Article
Chemistry, Physical
Stefanie Kieninger, Bettina G. Keller
Summary: This study shows that the GROMACS Stochastic Dynamics (GSD) integrator is equivalent to the less frequently used splitting method BAOA in molecular dynamics simulations. It indicates that GSD and BAOAB generate the same configurations with high configurational accuracy, but GSD/BAOA has higher kinetic accuracy than BAOAB.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Physical
Huayi Liu, Yi Zhou, Yingjie Song, Qianqian Zhang, Yeyi Kan, Xinyue Tang, Qingjie Xiao, Qianyin Xiang, Huanxiang Liu, Yunzi Luo, Rui Bao
Summary: CRISPR-Cas9 from Streptococcus pyogenes is a powerful biotechnological tool for DNA sequence modification. Understanding the dynamic mechanism of the REC lobe is crucial for designing and engineering better Cas9 enzymes. The structure analysis of the xCas9 P411T variant and molecular dynamics simulations revealed the central role of REC1 in the activation and target site binding process of Cas9.
Article
Biochemistry & Molecular Biology
Ilinka Clerc, Amin Sagar, Alessandro Barducci, Nathalie Sibille, Pau Bernado, Juan Cortes
Summary: Intrinsically disordered proteins and regions play crucial roles in biological processes by performing specialized functions related to the recognition of other biomolecules. Computational approaches have become essential tools for understanding the functional mechanisms of these proteins due to their conformational heterogeneity.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Article
Biochemistry & Molecular Biology
Samuel G. Holmes, Balaji Nagarajan, Umesh R. Desai
Summary: This study discovered novel compact topologies of heparan sulfate (HS) that are influenced by the 3-O-sulfation of cis-idoA residues. The transition in HS topology is driven by rotations that reduce like-charge repulsion, release water molecules, and establish specific interactions. These findings reveal a dynamic sulfation code in natural HS that could be utilized for selective recognition of target proteins.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Automation & Control Systems
Jakob Robnik, G. Bruno De Luca, Eva Silverstein, Uros Seljak
Summary: In this paper, we develop two models, Microcanonical Hamiltonian Monte Carlo (MCHMC) and Microcanonical Langevin Monte Carlo (MCLMC), which achieve sampling from the target distribution on a fixed energy surface by tuning the Hamiltonian function. These two methods exhibit favorable scalings with condition number and dimensionality.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Cristina Paissoni, Carlo Camilloni
Summary: The study shows that Metadynamics Metainference (M&M), combining molecular dynamics with Metadynamics enhanced sampling ability and Metainference capability of integrating experimental information, can provide converged estimate of population quantity, with higher precision obtained through block averaging and independent replicates. Metadynamics effectively reduces the number of effective frames, which is important for M&M simulations requiring a sufficient number of replicas to capture conformational heterogeneity. Monitoring relative error associated with conformational averaging can help determine the minimum number of replicas to be simulated.
FRONTIERS IN MOLECULAR BIOSCIENCES
(2021)
Article
Chemistry, Medicinal
Xiyu Chen, Sigrid Leyendecker, Henry van den Bedem
Summary: The conformation of a protein affects its function and interaction with ligands, with entropy playing a crucial role. This study presents a method to estimate the vibrational entropy of proteins using kinematic flexibility analysis, providing insights into protein-ligand binding.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Chemistry, Multidisciplinary
Aishwaryo Ghosh, Biswajit Pabi, Atindra Nath Pal, Tanusri Saha-Dasgupta
Summary: This study presents a machine learning approach using experimentally measured conductance traces to investigate the formation of atomic chains. By analyzing two data sets, the optimum conditions for forming longer chains are determined, and a deep learning method is used to classify breaking traces and identify features related to long-chain formation. Further insights are gained through ab initio molecular dynamics simulations.
Article
Chemistry, Medicinal
Lim Heo, Sangwoo Park, Chaok Seok
Summary: The article presents a novel method, GalaxyWater-wKGB, for predicting water positions on the protein surface, based on a statistical potential incorporating the generalized Born model. This method is accurate and rapid due to the effective statistical treatment, providing a more precise description of specific protein atom-water interactions compared to traditional methods.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2021)
Review
Biochemistry & Molecular Biology
Francisco Javier Canada, Angeles Canales, Pablo Valverde, Beatriz Fernandez de Toro, Monica Martinez-Orts, Paola Oquist Phillips, Amaia Pereda
Summary: Carbohydrates, either as free molecules or conjugated with other biomolecules, play important roles in various biological processes. Nuclear Magnetic Resonance spectroscopy (NMR) is a versatile tool for studying the structures and interactions of carbohydrates, providing information about their sequences, structures, and local geometries. Labeling carbohydrates with 13C enhances the resolution and detail of the analyzed structures. Moreover, combining NMR with molecular modeling and theoretical calculations offers insights into the conformational flexibility of carbohydrates. Additionally, the use of partially oriented media or paramagnetic perturbations allows for the study of longer and branched glycan chains. This review presents examples and an overview of recent and relevant NMR applications in the field of glycobiology.
CURRENT MEDICINAL CHEMISTRY
(2022)
Article
Multidisciplinary Sciences
Dhiman Ray, Ly Le, Ioan Andricioaei
Summary: Researchers are focusing on the correlation between the RBD region of the SARS-CoV-2 virus and residues distant from it to understand molecular recognition events and predict key mutations for therapeutics. Their model can identify multiple residues with long-distance coupling with the RBD opening and successfully predict some key mutations.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Chemistry, Applied
Ming Lei, Weian Huang, Zhehui Jin, Jinsheng Sun, Mingshan Zhang, Shuangliang Zhao
Summary: This study investigates the aggregation behavior of carboxymethyl chitosan (CMCS) in water using molecular dynamics simulations. The effects of degrees of deacetylation (DD) and substitution (DS) and ionization states on CMCS aggregation are also examined. The results show that CMCS prefers to aggregate in neutral condition, forming multimeric forms with interlaced stacking of molecular chains. The presence of specific intra- and intermolecular interactions stabilizes the aggregation structures.
CARBOHYDRATE POLYMERS
(2022)
Article
Mathematics, Applied
Susan Ghaderi, Masoud Ahookhosh, Adam Arany, Alexander Skupin, Panagiotis Patrinos, Yves Moreau
Summary: This paper proposes a gradient-based Markov Chain Monte Carlo (MCMC) method for sampling from the posterior distribution of problems with nonsmooth potential functions. By using smoothing techniques, the original potential function is approximated by a smooth function with the same critical points, leading to a smoothing ULA method called SULA. Non-asymptotic convergence results of SULA are established under mild assumptions on the original potential function. Numerical results demonstrate the promising performance of SULA on both synthetic and real chemoinformatics data.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Chemistry, Multidisciplinary
Yuanpeng Deng, Shubin Fu, Jingran Guo, Xiang Xu, Hui Li
Summary: Enhanced sampling MD simulations of complex ceramics are achieved by using anisotropic collective variables and machine learning potential, allowing for accurate identification of crystal structures and generation of free energy surfaces. This method demonstrates exceptional efficiency and ab initio quality in achieving crystallization, facilitating the analysis and design of complex crystalline materials.
Review
Biochemistry & Molecular Biology
Jun Li, Shi-Jie Chen
Summary: Understanding the 3D structures of RNA molecules is crucial for their biological functions, leading to the development of computational methods due to the laborious and difficult experimental determination. All-atom simulations are suitable for small RNA systems, while coarse-grained models are more widely applied.
FRONTIERS IN MOLECULAR BIOSCIENCES
(2021)