Although molecular dynamics simulations have become a useful tool in essentially all fields of chemistry, condensed matter physics, materials science, and biology, there is still large gap between the time scale which can be reached that observed experiments. To address problem, many enhanced sampling methods were introduced, effectively extend being approached simulations. In this perspective, we review variety methods. We first discuss collective-variables-based including metadynamics...

Protein surface hydration is fundamental to its structure and activity. We report here the direct mapping of global dynamics around a protein in native molten globular states, using tryptophan scan by site-specific mutations. With 16 mutants 29 different positions we observed two robust, distinct water layer on few ( approximately 1-8 ps) tens hundreds picoseconds 20-200 ps), representing initial local relaxation subsequent collective network restructuring, respectively. Both time scales are...

Protein surface hydration is fundamental to its structural stability and flexibility, water−protein fluctuations are essential biological function. Here, we report a systematic global mapping of water motions in the layer around model protein apomyoglobin both native molten globule states. With site-directed mutagenesis, use intrinsic tryptophan as local optical probe scan one at time with single-site specificity. femtosecond resolution, examined 16 mutants two states observed types...

Human serum albumin, the most abundant protein found in blood plasma, transports a great variety of ligands circulatory system and undergoes reversible conformational transitions over wide range pH values. We report here our systematic studies solvation dynamics local rigidity these conformations using single intrinsic tryptophan (W214) residue as molecular probe. With femtosecond resolution, we observed robust bimodal distribution time scales for all isomers. The initial occurs several...

Water motion at protein surfaces is fundamental to structure, stability, dynamics, and function. By using intrinsic tryptophans as local optical probes, with femtosecond resolution, it possible probe surface-water motions in the hydration layer. Here, we report our studies of dynamics surface enzyme Staphylococcus nuclease site-specific mutations. From these WT four related mutants, which change charge distribution are able ascertain contribution solvation by side chains relatively...

Ice nucleation is a process of great relevance in physics, chemistry, technology, and environmental sciences; much theoretical effort has been devoted to its understanding, but it still remains topic intense research. We shed light on this phenomenon by performing atomistic based simulations. Using metadynamics carefully designed set collective variables, reversible transitions between water ice are able be simulated. find that freezes into stacking disordered structure with the all-atom...

Boosting transitions of rare events is critical to simulations chemical and biophysical dynamic systems in order close the time scale gaps between theoretical modeling experiments. We present a novel approach, called targeted adversarial learning optimized sampling (TALOS), modify potential energy surface drive system user-defined target distribution where free-energy barrier lowered. Combining statistical mechanics generative learning, TALOS formulates competing game engine virtual...

Deep learning is transforming many areas in science, and it has great potential modeling molecular systems. However, unlike the mature deployment of deep computer vision natural language processing, its development simulations still at an early stage, largely because inductive biases molecules are completely different from those images or texts. Footed on these differences, we first reviewed limitations traditional models perspective physics wrapped up some relevant technical advancement...

The softmax function is a cornerstone of multi-class classification, integral to wide range machine learning applications, from large-scale retrieval and ranking models advanced large language models. However, its computational cost grows linearly with the number classes, which becomes prohibitively expensive in scenarios millions or even billions classes. sampled softmax, relies on self-normalized importance sampling, has emerged as powerful alternative, significantly reducing complexity....

Despite the apparent simplicity of water molecules, kinetics ice nucleation under natural conditions can be surprisingly intricate. Previous studies have yielded critical sizes that vary widely due to differences in experimental and computational approaches. In our investigation, we employed all-atom molecular dynamics simulations explore spontaneously grown ideal nuclei, revealing significant disparities their kinetics. Notably, defects challenge applicability classical theory (CNT) nuclei....

Inspired by the QM/MM methodology, ML/MM approach introduces a new opportunity for multiscale simulation, improving balance between accuracy and computational efficiency. Benefited from rapid advancements in molecular embedding methods, density functional theory level quantum mechanical (QM) calculations within framework can be accelerated several orders of magnitude through application machine learning (ML) potential energy surfaces. As problem inherited challenges exist designing...

Hypertensive nephropathy (HN), caused by long-term poorly controlled hypertension, is the second common cause of end-stage renal disease after diabetes mellitus, but pathogenesis HN unclear. The purpose this study was to identify biological pathways involved in progression and bile acid (BA)-related biomarkers, analyze role acids HN. Download gene microarray data from Gene Expression Omnibus. Differentially expressed genes (DEGs) associated with were identified, then DEGs subjected Ontology...

Introduction Long COVID significantly affects patients' quality of life, yet no standardized treatment has been established. Traditional Chinese Medicine (TCM) presents a promising potential approach with targeted therapeutic strategies. This study aims to develop an explainable machine learning (ML) model and nomogram identify patients who may benefit from TCM, enhancing clinical decision-making. Methods We analyzed data 1,331 treated TCM between December 2022 February 2024 at three...

The simulation of rare events is one the key problems in atomistic simulations. Towards its solution a plethora methods have been proposed. Here we combine two such metadynamics and inte-grated tempering sampling. In fluctuations carefully chosen collective variable are amplified, while integrated sampling system pushed to visit an approximately uniform interval energies allows exploring range temperatures single run. We describe our ap-proach apply it prototypical systems SN2 chemical...

RL^‡can automatically locate the transition states of chemical reactions through deep reinforcement learning feedback from molecular simulations.