Adaptively biased molecular dynamics for free energy calculations

Clustering one million molecular structures on GPU within seconds

Structure clustering is a general but time-consuming work in the study of life science. Up to now, most published tools do not support the clustering analysis on graphics processing unit (GPU) with root mean square deviation metric. In this work, we specially write codes to do the work. It supports multiple threads on multiple GPUs. To show the performance, we apply the program to a 33-residue fragment in protein Pin1 WW domain mutant. The dataset contains 1,400,000 snapshots, which are extracted from an enhanced sampling simulation and distribute widely in the conformational space. Various testing results present that our program is quite efficient. Particularly, with two NVIDIA RTX4090 GPUs and single precision data type, the clustering calculation on 1 million snapshots is completed in a few seconds (including the uploading time of data from memory to GPU and neglecting the reading time from hard disk). This is hundreds of times faster than central processing unit. Our program could be a powerful tool for fast extraction of representative states of a molecule among its thousands to millions of candidate structures.

A non-canonical mechanism of GPCR activation

The goal of designing safer, more effective drugs has led to tremendous interest in molecular mechanisms through which ligands can precisely manipulate the signaling of G-protein-coupled receptors (GPCRs), the largest class of drug targets. Decades of research have led to the widely accepted view that all agonists-ligands that trigger GPCR activation-function by causing rearrangement of the GPCR's transmembrane helices, opening an intracellular pocket for binding of transducer proteins. Here we demonstrate that certain agonists instead trigger activation of free fatty acid receptor 1 by directly rearranging an intracellular loop that interacts with transducers. We validate the predictions of our atomic-level simulations by targeted mutagenesis; specific mutations that disrupt interactions with the intracellular loop convert these agonists into inverse agonists. Further analysis suggests that allosteric ligands could regulate the signaling of many other GPCRs via a similar mechanism, offering rich possibilities for precise control of pharmaceutically important targets.

Calculating linear and nonlinear multi-ensemble slow collective variables for protein folding

Traditional molecular dynamics simulation of biomolecules suffers from the conformational sampling problem. It is often difficult to produce enough valid data for post analysis such as free energy calculation and transition path construction. To improve the sampling, one practical solution is putting an adaptive bias potential on some predefined collective variables. The quality of collective variables strongly affects the sampling ability of a molecule in the simulation. In the past, collective variables were built with the sampling data at a constant temperature. This is insufficient because of the same sampling problem. In this work, we apply the standard weighted histogram analysis method to calculate the multi-ensemble averages of pairs of time-lagged features for the construction of both linear and nonlinear slow collective variables. Compared to previous single-ensemble methods, the presented method produces averages with much smaller statistical uncertainties. The generated collective variables help a peptide and a miniprotein fold to their near-native states in a short simulation time period. By using the method, enhanced sampling simulations could be more effective and productive.

Structural and Dynamical Properties of Nucleic Acid Hairpins Implicated in Trinucleotide Repeat Expansion Diseases

Dynamic mutations in some human genes containing trinucleotide repeats are associated with severe neurodegenerative and neuromuscular disorders-known as Trinucleotide (or Triplet) Repeat Expansion Diseases (TREDs)-which arise when the repeat number of triplets expands beyond a critical threshold. While the mechanisms causing the DNA triplet expansion are complex and remain largely unknown, it is now recognized that the expandable repeats lead to the formation of nucleotide configurations with atypical structural characteristics that play a crucial role in TREDs. These nonstandard nucleic acid forms include single-stranded hairpins, Z-DNA, triplex structures, G-quartets and slipped-stranded duplexes. Of these, hairpin structures are the most prolific and are associated with the largest number of TREDs and have therefore been the focus of recent single-molecule FRET experiments and molecular dynamics investigations. Here, we review the structural and dynamical properties of nucleic acid hairpins that have emerged from these studies and the implications for repeat expansion mechanisms. The focus will be on CAG, GAC, CTG and GTC hairpins and their stems, their atomistic structures, their stability, and the important role played by structural interrupts.

Cas12a domain flexibility guides R-loop formation and forces RuvC resetting

The specific nature of CRISPR-Cas12a makes it a desirable RNA-guided endonuclease for biotechnology and therapeutic applications. To understand how R-loop formation within the compact Cas12a enables target recognition and nuclease activation, we used cryo-electron microscopy to capture wild-type Acidaminococcus sp. Cas12a R-loop intermediates and DNA delivery into the RuvC active site. Stages of Cas12a R-loop formation-starting from a 5-bp seed-are marked by distinct REC domain arrangements. Dramatic domain flexibility limits contacts until nearly complete R-loop formation, when the non-target strand is pulled across the RuvC nuclease and coordinated domain docking promotes efficient cleavage. Next, substantial domain movements enable target strand repositioning into the RuvC active site. Between cleavage events, the RuvC lid conformationally resets to occlude the active site, requiring re-activation. These snapshots build a structural model depicting Cas12a DNA targeting that rationalizes observed specificity and highlights mechanistic comparisons to other class 2 effectors.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Free-energy landscapes of transmembrane homodimers by bias-exchange adaptively biased molecular dynamics

Membrane proteins play essential roles in various biological functions within the cell. One of the most common functional regulations involves the dimerization of two single-pass transmembrane (TM) helices. Glycophorin A (GpA) and amyloid precursor protein (APP) form TM homodimers in the membrane, which have been investigated both experimentally and computationally. The homodimer structures are well characterized using only four collective variables (CVs) when each TM helix is stable. The CVs are the interhelical distance, the crossing angle, and the Crick angles for two TM helices. However, conformational sampling with multi-dimensional replica-exchange umbrella sampling (REUS) requires too many replicas to sample all the CVs for exploring the conformational landscapes. Here, we show that the bias-exchange adaptively biased molecular dynamics (BE-ABMD) with the four CVs effectively explores the free-energy landscapes of the TM helix dimers of GpA, wild-type APP and its mutants in the IMM1 implicit membrane. Compared to the original ABMD, the bias-exchange algorithm in BE-ABMD can provide a more rapidly converged conformational landscape. The BE-ABMD simulations could also reveal TM packing interfaces of the membrane proteins and the dependence of the free-energy landscapes on the membrane thickness. This approach is valuable for numerous other applications, including those involving explicit solvent and a lipid bilayer in all-atom force fields or Martini coarse-grained models, and enhances our understanding of protein-protein interactions in biological membranes.

Cation-π Interactions Greatly Influence Ion Transportability of the Light-Driven Sodium Pump KR2: A Molecular Dynamics Study

Krokinobacter eikastus rhodopsin 2 (KR2) is a typical light-driven sodium pump. Although wild-type KR2 exhibits high Na+ selectivity, mutagenesis performed on the residues constituting the entrance enables permeation of K+ and Cs+, while the underlying mechanism remains elusive. This study presents a comprehensive molecular dynamics investigation, including force field optimization, metadynamics, and alchemical free energy methods, to explore the N61L/G263F mutant of KR2, which exhibits transportability for K+ and Cs+. The introduced Phe263 residue can directly promote ion binding at the entrance through cation-π interactions, while the N61L mutation can enhance ion binding at Phe46 by relieving steric hindrance. These results suggest that cation-π interactions may significantly influence the ion transportability and selectivity of KR2, which can provide important insights for protein engineering and the design of artificial ion transporters.

Structural and Dynamic Changes of Nucleosome upon GATA3 Binding

Pioneer factors, which can directly bind to nucleosomes, have been considered to change chromatin conformations. However, the binding impact on the nucleosome is little known. Here, we show how the pioneer factor GATA3 binds to nucleosomal DNA and affects the conformation and dynamics of nucleosomes by using a combination of SAXS, molecular modeling, and molecular dynamics simulations. Our structural models, consistent with the SAXS data, indicate that only one of the two DNA binding domains, N- and C-fingers, of GATA3 binds to an end of the DNA in solution. Our MD simulations further showed that the other unbound end of the DNA increases the fluctuation and enhances the DNA dissociation from the histone core when the N-finger binds to a DNA end, a site near the entry or exit of the nucleosome. However, this was not true for the binding of the C-finger that binds to a location about 15 base pairs distant from the DNA end. In this case, DNA dissociation occurred on the bound end. Taken together, we suggest that the N-finger and C-finger bindings of GATA3 commonly enhance DNA dissociation at one of the two DNA ends (the bound end for the C-finger binding and the unbound end for the N-finger binding), leading to triggering a conformational change in the chromatin.

Efficient and accurate calculation of proline cis/trans isomerization free energies from Hamiltonian replica exchange molecular dynamics simulations

Proline cis/trans isomerization plays an important role in many biological processes but occurs on time scales not accessible to brute-force molecular dynamics (MD) simulations. We have designed a new Hamiltonian replica exchange scheme, ω-bias potential replica exchange molecular dynamics (ωBP-REMD), to efficiently and accurately calculate proline cis/trans isomerization free energies. ωBP-REMD is applied to various proline-containing tripeptides and a biologically important proline residue in the N2-domain of the gene-3-protein of phage fd in the wildtype and mutant variants of the protein. Excellent cis/trans transition rates are obtained. Reweighting of the sampled probability distribution along the peptide bond dihedral angle allows construction of the corresponding free-energy profile and calculation of the cis/trans isomerization free energy with high statistical precision. Very good agreement with experimental data is obtained. ωBP-REMD outperforms standard umbrella sampling in terms of convergence and agreement with experiment and strongly reduces perturbation of the local structure near the proline residue.

Structure and Dynamics of DNA and RNA Double Helices Formed by d(CTG), d(GTC), r(CUG), and r(GUC) Trinucleotide Repeats and Associated DNA-RNA Hybrids

Myotonic dystrophy type 1 is the most frequent form of muscular dystrophy in adults caused by an abnormal expansion of the CTG trinucleotide. Both the expanded DNA and the expanded CUG RNA transcript can fold into hairpins. Co-transcriptional formation of stable RNA·DNA hybrids can also enhance the instability of repeat tracts. We performed molecular dynamics simulations of homoduplexes associated with the disease, d(CTG)n and r(CUG)n, and their corresponding r(CAG)n:d(CTG)n and r(CUG)n:d(CAG)n hybrids that can form under bidirectional transcription and of non-pathological d(GTC)n and d(GUC)n homoduplexes. We characterized their conformations, stability, and dynamics and found that the U·U and T·T mismatches are dynamic, favoring anti-anti conformations inside the helical core, followed by anti-syn and syn-syn conformations. For DNA, the secondary minima in the non-expanding d(GTC)n helices are deeper, wider, and longer-lived than those in d(CTG)n, which constitutes another biophysical factor further differentiating the expanding and non-expanding sequences. The hybrid helices are closer to A-RNA, with the A-T and A-U pairs forming two stable Watson-Crick hydrogen bonds. The neutralizing ion distribution around the non-canonical pairs is also described.

Enhanced sampling in explicit solvent by deep learning module in FSATOOL

FSATOOL is an integrated molecular simulation and data analysis program. Its old molecular dynamics engine only supports simulations in vacuum or implicit solvent. In this work, we implement the well-known smooth particle mesh Ewald method for simulations in explicit solvent. The new developed engine is runnable on both CPU and GPU. All the existed analysis modules in the program are compatible with the new engine. Moreover, we also build a complete deep learning module in FSATOOL. Based on the module, we further implement two useful trajectory analysis methods: state-free reversible VAMPnets and time-lagged autoencoder. They are good at searching the collective variables related to the conformational transitions of biomolecules. In FSATOOL, these collective variables can be further used to construct a bias potential for the enhanced sampling purpose. We introduce the implementation details of the methods and present their actual performances in FSATOOL by a few enhanced sampling simulations.

Efficient Interrogation of the Kinetic Barriers Demarcating Catalytic States of a Tyrosine Kinase with Optimal Physical Descriptors and Mixture Models

Computer simulations are increasingly used to access thermo-kinetic information underlying structural transformation of protein kinases. Such information are necessary to probe their roles in disease progression and interactions with drug targets. However, the investigations are frequently challenged by forbiddingly high computational expense, and by the lack of standard protocols for the design of low dimensional physical descriptors that encode system features important for transitions. Here, we consider the demarcating characteristics of the different states of Abelson tyrosine kinase associated with distinct catalytic activity to construct a set of physically meaningful, orthogonal collective variables that preserve the slow modes of the system. Independent sampling of each metastable state is followed by the estimation of global partition function along the appropriate physical descriptors using the modified Expectation Maximized Molecular Dynamics method. The resultant free energy barriers are in excellent agreement with experimentally known rate-limiting dynamics and activation energy computed with conventional enhanced sampling methods. We discuss possible directions for further development and applications.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

We present Deep learning for Collective Variables (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.

Recent Advances of Studies on Cell-Penetrating Peptides Based on Molecular Dynamics Simulations

With the ability to transport cargo molecules across cell membranes with low toxicity, cell-penetrating peptides (CPPs) have become promising candidates for next generation peptide-based drug delivery vectors. Over the past three decades since the first CPP was discovered, a great deal of work has been done on the cellular uptake mechanisms and the applications for the delivery of therapeutic molecules, and significant advances have been made. But so far, we still do not have a precise and unified understanding of the structure-activity relationship of the CPPs. Molecular dynamics (MD) simulations provide a method to reveal peptide-membrane interactions at the atomistic level and have become an effective complement to experiments. In this paper, we review the progress of the MD simulations on CPP-membrane interactions, including the computational methods and technical improvements in the MD simulations, the research achievements in the CPP internalization mechanism, CPP decoration and coupling, and the peptide-induced membrane reactions during the penetration process, as well as the comparison of simulated and experimental results.

Kinetics of Drug Release from Clay Using Enhanced Sampling Methods

A key step in the development of a new drug, is the design of drug-excipient complexes that lead to optimal drug release kinetics. Computational chemistry and specifically enhanced sampling molecular dynamics methods can play a key role in this context, by minimizing the need for expensive experiments, and reducing cost and time. Here we show that recent advances in enhanced sampling methodologies can be brought to fruition in this area. We demonstrate the potential of these methodologies by simulating the drug release kinetics of the complex praziquantel-montmorillonite in water. Praziquantel finds promising applications in the treatment of schistosomiasis, but its biopharmaceutical profile needs to be improved, and a cheap material such as the montmorillonite clay would be a very convenient excipient. We simulate the drug release both from surface and interlayer space, and find that the diffusion of the praziquantel inside the interlayer space is the process that limits the rate of drug release.

Catalysis by [Ga4 L6 ]12- Metallocage on the Nazarov Cyclization: The Basicity of Complexed Alcohol is Key

The Nazarov cyclization is investigated in solution and within K12 [Ga4 L6 ] supramolecular organometallic cage by means of computational methods. The reaction needs acidic condition in solution but works at neutral pH in the presence of the metallocage. The reaction steps for the process are analogous in both media: (a) protonation of the alcohol group, (b) water loss and (c) cyclization. The relative Gibbs energies of all the steps are affected by changing the environment from solvent to the metallocage. The first step in the mechanism, the alcohol protonation, turns out to be the most critical one for the acceleration of the reaction inside the metallocage. In order to calculate the relative stability of protonated alcohol inside the cavity, we propose a computational scheme for the calculation of basicity for species inside cavities and can be of general use. These results are in excellent agreement with the experiments, identifying key steps of catalysis and providing an in-depth understanding of the impact of the metallocage on all the reaction steps.

A New Formulation for the Concerted Alchemical Calculation of van der Waals and Coulomb Components of Solvation Free Energies

Alchemical free energy calculations via molecular dynamics have been widely used to obtain thermodynamic properties related to protein-ligand binding and solute-solvent interactions. Although soft-core modeling is the most common approach, the linear basis function (LBF) methodology [Naden, L. N.; et al. J. Chem. Theory Comput.2014, 10 (3), 1128; 2015, 11 (6), 2536] has emerged as a suitable alternative. It overcomes the end-point singularity of the scaling method while maintaining essential advantages such as ease of implementation and high flexibility for postprocessing analysis. In the present work, we propose a simple LBF variant and formulate an efficient protocol for evaluating van der Waals and Coulomb components of an alchemical transformation in tandem, in contrast to the prevalent sequential evaluation mode. To validate our proposal, which results from a careful optimization study, we performed solvation free energy calculations and obtained octanol-water partition coefficients of small organic molecules. Comparisons with results obtained via the sequential mode using either another LBF approach or the soft-core model attest to the effectiveness and correctness of our method. In addition, we show that a reaction field model with an infinite dielectric constant can provide very accurate hydration free energies when used instead of a lattice-sum method to model solute-solvent electrostatics.

Transition Rates and Efficiency of Collective Variables from Time-Dependent Biased Simulations

Simulations with adaptive time-dependent bias enable an efficient exploration of the conformational space of a system. However, the dynamic information is altered by the bias. Infrequent metadynamics recovers the transition rate of crossing a barrier, if the collective variables are ideal and there is no bias deposition near the transition state. Unfortunately, these conditions are not always fulfilled. To overcome these limitations, and inspired by single-molecule force spectroscopy, we use Kramers' theory for calculating the barrier-crossing rate when a time-dependent bias is added to the system. We assess the efficiency of collective variables parameter by measuring how efficiently the bias accelerates the transitions. We present approximate analytical expressions of the survival probability, reproducing the barrier-crossing time statistics and enabling the extraction of the unbiased transition rate even for challenging cases. We explore the limits of our method and provide convergence criteria to assess its validity.

Accelerators for Classical Molecular Dynamics Simulations of Biomolecules

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.

Free Energy Landscape of H2A-H2B Displacement From Nucleosome

Nucleosome reconstitution plays an important role in many cellular functions. As an initial step, H2A-H2B dimer displacement, which is accompanied by disruption of many of the interactions within the nucleosome, should occur. To understand how H2A-H2B dimer displacement occurs, an adaptively biased molecular dynamics (ABMD) simulation was carried out to generate a variety of displacements of the H2A-H2B dimer from the fully wrapped to partially unwrapped nucleosome structures. With regards to these structures, the free energy landscape of the dimer displacement was investigated using umbrella sampling simulations. We found that the main contributors to the free energy were the docking domain of H2A and the C-terminal of H4. There were various paths for the dimer displacement which were dependent on the extent of nucleosomal DNA wrapping, suggesting that modulation of the intra-nucleosomal interaction by external factors such as histone chaperons could control the path for the H2A-H2B dimer displacement. Key residues which contributed to the free energy have also been reported to be involved in the mutations and posttranslational modifications (PTMs) which are important for assembling and/or reassembling the nucleosome at the molecular level and are found in cancer cells at the phenotypic level. Our results give insight into how the H2A-H2B dimer displacement proceeds along various paths according to different interactions within the nucleosome.

Identifying a Feasible Transition Pathway between Two Conformational States for a Protein

Proteins usually need to transit between different conformational states to fulfill their biological functions. In the mechanistic study of such transition processes by molecular dynamics simulations, identification of the minimum free energy path (MFEP) can substantially reduce the sampling space, thus enabling rigorous thermodynamic evaluation of the process. Conventionally, the MFEP is derived by iterative local optimization from an initial path, which is typically generated by simple brute force techniques like the targeted molecular dynamics (tMD). Therefore, the quality of the initial path determines the successfulness of MFEP estimation. In this work, we propose a method to improve derivation of the initial path. Through iterative relaxation-biasing simulations in a bidirectional manner, this method can construct a feasible transition pathway connecting two known states for a protein. Evaluation on small, fast-folding proteins against long equilibrium trajectories supports the good sampling efficiency of our method. When applied to larger proteins including the catalytic domain of human c-Src kinase as well as the converter domain of myosin VI, the paths generated by our method deviate significantly from those computed with the generic tMD approach. More importantly, free energy profiles and intermediate states obtained from our paths exhibit remarkable improvements over those from tMD paths with respect to both physical rationality and consistency with a priori knowledge.

Novel eGZ-motif formed by regularly extruded guanine bases in a left-handed Z-DNA helix as a major motif behind CGG trinucleotide repeats

The expansion of d(CGG) trinucleotide repeats (TRs) lies behind several important neurodegenerative diseases. Atypical DNA secondary structures have been shown to trigger TR expansion: their characterization is important for a molecular understanding of TR disease. CD spectroscopy experiments in the last decade have unequivocally demonstrated that CGG runs adopt a left-handed Z-DNA conformation, whose features remain uncertain because it entails accommodating GG mismatches. In order to find this missing motif, we have carried out molecular dynamics (MD) simulations to explore all the possible Z-DNA helices that potentially form after the transition from B- to Z-DNA. Such helices combine either CpG or GpC Watson-Crick steps in Z-DNA form with GG-mismatch conformations set as either intrahelical or extrahelical; and participating in BZ or ZZ junctions or in alternately extruded conformations. Characterization of the stability and structural features (especially overall left-handedness, higher-temperature and steered MD simulations) identified two novel Z-DNA helices: the most stable one displays alternately extruded Gs, and is followed by a helix with symmetrically extruded ZZ junctions. The G-extrusion favors a seamless stacking of the Watson-Crick base pairs; extruded Gs favor syn conformations and display hydrogen-bonding and stacking interactions. Such conformations could have the potential to hijack the MMR complex, thus triggering further expansion.

Structural basis for the synthesis of the core 1 structure by C1GalT1

C1GalT1 is an essential inverting glycosyltransferase responsible for synthesizing the core 1 structure, a common precursor for mucin-type O-glycans found in many glycoproteins. To date, the structure of C1GalT1 and the details of substrate recognition and catalysis remain unknown. Through biophysical and cellular studies, including X-ray crystallography of C1GalT1 complexed to a glycopeptide, we report that C1GalT1 is an obligate GT-A fold dimer that follows a S_N2 mechanism. The binding of the glycopeptides to the enzyme is mainly driven by the GalNAc moiety while the peptide sequence provides optimal kinetic and binding parameters. Interestingly, to achieve glycosylation, C1GalT1 recognizes a high-energy conformation of the α-GalNAc-Thr linkage, negligibly populated in solution. By imposing this 3D-arrangement on that fragment, characteristic of α-GalNAc-Ser peptides, C1GalT1 ensures broad glycosylation of both acceptor substrates. These findings illustrate a structural and mechanistic blueprint to explain glycosylation of multiple acceptor substrates, extending the repertoire of mechanisms adopted by glycosyltransferases.

The glycosyltransferase C1GalT1 directs a key step in protein O-glycosylation important for the expression of the cancer-associated Tn and T antigens. Here, the authors provide molecular insights into the function of C1GalT1 by solving the crystal structure of the Drosophila enzyme-substrate complex.

Molecular dynamics study of the internalization of cell-penetrating peptides containing unnatural amino acids across membranes

Peptide-based delivery systems that deliver target molecules into cells have been gaining traction. These systems need cell-penetrating peptides (CPPs), which have the remarkable ability to penetrate into biological membranes and help internalize different cargoes into cells through the cell membranes. The molecular internalization mechanism and structure-function relationships of CPPs are not clear, although the incorporation of nonproteinogenic amino acids such as α-aminoisobutyric acid (Aib) has been reported to increase their helicity, biostability and penetration efficiencies. Here, we used molecular dynamics to study two Aib-containing CPPs, poly(LysAibAla)₃ (KAibA) and poly(LysAibGly)₃ (KAibG), that previously showed high cell internalization efficiency. KAibA and KAibG displayed the lowest internalization energies among the studied CPPs, showing distinct internalization mechanisms depending on the lipid composition of the model membranes. The presence of Aib residues allows these CPPs to adopt amphipathic folding to efficiently penetrate through the membranes. Elucidating how Aib incorporation affects CPP-membrane binding and interactions is beneficial for the design of CPPs for efficient intracellular delivery.

QM/MM and MM MD simulations on decontamination of the V-type nerve agent VX by phosphotriesterase: Toward a comprehensive understanding of steroselectivity and activity

Due to deadly toxicity and high environmental stability of the nerve agent VX, an efficient decontamination approach is desperately needed in tackling its severe threat to human secu-rity. The enzymatic...

Peptide Dynamics and Metadynamics: Leveraging Enhanced Sampling Molecular Dynamics to Robustly Model Long-Timescale Transitions

Molecular dynamics simulations can in theory reveal the thermodynamics and kinetics of peptide conformational transitions at atomic-level resolution. However, even with modern computing power, they are limited in the timescales they can sample, which is especially problematic for peptides that are fully or partially disordered. Here, we discuss how the enhanced sampling methods accelerated molecular dynamics (aMD) and metadynamics can be leveraged in a complementary fashion to quickly explore conformational space and then robustly quantify the underlying free energy landscape. We apply these methods to two peptides that have an intrinsically disordered nature, the histone H3 and H4 N-terminal tails, and use metadynamics to compute the free energy landscape along collective variables discerned from aMD simulations. Results show that these peptides are largely disordered, with a slight preference for α-helical structures.

Affordable Ab Initio Path Integral for Thermodynamic Properties via Molecular Dynamics Simulations Using Semiempirical Reference Potential

Path integral molecular dynamics (PIMD) is becoming a routinely applied method for incorporating the nuclear quantum effect in computer simulations. However, direct PIMD simulations at an ab initio level of theory are formidably expensive. Using the protonated 1,8-bis(dimethylamino)naphthalene molecule as an example, we show in this work that the computational expense for the intramolecular proton transfer between the two nitrogen atoms can be remarkably reduced by implementing the idea of reference-potential methods. The simulation time can be easily extended to a scale of nanoseconds while maintaining the accuracy on an ab initio level of theory for thermodynamic properties. In addition, postprocessing can be carried out in parallel on massive computer nodes. A 545-fold reduction in the total CPU time can be achieved in this way as compared to a direct PIMD simulation at the same ab initio level of theory.

Development of enhanced conformational sampling methods to probe the activation landscape of GPCRs

G protein-coupled receptors (GPCRs) make up the largest superfamily of integral membrane proteins and play critical signal transduction roles in many physiological processes. Developments in molecular biology, biophysical, biochemical, pharmacological, and computational techniques aimed at these important therapeutic targets are beginning to provide unprecedented details on the structural as well as functional basis of their pleiotropic signaling mediated by G proteins, β arrestins, and other transducers. This pleiotropy presents a pharmacological challenge as the same ligand-receptor interaction can cause a therapeutic effect as well as an undesirable on-target side-effect through different downstream pathways. GPCRs don't function as simple binary on-off switches but as finely tuned shape-shifting machines described by conformational ensembles, where unique subsets of conformations may be responsible for specific signaling cascades. X-ray crystallography and more recently cryo-electron microscopy are providing snapshots of some of these functionally-important receptor conformations bound to ligands and/or transducers, which are being utilized by computational methods to describe the dynamic conformational energy landscape of GPCRs. In this chapter, we review the progress in computational conformational sampling methods based on molecular dynamics and discrete sampling approaches that have been successful in complementing biophysical and biochemical studies on these receptors in terms of their activation mechanisms, allosteric effects, actions of biased ligands, and effects of pathological mutations. Some of the sampled simulation time scales are beginning to approach receptor activation time scales. The list of conformational sampling methods and example uses discussed is not exhaustive but includes representative examples that have pushed the limits of classical molecular dynamics and discrete sampling methods to describe the activation energy landscape of GPCRs.

FSATOOL 2.0: An integrated molecular dynamics simulation and trajectory data analysis program

Molecular dynamics simulation is important in the computational study of the biomolecules. In this paper, we upgrade our previous FSATOOL to version 2.0. It is no longer a plugin as before. Besides the existed enhanced sampling and Markov state model analysis module, FSATOOL 2.0 has three new features now. First, it contains a molecular dynamics simulation engine on both CPU and GPU device. The engine works with an embedded enhanced sampling module. Second, it can do the free energy calculation by various practical methods, including the weighted histogram analysis method and Gaussian mixture model. Third, it has many subroutines to process the trajectory data, such as principal component analysis, time-structure based independent component analysis, contact analysis, and Φ-value analysis. Most importantly, all these calculations are integrated into one package. The trajectory data format is compatible with all the modules. With a proper input parameter file, users can do the molecular dynamics simulation and data analysis work by only a few simplified commands. The capabilities and theoretical backgrounds of FSATOOL 2.0 are introduced in the paper.

Fast and Accurate Multidimensional Free Energy Integration

Enhanced sampling and free energy calculation algorithms of the thermodynamic integration family (such as the adaptive biasing force (ABF) method) are not based on the direct computation of a free energy surface but rather of its gradient. Integrating the free energy surface is nontrivial in dimensions higher than one. Here, the author introduces a flexible, portable implementation of a Poisson equation formalism to integrate free energy surfaces from estimated gradients in dimensions 2 and 3 using any combination of periodic and nonperiodic (Neumann) boundary conditions. The algorithm is implemented in portable C++ and provided as a standalone tool that can be used to integrate multidimensional gradient fields estimated on a grid using any algorithm, such as umbrella integration as a post-treatment of umbrella sampling simulations. It is also included in the implementation of ABF (and its extended-system variant eABF) in the Collective Variables Module, enabling the seamless computation of multidimensional free energy surfaces within ABF and eABF simulations. A Python-based analysis toolchain is provided to easily plot and analyze multidimensional ABF simulation results, including metrics to assess their convergence. The Poisson integration algorithm can also be used to perform Helmholtz decomposition of noisy gradient estimates on the fly, resulting in an efficient implementation of the projected ABF (pABF) method proposed by Leliévre and co-workers. In numerical tests, pABF is found to lead to faster convergence with respect to ABF in simple cases of low intrinsic dimension but seems detrimental to convergence in a more realistic case involving degenerate coordinates and hidden barriers due to slower exploration. This suggests that variance reduction schemes do not always yield convergence improvements when applied to enhanced sampling methods.

Unlocking the Stereoselectivity and Substrate Acceptance of Enzymes: Proline-Induced Loop Engineering Test

Protein stability and evolvability influence each other. Although protein dynamics play essential roles in various catalytically important properties, their high flexibility and diversity makes it difficult to incorporate such properties into rational engineering. Therefore, how to unlock the potential evolvability in a user-friendly rational design process remains a challenge. In this endeavor, we describe a method for engineering an enantioselective alcohol dehydrogenase. It enables synthetically important substrate acceptance for 4-chlorophenyl pyridine-2-yl ketone, and perfect stereocontrol of both (S)- and (R)-configured products. Thermodynamic analysis unveiled the subtle interaction between enzyme stability and evolvability, while computational studies provided insights into the origin of selectivity and substrate recognition. Preparative-scale synthesis of the (S)-product (73 % yield; >99 % ee) was performed on a gram-scale. This proof-of-principle study demonstrates that interfaced proline residues can be rationally engineered to unlock evolvability and thus provide access to new biocatalysts with highly improved catalytic performance.

Torsional stress can regulate the unwrapping of two outer half superhelical turns of nucleosomal DNA

Torsional stress has a significant impact on the structure and stability of the nucleosome. RNA polymerase imposes torsional stress on the DNA in chromatin and unwraps the DNA from the nucleosome to access the genetic information encoded in the DNA. To understand how the torsional stress affects the stability of the nucleosome, we examined the unwrapping of two half superhelical turns of nucleosomal DNA from either end of the DNA under torsional stress with all-atom molecular dynamics simulations. The free energies for unwrapping the DNA indicate that positive stress that overtwists DNA facilitates a large-scale asymmetric unwrapping of the DNA without a large extension of the DNA. During the unwrapping, one end of the DNA was dissociated from H3 and H2A-H2B, while the other end of the DNA stably remained wrapped. The detailed analysis indicates that this asymmetric dissociation is facilitated by the geometry and bendability of the DNA under positive stress. The geometry stabilized the interaction between the major groove of the twisted DNA and the H3 αN-helix, and the straightened DNA destabilized the interaction with H2A-H2B. Under negative stress, the DNA became more bendable and flexible, which facilitated the binding of the unwrapped DNA to the octamer in a stable state. Consequently, we conclude that the torsional stress has a significant impact on the affinity of the DNA and the octamer through the inherent nature of the DNA and can change the accessibility of regulatory proteins.

Insights into the molecular mechanism of positive cooperativity between partial agonist MK-8666 and full allosteric agonist AP8 of hGPR40 by Gaussian accelerated molecular dynamics (GaMD) simulations

Activation of human free fatty acid receptor 1 (FFAR1, also called hGPR40) enhances insulin secretion in a glucose-dependent manner. Hence, the development of selective agonist targeting hGPR40 has been proposed as a therapeutic strategy of type 2 diabetes mellitus. Some agonists targeting hGPR40 were reported. The radioligand-binding studies and the crystal structures reveal that there are multiple sites on GPR40, and there exists positive binding cooperativity between the partial agonist MK-8666 and full allosteric agonist (AgoPAM) AP8. In this work, we carried out long-time Gaussian accelerated molecular dynamics (GaMD) simulations on hGPR40 to shed light on the mechanism of the cooperativity between the two agonists at different sites. Our results reveal that the induced-fit conformational coupling is bidirectional between the two sites. The movements and rotations of TM3, TM4, TM5 and TM6 due to their inherent flexibility are crucial in coupling the conformational changes of the two agonists binding sites. These helices adopt similar conformational states upon alternative ligand or both ligands binding. The Leu138^4.57, Leu186^5.42 and Leu190^5.46 play roles in coordinating the rearrangements of residues in the two pockets, which makes the movements of residues in the two sites like gear movements. These results provide detailed information at the atomic level about the conformational coupling between different sites of GPR40, and also provide the structural information for further design of new agonists of GPR40. In addition, these results suggest that it is necessary by considering the effect of other site bound in structure-based ligands discovery.

An ACE2 Triple Decoy that neutralizes SARS-CoV-2 shows enhanced affinity for virus variants

The SARS-CoV-2 variants replacing the first wave strain pose an increased threat by their potential ability to escape pre-existing humoral protection. An angiotensin converting enzyme 2 (ACE2) decoy that competes with endogenous ACE2 for binding of the SARS-CoV-2 spike receptor binding domain (S RBD) and inhibits infection may offer a therapeutic option with sustained efficacy against variants. Here, we used Molecular Dynamics (MD) simulation to predict ACE2 sequence substitutions that might increase its affinity for S RBD and screened candidate ACE2 decoys in vitro. The lead ACE2(T27Y/H34A)-IgG1FC fusion protein with enhanced S RBD affinity shows greater live SARS-CoV-2 virus neutralization capability than wild type ACE2. MD simulation was used to predict the effects of S RBD variant mutations on decoy affinity that was then confirmed by testing of an ACE2 Triple Decoy that included an additional enzyme activity-deactivating H374N substitution against mutated S RBD. The ACE2 Triple Decoy maintains high affinity for mutated S RBD, displays enhanced affinity for S RBD N501Y or L452R, and has the highest affinity for S RBD with both E484K and N501Y mutations, making it a viable therapeutic option for the prevention or treatment of SARS-CoV-2 infection with a high likelihood of efficacy against variants.

DESP: Deep Enhanced Sampling of Proteins' Conformation Spaces Using AI-Inspired Biasing Forces

The molecular structures (i.e., conformation spaces, CS) of bio-macromolecules and the dynamics that molecules exhibit are crucial to the understanding of the basis of many diseases and in the continuous attempts to retarget known drugs/medications, improve the efficacy of existing drugs, or develop novel drugs. These make a better understanding and the exploration of the CS of molecules a research hotspot. While it is generally easy to computationally explore the CS of small molecules (such as peptides and ligands), the exploration of the CS of a larger biomolecule beyond the local energy well and beyond the initial equilibrium structure of the molecule is generally nontrivial and can often be computationally prohibitive for molecules of considerable size. Therefore, research efforts in this area focus on the development of ways that systematically favor the sampling of new conformations while penalizing the resampling of previously sampled conformations. In this work, we present Deep Enhanced Sampling of Proteins’ Conformation Spaces Using AI-Inspired Biasing Forces (DESP), a technique for enhanced sampling that combines molecular dynamics (MD) simulations and deep neural networks (DNNs), in which biasing potentials for guiding the MD simulations are derived from the KL divergence between the DNN-learned latent space vectors of [a] the most recently sampled conformation and those of [b] the previously sampled conformations. Overall, DESP efficiently samples wide CS and outperforms conventional MD simulations as well as accelerated MD simulations. We acknowledge that this is an actively evolving research area, and we continue to further develop the techniques presented here and their derivatives tailored at achieving DNN-enhanced steered MD simulations and DNN-enhanced targeted MD simulations.

Molecular conformations and dynamics of nucleotide repeats associated with neurodegenerative diseases: double helices and CAG hairpin loops

Pathogenic DNA secondary structures have been identified as a common and causative factor for expansion in trinucleotide, hexanucleotide, and other simple sequence repeats. These expansions underlie about fifty neurological and neuromuscular disorders known as “anticipation diseases”. Cell toxicity and death have been linked to the pathogenic conformations and functional changes of the RNA transcripts, of DNA itself and, when trinucleotides are present in exons, of the translated proteins. We review some of our results for the conformations and dynamics of pathogenic structures for both RNA and DNA, which include mismatched homoduplexes formed by trinucleotide repeats CAG and GAC; CCG and CGG; CTG(CUG) and GTC(GUC); the dynamics of DNA CAG hairpins; mismatched homoduplexes formed by hexanucleotide repeats (GGGGCC) and (GGCCCC); and G-quadruplexes formed by (GGGGCC) and (GGGCCT). We also discuss the dynamics of strand slippage in DNA hairpins formed by CAG repeats as observed with single-molecule Fluorescence Resonance Energy Transfer. This review focuses on the rich behavior exhibited by the mismatches associated with these simple sequence repeat noncanonical structures.

Investigating the folding mechanism of the N-terminal domain of ribosomal protein L9

Protein folding is a popular topic in the life science. However, due to the limited sampling ability of experiments and simulations, the general folding mechanism is not yet clear to us. In this work, we study the folding of the N-terminal domain of ribosomal protein L9 (NTL9) in detail by a mixing replica exchange molecular dynamics method. The simulation results are close to previous experimental observations. According to the Markov state model, the folding of the protein follows a nucleation-condensation path. Moreover, after the comparison to its 39-residue β-α-β motif, we find that the helix at the C-terminal has a great influence on the folding process of the intact protein, including the nucleation of the key residues in the transition state ensemble and the packing of the hydrophobic residues in the native state.

Selecting Collective Variables and Free-Energy Methods for Peptide Translocation across Membranes

The selective permeability of cellular membranes is a crucial property for controlled transport into and out of cells. Molecules that can bypass the cellular machinery and spontaneously translocate across membranes could be used as therapeutics or drug carriers. Peptides are a prominent class of such molecules, which include natural and man-developed antimicrobial and cell-penetrating peptides. However, the necessary peptide properties for translocation remain elusive. Computer simulations could uncover these properties once we have a good collective variable (CV) that accurately describes the translocation process. Here, we developed a new CV, which includes a description of peptide insertion, local membrane deformation, and peptide internal degrees of freedom related to its charged groups. By comparison of CVs, we demonstrated that all these components are necessary for an accurate description of peptide translocation. Moreover, the advantages and disadvantages of three common methods for free-energy calculations with our CV were evaluated using the MARTINI coarse-grained model: umbrella sampling, umbrella sampling with replica exchange, and metadynamics. The developed CV leads to the reliable and effective calculation of the free energy of peptide translocation, and thus, it could be useful in the design of spontaneously translocating peptides.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.

A novel multiscale scheme to accelerate atomistic simulations of bio-macromolecules by adaptively driving coarse-grained coordinates

All-atom molecular dynamics (MD) simulations of bio-macromolecules can yield relatively accurate results while suffering from the limitation of insufficient conformational sampling. On the other hand, the coarse-grained (CG) MD simulations efficiently accelerate conformational changes in biomolecules but lose atomistic details and accuracy. Here, we propose a novel multiscale simulation method called the adaptively driving multiscale simulation (ADMS)-it efficiently accelerates biomolecular dynamics by adaptively driving virtual CG atoms on the fly while maintaining the atomistic details and focusing on important conformations of the original system with irrelevant conformations rarely sampled. Herein, the "adaptive driving" is based on the short-time-averaging response of the system (i.e., an approximate free energy surface of the original system), without requiring the construction of the CG force field. We apply the ADMS to two peptides (deca-alanine and Ace-GGPGGG-Nme) and one small protein (HP35) as illustrations. The simulations show that the ADMS not only efficiently captures important conformational states of biomolecules and drives fast interstate transitions but also yields, although it might be in part, reliable protein folding pathways. Remarkably, a ∼100-ns explicit-solvent ADMS trajectory of HP35 with three CG atoms realizes folding and unfolding repeatedly and captures the important states comparable to those from a 398-µs standard all-atom MD simulation.

Simulation Study of the Plasticity of k-Turn Motif in Different Environments

The k-turn is a widespread and important motif in RNA. According to the internal hydrogen bond network, it has two stable states, called N1 and N3. The relative stability between the states changes with the environment. It is able to accept different conformations in different environments. This is called the "plasticity" of a molecule. In this work, we study the plasticity of k-turn by the mixing REMD method in explicit solvent. The results are concluded as follows. First, N1 and N3 are almost equally stable when k-turn is in the solvent alone. The molecule is quite flexible as a hinge. However, after binding to different proteins, such as the proteins L7Ae and L24e, k-turn falls into one global minimum. The preferred state could be either N1 or N3. On the contrary, the other nonpreferred state becomes unstable with a weaker binding affinity to the protein. It reveals that RNA-binding protein is able to modulate the representative state of k-turn at equilibrium. This is in agreement with the findings in experiments. Moreover, free energy calculations show that the free energy barrier between the N1 and N3 states of k-turn increases in the complexes. The state-to-state transition is greatly impeded. We also give a deep discussion on the mechanism of the high plasticity of k-turn in different environments.

Enhanced Conformational Sampling Method Based on Anomaly Detection Parallel Cascade Selection Molecular Dynamics: ad-PaCS-MD

In the present study, we propose a rare-event sampling method called anomaly detection parallel cascade selection molecular dynamics (ad-PaCS-MD). The original PaCS-MD was designed to generate conformational transition pathways from a given reactant to a product when the latter is known a priori. As an extension of the original method, ad-PaCS-MD has been designed to efficiently search transition pathways from a given reactant without referring to a given product. In ad-PaCS-MD, rarely occurring but essential states (configurations) of proteins for the transitions are identified based on the degrees of an anomaly. In more detail, ad-PaCS-MD adopts an algorithm called an anomaly detection generative adversarial network (anoGAN) as a measure for detecting rarely occurring states to be resampled. Here, the essential configurations with higher degrees of the anomaly are selected with anoGAN and intensively resampled by restarting short-time MD simulations from the selected configurations. By repeating the detections and resampling of configurations with the higher degrees of the anomaly, ad-PaCS-MD automatically and efficiently promotes the rare events and gives a wide range of the free energy landscape by combining with the Markov state model construction. As demonstrations, open-closed transitions of two globular proteins (T4 lysozyme and maltose-binding protein) were promoted with ad-PaCS-MD by referring only to the given starting configurations. In each demonstration, ad-PaCS-MD promoted the large-amplitude open-closed transitions with nanosecond-order simulation times. In conclusion, our demonstrations showed a higher conformational sampling efficiency for ad-PaCS-MD than conventional MD (CMD) because CMD required computational costs of more than microsecond-order simulation times to promote the rare events.

Improved Alchemical Free Energy Calculations with Optimized Smoothstep Softcore Potentials

Progress in the development of GPU-accelerated free energy simulation software has enabled practical applications on complex biological systems and fueled efforts to develop more accurate and robust predictive methods. In particular, this work re-examines concerted (a.k.a., one-step or unified) alchemical transformations commonly used in the prediction of hydration and relative binding free energies (RBFEs). We first classify several known challenges in these calculations into three categories: endpoint catastrophes, particle collapse, and large gradient-jumps. While endpoint catastrophes have long been addressed using softcore potentials, the remaining two problems occur much more sporadically and can result in either numerical instability (i.e., complete failure of a simulation) or inconsistent estimation (i.e., stochastic convergence to an incorrect result). The particle collapse problem stems from an imbalance in short-range electrostatic and repulsive interactions and can, in principle, be solved by appropriately balancing the respective softcore parameters. However, the large gradient-jump problem itself arises from the sensitivity of the free energy to large values of the softcore parameters, as might be used in trying to solve the particle collapse issue. Often, no satisfactory compromise exists with the existing softcore potential form. As a framework for solving these problems, we developed a new family of smoothstep softcore (SSC) potentials motivated by an analysis of the derivatives along the alchemical path. The smoothstep polynomials generalize the monomial functions that are used in most implementations and provide an additional path-dependent smoothing parameter. The effectiveness of this approach is demonstrated on simple yet pathological cases that illustrate the three problems outlined. With appropriate parameter selection, we find that a second-order SSC(2) potential does at least as well as the conventional approach and provides vast improvement in terms of consistency across all cases. Last, we compare the concerted SSC(2) approach against the gold-standard stepwise (a.k.a., decoupled or multistep) scheme over a large set of RBFE calculations as might be encountered in drug discovery.

Frontier Expansion Sampling: A Method to Accelerate Conformational Search by Identifying Novel Seed Structures for Restart

Traditional molecular dynamics (MD) simulations have difficulties in tracking the slow molecular motions, at least partially due to the waste of sampling in already sampled regions. Here, we proposed a new enhanced sampling method, frontier expansion sampling (FEXS), to improve the sampling efficiency of molecular simulations by iteratively selecting seed structures diversely distributed at the "frontier" of an already sampled region to initiate new simulations. Different from other enhanced sampling methods, FEXS identifies the "frontier" seeds by integrating the Gaussian mixture model and the convex hull algorithm, which effectively improves the structural variation among the selected seeds and thus the descendant simulations. Validation in three protein systems, including the folding of chignolin, open-to-closed transition of maltodextrin binding protein, and internal conformational change of bovine pancreatic trypsin inhibitor, confirmed the effectiveness of this novel method in enhancing the sampling of conventional MD simulations to observe the large-scale protein conformational changes. When compared with other enhanced sampling methods like the structural dissimilarity sampling (SDS), FEXS reached at least the same level of sampling efficiency but was capable of providing complementary information in the three tested protein systems.

Computational study on the polymerization reaction of d-aminopeptidase for the synthesis of d-peptides

Almost all natural proteins are composed exclusively of l-amino acids, and this chirality influences their properties, functions, and selectivity. Proteases can recognize proteins composed of l-amino acids but display lower selectivity for their stereoisomers, d-amino acids. Taking this as an advantage, d-amino acids can be used to develop polypeptides or biobased materials with higher biostability. Chemoenzymatic peptide synthesis is a technique that uses proteases as biocatalysts to synthesize polypeptides, and d-stereospecific proteases can be used to synthesize polypeptides incorporating d-amino acids. However, engineered proteases with modified catalytic activities are required to allow the incorporation of d-amino acids with increased efficiency. To understand the stereospecificity presented by proteases and their involvement in polymerization reactions, we studied d-aminopeptidase. This enzyme displays the ability to efficiently synthesize poly d-alanine-based peptides under mild conditions. To elucidate the mechanisms involved in the unique specificity of d-aminopeptidase, we performed quantum mechanics/molecular mechanics simulations of its polymerization reaction and determined the energy barriers presented by the chiral substrates. The enzyme faces higher activation barriers for the acylation and aminolysis reactions with the l-stereoisomer than with the d-substrate (10.7 and 17.7 kcal mol-1 higher, respectively). The simulation results suggest that changes in the interaction of the substrate with Asn155 influence the stereospecificity of the polymerization reaction.