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.

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.

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.

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.

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.

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.

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

Accurate absolute binding free-energy estimation in silico, 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 2.0 of the binding free energy estimator (BFEE2) provides both standardized alchemical and geometrical workflows and obviates the need for extensive human intervention to guarantee complete reproducibility of the results. To achieve the largest gamut of protein-ligand and, more generally, of host-guest complexes, BFEE2 supports most academic force fields, such as CHARMM, Amber, OPLS, and GROMOS. Configurational files are generated in the NAMD and Gromacs formats, and all the post-treatments are performed in an automated fashion. Moreover, convergence of the free-energy calculation can be monitored from the intermediate files generated during the simulation. All in all, BFEE2 is a foolproof, versatile tool for accurate absolute binding free-energy calculations, assisting the end-user over a broad range of applications.

Taming Rugged Free Energy Landscapes Using an Average Force.

The observation of complex structural transitions in biological and abiological molecular objects within time scales amenable to molecular dynamics (MD) simulations is often hampered by significant free energy barriers associated with entangled movements. Importance-sampling algorithms, a powerful class of numerical schemes for the investigation of rare events, have been widely used to extend simulations beyond the time scale common to MD. However, probing processes spanning milliseconds through microsecond molecular simulations still constitutes in practice a daunting challenge because of the difficulty of taming the ruggedness of multidimensional free energy surfaces by means of naive transition coordinates. To address this limitation, in recent years we have elaborated importance-sampling methods relying on an adaptive biasing force (ABF). In this Account, we review recent developments of algorithms aimed at mapping rugged free energy landscapes that correspond to complex processes of physical, chemical, and biological relevance. Through these developments, we have broadened the spectrum of applications of the popular ABF algorithm while improving its computational efficiency, notably for multidimensional free energy calculations. One major algorithmic advance, coined meta-eABF, merges the key features of metadynamics and an extended Lagrangian variant of ABF (eABF) by simultaneously shaving the barriers and flooding the valleys of the free energy landscape, and it possesses a convergence rate up to 5-fold greater than those of other importance-sampling algorithms. Through faster convergence and enhanced ergodic properties, meta-eABF represents a significant step forward in the simulation of millisecond-time-scale events. Here we introduce extensions of the algorithm, notably its well-tempered and replica-exchange variants, which further boost the sampling efficiency while gaining in numerical stability, thus allowing quantum-mechanical/molecular-mechanical free energy calculations to be performed at a lower cost. As a paradigm to bridge microsecond simulations to millisecond events by means of free energy calculations, we have applied the ABF family of algorithms to decompose complex movements in molecular objects of biological and abiological nature. We show here how water lubricates the shuttling of an amide-based rotaxane by altering the mechanism that underlies the concerted translation and isomerization of the macrocycle. Introducing novel collective variables in a computational workflow for the rigorous determination of standard binding free energies, we predict with utmost accuracy the thermodynamics of protein-ligand reversible association. Because of their simplicity, versatility, and robust mathematical foundations, the algorithms of the ABF family represent an appealing option for the theoretical investigation of a broad range of problems relevant to physics, chemistry, and biology.

Finding an Optimal Pathway on a Multidimensional Free-Energy Landscape.

An ad-hoc, yet widely adopted approach to investigate complex molecular objects in motion using importance-sampling schemes involves two steps, namely (i) mapping the multidimensional free-energy landscape that characterizes the movements in the molecular object at hand and (ii) finding the most probable transition path connecting basins of the free-energy hyperplane. To achieve this goal, we turn to an importance-sampling algorithm, coined well-tempered metadynamics-extended adaptive biasing force (WTM-eABF), aimed at mapping rugged free-energy landscapes, combined with a path-searching algorithm, which we call multidimensional lowest energy (MULE), to identify the underlying minimum free-energy pathway in the collective-variable space of interest. First, the well-tempered feature of the importance-sampling scheme confers to the latter an asymptotic convergence, while the overall algorithm inherits the advantage of high sampling efficiency of its predecessor, meta-eABF, making its performance less sensitive to user-defined parameters. Second, the Dijkstra algorithm implemented in MULE is able to identify with utmost efficiency a pathway that satisfies minimum free energy of activation among all the possible routes in the multidimensional free-energy landscape. Numerical simulations of three molecular assemblies indicate that association of WTM-eABF and MULE constitutes a reliable, efficient and robust approach for exploring coupled movements in complex molecular objects. On account of its ease of use and intrinsic performance, we expect WTM-eABF and MULE to become a tool of choice for both experts and nonexperts interested in the thermodynamics and the kinetics of processes relevant to chemistry and biology.

Insights into directional movement in molecular machines from free-energy calculations.

A rotaxane composed of a symmetrical axle containing three binding stations and a cone-like macrocycle containing two secondary amines has been investigated at the atomic level. At high pH, the macrocycle binds to the intermediate di(quaternary ammonium) site, while at low pH, the protonated macrocycle selectively moves along the axle to one of the two symmetrical phenyl triazole binding sites facing its upper rim, but does not shuttle backward. The determined free-energy profile characterizing the translocation of the macrocycle indicates that the selected binding site is energetically more favorable than the one facing the lower rim of the macrocycle and the free-energy barrier against translocation to the former site is lower than to the latter one, rationalizing the directional movement. This selectivity mainly stems from the asymmetry of the macrocycle shape. The strong electrostatic repulsion between the ring and the axle is found to constitute the driving force for the shuttling of the ring and also the resistance for its reverse motion. Moreover, the effect of the solvent on the shuttling has been examined, suggesting that increasing the solvent polarity may weaken the directional preference of shuttling, due to the shielding effect of polar solvents on electrostatic interactions. Our study provides a theoretical framework for tuning the selectivity of directional movement in molecular machines.

Lysine Mutation of the Claw-Arm-Like Loop Accelerates Catalysis by Cellobiohydrolases.

Searching for viable strategies to accelerate the catalytic cycle of glycoside hydrolase family 7 (GH7) cellobiohydrolase I (CBHI)-the workhorse cellulose-degrading enzymes, we have performed a total of 12-µs molecular dynamics simulations on GH7 CBHI, which brought to light a new mechanism for cellobiose expulsion, coined "claw-arm" action. The loop flanking the product binding site plays the role of a flexible "arm" extending toward cellobiose, and residue Thr389 of this loop acts as a "claw" that captures cellobiose. Five mutations of residue Thr389 were considered to enhance the loop-cellobiose interaction. The lysine mutant was found to significantly accelerate cellobiose expulsion and facilitate polysaccharide-chain translocation. Lysine mutation of Thr393 in Talaromyces emersonii CBHI (TeCel7A) performed similarly. Lysine approaches the catalytic area and stabilizes the Michaelis complex, potentially affecting glycosylation, the rate-limiting step of the catalytic cycle. QM/MM calculations indicate that lysine replacement diminishes the barrier against proton transfer, the crucial step of glycosylation, by 2.3 kcal/mol. Experimental validation was performed using the full-length wild-type (WT) of TeCel7A and its mutants, recombinantly expressed in Pichia pastoris, to degrade the substrates. Compared with the WT, the lysine mutant revealed an associated higher enzymatic reaction rate. Furthermore, cellobiose yield was also increased by lysine mutation, indicating that dissociation of the enzyme from cellulose was accelerated, which largely stems from the enhanced flexibility of the "arm". The present work is envisioned to help design strategies for improving enzymatic activity, while decreasing enzyme cost.

Click RNA for Rapid Capture and Identification of Intracellular MicroRNA Targets.

Rapid capture and identification of the intracellular target genes of microRNAs (miRNAs) are the key to understanding miRNA functions and development of RNA-based therapeutics. However, developing biochemical tools that can fish out the target genes of miRNAs in live cells is a significant technical challenge. Here, we report a remarkably simple yet powerful technology capable of loading virtually any miRNA into Ago2 of the RNA-induced silencing complexes (RISCs). This surprising discovery enables rapid capture and identification of target mRNAs and long noncoding RNAs. It is achieved by linking dibenzocyclooctyne (DBCO), a classical chemical moiety in copper-free click chemistry, to the 3' end of miRNAs. DBCO serves as a high-affinity tag to the Ago2 protein, thus boosting the formation of RISCs with miRNA target genes in living cells. Upon cell lysing, DBCO's routine function in click chemistry allows rapid enrichment of target genes for analysis without the need of additional molecular handles. A series of miR-21 and miR-27a target genes that were previously unknown were pulled down from various cell lines and identified with qRT-PCR, demonstrating the utility of this innovative technology in both transcriptomic research and RNA-based studies.

Changes in Microenvironment Modulate the B- to A-DNA Transition.

B- to A-DNA transition is known to be sensitive to the macroscopic properties of the solution, such as salt and ethanol concentrations. Microenvironmental effects on DNA conformational transition have been broadly studied. Providing an intuitive picture of how DNA responds to environmental changes is, however, still needed. Analyzing the chemical equilibrium of B-to-A DNA transition at critical concentrations, employing explicit-solvent simulations, is envisioned to help understand such microenvironmental effects. In the present study, free-energy calculations characterizing the B- to A-DNA transition and the distribution of cations were carried out in solvents with different ethanol concentrations. With the addition of ethanol, the most stable structure of DNA changes from the B- to A-form, in agreement with previous experimental observation. In 60% ethanol, a chemical equilibrium is found, showing reversible transition between B- and A-DNA. Analysis of the microenvironment around DNA suggests that with the increase of ethanol concentration, the cations exhibit a significant tendency to move toward the backbone, and mobility of water molecules around the major groove and backbone decreases gradually, leading eventually to a B-to-A transition. The present results provide a free-energy view of DNA microenvironment and of the role of cation motion in the conformational transition.

ELF: An Extended-Lagrangian Free Energy Calculation Module for Multiple Molecular Dynamics Engines.

Extended adaptive biasing force (eABF), a collective variable (CV)-based importance-sampling algorithm, has proven to be very robust and efficient compared with the original ABF algorithm. Its implementation in Colvars, a software addition to molecular dynamics (MD) engines, is, however, currently limited to NAMD and LAMMPS. To broaden the scope of eABF and its variants, like its generalized form (egABF), and make them available to other MD engines, e.g., GROMACS, AMBER, CP2K, and openMM, we present a PLUMED-based implementation, called extended-Lagrangian free energy calculation (ELF). This implementation can be used as a stand-alone gradient estimator for other CV-based sampling algorithms, such as temperature-accelerated MD (TAMD) and extended-Lagrangian metadynamics (MtD). ELF provides the end user with a convenient framework to help select the best-suited importance-sampling algorithm for a given application without any commitment to a particular MD engine.

BFEE: A User-Friendly Graphical Interface Facilitating Absolute Binding Free-Energy Calculations.

Quantifying protein-ligand binding has attracted the attention of both theorists and experimentalists for decades. Many methods for estimating binding free energies in silico have been reported in recent years. Proper use of the proposed strategies requires, however, adequate knowledge of the protein-ligand complex, the mathematical background for deriving the underlying theory, and time for setting up the simulations, bookkeeping, and postprocessing. Here, to minimize human intervention, we propose a toolkit aimed at facilitating the accurate estimation of standard binding free energies using a geometrical route, coined the binding free-energy estimator (BFEE), and introduced it as a plug-in of the popular visualization program VMD. Benefitting from recent developments in new collective variables, BFEE can be used to generate the simulation input files, based solely on the structure of the complex. Once the simulations are completed, BFEE can also be utilized to perform the post-treatment of the free-energy calculations, allowing the absolute binding free energy to be estimated directly from the one-dimensional potentials of mean force in simulation outputs. The minimal amount of human intervention required during the whole process combined with the ergonomic graphical interface makes BFEE a very effective and practical tool for the end-user.

Conformational changes of DNA induced by a trans-azobenzene derivative via non-covalent interactions.

In biological environments and in aqueous solution, DNA generally adopts the canonical B conformation. Recently, an azobenzene photoswitch containing a polyamine chain with three positive charges was shown to induce a reversible conformational transition between the A and B forms of DNA, the transition being triggered by trans-cis isomerization of the photoswitch upon non-covalent intercalation. It was proposed that, in its trans conformation, azobenzene stabilizes the A form of DNA. The structural details and the mechanism upon which trans-azobenzene induces the B-to-A DNA transition remain, however, unclear. In the present work, two possible intercalating modes of trans-azobenzene, from the minor groove and from the major groove, were investigated with all-atom molecular-dynamics simulations. Intercalation from the major groove was found to be the most probable binding mode due to favorable electrostatic and π-π stacking interactions. The free-energy profile associated with the B-to-A conformational transition reveals that intercalation from the major groove leads to a conformational change of DNA, showing a slight tendency to interconvert from B- to A-DNA, in agreement with the CD spectrum obtained from the experiment. However, the presence of only one interacting azobenzene is not sufficient to lead to a global conformational change to A-DNA. The present results are expected to serve in the design of DNA switches, which can induce reversible DNA conformational changes.

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.

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.

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.