streak on Sorghum Caused by Klebsiella variicola in Haryana, India.

Sorghum (Sorghum bicolor [L.] Moench) is one of the top ten cereal crops in the world and is grown for fodder and seed purposes. During the fall of 2019 to 2022, a disease causing small to long streaks on leaves was observed in sorghum fields of Hisar (29° 9' 6.6996'' N, 75° 43' 16.0428'' E), Rohtak (28° 53' 43.8540'' N, 76° 36' 23.8068'' E) and Mohindergarh (28° 16' 6.0492'' N, 76° 9' 3.3552'' E) regions of Haryana between July and October. The reddish brown streaks were observed in the interveinal spaces of upper and lower leaves. The disease incidence reached 20-30% of plants in affected fields. The diseased leaf tissues were disinfected with 70% alcohol and placed in a tube with sterile water. After 30 minutes, 100 µl of the suspension was inoculated onto nutrient agar medium, incubated at 28 ± 2°C for three days, and a pure culture was obtained by restreaking on nutrient agar (Janse, 2005). The rod-shaped gram-negative bacterium with round, cream to white colonies was positive for methyl red, citrate utilization, urease activity, and glucose, lactose, sorbitol, rhamnose and sucrose fermentation tests. The genomic DNA of the bacterial suspension was extracted and 16S rDNA was amplified using universal 27F/1492R primers (Marchesi et al. 1998), resulting in tentative identification as Klebsiella sp. It was further confirmed with PCR amplification of Klebsiella specific primers (F:5'-CGCGTACTATACGCCATGAACGTA-3'; R:5'-ACCGTTGATCACTTCGGTCAGG-3') for gyrA gene (Brisse and Verhoef 2001). Discrete PCR amplicons of 1,500 (16S rDNA) and 300 bp (gyrA) were observed in a 1% (w/v) agarose gel. Forward and reverse DNA sequencing of both amplicons of the Hisar isolate (VMKV101) was carried out using a BDT v3.1 Cycle sequencing kit and consensus sequences were generated by using the program SeqMan Ultra (DNASTAR Lasergene). Sequences of the PCR products were deposited in GenBank with accession numbers MZ569433 (16S rDNA) and OP390080 (gyrA). The 16S rDNA sequence was 97.32% similar to K. variicola strain 13450 (CP026013; 1,450/1,490 bp) and the gyrA sequence had 99.66% similarity to K. variicola strain FH-1 (CP054254; 297/298 bp). A 16S RNA-based phylogenetic tree done by MEGA11 (Tamura et al. 2021) using the Maximum Likelihood method showed that strain VMKV101 clustered with K. variicola type strain F2R9. The complete bacterial genome (GCA025629215), sequenced by the Ion GeneStudio S5 system using Ion 530 chips (Thermo Fisher Scientific), was 99.03% identical by average nucleotide identity (ANI) to the type genome (CP045783) of Klebsiella variicola, with 87.8% genome coverage. For pathogenicity testing, a bacterial suspension (10 ml, 1×107 colony forming units/ml) was injected into the whole whorl after mechanical injury on 15-20 days old seedlings of the susceptible genotype HC-171, then plants were incubated at 35 ± 2°C, >80% relative humidity. Control plants were injected with sterile distilled water. Initial symptoms were observed on leaves of inoculated plants after 5 to 7 days as narrow, small longitudinal reddish brown streaks. As the disease progressed, the streaks on the leaf blade increased in number and size maintaining the reddish brown color. These streaks had slightly wavy margins and were surrounded by bright yellow halos. After 15 to 20 days, the streaks were 0.5 to 2.0 mm wide and 1.0 to 5.0 cm long, occasionally up to 10.0 cm long on both side of the leaves. Over time, neighboring streaks coalesced to form large necrotic areas. All inoculated plants exhibited identical symptoms. No symptoms were observed on control leaves. The reisolated bacterium from diseased sorghum leaves showed exactly the same morphological, biochemical and 16S RNA and gyrA molecular characteristics. To our knowledge, this is the first report of K. variicola causing a leaf streak disease on sorghum. Klebsiella species primarily cause diseases in humans and animals, but K. variicola has been found to incite banana soft rot (Fan et al. 2015) and K. aerogenes to cause stem rot in pearl millet (Malik et al. 2022). Differences of prevalence, spread and control between K. variicola and two other bacteria (Xanthomonas vasicola pv. holcicola causing Bacterial leaf streak; Paraburkholderia andropogonis causing Bacterial leaf stripe) causing leaf streak diseases on sorghum need to be determined. The identification of Klebsiella leaf streak disease lays the groundwork for future investigations into epidemiology and management of K. variicola on sorghum.

Structural and functional insights into the candidate genes associated with different developmental stages of flag leaf in bread wheat (Triticum aestivum L.).

Grain yield is one of the most important aims for combating the needs of the growing world population. The role of development and nutrient transfer in flag leaf for higher yields at the grain level is well known. It is a great challenge to properly exploit this knowledge because all the processes, starting from the emergence of the flag leaf to the grain filling stages of wheat (Triticum aestivum L.), are very complex biochemical and physiological processes to address. This study was conducted with the primary goal of functionally and structurally annotating the candidate genes associated with different developmental stages of flag leaf in a comprehensive manner using a plethora of in silico tools. Flag leaf-associated genes were analyzed for their structural and functional impacts using a set of bioinformatics tools and algorithms. The results revealed the association of 17 candidate genes with different stages of flag leaf development in wheat crop. Of these 17 candidate genes, the expression analysis results revealed the upregulation of genes such as TaSRT1-5D, TaPNH1-7B, and TaNfl1-2B and the downregulation of genes such as TaNAP1-7B, TaNOL-4D, and TaOsl2-2B can be utilized for the generation of high-yielding wheat varieties. Through MD simulation and other in silico analyses, all these proteins were found to be stable. Based on the outcome of bioinformatics and molecular analysis, the identified candidate genes were found to play principal roles in the flag leaf development process and can be utilized for higher-yield wheat production.

Reconciling conformational heterogeneity and substrate recognition in cytochrome P450.

Cytochrome P450, the ubiquitous metalloenzyme involved in detoxification of foreign components, has remained one of the most popular systems for substrate-recognition process. However, despite being known for its high substrate specificity, the mechanistic basis of substrate-binding by archetypal system cytochrome P450cam has remained at odds with the contrasting reports of multiple diverse crystallographic structures of its substrate-free form. Here, we address this issue by elucidating the probability of mutual dynamical transition to the other crystallographic pose of cytochrome P450cam and vice versa via unbiased all-atom computer simulation. A robust Markov state model, constructed using adaptively sampled 84-µs-long molecular dynamics simulation trajectories, maps the broad and heterogenous P450cam conformational landscape into five key substates. In particular, the Markov state model identifies an intermediate-assisted dynamic equilibrium between a pair of conformations of P450cam, in which the substrate-recognition sites remain "closed" and "open," respectively. However, the estimate of a significantly higher stationary population of closed conformation, coupled with faster rate of open → closed transition than its reverse process, dictates that the net conformational equilibrium would be swayed in favor of "closed" conformation. Together, the investigation quantitatively infers that although a potential substrate of cytochrome P450cam would, in principle, explore a diverse array of conformations of substrate-free protein, it would mostly encounter a "closed" or solvent-occluded conformation and hence would follow an induced-fit-based recognition process. Overall, the work reconciles multiple precedent crystallographic, spectroscopic investigations and establishes how a statistical elucidation of conformational heterogeneity in protein would provide crucial insights in the mechanism of potential substrate-recognition process.

An Appraisal of Computer Simulation Approaches in Elucidating Biomolecular Recognition Pathways.

Computer simulation approaches in biomolecular recognition processes have come a long way. In this Perspective, we highlight a series of recent success stories in which computer simulations have played a remarkable role in elucidating the atomic resolution mechanism of kinetic processes of protein-ligand binding in a quantitative fashion. In particular, we show that a robust combination of unbiased simulation, harnessed by a high-fidelity computing environment, and Markov state modeling approaches has been instrumental in revealing novel protein-ligand recognition pathways in multiple systems. We also elucidate the role of recent developments in enhanced sampling approaches in providing the much-needed impetus in accelerating simulation of the ligand recognition process. We identify multiple key issues, including force fields and the sampling bottleneck, which are currently preventing the field from achieving quantitative reconstruction of experimental measurements. Finally, we suggest a possible way forward via adoption of multiscale approaches and coarse-grained simulations as next steps toward efficient elucidation of ligand binding kinetics.

Role of allosteric switches and adaptor domains in long-distance cross-talk and transient tunnel formation.

Transient tunnels that assemble and disassemble to facilitate passage of unstable intermediates in enzymes containing multiple reaction centers are controlled by allosteric cues. Using the 140-kDa purine biosynthetic enzyme PurL as a model system and a combination of biochemical and x-ray crystallographic studies, we show that long-distance communication between ~25-Å distal active sites is initiated by an allosteric switch, residing in a conserved catalytic loop, adjacent to the synthetase active site. Further, combinatory experiments seeded from molecular dynamics simulations help to delineate transient states that bring out the central role of nonfunctional adaptor domains. We show that carefully orchestrated conformational changes, facilitated by interplay of dynamic interactions at the allosteric switch and adaptor-domain interface, control reactivity and concomitant formation of the ammonia tunnel. This study asserts that substrate channeling is modulated by allosteric hotspots that alter protein energy landscape, thereby allowing the protein to adopt transient conformations paramount to function.

Nonaffine Displacements Encode Collective Conformational Fluctuations in Proteins.

Identifying subtle conformational fluctuations underlying the dynamics of biomacromolecules is crucial for resolving their free energy landscape. We show that a collective variable, originally proposed for crystalline solids, is able to filter out essential macromolecular motions more efficiently than other approaches. While homogeneous or "affine" deformations of the biopolymer are trivial, biopolymer conformations are complicated by the occurrence of inhomogeneous or "nonaffine" displacements of atoms relative to their positions in the native structure. We show that these displacements encode functionally relevant conformations of macromolecules, and in combination with a formalism based upon time-structured independent component analysis, they quantitatively resolve the free energy landscape of a number of macromolecules of hierarchical complexity. The kinetics of conformational transitions among the basins can now be mapped within the framework of a Markov state model. The nonaffine modes, obtained by projecting out homogeneous fluctuations from the local displacements, are found to be responsible for local structural changes required for transitioning between pairs of macrostates.

On the role of solvent in hydrophobic cavity-ligand recognition kinetics.

A solvent often manifests itself as the key determinant of the kinetic aspect of the molecular recognition process. While the solvent is often depicted as a source of barrier in the ligand recognition process by the polar cavity, the nature of solvent's role in the recognition process involving hydrophobic cavity and hydrophobic ligand remains to be addressed. In this work, we quantitatively assess the role of solvent in dictating the kinetic process of recognition in a popular system involving the hydrophobic cavity and ligand. In this prototypical system, the hydrophobic cavity undergoes dewetting transition as the ligand approaches the cavity, which influences the cavity-ligand recognition kinetics. Here, we build a Markov state model (MSM) using adaptively sampled unrestrained molecular dynamics simulation trajectories to map the kinetic recognition process. The MSM-reconstructed free energy surface recovers a broad water distribution at an intermediate cavity-ligand separation, consistent with a previous report of dewetting transition in this system. Time-structured independent component analysis of the simulated trajectories quantitatively shows that cavity-solvent density contributes considerably in an optimized reaction coordinate involving cavity-ligand separation and water occupancy. Our approach quantifies two solvent-mediated macrostates at an intermediate separation of the cavity-ligand recognition pathways, apart from the fully ligand-bound and fully ligand-unbound macrostates. Interestingly, we find that these water-mediated intermediates, while transient in populations, can undergo slow mutual interconversion and create possibilities of multiple pathways of cavity recognition by the ligand. Overall, the work provides a quantitative assessment of the role that the solvent plays in facilitating the recognition process involving the hydrophobic cavity.

On identifying collective displacements in apo-proteins that reveal eventual binding pathways.

Binding of small molecules to proteins often involves large conformational changes in the latter, which open up pathways to the binding site. Observing and pinpointing these rare events in large scale, all-atom, computations of specific protein-ligand complexes, is expensive and to a great extent serendipitous. Further, relevant collective variables which characterise specific binding or un-binding scenarios are still difficult to identify despite the large body of work on the subject. Here, we show that possible primary and secondary binding pathways can be discovered from short simulations of the apo-protein without waiting for an actual binding event to occur. We use a projection formalism, introduced earlier to study deformation in solids, to analyse local atomic displacements into two mutually orthogonal subspaces-those which are "affine" i.e. expressible as a homogeneous deformation of the native structure, and those which are not. The susceptibility to non-affine displacements among the various residues in the apo- protein is then shown to correlate with typical binding pathways and sites crucial for allosteric modifications. We validate our observation with all-atom computations of three proteins, T4-Lysozyme, Src kinase and Cytochrome P450.

Mapping the Substrate Recognition Pathway in Cytochrome P450.

Cytochrome P450s are ubiquitous metalloenzymes involved in the metabolism and detoxification of foreign components via catalysis of the hydroxylation reactions of a vast array of organic substrates. However, the mechanism underlying the pharmaceutically critical process of substrate access to the catalytic center of cytochrome P450 is a long-standing puzzle, further complicated by the crystallographic evidence of a closed catalytic center in both substrate-free and substrate-bound cytochrome P450. Here, we address a crucial question whether the conformational heterogeneity prevalent in cytochrome P450 translates to heterogeneous pathways for substrate access to the catalytic center of these metalloenzymes. By atomistically capturing the full process of spontaneous substrate association from bulk solvent to the occluded catalytic center of an archetypal system P450cam in multi-microsecond-long continuous unbiased molecular dynamics simulations, we here demonstrate that the substrate recognition in P450cam always occurs through a single well-defined dominant pathway. The simulated final bound pose resulting from these unguided simulations is in striking resemblance with the crystallographic bound pose. Each individual binding trajectory reveals that the substrate, initially placed at random locations in bulk solvent, spontaneously lands on a single key channel on the protein-surface of P450cam and resides there for an uncharacteristically long period, before correctly identifying the occluded target-binding cavity. Surprisingly, the passage of substrate to the closed catalytic center is not accompanied by any large-scale opening in protein. Rather, the unbiased simulated trajectories (∼57 µs) and underlying Markov state model, in combination with free-energy analysis, unequivocally show that the substrate recognition process in P450cam needs a substrate-induced side-chain displacement coupled with a complex array of dynamical interconversions of multiple metastable substrate conformations. Further, the work reconciles multiple precedent experimental and theoretical observations on P450cam and establishes a comprehensive view of substrate-recognition in cytochrome P450 that only occurs via substrate-induced structural rearrangements.

Assessment and optimization of collective variables for protein conformational landscape: GB1 ß-hairpin as a case study.

Collective variables (CVs), when chosen judiciously, can play an important role in recognizing rate-limiting processes and rare events in any biomolecular systems. However, high dimensionality and inherent complexities associated with such biochemical systems render the identification of an optimal CV a challenging task, which in turn precludes the elucidation of an underlying conformational landscape in sufficient details. In this context, a relevant model system is presented by a 16-residue ß-hairpin of GB1 protein. Despite being the target of numerous theoretical and computational studies for understanding the protein folding, the set of CVs optimally characterizing the conformational landscape of the ß-hairpin of GB1 protein has remained elusive, resulting in a lack of consensus on its folding mechanism. Here we address this by proposing a pair of optimal CVs which can resolve the underlying free energy landscape of the GB1 hairpin quite efficiently. Expressed as a linear combination of a number of traditional CVs, the optimal CV for this system is derived by employing the recently introduced time-structured independent component analysis approach on a large number of independent unbiased simulations. By projecting the replica-exchange simulated trajectories along these pair of optimized CVs, the resulting free energy landscape of this system is able to resolve four distinct well-separated metastable states encompassing the extensive ensembles of folded, unfolded, and molten globule states. Importantly, the optimized CVs were found to be capable of automatically recovering a novel partial helical state of this protein, without needing to explicitly invoke helicity as a constituent CV. Furthermore, a quantitative sensitivity analysis of each constituent in the optimized CV provided key insights on the relative contributions of the constituent CVs in the overall free energy landscapes. Finally, the kinetic pathways connecting these metastable states, constructed using a Markov state model, provide an optimum description of the underlying folding mechanism of the peptide. Taken together, this work offers a quantitatively robust approach toward comprehensive mapping of the underlying folding landscape of a quintessential model system along its optimized CV.

Atomic resolution mechanism of ligand binding to a solvent inaccessible cavity in T4 lysozyme.

Ligand binding sites in proteins are often localized to deeply buried cavities, inaccessible to bulk solvent. Yet, in many cases binding of cognate ligands occurs rapidly. An intriguing system is presented by the L99A cavity mutant of T4 Lysozyme (T4L L99A) that rapidly binds benzene (~106 M-1s-1). Although the protein has long served as a model system for protein thermodynamics and crystal structures of both free and benzene-bound T4L L99A are available, the kinetic pathways by which benzene reaches its solvent-inaccessible binding cavity remain elusive. The current work, using extensive molecular dynamics simulation, achieves this by capturing the complete process of spontaneous recognition of benzene by T4L L99A at atomistic resolution. A series of multi-microsecond unbiased molecular dynamics simulation trajectories unequivocally reveal how benzene, starting in bulk solvent, diffuses to the protein and spontaneously reaches the solvent inaccessible cavity of T4L L99A. The simulated and high-resolution X-ray derived bound structures are in excellent agreement. A robust four-state Markov model, developed using cumulative 60 µs trajectories, identifies and quantifies multiple ligand binding pathways with low activation barriers. Interestingly, none of these identified binding pathways required large conformational changes for ligand access to the buried cavity. Rather, these involve transient but crucial opening of a channel to the cavity via subtle displacements in the positions of key helices (helix4/helix6, helix7/helix9) leading to rapid binding. Free energy simulations further elucidate that these channel-opening events would have been unfavorable in wild type T4L. Taken together and via integrating with results from experiments, these simulations provide unprecedented mechanistic insights into the complete ligand recognition process in a buried cavity. By illustrating the power of subtle helix movements in opening up multiple pathways for ligand access, this work offers an alternate view of ligand recognition in a solvent-inaccessible cavity, contrary to the common perception of a single dominant pathway for ligand binding.

Atomistic Insights into Structural Differences between E3 and E4 Isoforms of Apolipoprotein E.

Among various isoforms of Apolipoprotein E (ApoE), the E4 isoform (ApoE4) is considered to be the strongest risk factor for Alzheimer's disease, whereas the E3 isoform (ApoE3) is neutral to the disease. Interestingly, the sequence of ApoE4 differs from its wild-type ApoE3 by a single amino acid C112R in the 299-amino-acid-long sequence. Hence, the puzzle remains: how a single-amino-acid difference between the ApoE3 and ApoE4 sequences can give rise to structural dissimilarities between the two isoforms, which can potentially lead to functional differences with significant pathological consequences. The major obstacle in addressing this question has been the lack of a 3D atomistic structure of ApoE4 to date. In this work, we resolve the issue by computationally modeling a plausible atomistic 3D structure of ApoE4. Our microsecond-long atomistic simulations elucidate key structural differences between monomeric ApoE3 and ApoE4, which renders ApoE4 thermodynamically less stable, less structured, and topologically less rigid compared to ApoE3. Consistent with an experimental report of the molten globule state of ApoE4, simulations identify multiple partially folded intermediates for ApoE4, which are implicated in the stronger aggregation propensity of ApoE4.

Molecular Mechanism of Nucleotide-Dependent Allosteric Regulation in AMP-Activated Protein Kinase.

The AMP-activated protein kinase (AMPK), a central enzyme in the regulation of energy homeostasis, is an important drug target for type 2 diabetes, obesity, and cancer. Binding of adenosine nucleotides to the regulatory γ-subunit tightly regulates the activity of this enzyme. Though recent crystal structures of AMPK have provided important insights into the allosteric activation of AMPK, molecular details of the regulatory mechanism of AMPK activation is still elusive. Here, we have performed extensive all-atom molecular dynamics (MD) simulations and shown that the kinase domain (KD) and γ-subunit come closer resulting in a more compact heterotrimeric AMPK complex in AMP-bound state compared to the ATP-bound state. The binding of ATP at site 3 of regulatory γ-subunit allosterically inhibits AMPK by destabilizing different regulatory regions of α-subunit: the autoinhibitory domain, the linker region, and the activation loop of the kinase core. The catalytically important residues experience a change in mechanical stress, and major rearrangements in community structure derived from residue-residue interaction energy-based network are observed in KD and α-linker region upon binding of different nucleotides. Our results also highlight the role of conserved charged residues forming an ionic network near the site 3 of γ-subunit in allosteric communications.

Structural Ensemble of CD4 Cytoplasmic Tail (402-419) Reveals a Nearly Flat Free-Energy Landscape with Local α-Helical Order in Aqueous Solution.

The human cluster determinant 4 (CD4), expressed primarily on the surface of T helper cells, serves as a coreceptor in T-cell receptor recognition of MHC II antigen complexes. Besides its cellular functions, CD4 serves as a primary receptor of human immunodeficiency virus (HIV) type 1. The cytoplasmic tail of CD4 (residues 402-419) is known to be involved in direct interaction with the HIV-1 proteins Vpu and Nef. These two viral accessory proteins (Vpu and Nef) downregulate CD4 in HIV-1 infected cells by multiple strategies and make the body susceptible to all forms of infections. In this work, we carried out extensive replica exchange molecular dynamics simulations in explicit water with three popular protein force fields Amber ff99SB, Amber ff99SB*-ILDN, and CHARMM36 to characterize the equilibrium conformational ensemble of CD4-tail (402-419) and further validated the simulated ensembles with known NMR data. We found that ff99SB*-ILDN gives a better description of the structural ensemble of this peptide compared with ff99SB and CHARMM36. The peptide adopts multiple distinct conformations with varying degree of residual secondary structures. In particular, we observed 28, 7, and 5% average α-helical, ß-strand, and 3(10)-helix content, respectively, for ff99SB*-ILDN. The peptide chain shows the tendency of helix formation in a cooperative manner, seeding at residues 407-410, and subsequently extending toward both ends of the chain. Furthermore, we constructed Markov state model (MSM) from large-scale molecular dynamics simulations to study the dynamics of transitions between different metastable states explored by this peptide. The mean first passage times computed from MSM indicate rapid interconversion of these states, and the time scales of transitions range from several nanoseconds to hundreds of microseconds. Our results show good agreement with experimental data and could help to understand the key molecular mechanisms of T-cell activation and HIV-mediated receptor interference.

Activation of corticotropin-releasing factor 1 receptor: insights from molecular dynamics simulations.

G-protein-coupled receptors (GPCRs) constitute the largest family of membrane-bound proteins involved in translation of extracellular signals into intracellular responses. They regulate diverse physiological and pathophysiological processes, and hence, they are prime drug targets for therapeutic intervention. In spite of the recent advancements in membrane protein crystallography, limited information is available on the molecular signatures of activation of GPCRs. Although few studies have been reported for class A GPCRs, the activation mechanism of class B GPCRs remains unexplored. Corticotropin-releasing factor 1 receptor (CRF1R), a class B GPCR, is associated with various disease conditions including stress, anxiety, and irritable bowel syndrome. Here, we report the activation of CRF1R using accelerated molecular dynamics simulations of the apo receptor. The breakage of His155(2.50)-Glu209(3.50) and Glu209(3.50)-Thr316(6.42) interactions is found to be crucial in transition of the receptor to its active conformation. Compared to the inactive crystal structure, major structural rearrangements occurred in the intracellular region of the transmembrane (TM) domain upon activation: TM3 twisted away from TM2, and an opening of the G-protein binding site occurred as a result of the outward movements of TM5 and TM6 from the helical bundle. Further, an inward tilt of TM7 toward the helical core is observed at the extracellular side, in agreement with recent findings (Coin et al. Cell 2013, 155, 1258-1269), where it is proposed that this movement helps in establishing favorable interactions with peptide agonist. Moreover, different allosteric pathways in the inactive and active states are identified using the correlations in torsion angle space. The inactive state is found to be less dynamic as compared to the putative active state of the receptor. Results from the current study could present a model for class B GPCRs activation and aid in the design of CRF1R modulators against brain and metabolic disorders.