Accelerated molecular dynamics simulations of protein folding

Molecular Mechanisms Involved in the B Cell Growth and Clonogenic Activity of HIV-1 Matrix Protein p17 Variants

The human immunodeficiency virus (HIV-1) matrix protein p17 (p17) is released from infected cells as a protein capable of deregulating the biological activity of different cells. P17 variants (vp17s), more frequently detected in the plasma of HIV-1+ patients with rather than without lymphoma and characterized by amino acids insertions in their C-terminal region, were found to trigger B cell growth and clonogenicity. Vp17s endowed with B-cell-growth-promoting activity are drastically destabilized, whereas, in a properly folded state, reference p17 (refp17) does not exert any biological activity on B cell growth and clonogenicity. However, misfolding of refp17 is necessary to expose a masked functional epitope, interacting with the protease-activated receptor 1 (PAR-1), endowed with B cell clonogenicity. Indeed, it is worth noting that changes in the secondary structure can strongly impact the function of a protein. Here, we performed computational studies to show that the gain of function of vp17s is linked to dramatic conformational changes due to structural modification in the secondary-structure elements and in the rearrangement of the hydrogen bond (H-bond) network. In particular, all clonogenic vp17s showed the disengagement of two critical residues, namely Trp16 and Tyr29, from their hydrophobic core. Biological data showed that the mutation of Trp16 and Tyr29 to Ala in the refp17 backbone, alone or in combination, resulted in a protein endowed with B cell clonogenic activity. These data show the pivotal role of the hydrophobic component in maintaining refp17 stability and identify a novel potential therapeutic target to counteract vp17-driven lymphomagenesis in HIV-1+ patients.

Illuminating ligand-induced dynamics in nuclear receptors through MD simulations

Nuclear receptors (NRs) regulate gene expression in critical physiological processes, with their functionality finely tuned by ligand-induced conformational changes. While NRs may sometimes undergo significant conformational motions in response to ligand-binding, these effects are more commonly subtle and challenging to study by traditional structural or biophysical methods. Molecular dynamics (MD) simulations are a powerful tool to bridge the gap between static protein-ligand structures and dynamical changes that govern NR function. Here, we summarize a handful of recent studies that apply MD simulations to study NRs. We present diverse methodologies for analyzing simulation data with a detailed examination of the information each method can yield. By delving into the strengths, limitations and unique contributions of these tools, this review provides guidance for extracting meaningful data from MD simulations to advance the goal of understanding the intricate mechanisms by which ligands orchestrate a range of functional outcomes in NRs.

Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics

Ligand binding free energy simulations (LB-FES) that involve sampling of protein functional conformations have been longstanding challenges in research on molecular recognition. Particularly, modeling of the conformational transition pathway and design of the heuristic biasing mechanism are severe bottlenecks for the existing enhanced configurational sampling (ECS) methods. Inspired by the key role of hydration in regulating conformational dynamics of macromolecules, this report proposes a novel ECS approach that facilitates binding-associated structural dynamics by accelerated hydration transitions in combination with the λ-exchange of free energy perturbation (FEP). Two challenging protein-ligand binding processes involving large configurational transitions of the receptor are studied, with hydration transitions at binding sites accelerated by Hamiltonian-simulated annealing of the hydration layer. Without the need for pathway analysis or ad hoc barrier flattening potential, LB-FES were performed with FEP/λ-exchange molecular dynamics simulation at a minor overhead for annealing of the hydration layer. The LB-FES studies showed that the accelerated rehydration significantly enhances the collective conformational transitions of the receptor, and convergence of binding affinity calculations is obtained at a sweet-spot simulation time scale. Alchemical LB-FES with the proposed ECS strategy is free from the effort of trial and error for the setup and realizes efficient on-the-fly sampling for the collective functional response of the receptor and bound water and therefore presents a practical approach to high-throughput screening in drug discovery.

Computational design of scFv anti-receptor binding domain of SARS-CoV-2 spike protein based on antibody S230 anti-SARS-CoV-1

Developing therapeutics such as neutralizing antibodies targeting the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein is essential to halt the Covid-19 infection. However, antibody production is expensive and relatively inaccessible to many low-income countries. Therefore, a more efficient and smaller antibody fragment, such as a single-chain variable fragment (scFv), derived from a known neutralizing antibody structure, is of interest due to the lower cost of recombinant protein production and the ability to tailor scFvs against circulating viruses. In this study, we used computational design to create an scFv based on the structure of a known neutralizing antibody, S230, for SARS-CoV-1. By analyzing the interaction of S230 with the RBD of both SARS-CoV-1 and SARS-CoV-2, five mutations were introduced to improve the binding of the scFv to the RBD of SARS-CoV-2. These mutations were Ser32Thr, Trp99Val, Asn57Val, Lys65Glu, and Tyr106Ile. Molecular dynamics simulations were used to evaluate the stability and affinity of the designed scFv. Our results showed that the designed scFv improved binding to the RBD of SARS-CoV-2 compared to the original S230, as indicated by principal component analysis, distance analysis, and MM/GBSA interaction energy. Furthermore, a positive result in a spot test lateral flow assay of the expressed scFv against the RBD indicated that the mutations did not alter the protein's structure. The designed scFv showed a negative result when tested against human serum albumin as a negative control, indicating reasonable specificity. We hope that this study will be useful in designing a specific and low-cost therapeutic agent, particularly during early outbreaks when information on neutralizing antibodies is limited.Communicated by Ramaswamy H. Sarma.

Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters

Rational predictions on binding kinetics parameters of drugs to targets play significant roles in future drug designs. Full conformational samplings of targets are requisite for accurate predictions of binding kinetic parameters. In this review, we mainly focus on the applications of enhanced sampling technologies in calculations of binding kinetics parameters and residence time of drugs. The methods involved in molecular dynamics simulations are applied to not only probe conformational changes of targets but also reveal calculations of residence time that is significant for drug efficiency. For this review, special attention are paid to accelerated molecular dynamics (aMD) and Gaussian aMD (GaMD) simulations that have been adopted to predict the association or disassociation rate constant. We also expect that this review can provide useful information for future drug design.

Calculation of Protein Folding Thermodynamics Using Molecular Dynamics Simulations

Despite advances in artificial intelligence methods, protein folding remains in many ways an enigma to be solved. Accurate computation of protein folding energetics could help drive fields such as protein and drug design and genetic interpretation. However, the challenge of calculating the state functions governing protein folding from first-principles remains unaddressed. We present here a simple approach that allows us to accurately calculate the energetics of protein folding. It is based on computing the energy of the folded and unfolded states at different temperatures using molecular dynamics simulations. From this, two essential quantities (ΔH and ΔCp) are obtained and used to calculate the conformational stability of the protein (ΔG). With this approach, we have successfully calculated the energetics of two- and three-state proteins, representatives of the major structural classes, as well as small stability differences (ΔΔG) due to changes in solution conditions or variations in an amino acid residue.

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

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

Effect of Main and Side Chains on the Folding Mechanism of the Trp-Cage Miniprotein

Proteins that do not fold into their functional native state have been linked to diseases. In this study, the influence of the main and side chains of individual amino acids on the folding of the tryptophan cage (Trp-cage), a designed 20-residue miniprotein, was analyzed. For this purpose, we calculated the solvation free energy (SFE) contributions of individual atoms by using the 3D-reference interaction site model with the atomic decomposition method. The mechanism by which the Trp-cage is stabilized during the folding process was examined by calculating the total energy, which is the sum of the conformational energy and SFE. The folding process of the Trp-cage resulted in a stable native state, with a total energy that was 62.4 kcal/mol lower than that of the unfolded state. The solvation entropy, which is considered to be responsible for the hydrophobic effect, contributed 31.3 kcal/mol to structural stabilization. In other words, the contribution of the solvation entropy accounted for approximately half of the total contribution to Trp-cage folding. The hydrophobic core centered on Trp6 contributed 15.6 kcal/mol to the total energy, whereas the solvation entropy contribution was 6.3 kcal/mol. The salt bridge formed by the hydrophilic side chains of Asp9 and Arg16 contributed 10.9 and 5.0 kcal/mol, respectively. This indicates that not only the hydrophobic core but also the salt bridge of the hydrophilic side chains gain solvation entropy and contribute to stabilizing the native structure of the Trp-cage.

Accelerated molecular dynamics study to compare the thermostability of Bacillus licheniformis and Aspergillus niger α-amylase

The thermostability of enzymes plays a significant role in the starch hydrolysis process in the industry. The structural difference between thermostable Bacillus licheniformis α-amylase (BLA) and thermolabile Aspergillus niger α-amylase (ANA) is interesting to be explored. This work aimed to study the thermostability-determining factor of BLA as compared to a non-thermostable enzyme, ANA, using molecular dynamics (MD) simulation at a high temperature. A 100 ns of classical MD, which was followed by 200 ns accelerated MD was conducted to explore the conformational changes of the enzyme. It is revealed that the intramolecular interactions through salt bridge interactions and the presence of calcium ions dominates the stability effect of BLA, despite the absence of a disulfide bond in the structure. These results should be useful in designing a thermostable enzyme that can be used in industrial processes.Communicated by Ramaswamy H. Sarma.

An interoperable implementation of collective-variable based enhanced sampling methods in extended phase space within the OpenMM package

Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. In this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.

Functionalized Fullerene Potentially Inhibits SARS-CoV-2 Infection by Modulating Spike Protein Conformational Changes

The disease of SARS-CoV-2 has caused considerable morbidity and mortality globally. Spike proteins on the surface of SARS-CoV-2 allow it to bind with human cells, leading to infection. Fullerenes and their derivatives are promising SARS-CoV-2 inhibitors and drug-delivery vehicles. In this study, Gaussian accelerated molecular dynamics simulations and the Markov state model were employed to delve into the inhibitory mechanism of Fullerene-linear-polyglycerol-b-amine sulfate (F-LGPS) on spike proteins. During the study, it was discovered that fullerene derivatives can operate at the interface of the receptor-binding domain (RBD) and the N-terminal domain (NTD), keeping structural domains in a downward conformation. It was also observed that F-LGPS demonstrated superior inhibitory effects on the XBB variant in comparison to the wild-type variant. This study yielded invaluable insights for the potential development of efficient therapeutics targeting the spike protein of SARS-CoV-2.

Application of Molecular Dynamics Simulations to Determine Interactions between Canary Seed (Phalaris canariensis L.) Bioactive Peptides and Skin-Aging Enzymes

Food bioactive peptides are well recognized for their health benefits such as antimicrobial, antioxidant, and antihypertensive benefits, among others. Their drug-like behavior has led to their potential use in targeting skin-related aging factors like the inhibition of enzymes related with the skin-aging process. In this study, canary seed peptides (CSP) after simulated gastrointestinal digestion (<3 kDa) were fractioned by RP-HPLC and their enzyme-inhibition activity towards elastase and tyrosinase was evaluated in vitro. CSP inhibited elastase (IC50 = 6.2 mg/mL) and tyrosinase (IC50 = 6.1 mg/mL), while the hydrophobic fraction-VI (0.2 mg/mL) showed the highest inhibition towards elastase (93%) and tyrosinase (67%). The peptide fraction with the highest inhibition was further characterized by a multilevel in silico workflow, including physicochemical descriptor calculations, antioxidant activity predictions, and molecular dynamics-ensemble docking towards elastase and tyrosinase. To gain insights into the skin permeation process during molecular dynamics simulations, based on their docking scores, five peptides (GGWH, VPPH, EGLEPNHRVE, FLPH, and RPVNKYTPPQ) were identified to have favorable intermolecular interactions, such as hydrogen bonding of polar residues (W, H, and K) to lipid polar groups and 2-3 Å van der Waals close contact of hydrophobic aliphatic residues (P, V, and L). These interactions can play a critical role for the passive insertion of peptides into stratum corneum model skin-membranes, suggesting a promising application of CSP for skin-aging treatments.

Deep Boosted Molecular Dynamics (DBMD): Accelerating molecular simulations with Gaussian boost potentials generated using probabilistic Bayesian deep neural network

We have developed a new Deep Boosted Molecular Dynamics (DBMD) method. Probabilistic Bayesian neural network models were implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD was demonstrated on model systems of alanine dipeptide and the fast-folding protein and RNA structures. For alanine dipeptide, 30ns DBMD simulations captured up to 83-125 times more backbone dihedral transitions than 1μs conventional molecular dynamics (cMD) simulations and were able to accurately reproduce the original free energy profiles. Moreover, DBMD sampled multiple folding and unfolding events within 300ns simulations of the chignolin model protein and identified low-energy conformational states comparable to previous simulation findings. Finally, DBMD captured a general folding pathway of three hairpin RNAs with the GCAA, GAAA, and UUCG tetraloops. Based on Deep Learning neural network, DBMD provides a powerful and generally applicable approach to boosting biomolecular simulations. DBMD is available with open source in OpenMM at https://github.com/MiaoLab20/DBMD/.

Analysis of Protein Folding Simulation with Moving Root Mean Square Deviation

We apply moving root-mean-square deviation (mRMSD), which does not require a reference structure, as a method for analyzing protein dynamics. This method can be used to calculate the root-mean-square deviation (RMSD) of structure between two specified time points and to analyze protein dynamics behavior through time series analysis. We applied this method to the Trp-cage trajectory calculated by the Anton supercomputer and found that it shows regions of stable states as well as the conventional RMSD. In addition, we extracted a characteristic structure in which the side chains of Asp1 and Arg16 form hydrogen bonds near the most stable structure of the Trp-cage. We also determined that ≥20 ns is an appropriate time interval to investigate protein dynamics using mRMSD. Applying this method to NuG2 protein, we found that mRMSD can be used to detect regions of metastable states in addition to the stable state. This method can be applied to molecular dynamics simulations of proteins whose stable structures are unknown.

GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems

Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced DFTB calculations qualitatively agree with computationally-intensive hybrid DFT benchmarks, whereas classical force fields give significant errors. Most importantly, we show that our GPU-accelerated DFTB calculations are significantly faster than previous approaches by up to two orders of magnitude. To further extend our GPU-enhanced DFTB approach, we also carried out a 10 ns metadynamics simulation of remdesivir, which is prohibitively out of reach for routine DFT-based metadynamics calculations. We find that the free-energy surfaces of remdesivir obtained from DFTB and classical force fields differ significantly, where the latter overestimates the internal energy contribution of high free-energy states. Taken together, our benchmark tests, analyses, and extensions to large biochemical systems highlight the use of GPU-enhanced DFTB simulations for efficiently predicting the free-energy surfaces/thermodynamics of large biochemical systems.

Dimeric Lectin Chimeras as Novel Candidates for Gb3-Mediated Transcytotic Drug Delivery through Cellular Barriers

Receptor-mediated transcytosis is an elegant and promising strategy for drug delivery across biological barriers. Here, we describe a novel ligand-receptor pair based on a dimeric, engineered derivative of the Pseudomonas aeruginosa lectin LecA, here termed Di-LecA, and the host cell glycosphingolipid Gb3. We characterized the trafficking kinetics and transcytosis efficiencies in polarized Gb3-positive and -negative MDCK cells using mainly immunofluorescence in combination with confocal microscopy. To evaluate the delivery capacity of dimeric LecA chimeras, EGFP was chosen as a fluorescent model protein representing macromolecules, such as antibody fragments, and fused to either the N- or C-terminus of monomeric LecA using recombinant DNA technology. Both LecA/EGFP fusion proteins crossed cellular monolayers in vitro. Of note, the conjugate with EGFP at the N-terminus of LecA (EGFP-LecA) showed a higher release rate than the conjugate with EGFP at the C-terminus (LecA-EGFP). Based on molecular dynamics simulations and cross-linking studies of giant unilamellar vesicles, we speculate that EGFP-LecA tends to be a dimer while LecA-EGFP forms a tetramer. Overall, we confidently propose the dimeric LecA chimeras as transcytotic drug delivery tools through Gb3-positive cellular barriers for future in vivo tests.

Beta-rich intermediates in denaturation of lysozyme: accelerated molecular dynamics simulations

Amyloid fibrillar aggregates play a critical role in many neurodegenerative disorders. Conversion of globular proteins into fibrils is associated with global conformational rearrangement and involves the transformation of α-helices to β-sheets. In the present work, the accelerated molecular dynamics technique was applied to study the unfolding of hen egg-white lysozyme at elevated temperatures, and the transformation of the native structure to a disordered one was analyzed. The influence of the disulfide bonds on the conformational dynamics and the energy landscape of denaturation process was considered. Our results show that formation of the metastable β-enriched conformers of individual protein molecules may precede the aggregation process. These β-rich intermediates can play a role of bricks making up fibrils.Communicated by Ramaswamy H. Sarma.

A genetically encoded BRET-based SARS-CoV-2 Mpro protease activity sensor

The main protease, Mpro, is critical for SARS-CoV-2 replication and an appealing target for designing anti-SARS-CoV-2 agents. Therefore, there is a demand for the development of improved sensors to monitor its activity. Here, we report a pair of genetically encoded, bioluminescence resonance energy transfer (BRET)-based sensors for detecting Mpro proteolytic activity in live cells as well as in vitro. The sensors were generated by sandwiching peptides containing the Mpro N-terminal autocleavage sites, either AVLQSGFR (short) or KTSAVLQSGFRKME (long), in between the mNeonGreen and NanoLuc proteins. Co-expression of the sensors with Mpro in live cells resulted in their cleavage while mutation of the critical C145 residue (C145A) in Mpro completely abrogated their cleavage. Additionally, the sensors recapitulated the inhibition of Mpro by the well-characterized pharmacological agent GC376. Further, in vitro assays with the BRET-based Mpro sensors revealed a molecular crowding-mediated increase in the rate of Mpro activity and a decrease in the inhibitory potential of GC376. The sensors developed here will find direct utility in studies related to drug discovery targeting the SARS-CoV-2 Mpro and functional genomics application to determine the effect of sequence variation in Mpro.

Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins

Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.

An Efficient Gaussian-Accelerated Molecular Dynamics (GaMD) Multilevel Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

We introduce a novel multilevel enhanced sampling strategy grounded on Gaussian-accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups, thanks to the use of fast multiple-time step integrators. To further reduce the time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD─US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the solvated CD2-CD58 system (168 000 atoms), allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added, enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling-US-GaMD (ASUS-GaMD) scheme. The highly parallel ASUS-GaMD setup decreases time to convergence by, respectively, 10 and 20 times, compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water" enhanced sampling approaches increasing the applicability of polarizable force fields to large-scale simulations of biological systems.

Gaussian Accelerated Molecular Dynamics Simulations Investigation on the Mechanism of Angiotensin-Converting Enzyme (ACE) C-Domain Inhibition by Dipeptides

Angiotensin-converting enzyme (ACE)-inhibitory peptides extracted from food proteins can lower blood pressure by inhibiting ACE activity. A recent study showed that the inhibitory activity of IY (Ile-Tyr, a dipeptide derived from soybean protein) against ACE was much higher than that of LL (Leu-Leu), although they had similar hydrophobic and predicted activity values. It was difficult to reveal the deep molecular mechanism underlying this phenomenon by traditional experimental methods. The Apo and two complex systems (i.e., ACE–LL and ACE–IY) were therefore subjected to 1 μs long Gaussian accelerated molecular dynamics (GaMD) simulations. The results showed that the binding of IY can cause obvious contraction of the active site of ACE, mainly manifested by a significant lateral shift of α13, α14, and α15. In addition, hinge 2 and hinge 3 were more stable in the ACE–IY system, while these phenomena were not present in the ACE–LL system. Moreover, the α10 of the IY-bound ACE kept an inward state during the simulation progress, which facilitated the ACE to remain closed. However, for the LL-bound ACE, the α10 switched between two outward states. To sum up, our study provides detailed insights into inhibitor-induced conformational changes in ACE that may help in the design of specific inhibitors targeting ACE for the treatment of hypertension.

IgG1-b12-HIV-gp120 Interface in Solution: A Computational Study

The use of broadly neutralizing antibodies against human immunodeficiency virus type 1 (HIV-1) has been shown to be a promising therapeutic modality in the prevention of HIV infection. Understanding the b12-gp120 binding mechanism under physiological conditions may assist the development of more broadly effective antibodies. In this work, the main conformations and interactions between the receptor-binding domain (RBD) of spike glycoprotein gp120 of HIV-1 and the IgG1-b12 mAb are studied. Accelerated molecular dynamics (aMD) and ab initio hybrid molecular dynamics have been combined to determine the most persistent interactions between the most populated conformations of the antibody-antigen complex under physiological conditions. The results show the most persistent receptor-binding mapping in the conformations of the antibody-antigen interface in solution. The binding-free-energy decomposition reveals a small enhancement in the contribution played by the CDR-H3 region to the b12-gp120 interface compared to the crystal structure.

Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives

Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design.

Peptide stapling by late-stage Suzuki–Miyaura cross-coupling

The development of peptide stapling techniques to stabilise α-helical secondary structure motifs of peptides led to the design of modulators of protein–protein interactions, which had been considered undruggable for a long time. We disclose a novel approach towards peptide stapling utilising macrocyclisation by late-stage Suzuki–Miyaura cross-coupling of bromotryptophan-containing peptides of the catenin-binding domain of axin. Optimisation of the linker length in order to find a compromise between both sufficient linker rigidity and flexibility resulted in a peptide with an increased α-helicity and enhanced binding affinity to its native binding partner β-catenin. An increased proteolytic stability against proteinase K has been demonstrated.

Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics

Enhanced sampling methods such as metadynamics and umbrella sampling have become essential tools for exploring the configuration space of molecules and materials. At the same time, they have long faced a number of issues such as the inefficiency when dealing with a large number of collective variables (CVs) or systems with high free energy barriers. Here we show that, with clustering and adaptive tuning techniques, the reinforced dynamics (RiD) scheme can be used to efficiently explore the configuration space and free energy landscapes with a large number of CVs or systems with high free energy barriers. We illustrate this by studying various representative and challenging examples. First we demonstrate the efficiency of adaptive RiD compared with other methods and construct the nine-dimensional (9D) free energy landscape of a peptoid trimer, which has energy barriers of more than 8 kcal mol-1. We then study the folding of the protein chignolin using 18 CVs. In this case, both the folding and unfolding rates are observed to be 4.30 μs-1. Finally, we propose a protein structure refinement protocol based on RiD. This protocol allows us to efficiently employ more than 100 CVs for exploring the landscape of protein structures and it gives rise to an overall improvement of 14.6 units over the initial global distance test-high accuracy (GDT-HA) score.

Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery

Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.

Boosting the conformational sampling by combining replica exchange with solute tempering and well-sliced metadynamics

Methods that combine collective variable (CV) based enhanced sampling and global tempering approaches are used in speeding-up the conformational sampling and free energy calculation of large and soft systems with a plethora of energy minima. In this paper, a new method of this kind is proposed in which the well-sliced metadynamics approach (WSMTD) is united with replica exchange with solute tempering (REST2) method. WSMTD employs a divide-and-conquer strategy wherein high-dimensional slices of a free energy surface are independently sampled and combined. The method enables one to accomplish a controlled exploration of the CV-space with a restraining bias as in umbrella sampling, and enhance-sampling of one or more orthogonal CVs using a metadynamics like bias. The new hybrid method proposed here enables boosting the sampling of more slow degrees of freedom in WSMTD simulations, without the need to specify associated CVs, through a replica exchange scheme within the framework of REST2. The high-dimensional slices of the probability distributions of CVs computed from the united WSMTD and REST2 simulations are subsequently combined using the weighted histogram analysis method to obtain the free energy surface. We show that the new method proposed here is accurate, improves the conformational sampling, and achieves quick convergence in free energy estimates. We demonstrate this by computing the conformational free energy landscapes of solvated alanine tripeptide and Trp-cage mini protein in explicit water.

Unravelling the molecular interactions between the SARS-CoV-2 RBD spike protein and various specific monoclonal antibodies

Vaccination against SARS-CoV-2 just started in most of the countries. However, the development of specific vaccines against SARS-CoV-2 is not the only approach to control the virus and monoclonal antibodies (mAbs) start to merit special attention as a therapeutic option to treat COVID-19 disease. Here, the main conformations and interactions between the receptor-binding domain (RBD) of spike glycoprotein of SARS-CoV-2 (S protein) with two mAbs (CR3022 and S309) and the ACE2 cell receptor are studied as the main representatives of three different epitopes on the RBD of S protein. The combined approach of 1 μs accelerated molecular dynamics (aMD) and ab-initio hybrid molecular dynamics is used to identify the most predominant interactions under physiological conditions. Results allow to determine the main receptor-binding mapping, hydrogen bonding network and salt bridges in the most populated antigen-antibody interface conformations. The deep knowledge on the protein-protein interactions involving mAbs and ACE2 receptor with the spike glycoprotein of SARS-CoV-2 increases background knowledge to speed up the development of new vaccines and therapeutic drugs.

Specific Engineered G Protein Coupling to Histamine Receptors Revealed from Cellular Assay Experiments and Accelerated Molecular Dynamics Simulations

G protein-coupled receptors (GPCRs) are targets of extracellular stimuli and hence occupy a key position in drug discovery. By specific and not yet fully elucidated coupling profiles with α subunits of distinct G protein families, they regulate cellular responses. The histamine H2 and H4 receptors (H2R and H4R) are prominent members of Gs- and Gi-coupled GPCRs. Nevertheless, promiscuous G protein and selective Gi signaling have been reported for the H2R and H4R, respectively, the molecular mechanism of which remained unclear. Using a combination of cellular experimental assays and Gaussian accelerated molecular dynamics (GaMD) simulations, we investigated the coupling profiles of the H2R and H4R to engineered mini-G proteins (mG). We obtained coupling profiles of the mGs, mGsi, or mGsq proteins to the H2R and H4R from the mini-G protein recruitment assays using HEK293T cells. Compared to H2R-mGs expressing cells, histamine responses were weaker (pEC50, Emax) for H2R-mGsi and -mGsq. By contrast, the H4R selectively bound to mGsi. Similarly, in all-atom GaMD simulations, we observed a preferential binding of H2R to mGs and H4R to mGsi revealed by the structural flexibility and free energy landscapes of the complexes. Although the mG α5 helices were consistently located within the HR binding cavity, alternative binding orientations were detected in the complexes. Due to the specific residue interactions, all mG α5 helices of the H2R complexes adopted the Gs-like orientation toward the receptor transmembrane (TM) 6 domain, whereas in H4R complexes, only mGsi was in the Gi-like orientation toward TM2, which was in agreement with Gs- and Gi-coupled GPCRs structures resolved by X-ray/cryo-EM. These cellular and molecular insights support (patho)physiological profiles of the histamine receptors, especially the hitherto little studied H2R function in the brain, as well as of the pharmacological potential of H4R selective drugs.

Gaussian accelerated molecular dynamics (GaMD): principles and applications

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

Unraveling the cGAS catalytic mechanism upon DNA activation through molecular dynamics simulations

Cyclic GMP-AMP Synthase (cGAS) is activated upon DNA binding and catalyzes the synthesis of 2',3'-cGAMP from GTP and ATP. This cyclic dinucleotide is a messenger that triggers the autoimmune system of eukaryotic cells. In this study, we propose a Molecular Dynamics (MD) investigation of cGAS activation. We notably provide insights into the motion of the activation loop, both from a mechanical point of view and considering its role in the catalysis of cGAMP production. We finally shed light on the reaction resulting in cGAMP synthesis. Two possible catalytic routes (referred to as GTP-ATP and ATP-GTP) are proposed based on the active site occupancy, paving the way toward further exploration of the reaction mechanism.

Comprehensive Characterization of the Self-Folding Cavitand Dynamics

The conformational equilibria and guest exchange process of a resorcin[4]arene derived self-folding cavitand receptor have been characterized in detail by molecular dynamics simulations (MD) and ¹ H EXSY NMR experiments. A multi-timescale strategy for exploring the fluxional behaviour of this system has been constructed, exploiting conventional MD and accelerated MD (aMD) techniques. The use of aMD allows the reconstruction of the folding/unfolding process of the receptor by sampling high-energy barrier processes unattainable by conventional MD simulations. We obtained MD trajectories sampling events occurring at different timescales from ns to s: 1) rearrangement of the directional hydrogen bond seam stabilizing the receptor, 2) folding/unfolding of the structure transiting partially open intermediates, and 3) guest departure from different folding stages. Most remarkably, reweighing of the biased aMD simulations provided kinetic barriers that are in very good agreement with those determined experimentally by ¹ H NMR. These results constitute the first comprehensive characterization of the complex dynamic features of cavitand receptors. Our approach emerges as a valuable rational design tool for synthetic host-guest systems.

Biomolecular modeling thrives in the age of technology

The biomolecular modeling field has flourished since its early days in the 1970s due to the rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase in size and timespan of biomolecular simulations has outpaced Moore's law. Here, we discuss the role of knowledge-based versus physics-based methods and hardware versus software advances in propelling the field forward. This rapid adaptation and outreach suggests a bright future for modeling, where theory, experimentation and simulation define three pillars needed to address future scientific and biomedical challenges.

Pathway regulation mechanism revealed by cotranslational folding of villin headpiece subdomain HP35

Cotranslational folding is one of the most important features of protein folding in vivo. Although many studies have shown that the folding pathways of cotranslational folding are different from free folding in vitro, the detailed mechanism of folding dynamics is lacking. Here we combine all-atom molecular simulations with an ideal ribosome tunnel model to investigate the cotranslational folding of villin headpiece subdomain HP35. By comparing the folding dynamics between cotranslational folding and free folding, we found that cotranslational folding tends to fold along the pathway that is easier to fold into native state in the latter. In addition, the roles of the ribosome tunnel and sequential folding are analyzed separately. Our results show that the ribosome can prevent the untimely folding of the C segment of HP35 to reduce the non-native interactions, while the translation speed can regulate the amounts of native and non-native interactions and the balance between them. Overall, these results give insights into the general mechanisms of cotranslational protein folding.

Quality of force fields and sampling methods in simulating pepX peptides: a case study for intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are a group of proteins that lack well-defined structures under native conditions and carry out crucial physiological functions in various biochemical pathways. Due to the heterogeneous nature of IDPs, molecular dynamics simulations have been extensively adopted to investigate the conformational ensembles and dynamic properties of these proteins. However, their accuracy remains limited by the development of force fields and sampling algorithms. Here, we evaluated the quality of both force fields and enhanced sampling algorithms based on five short pepX peptides. Our results show that the more extended conformational ensembles sampled by the AMOEBA polarizable force field present a higher ability to reproduce experimental NMR observables than AMBER and CHARMM classical force fields. Moreover, a better agreement with experiments is achieved in the simulation of IaMD (integrated accelerated molecular dynamics) than in aMD (accelerated molecular dynamics). The results together indicate that the combination of AMOEBA force field and IaMD enhanced sampling might be a better choice for simulating IDPs. This work may provide important clues for developments and applications of force fields and enhanced sampling methods in future simulations of IDPs.

Fibril fragments from the amyloid core of lysozyme: An accelerated molecular dynamics study

Protein aggregation and formation of amyloid fibrils are associated with many diseases and present a ubiquitous problem in protein science. Hen egg white lysozyme (HEWL) can form fibrils both from the full length protein and from its fragments. In the present study, we simulated unfolding of the amyloidogenic fragment of HEWL encompassing residues 49-101 to study the conformational aspects of amyloidogenesis. The accelerated molecular dynamics approach was used to speed up the sampling of the fragment conformers under enhanced temperature. Analysis of conformational transformation and intermediate structures was performed. During the unfolding, the novel short-living and long-living β-structures are formed along with the unstructured random coils. Such β-structure enriched monomers can interact with each other and propagate into fibril-like forms. The stability of oligomers assembled from these monomers was evaluated in the course of MD simulations with explicit water. The residues playing a key role in fibril stabilization were determined. The work provides new insights into the processes occurring at the early stages of amyloid fibril assembly.

Adaptively Iterative Multiscale Switching Simulation Strategy and Applications to Protein Folding and Structure Prediction

Structure prediction is an important means to quickly understand new protein functions. However, the prediction of effects of proteins that have no detectable templates is still to be improved. Molecular dynamics simulation is supposed to be the primary research tool for structure predictions, but it still has limitations of huge computational cost in all-atom (AA) models and rough accuracy in coarse-grained (CG) models. We propose a universal multiscale simulation strategy named AIMS in which simulations can iteratively switch among multiple resolutions in order to adaptively trade off AA accuracy and CG high-efficiency. AIMS follows the idea of CG-guided enhanced sampling so that final results always keep AA accuracy. We successfully achieve four ab initio and four data-assisted protein structure predictions using AIMS. The prediction result is an ensemble rather than a structure and provides special insights on folding metastable states. AIMS is estimated to achieve a computational speed about 40 times faster than that of conventional AA simulations.

Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside-the-box, predictions not found in other state-of-the-art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.

Anti-TNF Alpha Antibody Humira with pH-dependent Binding Characteristics: A constant-pH Molecular Dynamics, Gaussian Accelerated Molecular Dynamics, and In Vitro Study

Humira is a monoclonal antibody that binds to TNF alpha, inactivates TNF alpha receptors, and inhibits inflammation. Neonatal Fc receptors can mediate the transcytosis of Humira-TNF alpha complex structures and process them toward degradation pathways, which reduces the therapeutic effect of Humira. Allowing the Humira-TNF alpha complex structures to dissociate to Humira and soluble TNF alpha in the early endosome to enable Humira recycling is crucial. We used the cytoplasmic pH (7.4), the early endosomal pH (6.0), and pKa of histidine side chains (6.0-6.4) to mutate the residues of complementarity-determining regions with histidine. Our engineered Humira (W1-Humira) can bind to TNF alpha in plasma at neutral pH and dissociate from the TNF alpha in the endosome at acidic pH. We used the constant-pH molecular dynamics, Gaussian accelerated molecular dynamics, two-dimensional potential mean force profiles, and in vitro methods to investigate the characteristics of W1-Humira. Our results revealed that the proposed Humira can bind TNF alpha with pH-dependent affinity in vitro. The W1-Humira was weaker than wild-type Humira at neutral pH in vitro, and our prediction results were close to the in vitro results. Furthermore, our approach displayed a high accuracy in antibody pH-dependent binding characteristics prediction, which may facilitate antibody drug design. Advancements in computational methods and computing power may further aid in addressing the challenges in antibody drug design.

Unveiling conformational dynamics changes of H-Ras induced by mutations based on accelerated molecular dynamics

Uncovering molecular basis with regard to the conformational change of two switches I and II in the GppNHp (GNP)-bound H-Ras is highly significant for the understanding of Ras signaling. For this purpose, accelerated molecular dynamics (aMD) simulations and principal component (PC) analysis are integrated to probe the effect of mutations G12V, T35S and Q61K on conformational transformation between two switches of the GNP-bound H-Ras. The RMSF and cross-correlation analyses suggest that three mutations exert a vital effect on the flexibility and internal dynamics of two switches in the GNP-bound H-Ras. The results stemming from PC analysis indicate that two switches in the GNP-bound WT H-Ras tend to form a closed state in most conformations, while those in the GNP-bound mutated H-Ras display transformation between different states. This conclusion is further supported by free energy landscapes constructed by using the distances of residues 12 away from 35 and 35 away from 61 as reaction coordinates and different experimental studies. Interaction scanning is performed on aMD trajectories and the information shows that conformational transformations of two switches I and II induced by mutations extremely affect the GNP-residue interactions. Meanwhile, the scanning results also signify that residues G15, A18, F28, K117, A146 and K147 form stable contacts with GNP, while residues D30, E31, Y32, D33, P34 and E62 in two switches I and II produce unstable contacts with GNP. This study not only reveals dynamic behavior changes of two switches in H-Ras induced by mutations, but also unveils general principles and mechanisms with regard to functional conformational changes of H-Ras.

Group II metabotropic glutamate receptor activation in the basolateral amygdala mediates individual differences in stress-induced changes in rapid eye movement sleep

Group II metabotropic glutamate receptors (mGluR2/3s) have been implicated in stress and trauma related disorders including post-traumatic stress disorder (PTSD). PTSD is characterized by flashbacks, anxiety, and sleep disturbances. While many people are exposed to trauma in their lifetime, only a small percentage go on to develop PTSD, indicating individual differences in stress and emotional processing. Wistar strain rats display directionally different rapid-eye movement sleep (REM) responses to footshock stress, with resilient rats having no change or an increase in REM and vulnerable rats having a significant reduction in REM compared to baseline. The basolateral nucleus of the amygdala (BLA) is key in regulating individual differences in stress-induced alterations in sleep. Group II metabotropic glutamate receptors (mGluR2/3s) negatively modulate glutamate and are implicated in fear, fear memory, and sleep. The current study evaluated the effect of mGluR2/3 agonist LY379268 (LY37) in BLA on stress and fear memory induced changes in sleep, EEG spectra, behavioral fear expression and physiological stress. These data indicate that vulnerable rats treated with LY37 have an attenuation of the REM reductions generally seen in vulnerable rats. Furthermore, LY37 altered EEG spectra in the delta (0.5-4.5 Hz) and theta (5-9.5 Hz) frequency. LY37 did not impact behavioral fear expression or physiological stress. Therefore, mGluR2/3s within BLA are implicated in regulating individual differences in sleep responses to fear- and stress-related memories.

Characterizing an allosteric inhibitor-induced inactive state in with-no-lysine kinase 1 using Gaussian accelerated molecular dynamics simulations

The binding of an allosteric inhibitor in WNK1 leads to the inactive state.

Tuning the Biological Activity of RGD Peptides with Halotryptophans†

An array of l- and d-halotryptophans with different substituents at the indole moiety was synthesized employing either enzymatic halogenation by halogenases or incorporation of haloindoles using tryptophan synthase. Introduction of these Trp derivatives into RGD peptides as a benchmark system was performed to investigate their influence on bioactivity. Halotryptophan-containing RGD peptides display increased affinity toward integrin α_vβ₃ and enhanced selectivity over integrin α₅β₁. In addition, bromotryptophan was exploited as a platform for late-stage diversification by Suzuki-Miyaura cross-coupling (SMC), resulting in new-to-nature biaryl motifs. These peptides show enhanced affinity toward α_vβ₃, good affinity to α_vβ₈, and remarkable selectivity over α₅β₁ and α_IIbβ₃ while featuring fluorogenic properties. Their feasibility as a probe was demonstrated in vitro. Extensive molecular dynamics simulations were undertaken to elucidate NMR and high-performance liquid chromatography (HPLC) data for these late-stage diversified cyclic RGD peptides and to further characterize their conformational preferences.

EasyAmber: A comprehensive toolbox to automate the molecular dynamics simulation of proteins

Conformational plasticity of the functionally important regions and binding sites in protein/enzyme structures is one of the key factors affecting their function and interaction with substrates/ligands. Molecular dynamics (MD) can address the challenge of accounting for protein flexibility by predicting the time-dependent behavior of a molecular system. It has a potential of becoming a particularly important tool in protein engineering and drug discovery, but requires specialized training and skills, what impedes practical use by many investigators. We have developed the easyAmber - a comprehensive set of programs to automate the molecular dynamics routines implemented in the Amber package. The toolbox can address a wide set of tasks in computational biology struggling to account for protein flexibility. The automated workflow includes a complete set of steps from the initial "static" molecular model to the MD "production run": the full-atom model building, optimization/equilibration of the molecular system, classical/conventional and accelerated molecular dynamics simulations. The easyAmber implements advanced MD protocols, but is highly automated and easy-to-operate to attract a broad audience. The toolbox can be used on a personal desktop station equipped with a compatible gaming GPU-accelerator, as well as help to manage huge workloads on a powerful supercomputer. The software provides an opportunity to operate multiple simulations of different proteins at the same time, thus significantly increasing work efficiency. The easyAmber takes the molecular dynamics to the next level in terms of usability for complex processing of large volumes of data, thus supporting the recent trend away from inefficient "static" approaches in biology toward a deeper understanding of the dynamics in protein structures. The software is freely available for download at https://biokinet.belozersky.msu.ru/easyAmber, no login required.

Enhanced sampling and free energy calculations for protein simulations

Molecular dynamics simulation is a powerful computational technique to study biomolecular systems, which complements experiments by providing insights into the structural dynamics relevant to biological functions at atomic scale. It can also be used to calculate the free energy landscapes of the conformational transitions to better understand the functions of the biomolecules. However, the sampling of biomolecular configurations is limited by the free energy barriers that need to be overcome, leading to considerable gaps between the timescales reached by MD simulation and those governing biological processes. To address this issue, many enhanced sampling methodologies have been developed to increase the sampling efficiency of molecular dynamics simulations and free energy calculations. Usually, enhanced sampling algorithms can be classified into methods based on collective variables (CV-based) and approaches which do not require predefined CVs (CV-free). In this chapter, the theoretical basis of free energy estimation is briefly reviewed first, followed by the reviews of the most common CV-based and CV-free methods including the presentation of some examples and recent developments. Finally, the combination of different enhanced sampling methods is discussed.