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. RESULTS: To present an ultra-accurate model for ABP identification, we propose a novel deep learning approach, PGAT-ABPp. PGAT-ABPp leverages structures predicted by AlphaFold2 and a pretrained protein language model, ProtT5-XL-U50 (ProtT5), to construct graphs. Then the graph attention network (GAT) is adopted to learn global discriminative features from the graphs. PGAT-ABPp outperforms the other fourteen state-of-the-art models in terms of accuracy, F1-score and Matthews Correlation Coefficient on the independent test dataset. The results show that ProtT5 has significant advantages in the identification of ABPs and the introduction of spatial information further improves the prediction performance of the model. The interpretability analysis of key residues in known active ABPs further underscores the superiority of PGAT-ABPp. AVAILABILITY AND IMPLEMENTATION: The datasets and source codes for the PGAT-ABPp model are available at https://github.com/moonseter/PGAT-ABPp/.

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. Therefore, this study not only elucidates the role of noninteracting chains in the reversible binding of the insulin dimer but also offers a methodological guide for investigating the reversible binding of multichain protein-protein complexes utilizing streamlined free-energy calculations.

In-situ 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 situ in the nucleus of live cells has not been reported. Here, we have achieved in-situ synthesis of CdSxSe1-x quantum dots (QDs) in the nucleus by regulation of the glutathione (GSH) metabolic pathway. High enrichment of GSH in the nucleus can be accomplished by the addition of GSH with the help of the Bcl-2 protein. Then, high-valence Se is reduced to low-valence Se by glutathione-reductase-catalyzed GSH, and interacts with the Cd precursor formed through Cd and thiol-rich proteins, eventually generating QDs in the nucleus. Our work contributes to a new understanding of the syntheses of substances in the cell nucleus and will pave the way for the development of advanced 'supercells'.

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. We also introduce path-sampling algorithms, which can identify path-like CVs in a high-dimensional free-energy space. Machine-learning algorithms offer a viable approach to finding suitable CVs by analyzing trajectories from preliminary simulations. We discuss both the performance of machine-learning-derived CVs in enhanced-sampling simulations of experimental models and the challenges involved in applying these CVs to realistic, complex molecular assemblies. Moreover, we provide a prospective view of the potential advancements of machine-learning algorithms for the development of CVs in the field of enhanced-sampling simulations.

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., important intermediate states and collective variables (CVs) that describe the major movement modes, from molecular trajectories to understand the underlying mechanisms of biological processes presents a significant challenge. To achieve this goal, we introduce a deep learning approach, coined DIKI (deep identification of key intermediates), to determine low-dimensional CVs distinguishing key intermediate conformations without a-priori assumptions. DIKI dynamically plans the distribution of latent space and groups together similar conformations within the same cluster. Moreover, by incorporating two user-defined parameters, namely, coarse focus knob and fine focus knob, to help identify conformations with low free energy and differentiate the subtle distinctions among these conformations, resolution-tunable clustering was achieved. Furthermore, the integration of DIKI with a path-finding algorithm contributes to the identification of crucial intermediates along the lowest free-energy pathway. We postulate that DIKI is a robust and flexible tool that can find widespread applications in the analysis of complex biochemical processes.

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. To elucidate the mechanisms underlying this resistance and inform future drug development, we examined the binding affinities of BLU-945, a recently reported fourth-generation TKI, to wild-type EGFR (EGFRWT) and its double-mutant (L858R/T790M; EGFRDM) and triple-mutant (L858R/T790M/C797S; EGFRTM) forms. We compared the binding affinities of BLU-945, BLU-945 analogues, CH7233163 (another fourth-generation TKI), and erlotinib (a first-generation TKI) using absolute binding free energy calculations. Our findings reveal that BLU-945 and CH7233163 exhibit binding affinities to both EGFRDM and EGFRTM stronger than those of erlotinib, corroborating experimental data. We identified K745 and T854 as the key residues in the binding of fourth-generation EGFR TKIs. Electrostatic forces were the predominant driving force for the binding of fourth-generation TKIs to EGFR mutants. Furthermore, we discovered that the incorporation of piperidinol and sulfone groups in BLU-945 substantially enhanced its binding capacity to EGFR mutants. Our study offers valuable theoretical insights for optimizing fourth-generation EGFR TKIs.

Deep-Learning-Assisted Enhanced Sampling for Exploring Molecular Conformational Changes.

We present a novel strategy to explore conformational changes and identify stable states of molecular objects, eliminating the need for a priori knowledge. The approach applies a deep learning method to extract information about the movement modes of the molecular object from a short, high-dimensional, and parameter-free preliminary enhanced-sampling simulation. The gathered information is described by a small set of deep-learning-based collective variables (dCVs), which steer the production-enhanced-sampling simulation. Considering the challenge of adequately exploring the configurational space using the low-dimensional, suboptimal dCVs, we incorporate a method designed for ergodic sampling, namely, Gaussian-accelerated molecular dynamics (MD), into the framework of CV-based enhanced sampling. MD simulations on both toy models and nontrivial examples demonstrate the remarkable computational efficiency of the strategy in capturing the conformational changes of molecular objects without a priori knowledge. Specifically, we achieved the blind folding of two fast folders, chignolin and villin, within a time scale of hundreds of nanoseconds and successfully reconstructed the free-energy landscapes that characterize their reversible folding. All in all, the presented strategy holds significant promise for investigating conformational changes in macromolecules, and it is anticipated to find extensive applications in the fields of chemistry and biology.

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. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.

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 40.0% for chiral inorganic QDs, along with absorption dissymmetry factor (|gabs|) and luminescence dissymmetry factor (|glum|) values of 10-2, exceeding the range of 10-5-10-3 and 10-4-10-2, respectively. Detailed investigations of the synthetic pathway reveal that the interface, as a binary synthetic environment, facilitates the coordinated ligand exchange and shell growth mediated by chiral His-Zn2+ coordination complexes, leading to a maximum fluorescent brightness and chiroptical activities. The growth process, regulated by the His-Zn2+ coordination complex, not only reduces trap states on the CdSe surface, thereby enhancing the fluorescence intensity, but also significantly promotes the orbital hybridization between QDs and chiral ligands, effectively overcoming the shielding effect of the wide bandgap shell and imparting pronounced chirality. The proposed growth pathway elucidates the origin of chirality and provides insights into the regulation of the CPL intensity in chiral QDs. Furthermore, the application of CPL QDs in multilevel anticounterfeiting systems overcomes the limitations of replication in achiral fluorescence materials and enhances the system's resistance to counterfeiting, thus opening new opportunities for chiral QDs in optical anticounterfeiting and intelligent information encryption.

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. Therefore, independent simulations capturing the isomerization of proteins are no longer needed, reducing both the computational cost and human effort. Numerical applications indicate errors on the order of 0.1 kcal/mol for the Abl-SH3 domain binding a decapeptide, of 2.6 kcal/mol for the barnase-barstar complex, and of 0.2 kcal/mol for human leukocyte elastase binding the third domain of the turkey ovomucoid inhibitor. Compared with the classical geometrical route, which resorts to collective variables to describe the isomerization of proteins, our new strategy possesses remarkable convergence properties and robustness for protein-protein complexes owing to improved ergodic sampling. We are confident that the strategy presented in this study will have a broad range of applications, helping us understand recognition-association phenomena in the areas of physical, biological, and medicinal chemistry.

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. Due to the microscopic ability of WPT in both position and frequency, the fine details of a spectrum can be observed in the spectral components of different frequencies obtained by WPT decomposition. Ultra-high resolution spectrum can be obtained from the high-frequency component representing the spectral features. Spectral features of different hydrogen-bonded OH, as well as the OH in HOH and HOD, were identified from the high-resolution NIR spectra of water and heavy water mixtures and validated by the variation of the spectral intensity with the mole ratio of H2O and D2O. The high-resolution spectrum was further applied in analyzing the interaction of amine and water. The spectral features of the hydrogen bonding between CH/NH in tert-butylamine (TBA) and OH in water were observed. The structures of CH bonded to one water molecule, and the structures of NH connecting with one and two water molecules were identified.

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. The results indicate that AFGP8 can bind to ice both reversibly through its hydrophobic methyl groups (peptide binding) and irreversibly through its hydrophilic disaccharide moieties (saccharide binding). Although peptide binding occurs faster than saccharide binding, free-energy calculations indicate that the latter is energetically more favorable. In saccharide binding, at least one disaccharide moiety is frozen in the grown ice, resulting in irreversible binding, while the other moieties significantly perturb the water hydrogen-bonding network, thus inhibiting ice growth more effectively. The present study reveals the coexistence of reversible and irreversible bindings of AFGP8, both contributing to the inhibition of ice growth and further provides molecular mechanism of irreversible binding.

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

Systematic and quantitative analysis of the reliability of formally exact methods that in silico 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 = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.

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. First, the interaction of a cation and an anion is characterized by hybrid quantum mechanical/molecular mechanics (QM/MM) potential of mean force (PMF) calculations, whereby constantly exchanging solvent molecules around the ions are treated at the quantum mechanical level. The Lennard-Jones (LJ) parameters describing the salt-bridge ion pairs are then optimized to match the reference QM/MM PMFs through the so-called nonbonded FIX, or NBFIX, feature of the CHARMM force field. We apply the new set of parameters, coined CHARMM36m-SBFIX, to the calculation of association constants for the ammonium-acetate and guanidinium-acetate complexes, the osmotic pressures for glycine zwitterions, guanidinium, and acetate ions, and to the simulation of both folded and intrinsically disordered proteins. Our findings indicate that CHARMM36m-SBFIX improves the description of solvent-exposed salt-bridge interactions, both structurally and thermodynamically. However, application of this force field to the standard binding free-energy calculation of a protein-ligand complex featuring solvent-excluded salt-bridge interactions leads to a poor reproduction of the experimental value, suggesting that the parameters optimized in an aqueous solution cannot be readily transferred to describe solvent-excluded salt-bridge interactions. Put together, owing to their sensitivity to the environment, modeling salt-bridge interactions by means of a single, universal set of LJ parameters remains a daunting theoretical challenge.

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. In this work, to explore the detailed mechanism of the drug resistance due to the T790M mutation, molecular dynamics simulations and absolute binding free energy calculations have been performed. The results show that the binding affinity of ATP with respect to the L858R/T790M mutant is higher compared with the L858R mutant, in good agreement with experiments. Further analysis demonstrates that the T790M mutation significantly changes the van der Waals interaction of ATP and the binding site. We also find that the favorable binding of ATP to the L858R/T790M mutant, compared with the L858R mutant, is due to a conformational change of the αC-helix, the A-loop and the P-loop of the latter induced by the T790M mutation. This change makes the interaction of ATP and P-loop, αC-helix in the L858R/T790M mutant higher than that in the L858R mutant, therefore increasing the binding affinity of ATP to EGFR. We believe the drug-resistance mechanism proposed in this study will provide valuable guidance for the design of drugs for NSCLC.

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

Glucuronoyl esterases (GEs) are α/ß 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. By QM/MM calculations, we identified deacylation as the decisive step in catalysis, and quantified the role of Asp, Glu and Arg, showing the latter to be particularly important. The results agree well with experimental and structural data. We further calculated the free-energy barrier of post-catalysis dissociation from a complex natural substrate, suggesting that in industrial settings non-catalytic processes may constitute the rate-limiting step, and pointing to future directions for enzyme engineering in biomass utilization.

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. With the knowledge of the bound state, available from experiments or docking, application of the BFEE2 protocol with a reliable force field supplies in a matter of days standard binding free energies within chemical accuracy, for a broad range of protein:ligand complexes. Limiting undesirable human intervention, BFEE2 assists the end user in preparing all the necessary input files and performing the post-treatment of the simulations towards the final estimate of the binding affinity.

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. Computer-aided molecular-machine design, therefore, has attracted widespread attention on account of its ability to model and investigate complex molecular architectures without too much time and expense required for synthetic experiments. Using leading-edge numerical-simulation techniques, the mechanisms underlying achieving tasks through response to external stimuli of a large number of existing molecular machines have been successfully explored. Based on the experience of studying existing molecular machines, generalized methodologies of predicting the properties and working principles of molecular candidates have been established, paving the way for de novo computer-aided design of molecular machines. In this perspective, we introduce cutting-edge techniques that have been applied for investigating and designing molecular machines. We show paradigms of computer-aided design of molecular machines, which can serve as guidelines for the investigation of new supramolecular architectures. Moreover, we discuss the limitations and possible future developments of current techniques and methodologies in the field of computer-aided design of molecular machines.

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. To solve this issue, remarkable progress has been made in employing machine-learning techniques, such as autoencoders, to extract the low-dimensional RC model from a large set of CVs. Implementation of the differentiable, nonlinear machine-learned CVs in common MD engines to perform free-energy calculations is, however, particularly cumbersome. To address this issue, we present here a user-friendly tool (called MLCV) that facilitates the use of machine-learned CVs in importance-sampling simulations through the popular Colvars module. Our approach is critically probed with three case examples consisting of small peptides, showcasing that through hard-coded neural network in Colvars, deep-learning and enhanced-sampling can be effectively bridged with MD simulations. The MLCV code is versatile, applicable to all the CVs available in Colvars, and can be connected to any kind of dense neural networks. We believe that MLCV provides an effective, powerful, and user-friendly platform accessible to experts and nonexperts alike for machine-learning (ML)-guided CV discovery and enhanced-sampling simulations to unveil the molecular mechanisms underlying complex biochemical processes.

Breaking through the Size Control Dilemma of Silver Chalcogenide Quantum Dots via Trialkylphosphine-Induced Ripening: Leading to Ag2Te Emitting from 950 to 2100 nm.

Ag2Te 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 Ag2Te quantum dots (QDs) precisely and continuously in a wide range. Herein, Ag2Te 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 Ag2Te QDs due to its stronger ability to coordinate to the Ag ion than that of 1-octanethiol, predicated by the density functional theory. By controlling this dissolution effect, the monomer supply kinetics can be regulated, achieving precise size control of Ag2Te QDs. This synthetic strategy results in state-of-the-art silver-based QDs with emission tunability. Only by taking advantage of such an ultrawide emission has the sizing curve of Ag2Te been obtained. Moreover, the absolute photoluminescence quantum yield of Ag2Te QDs can reach 12.0% due to their well-passivated Ag-enriched surface with a density of 5.0 ligands/nm2, facilitating noninvasive in vivo fluorescence imaging. The high brightness in the long-wavelength near-infrared (NIR) region makes the cerebral vasculature and the tiny vessel with a width of only 60 µm clearly discriminable. This work reveals a nonclassical growth mechanism of Ag2Te QDs, providing new insight into precisely controlling the size and corresponding photoluminescence properties of semiconductor nanocrystals. The ultrasmall, low-toxicity, emission-tunable, and bright NIR-II Ag2Te QDs synthesized in this work offer a tremendous promise for multicolor and deep-tissue in vivo fluorescence imaging.