Benchmarking Molecular Dynamics Force Fields for All-Atom Simulations of Biological Condensates.

Proteins containing intrinsically disordered regions are integral parts of the cellular signaling pathways and common components of biological condensates. Point mutations in the protein sequence, genetic at birth or acquired through aging, can alter the properties of the condensates, marking the onset of neurodegenerative diseases such as ALS and dementia. While the all-atom molecular dynamics method can, in principle, elucidate the conformational changes that arise from point mutations, the applications of this method to protein condensate systems is conditioned upon the availability of molecular force fields that can accurately describe both structured and disordered regions of such proteins. Using the special-purpose Anton 2 supercomputer, we benchmarked the efficacy of nine presently available molecular force fields in describing the structure and dynamics of a Fused in sarcoma (FUS) protein. Five-microsecond simulations of the full-length FUS protein characterized the effect of the force field on the global conformation of the protein, self-interactions among its side chains, solvent accessible surface area, and the diffusion constant. Using the results of dynamic light scattering as a benchmark for the FUS radius of gyration, we identified several force fields that produced FUS conformations within the experimental range. Next, we used these force fields to perform ten-microsecond simulations of two structured RNA binding domains of FUS bound to their respective RNA targets, finding the choice of the force field to affect stability of the RNA-FUS complex. Taken together, our data suggest that a combination of protein and RNA force fields sharing a common four-point water model provides an optimal description of proteins containing both disordered and structured regions and RNA-protein interactions. To make simulations of such systems available beyond the Anton 2 machines, we describe and validate implementation of the best performing force fields in a publicly available molecular dynamics program NAMD. Our NAMD implementation enables simulations of large (tens of millions of atoms) biological condensate systems and makes such simulations accessible to a broader scientific community.

Benchmarking Molecular Dynamics Force Fields for All-Atom Simulations of Biological Condensates.

Proteins containing intrinsically disordered regions are integral components of the cellular signaling pathways and common components of biological condensates. Point mutations in the protein sequence, genetic at birth or acquired through aging, can alter the properties of the condensates, marking the onset of neurodegenerative diseases such as ALS and dementia. While all-atom molecular dynamics method can, in principle, elucidate the conformational changes responsible for the aging of the condensate, the applications of this method to protein condensate systems is conditioned by the availability of molecular force fields that can accurately describe both structured and disordered regions of such proteins. Using the special-purpose Anton 2 supercomputer, we benchmarked the efficacy of nine presently available molecular force fields in describing the structure and dynamics of a Fused in sarcoma (FUS) protein. Five-microsecond simulations of the full-length FUS protein characterized the effect of the force field on the global conformation of the protein, self-interactions among its side chains, solvent accessible surface area and the diffusion constant. Using the results of dynamic light scattering as a benchmark for the FUS radius of gyration, we identified several force field that produced FUS conformations within the experimental range. Next, we used these force fields to perform ten-microsecond simulations of two structured RNA binding domains of FUS bound to their respective RNA targets, finding the choice of the force field to affect stability of the RNA-FUS complex. Taken together, our data suggest that a combination of protein and RNA force fields sharing a common four-point water model provides an optimal description of proteins containing both disordered and structured regions and RNA-protein interactions. To make simulations of such systems available beyond the Anton 2 machines, we describe and validate implementation of the best performing force fields in a publicly available molecular dynamics program NAMD. Our NAMD implementation enables simulations of large (tens of millions of atoms) biological condensate systems and makes such simulations accessible to a broader scientific community.

Glazing as a bonding system for zirconia dental ceramics.

Studies have reported challenges of debonding of dental zirconia crowns to from luting cement and prepared teeth. The aim of the study was to explore the application of dental glazing systems for enhancing the bonding of zirconia dental ceramics to luting resin cement. Commercial glaze powder and liquid (Vita Akzent) and experimental mica-based glaze powders were used for the study. X-ray diffraction analysis of the glaze powders (XRD) and Fourier Transform InfraRed Spectroscopy (FTIR) was done on the glaze liquid. Sandblasted sintered dental zirconia (Katana, Noritake) were the control samples. Glazed zirconia samples were coated with commercial glaze and experimental glaze powders which were further etched with 5% hydrofluoric acid. Shear bond strengths of sandblasted and glazed zirconia samples to resin composites were evaluated. XRD of commercial and experimental glaze powders revealed a broad peak confirming the amorphous nature of glass and FTIR analysis of the glaze liquid revealed symmetrical stretching (CH2-CH3) of the alcohol group indicating a mixture of iso-butane and ethanol. Glazed and etched zirconia showed significantly higher shear bond strength to resin cement compared to sand-blasted zirconia. The study confirms the glassy nature of dental glaze powders and the presence of ethanol-based mixtures in the commercial glaze liquid. Glazing systems have the potential to be explored for enhancing the bonding of non-etchable zirconia ceramics to resin cement and tooth substrates.

Factors propelling fatalities during road crashes: A detailed investigation and modelling of historical crash data with field studies.

One of the major concerns in developing countries like India is to maintain traffic safety under mixed and heterogenous scenario. Although zero accidents is the need of the hour, the first step to attain it is ensuring zero deaths and no serious long-term disabling injuries in road crashes. To reduce the road crash fatalities, explicit and detailed studies have been conducted by utilising historical road crash data of two emerging smart cities of India - Bhubaneswar and Visakhapatnam. Traffic flow data and characteristics of road infrastructure has also been collected by performing field studies at accident prone locations. Various factors including vehicular characteristics, road user characteristics, and road infrastructure have been analyzed using various non-parametric tests to identify the contributing factors resulting in fatalities. It is observed that out of 14 variables used for study, 8 factors were significantly related to fatal crashes. These included categories of victim and accused, 85th percentile speed, presence of road markings, availability of sight distance, etc. The significant factors were subjected to binary logistic regression to determine the odd's ratio of significant factors. The logistic regression predicted 79% of deaths correctly. Crash fatality prediction models are developed using both Classification and Regression Tree (CART) classification tree with 83% accuracy. Although CART classification led to higher accuracy, binary logistic regression is more robust as it considered more significant factors as compared to CART. Subsequently, a severity index has been proposed based on proportions of actual fatal crashes and usage of K-means clustering technique. The proposed indices shall be really helpful in traffic safety management, specifically in reduction of fatalities during road crashes.

Resolving Isomeric Posttranslational Modifications Using a Biological Nanopore as a Sensor of Molecular Shape.

The chemical nature and precise position of posttranslational modifications (PTMs) in proteins or peptides are crucial for various severe diseases, such as cancer. State-of-the-art PTM diagnosis is based on elaborate and costly mass-spectrometry or immunoassay-based approaches, which are limited in selectivity and specificity. Here, we demonstrate the use of a protein nanopore to differentiate peptides─derived from human histone H4 protein─of identical mass according to the positions of acetylated and methylated lysine residues. Unlike sequencing by stepwise threading, our method detects PTMs and their positions by sensing the shape of a fully entrapped peptide, thus eliminating the need for controlled translocation. Molecular dynamics simulations show that the sensitivity to molecular shape derives from a highly nonuniform electric field along the pore. This molecular shape-sensing principle offers a path to versatile, label-free, and high-throughput characterizations of protein isoforms.

The emerging landscape of single-molecule protein sequencing technologies.

Single-cell profiling methods have had a profound impact on the understanding of cellular heterogeneity. While genomes and transcriptomes can be explored at the single-cell level, single-cell profiling of proteomes is not yet established. Here we describe new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell profiling. These technologies will in turn facilitate biological discovery and open new avenues for ultrasensitive disease diagnostics.

The Manipulation of the Internal Hydrophobicity of FraC Nanopores Augments Peptide Capture and Recognition.

The detection of analytes and the sequencing of DNA using biological nanopores have seen major advances over recent years. The analysis of proteins and peptides with nanopores, however, is complicated by the complex physicochemical structure of polypeptides and the lack of understanding of the mechanism of capture and recognition of polypeptides by nanopores. In this work, we show that introducing aromatic amino acids at precise positions within the lumen of α-helical fragaceatoxin C (FraC) nanopores increased the capture frequency of peptides and largely improved the discrimination among peptides of similar size. Molecular dynamics simulations determined the sensing region of the nanopore, elucidated the microscopic mechanism enabling accurate characterization of the peptides via ionic current blockades in FraC, and characterized the effect of the pore modification on peptide discrimination. This work provides insights to improve the recognition and to augment the capture of peptides by nanopores, which is important for developing a real-time and single-molecule size analyzer for peptide recognition and identification.

High-Fidelity Capture, Threading, and Infinite-Depth Sequencing of Single DNA Molecules with a Double-Nanopore System.

Nanopore sequencing of nucleic acids has an illustrious history of innovations that eventually made commercial nanopore sequencing possible. Nevertheless, the present nanopore sequencing technology leaves much room for improvement, especially with respect to accuracy of raw reads and detection of nucleotide modifications. Double-nanopore sequencing-an approach where a DNA molecule is pulled back and forth by a tug-of-war of two nanopores-could potentially improve single-molecule read accuracy and modification detection by offering multiple reads of the same DNA fragment. One principle difficulty in realizing such a technology is threading single-stranded DNA through both nanopores. Here, we describe and demonstrate through simulations a nanofluidic system for loading and threading DNA strands through a double-nanopore setup with nearly 100% fidelity. The high-efficiency loading is realized by using hourglass-shaped side channels that not only deliver the molecules to the nanopore but also retain molecules that missed the nanopore at the first passage to attempt the nanopore capture again. The second nanopore capture is facilitated by an orthogonal microfluidic flow that unravels the molecule captured by the first nanopore and delivers it to the capture volume of the second nanopore. We demonstrate the potential utility of our double-nanopore system for DNA sequencing by simulating repeat back-and-forth motion-flossing-of a DNA strand through the double-nanopore system. We show that repeat exposure of the same DNA fragments to the nanopore sensing volume considerably increases accuracy of the nucleotide sequence determination and that correlated displacement of ssDNA through the two nanopores may facilitate recognition of homopolymer fragments.

Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore.

Efforts to sequence single protein molecules in nanopores1-5 have been hampered by the lack of techniques with sufficient sensitivity to discern the subtle molecular differences among all twenty amino acids. Here we report ionic current detection of all twenty proteinogenic amino acids in an aerolysin nanopore with the help of a short polycationic carrier. Application of molecular dynamics simulations revealed that the aerolysin nanopore has a built-in single-molecule trap that fully confines a polycationic carrier-bound amino acid inside the sensing region of the aerolysin. This structural feature means that each amino acid spends sufficient time in the pore for sensitive measurement of the excluded volume of the amino acid. We show that distinct current blockades in wild-type aerolysin can be used to identify 13 of the 20 natural amino acids. Furthermore, we show that chemical modifications, instrumentation advances and nanopore engineering offer a route toward identification of the remaining seven amino acids. These findings may pave the way to nanopore protein sequencing.

Rapid and Accurate Determination of Nanopore Ionic Current Using a Steric Exclusion Model.

Nanopore sensing has emerged as a versatile approach to detection and identification of biomolecules. Presently, researchers rely on experience and intuition for choosing or modifying the nanopores to detect a target analyte. The field would greatly benefit from a computational method that could relate the atomic-scale geometry of the nanopores and analytes to the blockade nanopore currents they produce. Existing computational methods are either computationally too expensive to be used routinely in experimental laboratories or not sensitive enough to account for the atomic structure of the pore and the analytes. Here, we demonstrate a robust and inexpensive computational approach-the steric exclusion model (SEM) of nanopore conductance-that is orders of magnitude more efficient than all-atom MD and yet is sensitive enough to account for the atomic structure of the nanopore and the analyte. The method combines the computational efficiency of a finite element solver with the atomic precision of a nanopore conductance map to yield unprecedented speed and accuracy of ionic current prediction. We validate our SEM approach through comparison with the current blockades computed using the all-atom molecular dynamics method for a range of proteins confined to a solid-state nanopore, biological channels embedded in a lipid bilayer membranes, and blockade currents produced by DNA homopolymers in MspA. We illustrate potential applications of SEM by computing blockade currents produced by nucleosome proteins in a solid-state nanopore, individual amino acids in MspA, and by testing the effect of point mutations on amino acid distinguishability. We expect our SEM approach to become an integral part of future development of the nanopore sensing field.

A comparative analysis of quality of life after postoperative intensity-modulated radiotherapy or three-dimensional conformal radiotherapy for cervical cancer.

AIMS: The aim of this study is to compare the quality of life (QOL) between adjuvant three-dimensional conformal radiotherapy (3DCRT) or intensity-modulated radiotherapy (IMRT) for cervical cancer. MATERIALS AND METHODS: QOL assessment was done at the baseline and then longitudinally after completing (chemo) radiation. All patients completed EORTC QLQ-C30 and EORTC QLQ Cx-24 modules. Independent-sample t-test was used to compare the mean scores between the two groups. Analysis of variance was used to compare differences in QOL measures over the six time points (baseline, post-RT, 3, 6, 9, and 12 months after treatment) and between treatment groups (3DCRT vs. IMRT). Linear mixed model was also performed to account for attrition. RESULTS: Overall, 64 patients (image-guided IMRT, n = 40 and 3DCRT, n = 24) completed QOL assessment. The median age and follow-up period were 48 years and 15.5 months, respectively. General QOL domains such as emotional (at 12 months, P = 0.04) and social (at 3 months, P = 0.02 and 12 months, P = 0.03) were better with IMRT. Pain (12 months, P = 0.03); fatigue (12 months, P = 0.05); nausea and vomiting (12 months, P = 0.03); insomnia (post-RT, P = 0.05 and 12 months, P = 0.03); appetite loss (post-RT and 12 months, P = 0.04); and diarrhea (6 months, P = 0.02 and 12 months, P = 0.003) scores were significantly better with IMRT. On linear mixed model analysis, there was a significant interaction between treatment cohort and assessment intervals for physical, emotional, and social functioning, appetite loss, diarrhea, lymphedema, and menopausal symptom scores were significantly better with IMRT. CONCLUSIONS: Treatment technique (IMRT vs. 3DCRT) impacts early QOL in undergoing adjuvant radiation for cervical cancer.