Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.

<p>Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled ϕ/ψ parameters using 2D ϕ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, we have performed a total of ~5 milliseconds MD simulations in explicit solvent. Our results show that after amino-acid specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino acid specific Protein Data Bank (PDB) Ramachandran maps better, but also shows significantly improved capability to differentiate amino acid dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. </p>

Associations between gut microbiota and colorectal cancer (CRC) have been widely investigated. However, the replicable markers for early-stage adenoma diagnosis across multiple populations remain elusive. Here, we perform an integrated analysis on 1056 public fecal samples, to identify adenoma-associated microbial markers for early detection of CRC. After adjusting for potential confounders, Random Forest classifiers are constructed with 11 markers to discriminate adenoma from control (area under the ROC curve (AUC) = 0.80), and 26 markers to discriminate adenoma from CRC (AUC = 0.89), respectively. Moreover, we validate the classifiers in two independent cohorts achieving AUCs of 0.78 and 0.84, respectively. Functional analysis reveals that the altered microbiome is characterized with increased ADP-l-glycero-beta-d-manno-heptose biosynthesis in adenoma and elevated menaquinone-10 biosynthesis in CRC. These findings are validated in a newly-collected cohort of 43 samples using quantitative real-time PCR. This work proves the validity of adenoma-specific markers across multi-populations, which would contribute to the early diagnosis and treatment of CRC.

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

Potassium channels participate in many critical biological functions and play important roles in a variety of diseases. In recent years, many significant discoveries have been made which motivate us to review these achievements. The focus of our review is mainly on three aspects. Firstly, we try to summarize the latest developments in structure determinants and regulation mechanism of all types of potassium channels. Secondly, we review some diseases induced by or related to these channels. Thirdly, both qualitative and quantitative approaches are utilized to analyze structural features of modulators of potassium channels. Our analyses further prove that modulators possess some certain natural-product scaffolds. And pharmacokinetic parameters are important properties for organic molecules. Besides, with in silico methods, some features that can be used to differentiate modulators are derived. There is no doubt that all these studies on potassium channels as possible pharmaceutical targets will facilitate future translational research. All the strategies developed in this review could be extended to studies on other ion channels and proteins as well.

Ceramic samples of MnW 1−x Mo x O 4 (x ≤ 0.3) solid solution were prepared by a solid-state route with the goal of increasing the magnitude of the spin-exchange couplings among the Mn 2+ ions in the spin spiral multiferroic MnWO 4 . Samples were characterized by X-ray diffraction, optical spectroscopy, magnetization, and dielectric permittivity measurements. It was observed that the Neél temperature T N , the spin spiral ordering temperature T M2 , and the ferroelectric phase-transition temperature T FE2 of MnWO 4 increased upon the nonmagnetic substitution of Mo 6+ for W 6+ . Like pure MnWO 4 , the ferroelectric critical temperature T FE2 (x) coincides with the magnetic ordering temperature T M2 (x). A density functional analysis of the spin-exchange interactions for a hypothetical MnMoO 4 that is isostructural with MnWO 4 suggests that Mo substitution increases the strength of the spin-exchange couplings among Mn 2+ in the vicinity of a Mo 6+ ion. Our study shows that the Mo-doped MnW 1−x Mo x O 4 (x ≤ 0.3) compounds are spin-frustrated materials that have higher magnetic and ferroelectric phase-transition temperatures than does pure MnWO 4 .

The local vibrational mode analysis developed by Konkoli and Cremer has been successfully applied to characterize the intrinsic bond strength via local bond stretching force constants in molecular systems. A wealth of new insights into covalent bonding and weak chemical interactions ranging from hydrogen, halogen, pnicogen, and chalcogen to tetrel bonding has been obtained. In this work we extend the local vibrational mode analysis to periodic systems, i.e. crystals, allowing for the first time a quantitative in situ measure of bond strength in the extended systems of one, two, and three dimensions. We present the study of onedimensional polyacetylene and hydrogen fluoride chains and two-dimensional layers of graphene, water, and melamine-cyanurate as well as three-dimensional ice I h and crystalline acetone. Besides serving as a new powerful tool for the analysis of bonding in crystals, a systematic comparison of the intrinsic bond strength in periodic systems and that in isolated molecules becomes possible, providing new details into structure and bonding changes upon crystallization. The potential application for the analysis of solid-state vibrational spectra will be discussed.

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