Is an intuitive convergence definition of molecular dynamics simulations solely based on the root mean square deviation possible?

High throughput nonparametric probability density estimation

In high throughput applications, such as those found in bioinformatics and finance, it is important to determine accurate probability distribution functions despite only minimal information about data characteristics, and without using human subjectivity. Such an automated process for univariate data is implemented to achieve this goal by merging the maximum entropy method with single order statistics and maximum likelihood. The only required properties of the random variables are that they are continuous and that they are, or can be approximated as, independent and identically distributed. A quasi-log-likelihood function based on single order statistics for sampled uniform random data is used to empirically construct a sample size invariant universal scoring function. Then a probability density estimate is determined by iteratively improving trial cumulative distribution functions, where better estimates are quantified by the scoring function that identifies atypical fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian or Akaike information criterion. Multiple estimates for the probability density reflect uncertainties due to statistical fluctuations in random samples. Scaled quantile residual plots are also introduced as an effective diagnostic to visualize the quality of the estimated probability densities. Benchmark tests show that estimates for the probability density function (PDF) converge to the true PDF as sample size increases on particularly difficult test probability densities that include cases with discontinuities, multi-resolution scales, heavy tails, and singularities. These results indicate the method has general applicability for high throughput statistical inference.

Human DHEA sulfation requires direct interaction between PAPS synthase 2 and DHEA sulfotransferase SULT2A1

The high-energy sulfate donor 3′-phosphoadenosine-5′-phosphosulfate (PAPS), generated by human PAPS synthase isoforms PAPSS1 and PAPSS2, is required for all human sulfation pathways. Sulfotransferase SULT2A1 uses PAPS for sulfation of the androgen precursor dehydroepiandrosterone (DHEA), thereby reducing downstream activation of DHEA to active androgens. Human PAPSS2 mutations manifest with undetectable DHEA sulfate, androgen excess, and metabolic disease, suggesting that ubiquitous PAPSS1 cannot compensate for deficient PAPSS2 in supporting DHEA sulfation. In knockdown studies in human adrenocortical NCI-H295R1 cells, we found that PAPSS2, but not PAPSS1, is required for efficient DHEA sulfation. Specific APS kinase activity, the rate-limiting step in PAPS biosynthesis, did not differ between PAPSS1 and PAPSS2. Co-expression of cytoplasmic SULT2A1 with a cytoplasmic PAPSS2 variant supported DHEA sulfation more efficiently than co-expression with nuclear PAPSS2 or nuclear/cytosolic PAPSS1. Proximity ligation assays revealed protein–protein interactions between SULT2A1 and PAPSS2 and, to a lesser extent, PAPSS1. Molecular docking studies showed a putative binding site for SULT2A1 within the PAPSS2 APS kinase domain. Energy-dependent scoring of docking solutions identified the interaction as specific for the PAPSS2 and SULT2A1 isoforms. These findings elucidate the mechanistic basis for the selective requirement for PAPSS2 in human DHEA sulfation.

Interactions of 2'-O-methyl oligoribonucleotides with the RNA models of the 30S subunit A-site

Synthetic oligonucleotides targeting functional regions of the prokaryotic rRNA could be promising antimicrobial agents. Indeed, such oligonucleotides were proven to inhibit bacterial growth. 2’-O-methylated (2’-O-Me) oligoribonucleotides with a sequence complementary to the decoding site in 16S rRNA were reported as inhibitors of bacterial translation. However, the binding mode and structures of the formed complexes, as well as the level of selectivity of the oligonucleotides between the prokaryotic and eukaryotic target, were not determined. We have analyzed three 2’-O-Me oligoribonucleotides designed to hybridize with the models of the prokaryotic rRNA containing two neighboring aminoglycoside binding pockets. One pocket is the paromomycin/kanamycin binding site corresponding to the decoding site in the small ribosomal subunit and the other one is the close-by hygromycin B binding site whose dynamics has not been previously reported. Molecular dynamics (MD) simulations, as well as isothermal titration calorimetry, gel electrophoresis and spectroscopic studies have shown that the eukaryotic rRNA model is less conformationally stable (in terms of hydrogen bonds and stacking interactions) than the corresponding prokaryotic one. In MD simulations of the eukaryotic construct, the nucleotide U1498, which plays an important role in correct positioning of mRNA during translation, is flexible and spontaneously flips out into the solvent. In solution studies, the 2’-O-Me oligoribonucleotides did not interact with the double stranded rRNA models but all formed stable complexes with the single-stranded prokaryotic target. 2’-O-Me oligoribonucleotides with one and two mismatches bound less tightly to the eukaryotic target. This shows that at least three mismatches between the 2’-O-Me oligoribonucleotide and eukaryotic rRNA are required to ensure target selectivity. The results also suggest that, in the ribosome environment, the strand invasion is the preferred binding mode of 2’-O-Me oligoribonucleotides targeting the aminoglycoside binding sites in 16S rRNA.

Statistical Measures to Quantify Similarity between Molecular Dynamics Simulation Trajectories

Molecular dynamics simulation is commonly employed to explore protein dynamics. Despite the disparate timescales between functional mechanisms and molecular dynamics (MD) trajectories, functional differences are often inferred from differences in conformational ensembles between two proteins in structure-function studies that investigate the effect of mutations. A common measure to quantify differences in dynamics is the root mean square fluctuation (RMSF) about the average position of residues defined by Cα-atoms. Using six MD trajectories describing three native/mutant pairs of beta-lactamase, we make comparisons with additional measures that include Jensen-Shannon, modifications of Kullback-Leibler divergence, and local p-values from 1-sample Kolmogorov-Smirnov tests. These additional measures require knowing a probability density function, which we estimate by using a nonparametric maximum entropy method that quantifies rare events well. The same measures are applied to distance fluctuations between Cα-atom pairs. Results from several implementations for quantitative comparison of a pair of MD trajectories are made based on fluctuations for on-residue and residue-residue local dynamics. We conclude that there is almost always a statistically significant difference between pairs of 100 ns all-atom simulations on moderate-sized proteins as evident from extraordinarily low p-values.

Design and evaluation of an apta-nano-sensor to detect Acetamiprid in vitro and in silico

Pesticide detection is a main concern of food safety experts. Therefore, it is urgent to design an accurate, rapid, and cheap test. Biosensors that detect pesticide residues could replace current methods, such as HPLC or GC-MC. This research designs a biosensor based on aptamer (Oligonucleotide ss-DNA) in the receptor role, silver nanoparticles (AgNPs) as optical sensors and salt (NaCl) as the aggregative inducer of AgNPs to detect the presence of Acetamiprid. After optimization, .6 μM aptamer and 100 mM salt were employed. The selectivity and sensitivity of the complex were examined by different pesticides and different Acetamiprid concentrations. To simulate in vitro experimental conditions, bioinformatics software was used as in silico analysis. The results showed the detection of Acetamiprid at the .02 ppm (89.8 nM) level in addition to selectivity. Docking outputs introduced two loops as active sites in aptamer and confirmed aptamer-Acetamiprid bonding. Circular dichroism spectroscopy (CD) confirmed upon Acetamiprid binding, aptamer was folded due to stem-loop formation. Stability of the Apt-Acetamiprid complex in a simulated aqueous media was examined by molecular dynamic studies.

Exploring peptide/MHC detachment processes using hierarchical natural move Monte Carlo

MOTIVATION

The binding between a peptide and a major histocompatibility complex (MHC) is one of the most important processes for the induction of an adaptive immune response. Many algorithms have been developed to predict peptide/MHC (pMHC) binding. However, no approach has yet been able to give structural insight into how peptides detach from the MHC.

RESULTS

In this study, we used a combination of coarse graining, hierarchical natural move Monte Carlo and stochastic conformational optimization to explore the detachment processes of 32 different peptides from HLA-A*02:01. We performed 100 independent repeats of each stochastic simulation and found that the presence of experimentally known anchor amino acids affects the detachment trajectories of our peptides. Comparison with experimental binding affinity data indicates the reliability of our approach (area under the receiver operating characteristic curve 0.85). We also compared to a 1000 ns molecular dynamics simulation of a non-binding peptide (AAAKTPVIV) and HLA-A*02:01. Even in this simulation, the longest published for pMHC, the peptide does not fully detach. Our approach is orders of magnitude faster and as such allows us to explore pMHC detachment processes in a way not possible with all-atom molecular dynamics simulations.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available for download at http://www.cs.ox.ac.uk/mosaics/.

CONTACT

bernhard.knapp@stats.ox.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Calcium Goes Meow: Effects of Ions and Glycosylation on Fel d 1, the Major Cat Allergen

The major cat allergen, Fel d 1, is a structurally complex protein with two N-glycosylation sites that may be filled by different glycoforms. In addition, the protein contains three putative Ca2+ binding sites. Since the impact of these Fel d 1 structure modifications on the protein dynamics, physiology and pathology are not well established, the present work employed computational biology techniques to tackle these issues. While conformational effects brought upon by glycosylation were identified, potentially involved in cavity volume regulation, our results indicate that only the central Ca2+ ion remains coordinated to Fel d 1 in biological solutions, impairing its proposed role in modulating phospholipase A2 activity. As these results increase our understanding of Fel d 1 structural biology, they may offer new support for understanding its physiological role and impact into cat-promoted allergy.

The Calcium Goes Meow: Effects of Ions and Glycosylation on Fel d 1, the Major Cat Allergen

The major cat allergen, Fel d 1, is a structurally complex protein with two N-glycosylation sites that may be filled by different glycoforms. In addition, the protein contains three putative Ca²⁺ binding sites. Since the impact of these Fel d 1 structure modifications on the protein dynamics, physiology and pathology are not well established, the present work employed computational biology techniques to tackle these issues. While conformational effects brought upon by glycosylation were identified, potentially involved in cavity volume regulation, our results indicate that only the central Ca²⁺ ion remains coordinated to Fel d 1 in biological solutions, impairing its proposed role in modulating phospholipase A₂ activity. As these results increase our understanding of Fel d 1 structural biology, they may offer new support for understanding its physiological role and impact into cat-promoted allergy.

Relaxation estimation of RMSD in molecular dynamics immunosimulations

Molecular dynamics simulations have to be sufficiently long to draw reliable conclusions. However, no method exists to prove that a simulation has converged. We suggest the method of "lagged RMSD-analysis" as a tool to judge if an MD simulation has not yet run long enough. The analysis is based on RMSD values between pairs of configurations separated by variable time intervals Δt. Unless RMSD(Δt) has reached a stationary shape, the simulation has not yet converged.