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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. [ABSTRACT FROM AUTHOR]

Collective variables (CVs), when chosen judiciously, can play an important role in recognizing ratelimiting 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 β-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 β-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. [ABSTRACT FROM AUTHOR]

Thehuman cluster determinant 4 (CD4), expressed primarily on thesurface of T helper cells, serves as a coreceptor in T-cell receptorrecognition 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 knownto be involved in direct interaction with the HIV-1 proteins Vpu andNef. These two viral accessory proteins (Vpu and Nef) downregulateCD4 in HIV-1 infected cells by multiple strategies and make the bodysusceptible to all forms of infections. In this work, we carried outextensive replica exchange molecular dynamics simulations in explicitwater with three popular protein force fields Amber ff99SB, Amberff99SB*-ILDN, and CHARMM36 to characterize the equilibrium conformationalensemble of CD4-tail (402–419) and further validated the simulatedensembles with known NMR data. We found that ff99SB*-ILDN gives abetter description of the structural ensemble of this peptide comparedwith ff99SB and CHARMM36. The peptide adopts multiple distinct conformationswith varying degree of residual secondary structures. In particular,we observed 28, 7, and 5% average α-helical, β-strand,and 310-helix content, respectively, for ff99SB*-ILDN.The peptide chain shows the tendency of helix formation in a cooperativemanner, seeding at residues 407–410, and subsequently extendingtoward both ends of the chain. Furthermore, we constructed Markovstate model (MSM) from large-scale molecular dynamics simulationsto study the dynamics of transitions between different metastablestates explored by this peptide. The mean first passage times computedfrom MSM indicate rapid interconversion of these states, and the timescales of transitions range from several nanoseconds to hundreds ofmicroseconds. Our results show good agreement with experimental dataand could help to understand the key molecular mechanisms of T-cellactivation and HIV-mediated receptor interference. [ABSTRACT FROM AUTHOR]

G-protein-coupledreceptors (GPCRs) constitute the largest familyof membrane-bound proteins involved in translation of extracellularsignals into intracellular responses. They regulate diverse physiologicaland pathophysiological processes, and hence, they are prime drug targetsfor therapeutic intervention. In spite of the recent advancementsin membrane protein crystallography, limited information is availableon the molecular signatures of activation of GPCRs. Although few studieshave been reported for class A GPCRs, the activation mechanism ofclass B GPCRs remains unexplored. Corticotropin-releasing factor 1receptor (CRF1R), a class B GPCR, is associated with various diseaseconditions including stress, anxiety, and irritable bowel syndrome.Here, we report the activation of CRF1R using accelerated moleculardynamics simulations of the apo receptor. The breakage of His1552.50–Glu2093.50and Glu2093.50–Thr3166.42interactions is found to be crucialin transition of the receptor to its active conformation. Comparedto the inactive crystal structure, major structural rearrangementsoccurred in the intracellular region of the transmembrane (TM) domainupon activation: TM3 twisted away from TM2, and an opening of theG-protein binding site occurred as a result of the outward movementsof TM5 and TM6 from the helical bundle. Further, an inward tilt ofTM7 toward the helical core is observed at the extracellular side,in agreement with recent findings (Coin et al. Cell2013, 155, 1258–1269), whereit is proposed that this movement helps in establishing favorableinteractions with peptide agonist. Moreover, different allostericpathways in the inactive and active states are identified using thecorrelations in torsion angle space. The inactive state is found tobe less dynamic as compared to the putative active state of the receptor.Results from the current study could present a model for class B GPCRsactivation and aid in the design of CRF1R modulators against brainand metabolic disorders. [ABSTRACT FROM AUTHOR]

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.

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.

The reaction of KSeO(t)Bu with 2-iodo-arylbenzamides gave benzamide ring-substituted, quinine-derived isoselenazolones 1b–1d. The reaction of PhSH with ortho-methyl-substituted isoselenazolone 1b gave selenol 3b, which is oxidized by H2O2 to regenerate 1b. Isoselenazolone 1b shows a high rate (0.33 × 103 μM min(−1)) of oxidation of PhSH with H2O2, which is ∼103-fold more active than ebselen (1a) and ≥30-fold more active than the other isoselenazolones of this study. Compound 1b shows less inhibition of the growth of yeast cells than 1a.