Overcoming Sampling Issues and Improving Computational Efficiency in Collective-Variable-Based Enhanced-Sampling Simulations: A Tutorial.

This tutorial is designed to help users overcome sampling challenges and improve computational efficiency in collective-variable (CV)-based enhanced-sampling, or importance-sampling, simulations. Toward this end, we introduce well-tempered metadynamics-extended adaptive biasing force (WTM-eABF) and its integration with Gaussian accelerated molecular dynamics (GaMD). Additionally, use will be made of a method for identifying the least-free-energy pathway (LFEP) and multiple concurrent pathways on high-dimensional free-energy surfaces.

PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy.

Motivation: The emergence of drug-resistant pathogens represents a formidable challenge to global health. Using computational methods to identify the antibacterial peptides (ABPs), an alternative antimicrobial agent, has demonstrated advantages in further drug design studies. Most of the current approaches, however, rely on handcrafted features and underutilize structural information, which may affect prediction performance.

Understanding the Reversible Binding of a Multichain Protein-Protein Complex through Free-Energy Calculations.

We demonstrate that the binding affinity of a multichain protein-protein complex, insulin dimer, can be accurately predicted using a streamlined route of standard binding free-energy calculations. We find that chains A and C, which do not interact directly during binding, stabilize the insulin monomer structures and reduce the binding affinity of the two monomers, therefore enabling their reversible association. Notably, we confirm that although classical methods can estimate the binding affinity of the insulin dimer, conventional molecular dynamics, enhanced sampling algorithms, and classical geometrical routes of binding free-energy calculations may not fully capture certain aspects of the role played by the noninteracting chains in the binding dynamics.

synthesis of quantum dots in the nucleus of live cells.

The cell nucleus is the main site for the storage and replication of genetic material, and the synthesis of substances in the nucleus is rhythmic, regular and strictly regulated by physiological processes. However, whether exogenous substances, such as nanoparticles, can be synthesized in the nucleus of live cells has not been reported. Here, we have achieved synthesis of CdSSe quantum dots (QDs) in the nucleus by regulation of the glutathione (GSH) metabolic pathway.

Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning.

Enhanced-sampling algorithms relying on collective variables (CVs) are extensively employed to study complex (bio)chemical processes that are not amenable to brute-force molecular simulations. The selection of appropriate CVs characterizing the slow movement modes is of paramount importance for reliable and efficient enhanced-sampling simulations. In this Perspective, we first review the application and limitations of CVs obtained from chemical and geometrical intuition.

Analyzing Molecular Dynamics Trajectories Thermodynamically through Artificial Intelligence.

Molecular dynamics simulations produce trajectories that correspond to vast amounts of structure when exploring biochemical processes. Extracting valuable information, e.g.

Binding Thermodynamics of Fourth-Generation EGFR Inhibitors Revealed by Absolute Binding Free Energy Calculations.

The overexpression or mutation of the kinase domain of the epidermal growth factor receptor (EGFR) is strongly associated with non-small-cell lung cancer (NSCLC). EGFR tyrosine kinase inhibitors (TKIs) have proven to be effective in treating NSCLC patients. However, EGFR mutations can result in drug resistance.

Enhancing Circularly Polarized Luminescence in Quantum Dots through Chiral Coordination-Mediated Growth at the Liquid/Liquid Interface.

Here, we develop a novel methodology for synthesizing chiral CdSe@ZnS quantum dots (QDs) with enhanced circularly polarized luminescence (CPL) by incorporating l-/d-histidine (l-/d-His) ligands during ZnS shell growth at the water/oil interface. The resulting chiral QDs exhibit exceptional absolute photoluminescence quantum yield of up to 67.2%, surpassing the reported limits of 40.

Standard Binding Free-Energy Calculations: How Far Are We from Automation?

Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement.

Achieving Accurate Standard Protein-Protein Binding Free Energy Calculations through the Geometrical Route and Ergodic Sampling.

A new strategy for the prediction of binding free energies of protein-protein complexes is reported in the present article. By combining an ergodic-sampling algorithm with the so-called "geometrical route", which introduces a series of geometrical restraints as a preamble to the physical separation of the two partners, we achieve accurate binding free energy calculations for medium-sized protein-protein complexes within the microsecond timescale. The ergodic-sampling algorithm, namely, Gaussian-accelerated molecular dynamics (GaMD), implicitly helps explore the conformational change of the two binding partners as they associate reversibly by raising the energy wells.

Ultra-high resolution near-infrared spectrum by wavelet packet transform revealing the hydrogen bond interactions.

Resolution is always an obstacle to analyzing the fine structure of a spectrum. The problem is particularly serious in the analysis of the near-infrared (NIR) spectra of aqueous solutions, because the spectrum is generally composed of overlapping broad peaks making the understanding of the structures and the interactions notoriously difficult. In this work, wavelet packet transform (WPT) was adopted to enhance the resolution of the NIR spectra of aqueous mixtures.

Revealing the Mechanism of Irreversible Binding of Antifreeze Glycoproteins to Ice.

Antifreeze glycoproteins (AFGPs) are a special kind of antifreeze proteins with strong flexibility. Whether their antifreeze activity is achieved by reversibly or irreversibly binding to ice is widely debated, and the molecular mechanism of irreversible binding remains unclear. In this work, the antifreeze mechanism of the smallest AFGP isoform, AFGP8, is investigated at the atomic level.

Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy.

Systematic and quantitative analysis of the reliability of formally exact methods that calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.

Accurate Description of Solvent-Exposed Salt Bridges with a Non-polarizable Force Field Incorporating Solvent Effects.

The strength of salt bridges resulting from the interaction of cations and anions is modulated by their environment. However, polarization of the solvent molecules by the charged moieties makes the accurate description of cation-anion interactions in an aqueous solution by means of a pairwise additive potential energy function and classical combination rules particularly challenging. In this contribution, aiming at improving the representation of solvent-exposed salt-bridge interactions with an all-atom non-polarizable force field, we put forth here a parametrization strategy.

Uncovering the Mechanism of Drug Resistance Caused by the T790M Mutation in EGFR Kinase From Absolute Binding Free Energy Calculations.

The emergence of drug resistance may increase the death rates in advanced non-small cell lung cancer (NSCLC) patients. The resistance of erlotinib, the effective first-line antitumor drug for NSCLC with the L858R mutation of epidermal growth factor receptor (EGFR), happens after the T790M mutation of EGFR, because this mutation causes the binding of adenosine triphosphate (ATP) to EGFR more favorable than erlotinib. However, the mechanism of the enhancement of the binding affinity of ATP to EGFR, which is of paramount importance for the development of new inhibitors, is still unclear.

Mechanism and biomass association of glucuronoyl esterase: an α/β hydrolase with potential in biomass conversion.

Glucuronoyl esterases (GEs) are α/β serine hydrolases and a relatively new addition in the toolbox to reduce the recalcitrance of lignocellulose, the biggest obstacle in cost-effective utilization of this important renewable resource. While biochemical and structural characterization of GEs have progressed greatly recently, there have yet been no mechanistic studies shedding light onto the rate-limiting steps relevant for biomass conversion. The bacterial GE OtCE15A possesses a classical yet distinctive catalytic machinery, with easily identifiable catalytic Ser/His completed by two acidic residues (Glu and Asp) rather than one as in the classical triad, and an Arg side chain participating in the oxyanion hole.

Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations.

Designing a reliable computational methodology to calculate protein:ligand standard binding free energies is extremely challenging. The large change in configurational enthalpy and entropy that accompanies the association of ligand and protein is notoriously difficult to capture in naive brute-force simulations. Addressing this issue, the present protocol rests upon a rigorous statistical mechanical framework for the determination of protein:ligand binding affinities together with the comprehensive Binding Free-Energy Estimator 2 (BFEE2) application software.

Computer-aided design of molecular machines: techniques, paradigms and difficulties.

With their development in the past decade, molecular machines, which achieve specific tasks by responding to external stimuli, have gradually come to be regarded as powerful tools for a wide range of applications, rather than interesting molecular toys. This conceptual change in turn motivates scientists to design molecular machines with complex architectures. Due to the lack of general principles bridging the functions and the chemical structures of molecular machines, experience-based design becomes difficult with the increase of size and complexity of the architectures.

MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation.

Importance-sampling algorithms leaning on the definition of a model reaction coordinate (RC) are widely employed to probe processes relevant to chemistry and biology alike, spanning time scales not amenable to common, brute-force molecular dynamics (MD) simulations. In practice, the model RC often consists of a handful of collective variables (CVs) chosen on the basis of chemical intuition. However, constructing manually a low-dimensional RC model to describe an intricate geometrical transformation for the purpose of free-energy calculations and analyses remains a daunting challenge due to the inherent complexity of the conformational transitions at play.

Breaking through the Size Control Dilemma of Silver Chalcogenide Quantum Dots via Trialkylphosphine-Induced Ripening: Leading to AgTe Emitting from 950 to 2100 nm.

AgTe is one of the most promising semiconductors with a narrow band gap and low toxicity; however, it remains a challenge to tune the emission of AgTe quantum dots (QDs) precisely and continuously in a wide range. Herein, AgTe QDs emitting from 950 to 2100 nm have been synthesized via trialkylphosphine-controlled growth. Trialkylphosphine has been found to induce the dissolution of small-sized AgTe QDs due to its stronger ability to coordinate to the Ag ion than that of 1-octanethiol, predicated by the density functional theory.

Accuracy of Alternate Nonpolarizable Force Fields for the Determination of Protein-Ligand Binding Affinities Dominated by Cation-π Interactions.

Modifying pair-specific Lennard-Jones parameters through the nonbonded FIX (NBFIX) feature of the CHARMM36 force field has proven cost-effective for improving the description of cation-π interactions in biological objects by means of pairwise additive potential energy functions. Here, two sets of newly optimized CHARMM36 force-field parameters including NBFIX corrections, coined CHARMM36m-NBF and CHARMM36-WYF, and the original force fields, namely CHARMM36m and Amber ff14SB, are used to determine the standard binding free energies of seven protein-ligand complexes containing cation-π interactions. Compared with precise experimental measurements, our results indicate that the uncorrected, original force fields significantly underestimate the binding free energies, with a mean error of 5.

Overcoming Free-Energy Barriers with a Seamless Combination of a Biasing Force and a Collective Variable-Independent Boost Potential.

Amid collective-variable (CV)-based importance-sampling algorithms, a hybrid of the extended adaptive biasing force and the well-tempered metadynamics algorithms (WTM-eABF) has proven particularly cost-effective for exploring the rugged free-energy landscapes that underlie biological processes. However, as an inherently CV-based algorithm, this hybrid scheme does not explicitly accelerate sampling in the space orthogonal to the chosen CVs, thereby limiting its efficiency and accuracy, most notably in those cases where the slow degrees of freedom of the process at hand are not accounted for in the model transition coordinate. Here, inspired by Gaussian-accelerated molecular dynamics (GaMD), we introduce the same CV-independent harmonic boost potential into WTM-eABF, yielding a hybrid algorithm coined GaWTM-eABF.

BFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations.

Accurate absolute binding free-energy estimation , following either an alchemical or a geometrical route, involves several subprocesses and requires the introduction of geometric restraints. Human intervention, for instance, to define the necessary collective variables, prepare the input files, monitor the simulation, and perform post-treatments is, however, tedious, cumbersome, and prone to errors. With the aim of automating and streamlining free-energy calculations, especially for nonexperts, version 2.