Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening.

Binding free energy calculation of a ligand to a protein receptor is a fundamental objective in drug discovery. Molecular mechanics/Generalized-Born (Poisson-Boltzmann) surface area (MM/GB(PB)SA) is one of the most popular methods for binding free energy calculations. It is more accurate than most scoring functions and more computationally efficient than alchemical free energy methods. Several open-source tools for performing MM/GB(PB)SA calculations have been developed, but they have limitations and high entry barriers to users. Here, we introduce Uni-GBSA, a user-friendly automatic workflow to perform MM/GB(PB)SA calculations, which can perform topology preparation, structure optimization, binding free energy calculation and parameter scanning for MM/GB(PB)SA calculations. It also offers a batch mode that evaluates thousands of molecules against one protein target in parallel for efficient application in virtual screening. The default parameters are selected after systematic testing on the PDBBind-2011 refined dataset. In our case studies, Uni-GBSA produced a satisfactory correlation with the experimental binding affinities and outperformed AutoDock Vina in molecular enrichment. Uni-GBSA is available as an open-source package at https://github.com/dptech-corp/Uni-GBSA. It can also be accessed for virtual screening from the Hermite web platform at https://hermite.dp.tech. A free Uni-GBSA web server of a lab version is available at https://labs.dp.tech/projects/uni-gbsa/. This increases user-friendliness because the web server frees users from package installations and provides users with validated workflows for input data and parameter settings, cloud computing resources for efficient job completions, a user-friendly interface and professional support and maintenance.

Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis.

Understanding drug selectivity mechanism is a long-standing issue for helping design drugs with high specificity. Designing drugs targeting cyclin-dependent kinases (CDKs) with high selectivity is challenging because of their highly conserved binding pockets. To reveal the underlying general selectivity mechanism, we carried out comprehensive analyses from both the thermodynamics and kinetics points of view on a representative CDK12 inhibitor. To fully capture the binding features of the drug-target recognition process, we proposed to use kinetic residue energy analysis (KREA) in conjunction with the community network analysis (CNA) to reveal the underlying cooperation effect between individual residues/protein motifs to the binding/dissociating process of the ligand. The general mechanism of drug selectivity in CDKs can be summarized as that the difference of structural cooperation between the ligand and the protein motifs leads to the difference of the energetic contribution of the key residues to the ligand. The proposed mechanisms may be prevalent in drug selectivity issues, and the insights may help design new strategies to overcome/attenuate the drug selectivity associated problems.

Solvent Isotherms and Structural Transitions in Nanoparticle Superlattice Assembly.

We introduce a Molecular Theory for Compressible Fluids (MOLT-CF) that enables us to compute free energies and other thermodynamic functions for nanoparticle superlattices with any solvent content, including the dry limit. Quantitative agreement is observed between MOLT-CF and united-atom molecular dynamics simulations performed to assess the reliability and precision of the theory. Among other predictions, MOLT-CF shows that the amount of solvent within the superlattice decreases approximately linearly with its vapor pressure and that in the late stages of drying, solvent-filled voids form at lattice interstitials. Applied to single-component superlattices, MOLT-CF predicts fcc-to-bcc Bain transitions for decreasing vapor pressure and for increasing ligand length, both in agreement with experimental results. We explore the stability of other single-component phases and show that the C14 Frank-Kasper phase, which has been reported in experiments, is not a global free-energy minimum. Implications for precise assembly and prediction of multicomponent nanoparticle systems are discussed.

Computational Dissection of Membrane Transport at a Microscopic Level.

Membrane transporters are key gatekeeper proteins at cellular membranes that closely control the traffic of materials. Their function relies on structural rearrangements of varying degrees that facilitate substrate translocation across the membrane. Characterizing these functionally important molecular events at a microscopic level is key to our understanding of membrane transport, yet challenging to achieve experimentally. Recent advances in simulation technology and computing power have rendered molecular dynamics (MD) simulation a powerful biophysical tool to investigate a wide range of dynamical events spanning multiple spatial and temporal scales. Here, we review recent studies of diverse membrane transporters using computational methods, with an emphasis on highlighting the technical challenges, key lessons learned, and new opportunities to illuminate transporter structure and function.

Efficient and accurate binding free energy calculation of Aß9-40 protofilament propagation.

Self-assembled aggregation of peptides and proteins into regular amyloid fibrils is associated with several neurodegenerative diseases. In case of Alzheimer's disease proteolytic cleavage products of the amyloid precursor protein form pathological amyloid-beta fibrils in a nucleation and propagation phase. The molecular details and thermodynamic driving forces of amyloid formation are not well understood, but are of high relevance for potential pharmacological interference. We used atomistic binding free energy simulations to calculate the free energy of protofilament propagation by an additional Aß9-40 peptide binding to the protofilament tip. It requires sampling of relevant conformational transitions which is challenging since the monomeric Aß9-40 peptide is intrinsically disordered. However, the convergence of umbrella simulations can be enhanced by applying additional restraining potentials on the axial, orientational and conformational degrees of freedom. The improved convergence leads to a much closer agreement with experimental binding free energy data compared to unrestrained umbrella sampling. Moreover, the restraining approach results in a separation of contributions to the total binding free energy. The calculated contributions indicate that the free energy change associated with the restriction of conformational freedom upon propagation makes a large opposing contribution of higher magnitude than the total binding free energy. Finally, optimization of the approach leads to further significant reduction of the computational demand which is crucial for systematic studies on mutations, denaturants and inhibitors in the fibril propagation step.

Synthesis and unexpected binding of monofluorinated N,N'-diacetylchitobiose and LacdiNAc to wheat germ agglutinin.

Fluorination of carbohydrate ligands of lectins is a useful approach to examine their binding profile, improve their metabolic stability and lipophilicity, and convert them into 19F NMR-active probes. However, monofluorination of monovalent carbohydrate ligands often leads to a decreased or completely lost affinity. By chemical glycosylation, we synthesized the full series of methyl ß-glycosides of N,N'-diacetylchitobiose (GlcNAcß(1-4)GlcNAcß1-OMe) and LacdiNAc (GalNAcß(1-4)GlcNAcß1-OMe) systematically monofluorinated at all hydroxyl positions. A competitive enzyme-linked lectin assay revealed that the fluorination at the 6'-position of chitobioside resulted in an unprecedented increase in affinity to wheat germ agglutinin (WGA) by one order of magnitude. For the first time, we have characterized the binding profile of a previously underexplored WGA ligand LacdiNAc. Surprisingly, 4'-fluoro-LacdiNAc bound WGA even stronger than unmodified LacdiNAc. These observations were interpreted using molecular dynamic calculations along with STD and transferred NOESY NMR techniques, which gave evidence for the strengthening of CH/π interactions after deoxyfluorination of the side chain of the non-reducing GlcNAc. These results highlight the potential of fluorinated glycomimetics as high-affinity ligands of lectins and 19F NMR-active probes.

Effect of phenolic-hydroxy-group incorporation on the biological activity of a simplified aplysiatoxin analog with an (R)-(-)-carvone-based core.

We synthesized a phenolic hydroxy group-bearing version (1) of a simplified analog of aplysiatoxin comprising a carvone-based conformation-controlling unit. Thereafter, we evaluated its antiproliferative activity against human cancer cell lines and its binding affinity to protein kinase C (PKC) isozymes. The antiproliferative activity and PKC-binding ability increased with the introduction of the phenolic hydroxy group. The results of molecular dynamics simulations and subsequent relative binding free-energy calculations conducted using an alchemical transformation procedure showed that the phenolic hydroxy group in 1 could form a hydrogen bond with a phospholipid and the PKC. The former hydrogen bonding formation facilitated the partitioning of the compound from water to the phospholipid membrane and the latter compensated for the loss of hydrogen bond with the phospholipid upon binding to the PKC. This information may facilitate the development of rational design methods for PKC ligands with additional hydrogen bonding groups.

Highlighting the Major Role of Cyclin C in Cyclin-Dependent Kinase 8 Activity through Molecular Dynamics Simulations.

Dysregulation of cyclin-dependent kinase 8 (CDK8) activity has been associated with many diseases, including colorectal and breast cancer. As usual in the CDK family, the activity of CDK8 is controlled by a regulatory protein called cyclin C (CycC). But, while human CDK family members are generally activated in two steps, that is, the binding of the cyclin to CDK and the phosphorylation of a residue in the CDK activation loop, CDK8 does not require the phosphorylation step to be active. Another peculiarity of CDK8 is its ability to be associated with CycC while adopting an inactive form. These specificities raise the question of the role of CycC in the complex CDK8-CycC, which appears to be more complex than the other members of the CDK family. Through molecular dynamics (MD) simulations and binding free energy calculations, we investigated the effect of CycC on the structure and dynamics of CDK8. In a second step, we particularly focused our investigation on the structural and molecular basis of the protein-protein interaction between the two partners by finely analyzing the energetic contribution of residues and simulating the transition between the active and the inactive form. We found that CycC has a stabilizing effect on CDK8, and we identified specific interaction hotspots within its interaction surface compared to other human CDK/Cyc pairs. Targeting these specific interaction hotspots could be a promising approach in terms of specificity to effectively disrupt the interaction between CDK8. The simulation of the conformational transition from the inactive to the active form of CDK8 suggests that the residue Glu99 of CycC is involved in the orientation of three conserved arginines of CDK8. Thus, this residue may assume the role of the missing phosphorylation step in the activation mechanism of CDK8. In a more general view, these results point to the importance of keeping the CycC in computational studies when studying the human CDK8 protein in both the active and the inactive form.

Unraveling the Binding Mode of Cyclic Adenosine-Inosine Monophosphate (cAIMP) to STING through Molecular Dynamics Simulations.

The stimulator of interferon genes (STING) plays a significant role in immune defense and protection against tumor proliferation. Many cyclic dinucleotide (CDN) analogues have been reported to regulate its activity, but the dynamic process involved when the ligands activate STING remains unclear. In this work, all-atom molecular dynamics simulations were performed to explore the binding mode between human STING (hSTING) and four cyclic adenosine-inosine monophosphate analogs (cAIMPs), as well as 2',3'-cGMP-AMP (2',3'-cGAMP). The results indicate that these cAIMPs adopt a U-shaped configuration within the binding pocket, forming extensive non-covalent interaction networks with hSTING. These interactions play a significant role in augmenting the binding, particularly in interactions with Tyr167, Arg238, Thr263, and Thr267. Additionally, the presence of hydrophobic interactions between the ligand and the receptor further contributes to the overall stability of the binding. In this work, the conformational changes in hSTING upon binding these cAIMPs were also studied and a significant tendency for hSTING to shift from open to closed state was observed after binding some of the cAIMP ligands.

Total free energy analysis of fully hydrated proteins.

The total free energy of a hydrated biomolecule and its corresponding decomposition of energy and entropy provides detailed information about regions of thermodynamic stability or instability. The free energies of four hydrated globular proteins with different net charges are calculated from a molecular dynamics simulation, with the energy coming from the system Hamiltonian and entropy using multiscale cell correlation. Water is found to be most stable around anionic residues, intermediate around cationic and polar residues, and least stable near hydrophobic residues, especially when more buried, with stability displaying moderate entropy-enthalpy compensation. Conversely, anionic residues in the proteins are energetically destabilized relative to singly solvated amino acids, while trends for other residues are less clear-cut. Almost all residues lose intraresidue entropy when in the protein, enthalpy changes are negative on average but may be positive or negative, and the resulting overall stability is moderate for some proteins and negligible for others. The free energy of water around single amino acids is found to closely match existing hydrophobicity scales. Regarding the effect of secondary structure, water is slightly more stable around loops, of intermediate stability around ß strands and turns, and least stable around helices. An interesting asymmetry observed is that cationic residues stabilize a residue when bonded to its N-terminal side but destabilize it when on the C-terminal side, with a weaker reversed trend for anionic residues.

Mechanistic insights into the role of vitamin D and computational identification of potential lead compounds for Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects dopaminergic neurons in the midbrain. A recent study suggests that Orphan Nuclear Receptor 1 (NURR1) impairment may contribute to PD pathogenesis. Our study found three potent agonists for NURR1 protein based on structural and ligand-based screening methods. The pharmacophore is comprised of a hydrogen bond donor, a hydrophobic group, and two aromatic rings (DHRR). The Pharmacophore screening method screened 3142 compounds, of which 3 were screened using structure-based screening. An analysis of the molecules using Molecular Mechanics-Generalized Born Surface Area (binding free energy) revealed a range of -46.77 to -59.06 Kcal/mol. After that, chemical reactivity was investigated by density functional theory, and molecular dynamics simulation was performed (protein-ligand stability). Based on the computational studies, Lifechemical_16901310, Maybridge_2815310, and NPACT_392450 are promising agonists with respect to NURR1. To confirm the potency of the identified compounds, further validation and experiments must be conducted.

Acceleration of generalized replica exchange with solute tempering simulations of large biological systems on massively parallel supercomputer.

Generalized replica exchange with solute tempering (gREST) is one of the enhanced sampling algorithms for proteins or other systems with rugged energy landscapes. Unlike the replica-exchange molecular dynamics (REMD) method, solvent temperatures are the same in all replicas, while solute temperatures are different and are exchanged frequently between replicas for exploring various solute structures. Here, we apply the gREST scheme to large biological systems containing over one million atoms using a large number of processors in a supercomputer. First, communication time on a multi-dimensional torus network is reduced by matching each replica to MPI processors optimally. This is applicable not only to gREST but also to other multi-copy algorithms. Second, energy evaluations, which are necessary for the multistate bennet acceptance ratio (MBAR) method for free energy estimations, are performed on-the-fly during the gREST simulations. Using these two advanced schemes, we observed 57.72 ns/day performance in 128-replica gREST calculations with 1.5 million atoms system using 16,384 nodes in Fugaku. These schemes implemented in the latest version of GENESIS software could open new possibilities to answer unresolved questions on large biomolecular complex systems with slow conformational dynamics.

Modeling disruption of Apis mellifera (honey bee) odorant-binding protein function with high-affinity binders.

Chemical toxins pose a great threat to honey bee health because they affect memory and cognition, diminish immunity, and increase susceptibility to infection, resulting in decreased colony performance, reproduction, and survival. Although the behavioral effects of sub-lethal chemical exposure on honey bees have been intensively studied, how xenobiotics affect olfaction, at the molecular level, still needs to be elucidated. In the present work, in silico tools, such as molecular docking, binding free energy calculations, and molecular dynamics simulations are used to predict if environmental chemicals have stronger binding affinities to honey bee antennal odorant-binding protein 14 (OBP14) than the representative floral odors citralva, eugenol, and the fluorescent probe 1-N-phenylnaphthylamine. Based on structural analysis, 21 chemicals from crop pesticides, household appliances, cosmetics, food, public health-related products, and other sources, many of which are pervasive in the hive environment, have higher binding affinities than the floral odors. These results suggest that chemical exposures are likely to interfere with the honey bee's sense of smell and this disruptive mechanism may be responsible for the lower associative learning and memory based on olfaction found in bees exposed to pesticides. Moreover, bees mainly rely on olfactory cues to perceive their environment and orient themselves as well as to discriminate and identify their food, predators, nestmates, and diseased individuals that need to be removed with hygienic behavior. In summary, sub-lethal exposure to environmental toxins can contribute to colony collapse in several ways from the disruption of proper olfaction functioning.

In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor.

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.

Identification and in silicon binding study of a novel NR2B selective NMDAR antagonist.

Alzheimer's disease (AD) is a major group of diseases that threaten human health, and the search for drugs and treatments for it has never stopped. Research and development of NMDA receptor antagonists as potential therapeutic targets have also been ongoing. Our group designed and synthesized 22 new tetrahydropyrrolo[2,1-b]quinazolines based on NR2B-NMDARs targets and evaluated them for their neuroprotective activity against NMDA-induced cytotoxicity in vitro, A21 exhibited excellent neuroprotective activity. Subsequently, the structure-activity relationships and inhibitor binding modes of the tetrahydropyrrolo[2,1-b]quinazolines were further analyzed by molecular docking, molecular dynamics (MD) simulations and binding free energy calculations. The results showed that A21 could match the two binding pockets of NR2B-NMDARs. The research results of this project will lay a certain foundation for the research of novel NR2B-NMDA receptor antagonists and also provide new ideas for the subsequent research and development of this target.

Evaluation of interactions between the hepatitis C virus NS3/4A and sulfonamidobenzamide based molecules using molecular docking, molecular dynamics simulations and binding free energy calculations.

The Hepatitis C Virus (HCV) NS3/4A is an attractive target for the treatment of Hepatitis C infection. Herein, we present an investigation of HCV NS3/4A inhibitors based on a sulfonamidobenzamide scaffold. Inhibitor interactions with HCV NS3/4A were explored by molecular docking, molecular dynamics simulations, and MM/PBSA binding free energy calculations. All of the inhibitors adopt similar molecular docking poses in the catalytic site of the protease that are stabilized by hydrogen bond interactions with G137 and the catalytic S139, which are known to be important for potency and binding stability. The quantitative assessments of binding free energies from MM/PBSA correlate well with the experimental results, with a high coefficient of determination, R2 of 0.92. Binding free energy decomposition analyses elucidate the different contributions of Q41, F43, H57, R109, K136, G137, S138, S139, A156, M485, and Q526 in binding different inhibitors. The importance of these sidechain contributions was further confirmed by computational alanine scanning mutagenesis. In addition, the sidechains of K136 and S139 show crucial but distinct contributions to inhibitor binding with HCV NS3/4A. The structural basis of the potency has been elucidated, demonstrating the importance of the R155 sidechain conformation. This extensive exploration of binding energies and interactions between these compounds and HCV NS3/4A at the atomic level should benefit future antiviral drug design.

Computational Exploration of the Effects of Mutations on GABA Aminotransferase in GABA Aminotransferase Deficiency.

Gamma-aminobutyric acid (GABA) transaminase-also called GABA aminotransferase (GABA-AT)-deficiency is a rare autosomal recessive disorder characterized by a severe neonatal-infantile epileptic encephalopathy with symptoms such as seizures, hypotonia, hyperreflexia, developmental delay, and growth acceleration. GABA transaminase deficiency is caused by mutations in GABA-AT, the enzyme responsible for the catabolism of GABA. Mutations in multiple locations on GABA-AT have been reported and their locations have been shown to influence the onset of the disease and the severity of symptoms. We examined how GABA-AT mutations influence the structural stability of the enzyme and GABA-binding affinity using computational methodologies such as molecular dynamics simulation and binding free energy calculation to understand the underlying mechanism through which GABA-AT mutations cause GABA-AT deficiency. GABA-AT 3D model depiction was carried out together with seven individual mutated models of GABA-AT. The structural stability of all the predicted models was analyzed using several tools and web servers. All models were evaluated based on their phytochemical values. Additionally, 100 ns MD simulation was carried out and the mutated models were evaluated using RMSD, RMSF, Rg, and SASA. gmxMMPBSA free energy calculation was carried out. Moreover, RMSD and free energy calculations were also compared with those obtained using online web servers. Our study demonstrates that P152S, Q296H, and R92Q play a more critical role in the structural instability of GABA-AT compared with the other mutated models: G465R, L211F, L478P, and R220K.

How phosphorylation of peptides affects their interaction with 14-3-3η domains.

Members of the 14-3-3 domain family have important functions as adapter domains. Via an amphipathic groove on their protein surface they typically bind to disordered C-terminals of other proteins. Importantly, binding partners of 14-3-3 domains usually contain a phosphorylated serine or threonine residue at their binding interface and possess one of three different sequence motifs. Binding of the respective unphosphorylated versions of the peptides is typically strongly disfavored. There is a wealth of structural and thermodynamic data available for the phosphorylated forms but not for the unphosphorylated forms as the binding affinities seem to be too weak to be measurable experimentally. Here, we characterized the mechanistic details that govern the preference for the binding of phosphorylated peptides to 14-3-3η domains by means of molecular dynamics (MD) simulations. We found that the phosphate group is ideally coordinated in the binding pocket whereas the respective unphosphorylated side-chain counterpart is not. Thus, the binding preference results from the tight coordination of the phosphorylated residue at the center of the binding interface. Furthermore, MD simulations of 14-3-3η dimers showed a preference for the simultaneous binding of two phosphorylated peptides in agreement with their experimentally observed cooperativity.

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.

Relative free-energy calculations for scaffold hopping-type transformations with an automated RE-EDS sampling procedure.

The calculation of relative free-energy differences between different compounds plays an important role in drug design to identify potent binders for a given protein target. Most rigorous methods based on molecular dynamics simulations estimate the free-energy difference between pairs of ligands. Thus, the comparison of multiple ligands requires the construction of a "state graph", in which the compounds are connected by alchemical transformations. The computational cost can be optimized by reducing the state graph to a minimal set of transformations. However, this may require individual adaptation of the sampling strategy if a transformation process does not converge in a given simulation time. In contrast, path-free methods like replica-exchange enveloping distribution sampling (RE-EDS) allow the sampling of multiple states within a single simulation without the pre-definition of alchemical transition paths. To optimize sampling and convergence, a set of RE-EDS parameters needs to be estimated in a pre-processing step. Here, we present an automated procedure for this step that determines all required parameters, improving the robustness and ease of use of the methodology. To illustrate the performance, the relative binding free energies are calculated for a series of checkpoint kinase 1 inhibitors containing challenging transformations in ring size, opening/closing, and extension, which reflect changes observed in scaffold hopping. The simulation of such transformations with RE-EDS can be conducted with conventional force fields and, in particular, without soft bond-stretching terms.