Structure-guided engineering and molecular simulations to design a potent monoclonal antibody to target aP2 antigen for adaptive immune response instigation against type 2 diabetes

Introduction

Diabetes mellitus (DM) is recognized as one of the oldest chronic diseases and has become a significant public health issue, necessitating innovative therapeutic strategies to enhance patient outcomes. Traditional treatments have provided limited success, highlighting the need for novel approaches in managing this complex disease.

Methods

In our study, we employed graph signature-based methodologies in conjunction with molecular simulation and free energy calculations. The objective was to engineer the CA33 monoclonal antibody for effective targeting of the aP2 antigen, aiming to elicit a potent immune response. This approach involved screening a mutational landscape comprising 57 mutants to identify modifications that yield significant enhancements in binding efficacy and stability.

Results

Analysis of the mutational landscape revealed that only five substitutions resulted in noteworthy improvements. Among these, mutations T94M, A96E, A96Q, and T94W were identified through molecular docking experiments to exhibit higher docking scores compared to the wild-type. Further validation was provided by calculating the dissociation constant (KD), which showed a similar trend in favor of these mutations. Molecular simulation analyses highlighted T94M as the most stable complex, with reduced internal fluctuations upon binding. Principal components analysis (PCA) indicated that both the wild-type and T94M mutant displayed similar patterns of constrained and restricted motion across principal components. The free energy landscape analysis underscored a single metastable state for all complexes, indicating limited structural variability and potential for high therapeutic efficacy against aP2. Total binding free energy (TBE) calculations further supported the superior performance of the T94M mutation, with TBE values demonstrating the enhanced binding affinity of selected mutants over the wild-type.

Discussion

Our findings suggest that the T94M substitution, along with other identified mutations, significantly enhances the therapeutic potential of the CA33 antibody against DM by improving its binding affinity and stability. These results not only contribute to a deeper understanding of antibody-antigen interactions in the context of DM but also provide a valuable framework for the rational design of antibodies aimed at targeting this disease more effectively.

Network pharmacology, molecular simulation, and binding free energy calculation-based investigation of Neosetophomone B revealed key targets for the treatment of cancer

In the current study, Neosetophomone B (NSP-B) was investigated for its anti-cancerous potential using network pharmacology, quantum polarized ligand docking, molecular simulation, and binding free energy calculation. Using SwissTarget prediction, and Superpred, the molecular targets for NSP-B were predicted while cancer-associated genes were obtained from DisGeNet. Among the total predicted proteins, only 25 were reported to overlap with the disease-associated genes. A protein-protein interaction network was constructed by using Cytoscape and STRING databases. MCODE was used to detect the densely connected subnetworks which revealed three sub-clusters. Cytohubba predicted four targets, i.e., fibroblast growth factor , FGF20, FGF22, and FGF23 as hub genes. Molecular docking of NSP-B based on a quantum-polarized docking approach with FGF6, FGF20, FGF22, and FGF23 revealed stronger interactions with the key hotspot residues. Moreover, molecular simulation revealed a stable dynamic behavior, good structural packing, and residues' flexibility of each complex. Hydrogen bonding in each complex was also observed to be above the minimum. In addition, the binding free energy was calculated using the MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) and MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) approaches. The total binding free energy calculated using the MM/GBSA approach revealed values of -36.85 kcal/mol for the FGF6-NSP-B complex, -43.87 kcal/mol for the FGF20-NSP-B complex, and -37.42 kcal/mol for the FGF22-NSP-B complex, and -41.91 kcal/mol for the FGF23-NSP-B complex. The total binding free energy calculated using the MM/PBSA approach showed values of -30.05 kcal/mol for the FGF6-NSP-B complex, -39.62 kcal/mol for the FGF20-NSP-B complex, -34.89 kcal/mol for the FGF22-NSP-B complex, and -37.18 kcal/mol for the FGF23-NSP-B complex. These findings underscore the promising potential of NSP-B against FGF6, FGF20, FGF22, and FGF23, which are reported to be essential for cancer signaling. These results significantly bolster the potential of NSP-B as a promising candidate for cancer therapy.

Investigation of the Binding Interaction of Mfsd2a with NEDD4-2 via Molecular Dynamics Simulations

Major facilitator superfamily domain-containing 2a (Mfsd2a) is a sodium-dependent lysophosphatidylcholine cotransporter that plays an important role in maintaining the integrity of the blood-brain barrier and neurological function. Abnormal degradation of Mfsd2a often leads to dysfunction of the blood-brain barrier, while upregulation of Mfsd2a can retrieve neurological damage. It has been reported that Mfsd2a can be specifically recognized and ubiquitinated by neural precursor cell-expressed developmentally downregulated gene 4 type 2 (NEDD4-2) ubiquitin ligase and finally degraded through the proteasome pathway. However, the structural basis for the specific binding of Mfsd2a to NEDD4-2 is unclear. In this work, we combined deep learning and molecular dynamics simulations to obtain a Mfsd2a structure with high quality and a stable Mfsd2a/NEDD4-2-WW3 interaction model. Moreover, molecular mechanics generalized Born surface area (MM-GBSA) methods coupled with per-residue energy decomposition studies were carried out to analyze the key residues that dominate the binding interaction. Based on these results, we designed three peptides containing the key residues by truncating the Mfsd2a sequences. One of them was found to significantly inhibit Mfsd2a ubiquitination, which was further validated in an oxygen-glucose deprivation (OGD) model in a human microvascular endothelial cell line. This work provides some new insights into the understanding of Mfsd2a and NEDD4-2 interaction and might promote further development of drugs targeting Mfsd2a ubiquitination.

Identification of defactinib derivatives targeting focal adhesion kinase using ensemble docking, molecular dynamics simulations and binding free energy calculations

Focal adhesion kinase (FAK) belongs to the nonreceptor tyrosine kinases, which selectively phosphorylate tyrosine residues on substrate proteins. FAK is associated with bladder, esophageal, gastric, neck, breast, ovarian and lung cancers. Thus, FAK has been considered as a potential target for tumor treatment. Currently, there are six adenosine triphosphate (ATP)-competitive FAK inhibitors tested in clinical trials but no approved inhibitors targeting FAK. Defactinib (VS-6063) is a second-generation FAK inhibitor with an IC50 of 0.6 nM. The binding model of VS-6063 with FAK may provide a reference model for developing new antitumor FAK-targeting drugs. In this study, the VS-6063/FAK binding model was constructed using ensemble docking and molecular dynamics simulations. Furthermore, the molecular mechanics/generalized Born (GB) surface area (MM/GBSA) method was employed to estimate the binding free energy between VS-6063 and FAK. The key residues involved in VS-6063/FAK binding were also determined using per-residue energy decomposition analysis. Based on the binding model, VS-6063 could be separated into seven regions to enhance its binding affinity with FAK. Meanwhile, 60 novel defactinib-based compounds were designed and verified using ensemble docking. Overall, the present study improves our understanding of the binding mechanism of human FAK with VS-6063 and provides new insights into future drug designs targeting FAK.Communicated by Ramaswamy H. Sarma.

Imidazole-4-N-acetamide Derivatives as a Novel Scaffold for Selective Targeting of Cyclin Dependent Kinases

The rational design of cyclin-dependent protein kinase (CDK) inhibitors presumes the development of approaches for accurate prediction of selectivity and the activity of small molecular weight anticancer drug candidates. Aiming at attenuation of general toxicity of low selectivity compounds, we herein explored the new chemotype of imidazole-4-N-acetamide substituted derivatives of the pan-CDK inhibitor PHA-793887. Newly synthesized compounds 1-4 containing an aliphatic methyl group or aromatic radicals at the periphery of the scaffold were analyzed for the prediction of relative free energies of binding to CDK1, -2, -5, and -9 using a protocol based on non-equilibrium (NEQ) thermodynamics. This methodology allows for the demonstration of a good correlation between the calculated parameters of interaction of 1-4 with individual targets and the values of inhibitory potencies in in vitro kinase assays. We provide evidence in support of NEQ thermodynamics as a time sparing, precise, and productive approach for generating chemical inhibitors of clinically relevant anticancer targets.

Targeting NMDA receptor in Alzheimer's disease: identifying novel inhibitors using computational approaches

The glutamate-gated ion channels known as N-methyl-d-aspartate receptors (NMDARs) are important for both normal and pathological brain function. Subunit-selective antagonists have high therapeutic promise since many pathological conditions involve NMDAR over activation, although few clinical successes have been reported. Allosteric inhibitors of GluN2B-containing receptors are among the most potential NMDAR targeting drugs. Since the discovery of ifenprodil, a variety of GluN2B-selective compounds have been discovered, each with remarkably unique structural motifs. These results expand the allosteric and pharmacolog-ical spectrum of NMDARs and provide a new structural basis for the development of next-generation GluN2B antagonists that have therapeutic potential in brain diseases. Small molecule therapeutic inhibitors targeting NMDA have recently been developed to target CNS disorders such as Alzheimer's disease. In the current study, a cheminformatics method was used to discover potential antagonists and to identify the structural requirements for Gly/NMDA antagonism. In this case we have created a useful pharmacophore model with solid statistical values. Through pharmacophore mapping, the verified model was used to filter out virtual matches from the ZINC database. Assessing receptor-ligand binding mechanisms and affinities used molecular docking. To find the best hits, the GlideScore and the interaction of molecules with important amino acids were considered essential features. We found some molecular inhibitors, namely, ZINC13729211, ZINC07430424, ZINC08614951, ZINC60927204, ZINC12447511, and ZINC18889258 with high binding affinity using computational methods. The molecules in our studies showed characteristics such as good stability, hydrogen bonding and higher binding affinities in the solvation-based assessment method than ifenprodil with acceptable ADMET profile. Moreover, these six leads have been proposed as potential new perspectives for exploring potent Gly/NMDA receptor antagonists. In addition, it can be tested in the laboratory for potential therapeutic strategies for both in vitro and in vivo research.

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.

Modeling the Effects of Phosphorylation on Phase Separation of the FUS Low-Complexity Domain

Aggregation of the RNA-binding protein fused in sarcoma (FUS) is a hallmark of neurodegenerative diseases. Phosphorylation of Ser/Thr in the FUS low-complexity domain (FUS-LC) may regulate phase separation of FUS and prevent pathological aggregation in cells. However, many details of this process remain elusive to date. In this work, we systematically investigated the phosphorylation of FUS-LC and the underlying molecular mechanism by molecular dynamics (MD) simulations and free energy calculations. The results clearly show that phosphorylation can destroy the fibril core structure of FUS-LC by breaking interchain interactions, particularly contacts involving residues like Tyr, Ser, and Gln. Among the six phosphorylation sites, Ser61 and Ser84 may have more important effects on the stability of the fibril core. Our study reveals structural and dynamic details of FUS-LC phase separation modulated by phosphorylation.

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.

Structural dynamics and susceptibility of isobutylamido thiazolyl resorcinol (ThiamidolTM) against human and mushroom tyrosinases

Tyrosinase, a key enzyme catalyzing a rate-limiting step of the melanin production, has been the most promising target for suppressing hyperpigmentation. Although a number of tyrosinase inhibitors have been developed, most of those lack clinical efficacy as they were identified from using mushroom tyrosinase (mTyr) as the target. Previous study revealed that the inhibitory effect of isobutylamido thiazolyl resorcinol (ThiamidolTM) on human tyrosinase (hTyr) is ∼100 times higher than that on mTyr. In the present study, we aimed to investigate the structural dynamics and susceptibility of ThiamidolTM against hTyr and mTyr at the atomic level using molecular docking, molecular dynamics simulation, and free energy calculation based on the molecular mechanics/Poisson-Boltzmann surface area method. The obtained results revealed that the resorcinol moiety of ThiamidolTM was found to be embedded in the catalytic copper center, interacting with H180, H202, H211, F386, and H390 residues of hTyr as well as with F264 residue of mTyr, mostly through van der Waals interactions. However, the number of destabilizing residues was found to be more pronounced in the ThiamidolTM/mTyr complex than the ThiamidolTM/hTyr system, supported by the lower binding affinity of ThiamidolTM/mTyr complex as well as the higher water accessibility and the lower number of atomic contacts at the active site of mTyr. Altogether, the structural and energetic information from this work would be useful for further optimization of more potent human tyrosinase inhibitors based on ThiamidolTM scaffold.Communicated by Ramaswamy H. Sarma.

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 β 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.

Pressure-induced structural phase transitions of zirconium: anab initiostudy based on statistical ensemble theory

Recently, we put forward a direct integral approach to solve the partition function with ultrahigh efficiency and precision, which enables the rigorous ensemble theory to investigate phase behaviors of realistic condensed matters and has been successfully applied to the phase transition of vanadium metal (Ninget al2022J. Phys.: Condens. Matter34425404). In this work, the approach is applied to the structural phase transitions of zirconium metal under compressions up to 160 GPa and ultrahigh calculation precision is achieved. For the obtained equation of state with pressure over 40 GPa, the deviations from latest experiments are within0.7%and the computed transition pressure ofα→ωis 6.93 GPa, which is about five times larger than previous theoretical predictions and in excellent agreement with the measured range of 5-15 GPa. Our results support the argument that there is no existence of the isostructural phase transition of Zr metal that was asserted by recent experimental observations.

Computational study on the binding of Mango-II RNA aptamer and fluorogen using the polarizable force field AMOEBA

Fluorescent light-up aptamers (FLAPs) are well-performed biosensors for cellular imaging and the detection of different targets of interest, including RNA, non-nucleic acid molecules, metal ions, and so on. They could be easily designed and emit a strong fluorescence signal once bound to specified fluorogens. Recently, one unique aptamer called Mango-II has been discovered to possess a strong affinity and excellent fluorescent properties with fluorogens TO1-Biotin and TO3-Biotin. To explore the binding mechanisms, computational simulations have been performed to obtain structural and thermodynamic information about FLAPs at atomic resolution. AMOEBA polarizable force field, with the capability of handling the highly charged and flexible RNA system, was utilized for the simulation of Mango-II with TO1-Biotin and TO3-Biotin in this work. The calculated binding free energy using published crystal structures is in excellent agreement with the experimental values. Given the challenges in modeling complex RNA dynamics, our work demonstrates that MD simulation with a polarizable force field is valuable for understanding aptamer-fluorogen binding and potentially designing new aptamers or fluorogens with better performance.

Predicting Adhesive Free Energies of Polymer-Surface Interactions with Machine Learning

Polymer-surface interactions are crucial to many biological processes and industrial applications. Here we propose a machine learning method to connect a model polymer's sequence with its adhesion to decorated surfaces. We simulate the adhesive free energies of 20000 unique coarse-grained one-dimensional polymer sequences interacting with functionalized surfaces and build support vector regression models that demonstrate inexpensive and reliable prediction of the adhesive free energy as a function of sequence. Our work highlights the promising integration of coarse-grained simulation with data-driven machine learning methods for the design of functional polymers and represents an important step toward linking polymer compositions with polymer-surface interactions.

A strategy to optimize the peptide-based inhibitors against different mutants of the spike protein of SARS-CoV-2

The SARS-CoV-2 virus has caused high-priority health concerns at a global level. Vaccines have stalled the proliferation of viruses to some extent. Yet, the emergence of newer, potentially more infectious, and dangerous mutants such as Delta and Omicron are among the major challenges in finding a more permanent solution for this pandemic. The effectiveness of antivirals Molnupiravir and Paxlovid, authorized for emergency use by the FDA, are yet to be assessed on a larger population. Patients with a high risk of disease progression have received treatment with antibody-cocktail. Most of the mutations leading to the new lineage of SARS-CoV-2 are found in the spike protein of this virus that plays a key role in facilitating host entry. The current study has investigated how to modify a promising peptide-based inhibitor of spike protein, LCB3, against common mutations, N501Y and K417N in the target protein so that it retains its efficacy against the spike protein. LCB3 being a prototype for protein-based inhibitors is an ideal testing system to learn about protein-based inhibitors. This study proposes the substitutions of amino acid residues of LCB3 inhibitor using a structure-based approach that considers free energy decomposition of residues, the distance between atoms, and the interaction among amino acids. The binding free energy calculations suggest a possible improvement in the binding affinity of existing inhibitor LCB3 to the mutant forms of the S-protein using simple substitutions at specific positions of the inhibitor. This approach, being general, can be used in different inhibitors and other mutations and help in fighting against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Simulation analysis of selective alanine mutation effect on stability of human prion protein

Prion diseases are neurodegenerative disorders caused by spongiform degeneration of the brain. Understanding the fundamental mechanism of prion protein aggregation caused by mutations is very crucial to resolve the pathology of prion diseases. To help understand the roles of individual residues on the stability of the human prion protein, the computational method of free energy simulations based on atomistic molecular dynamics trajectories is applied to Phe175 → Ala, Val180 → Ala, and Val209 → Ala mutations of the human prion protein. The simulations show that all three alanine mutations destabilize the human prion protein. The calculated free energy change differences, ΔΔG, for the Phe175 → Ala, Val180 → Ala, and Val209 → Ala mutations are in good agreement with the experimental values. The significant destabilizing effects on the mutants relative to the wild-type protein arise from van der Waals terms. Furthermore, our free energy decomposition analysis shows that the major contribution to destabilizing the V180A and V209A mutants relative to the wild-type protein is originated from van der Waals interactions from residues near the mutation sites. In contrast, the contribution to destabilizing the F175A mutant is mainly caused by van der Waals interactions from residues near and far away from the mutation site. Our results show that the free energy simulation with a thermodynamic integration approach for selected alanine scanning mutations is beneficial for understanding the detailed mechanism of human prion protein destabilization, specific residues' role, and the hydrophobic effect on protein stability.Communicated by Ramaswamy H. Sarma.

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.

Diptool-A Novel Numerical Tool for Membrane Interactions Analysis, Applying to Antimicrobial Detergents and Drug Delivery Aids

The widespread problem of resistance development in bacteria has become a critical issue for modern medicine. To limit that phenomenon, many compounds have been extensively studied. Among them were derivatives of available drugs, but also alternative novel detergents such as Gemini surfactants. Over the last decade, they have been massively synthesized and studied to obtain the most effective antimicrobial agents, as well as the most selective aids for nanoparticles drug delivery. Various protocols and distinct bacterial strains used in Minimal Inhibitory Concentration experimental studies prevented performance benchmarking of different surfactant classes over these last years. Motivated by this limitation, we designed a theoretical methodology implemented in custom fast screening software to assess the surfactant activity on model lipid membranes. Experimentally based QSAR (quantitative structure-activity relationship) prediction delivered a set of parameters underlying the Diptool software engine for high-throughput agent-membrane interactions analysis. We validated our software by comparing score energy profiles with Gibbs free energy from the Adaptive Biasing Force approach on octenidine and chlorhexidine, popular antimicrobials. Results from Diptool can reflect the molecule behavior in the lipid membrane and correctly predict free energy of translocation much faster than classic molecular dynamics. This opens a new venue for searching novel classes of detergents with sharp biologic activity.

Computational Evaluation of Abrogation of HBx-Bcl-xL Complex with High-Affinity Carbon Nanotubes (Fullerene) to Halt the Hepatitis B Virus Replication

Hepatitis B virus (HBV) is the world's most prevalent chronic viral infection. More than 350 million individuals are chronic carriers of the virus, with an estimated 2 billion infected persons. For instance, the role of HBx protein in attachment and infection is very obvious and consequently deemed as an important druggable target. Targeting the interface and discovering novel drugs greatly advanced the field of therapeutics development. Therefore, in the current study, HBx to Bcl-xL is abrogated on high-affinity carbon nanotubes using computational structural biology tools. Our analysis revealed that among the total 62 carbon fullerenes, only 13 compounds exhibited inhibitory activity against HBx, which was further confirmed through IFD-based rescoring. Structural dynamics investigation revealed stable binding, compactness, and hydrogen bonds reprogramming. Moreover, the binding free energy calculation results revealed that the top hits1-4 possess the total binding energy of -54.36 kcal/mol (hit1), -50.81 kcal/mol (hit2), -47.09 kcal/mol (hit3), and -45.59 kcal/mol for hit4. In addition, the predicted KD values and bioactivity scores further validated the inhibitory potential of these top hits. The identified compounds need further in vitro and in vivo validation to aid the treatment process of HBV.

Fighting Against Bacterial Lipopolysaccharide-Caused Infections through Molecular Dynamics Simulations: A Review

Lipopolysaccharide (LPS) is the primary component of the outer leaflet of Gram-negative bacterial outer membranes. LPS elicits an overwhelming immune response during infection, which can lead to life-threatening sepsis or septic shock for which no suitable treatment is available so far. As a result of the worldwide expanding multidrug-resistant bacteria, the occurrence and frequency of sepsis are expected to increase; thus, there is an urge to develop novel strategies for treating bacterial infections. In this regard, gaining an in-depth understanding about the ability of LPS to both stimulate the host immune system and interact with several molecules is crucial for fighting against LPS-caused infections and allowing for the rational design of novel antisepsis drugs, vaccines and LPS sequestration and detection methods. Molecular dynamics (MD) simulations, which are understood as being a computational microscope, have proven to be of significant value to understand LPS-related phenomena, driving and optimizing experimental research studies. In this work, a comprehensive review on the methods that can be combined with MD simulations, recently applied in LPS research, is provided. We focus especially on both enhanced sampling methods, which enable the exploration of more complex systems and access to larger time scales, and free energy calculation approaches. Thereby, apart from outlining several strategies for surmounting LPS-caused infections, this work reports the current state-of-the-art of the methods applied with MD simulations for moving a step forward in the development of such strategies.

Efficient workflow for the investigation of the catalytic cycle of water oxidation catalysts: Combining GFN-xTB and density functional theory

Photocatalytic water oxidation remains the bottleneck in many artificial photosynthesis devices. The efficiency of this challenging process is inherently linked to the thermodynamic and electronic properties of the chromophore and the water oxidation catalyst (WOC). Computational investigations can facilitate the search for favorable chromophore‐catalyst combinations. However, this remains a demanding task due to the requirements on the computational method that should be able to correctly describe different spin and oxidation states of the transition metal, the influence of solvation and the different rates of the charge transfer and water oxidation processes. To determine a suitable method with favorable cost/accuracy ratios, the full catalytic cycle of a molecular ruthenium based WOC is investigated using different computational methods, including density functional theory (DFT) with different functionals (GGA, Hybrid, Double Hybrid) as well as the semi‐empirical tight binding approach GFN‐xTB. A workflow with low computational cost is proposed that combines GFN‐xTB and DFT and provides reliable results. GFN‐xTB geometries and frequencies combined with single‐point DFT energies give free energy changes along the catalytic cycle that closely follow the full DFT results and show satisfactory agreement with experiment, while significantly decreasing the computational cost. This workflow allows for cost efficient determination of energetic, thermodynamic and dynamic properties of WOCs.

Exploring the molecular basis of UG-rich RNA recognition by the human splicing factor TDP-43 using molecular dynamics simulation and free energy calculation

Transactivation response element RNA/DNA-binding protein 43 (TDP-43) is involved in the regulation of alternative splicing of human neurodegenerative disease-related genes through binding to long UG-rich RNA sequences. Mutations in TDP-43, most in the homeodomain, cause neurological disorders such as amyotrophic lateral sclerosis and fronto temporal lobar degeneration. Several mutants destabilize the structure and disrupt RNA-binding activity. The biological functions of these mutants have been characterized, but the structural basis behind the loss of RNA-binding activity is unclear. Focused on the specific TDP-43-ssRNA complex (PDB code 4BS2), we applied molecular dynamics simulations and the molecular mechanics Poisson-Boltzmann surface area free energy calculation to characterize and explore the structural and dynamic effects between ssRNA and TDP-43. The energetic analysis indicated that the intermolecular van der Waals interaction and nonpolar solvation energy play an important role in the binding process of TDP-43 and ssRNA. Compared with the wild-type TDP-43, the reduction of the polar or non-polar interaction between all the mutants F149A, D105A/S254A, R171A/D174A, F147L/F149L/F229L/F231L and ssRNA is the main reason for the reduction of its binding free energy. Decomposing energies suggested that the extensive interactions between TDP-43 and the nitrogenous bases of ssRNA are responsible for the specific ssRNA recognition by TDP-43. These results elucidated the TDP-43-ssRNA interaction comprehensively and further extended our understanding of the previous experimental data. The uncovering of TDP-43-ssRNA recognition mechanism will provide us useful insights and new chances for the development of anti-neurodegenerative drugs.

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.

Molecular Dynamics Simulations Reveal Interactions of an IgG1 Antibody With Selected Fc Receptors

In a survey of novel interactions between an IgG1 antibody and different Fcγ receptors (FcγR), molecular dynamics simulations were performed of interactions of monoclonal antibody involved complexes with FcγRs. Free energy simulations were also performed of isolated wild-type and substituted Fc regions bound to FcγRs with the aim of assessing their relative binding affinities. Two different free energy calculation methods, Molecular Mechanical/Generalized Born Molecular Volume (MM/GBMV) and Bennett Acceptance Ratio (BAR), were used to evaluate the known effector substitution G236A that is known to selectively increase antibody dependent cellular phagocytosis. The obtained results for the MM/GBMV binding affinity between different FcγRs are in good agreement with previous experiments, and those obtained using the BAR method for the complete antibody and the Fc-FcγR simulations show increased affinity across all FcγRs when binding to the substituted antibody. The FcγRIIa, a key determinant of antibody agonistic efficacy, shows a 10-fold increase in binding affinity, which is also consistent with the published experimental results. Novel interactions between the Fab region of the antibody and the FcγRs were discovered with this in silico approach, and provide insights into the antibody-FcγR binding mechanism and show promise for future improvements of therapeutic antibodies for preclinical studies of biological drugs.

Uncertainty quantification in classical molecular dynamics

Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalizing experimental observations to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable it to be used to report actionable results. The approach adopted is a standard one in the field of uncertainty quantification, namely using ensemble methods, in which a sufficiently large number of replicas are run concurrently, from which reliable statistics can be extracted. Indeed, because molecular dynamics is intrinsically chaotic, the need to use ensemble methods is fundamental and holds regardless of the duration of the simulations performed. We discuss the approach and illustrate it in a range of applications from materials science to ligand-protein binding free energy estimation. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Design of new drug delivery platform based on surface functionalization of black phosphorus nanosheet with a smart polymer for enhancing the efficiency of doxorubicin in the treatment of cancer

The development of drug delivery systems (DDSs) has raised hopes for targeted cancer therapy. Smart polymers can be conjugated with several nanoparticles and increase their efficiency in biomedical applications. In this work, the classical molecular dynamics and well-tempered metadynamics simulations are performed to study the behavior of black phosphorus (BPH) nanosheet functionalized with polyethylenimine (PEI) in adsorption, diffusion, and release of doxorubicin (DOX) anticancer drug. Adsorption of the drug on PEI-BPH surface is mainly due to the formation of strong pi-pi interaction between the drug and BPH. The drug-binding to the nanosheet is enhanced by the intermolecular hydrogen bond that formed between DOX and PEI. The energy values for the interaction of DOX with BPH and PEI are calculated to be about - 180 and - 50 kJ/mol, respectively. The obtained results indicated that the adsorption of the drug molecules on the nanosheet destroyed the hydration layer around the BPH-PEI surface. The free energy calculation for DDS shows a global minimum in which the distances of DOX from BPH surface and PEI are about 1.0 and 0.5 nm, respectively. Furthermore, the diffusion of DDS into the membrane has a macropinocytosis pathway that is in line with experimental observations. Moreover, it is found that, unlike the isolated DOX, the drug in complex with BPH-PEI can be easily penetrated membrane cells. The study of the pH-responsive release of the drug shows the high solubility of the polymer in the water environment plays the main role in swelling of DDS and the release of the DOX molecules.

Beta-1,3 Oligoglucans Specifically Bind to Immune Receptor CD28 and May Enhance T Cell Activation

Beta glucans are known to have immunomodulatory effects that mediated by a variety of mechanisms. In this article, we describe experiments and simulations suggesting that beta-1,3 glucans may promote activation of T cells by a previously unknown mechanism. First, we find that treatment of a T lymphoblast cell line with beta-1,3 oligoglucan significantly increases mRNA levels of T cell activation-associated cytokines, especially in the presence of the agonistic anti-CD3 antibody. This immunostimulatory activity was observed in the absence of dectin-1, a known receptor for beta-1,3 glucans. To clarify the molecular mechanism underlying this activity, we performed a series of molecular dynamics simulations and free-energy calculations to explore the interaction of beta-1,3 oligoglucans with potential immune receptors. While the simulations reveal little association between beta-1,3 oligoglucan and the immune receptor CD3, we find that beta-1,3 oligoglucans bind to CD28 near the region identified as the binding site for its natural ligands CD80 and CD86. Using a rigorous absolute binding free-energy technique, we calculate a dissociation constant in the low millimolar range for binding of 8-mer beta-1,3 oligoglucan to this site on CD28. The simulations show this binding to be specific, as no such association is computed for alpha-1,4 oligoglucan. This study suggests that beta-1,3 glucans bind to CD28 and may stimulate T cell activation collaboratively with T cell receptor activation, thereby stimulating immune function.

Deciphering nucleotide modification-induced structure and stability changes

Nucleotide modification in RNA controls a bevy of biological processes, including RNA degradation, gene expression, and gene editing. In turn, misregulation of modified nucleotides is associated with a host of chronic diseases and disorders. However, the molecular mechanisms driving these processes remain poorly understood. To partially address this knowledge gap, we used alchemical and temperature replica exchange molecular dynamics (TREMD) simulations on an RNA duplex and an analogous hairpin to probe the structural effects of modified and/or mutant nucleotides. The simulations successfully predict the modification/mutation-induced relative free energy change for complementary duplex formation, and structural analyses highlight mechanisms driving stability changes. Furthermore, TREMD simulations for a hairpin-forming RNA with and without modification provide reliable estimations of the energy landscape. Illuminating the impact of methylated and/or mutated nucleotides on the structure-function relationship and the folding energy landscape, the simulations provide insights into modification-induced alterations to the folding mechanics of the hairpin. The results here may be biologically significant as hairpins are widespread structure motifs that play critical roles in gene expression and regulation. Specifically, the tetraloop of the probed hairpin is phylogenetically abundant, and the stem mirrors a miRNA seed region whose modification has been implicated in epilepsy pathogenesis.

Molecular Modeling Study of c-KIT/PDGFRα Dual Inhibitors for the Treatment of Gastrointestinal Stromal Tumors

Gastrointestinal stromal tumors (GISTs) are the most common Mesenchymal Neoplasm of the gastrointestinal tract. The tumorigenesis of GISTs has been associated with the gain-of-function mutation and abnormal activation of the stem cell factor receptor (c-KIT) and platelet-derived growth factor receptor alpha (PDGFRα) kinases. Hence, inhibitors that target c-KIT and PDGFRα could be a therapeutic option for the treatment of GISTs. The available approved c-KIT/PDGFRα inhibitors possessed low efficacy with off-target effects, which necessitated the development of potent inhibitors. We performed computational studies of 48 pyrazolopyridine derivatives that showed inhibitory activity against c-KIT and PDGFRα to study the structural properties important for inhibition of both the kinases. The derivative of phenylurea, which has high activities for both c-KIT (pIC₅₀ = 8.6) and PDGFRα (pIC₅₀ = 8.1), was used as the representative compound for the dataset. Molecular docking and molecular dynamics simulation (100 ns) of compound 14 was performed. Compound 14 showed the formation of hydrogen bonding with Cys673, Glu640, and Asp810 in c-KIT, and Cys677, Glu644, and Asp836 in PDGFRα. The results also suggested that Thr670/T674 substitution in c-KIT/PDGFRα induced conformational changes at the binding site of the receptors. Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed based on the inhibitors. Contour map analysis showed that electropositive and bulky substituents at the para-position and the meta-position of the benzyl ring of compound 14 was favorable and may increase the inhibitory activity against both c-KIT and PDGFRα. Analysis of the results suggested that having bulky and hydrophobic substituents that extend into the hydrophobic pocket of the binding site increases the activity for both c-KIT and PDGFRα. Based on the contour map analysis, 50 compounds were designed, and the activities were predicted. An evaluation of binding free energy showed that eight of the designed compounds have potential binding affinity with c-KIT/PDGFRα. Absorption, distribution, metabolism, excretion and toxicity (ADMET) and synthetic feasibility tests showed that the designed compounds have reasonable pharmaceutical properties and synthetic feasibility. Further experimental study of the designed compounds is recommended. The structural information from this study could provide useful insight into the future development of c-KIT and PDGFRα inhibitors.

How Mutations Perturb γ-Secretase Active Site Studied by Free Energy Simulations

γ-Secretase is involved in processing of the amyloid precursor protein (APP) and generation of short Aβ peptides that may play a key role in neurodegenerative diseases such as Alzheimer's disease (AD). Several mutations in γ-secretase influence its activity, resulting in early AD onset (Familial AD or FAD mutations). The molecular details of how mutations, not located close to the active site, can affect enzyme activity is not understood. In molecular dynamics simulations of γ-secretase in the absence of substrate (apo), we identified two active site conformational states characterized by a direct contact between catalytic Asp residues (closed state) and an open water-bridged state. In the presence of substrate, only conformations compatible with the open active site geometry are accessible. Systematic free energy simulations on wild type and FAD mutations indicate a free energy difference between closed and open states that is significantly modulated by FAD mutations and correlates with the corresponding experimental activity. For mutations with reduced activity, an increased penalty for open-state transitions was found. Only for two mutations located at the active site a direct perturbation of the open-state geometry was observed that could directly explain the drop of enzyme activity. The simulations suggest that modulation of the closed/open equilibrium and perturbation of the open (active) catalytic geometry are possible mechanisms of how FAD mutations affect γ-secretase activity. The results also offer an explanation for the experimental finding that FAD mutations, although not located at the interface to the substrate, mainly destabilize the enzyme-substrate complex.

Theoretical Studies on the Selectivity Mechanisms of Glycogen Synthase Kinase 3β (GSK3β) with Pyrazine ATP-competitive Inhibitors by 3DQSAR, Molecular Docking, Molecular Dynamics Simulation and Free Energy Calculations

Background

Glycogen synthase kinase-3 (GSK3) is associated with various key biological processes and has been considered as an important therapeutic target for the treatment of many diseases. Great efforts have been made on the development of GSK3 inhibitors, especially ATP-competitive GSK3β inhibitor, but it is still a great challenge to develop selective GSK3β inhibitors because of the high sequence homology with other kinases.

Objective

In order to reveal the selectivity mechanisms of GSK3β inhibition at the molecular level, a series of ATP-competitive GSK3β inhibitor was analyzed by a systematic computational method, combining 3D-QSAR, molecular docking, molecular dynamic simulations and free energy calculations.

Methods

Firstly, 3D-QSAR with CoMFA was built to explore the general structure activity relationships. Secondly, CDOCKER and Flexible docking were employed to predicted the reasonable docking poses of all studied inhibitors. And then, both GSK3β and CDK2 complexes were selected to conduct molecular dynamics simulations. Finally, the free energy calculations were employed to find the key selective-residues.

Results

CoMFA model suggested the steric, hydrophobic fields play key roles in the bioactivities of inhibitors, and the binding mechanisms were well analyzed through molecular docking. The binding free energies predicted are in good agreement with the experimental bioactivities and the free energy calculations showed that the binding of GSK3β/inhibitors was mainly contributed from hydrogen bonding and hydrophobic interaction.

Conclusion

Some key residues for selective binding were highlighted, which may afford important guidance for the rational design of novel ATP-competitive GSK3β inhibitors.

Recent Developments in Linear Interaction Energy Based Binding Free Energy Calculations

The linear interaction energy (LIE) approach is an end-point method to compute binding affinities. As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy ΔG _bind . This perspective summarizes our recent efforts to use molecular simulation and empirically calibrated LIE models for accurate and efficient calculation of ΔG _bind for diverse sets of compounds binding to flexible proteins (e.g., Cytochrome P450s and other proteins of direct pharmaceutical or biochemical interest). Such proteins pose challenges on ΔG _bind computation, which we tackle using a previously introduced statistically weighted LIE scheme. Because calibrated LIE models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space. To enable AD assessment of LIE predictions (or other protein-structure and -dynamic based ΔG _bind calculations) we recently introduced strategies for AD assignment of LIE models, based on simulation and training data only. These strategies are reviewed here as well, together with available tools to facilitate and/or automate LIE computation (including software for combined statistically-weighted LIE calculations and AD assessment).

Ligand-protein interactions in lysozyme investigated through a dual-resolution model

A fully atomistic (AT) modeling of biological macromolecules at relevant length- and time-scales is often cumbersome or not even desirable, both in terms of computational effort required and a posteriori analysis. This difficulty can be overcome with the use of multiresolution models, in which different regions of the same system are concurrently described at different levels of detail. In enzymes, computationally expensive AT detail is crucial in the modeling of the active site in order to capture, for example, the chemically subtle process of ligand binding. In contrast, important yet more collective properties of the remainder of the protein can be reproduced with a coarser description. In the present work, we demonstrate the effectiveness of this approach through the calculation of the binding free energy of hen egg white lysozyme with the inhibitor di-N-acetylchitotriose. Particular attention is payed to the impact of the mapping, that is, the selection of AT and coarse-grained residues, on the binding free energy. It is shown that, in spite of small variations of the binding free energy with respect to the active site resolution, the separate contributions coming from different energetic terms (such as electrostatic and van der Waals interactions) manifest a stronger dependence on the mapping, thus pointing to the existence of an optimal level of intermediate resolution.

Theoretical Exploring Selective-Binding Mechanisms of JAK3 by 3D-QSAR, Molecular Dynamics Simulation and Free Energy Calculation

Janus kinase 3 (JAK3) plays a critical role in the JAK/STAT signaling pathway and has become an attractive selective target for the treatment of immune-mediated disorders. Therefore, great efforts have been made for the development of JAK3 inhibitors, but developing selective JAK3 inhibitors remains a great challenge because of the high sequence homology with other kinases. In order to reveal the selective-binding mechanisms of JAK3 and to find the key structural features that refer to specific JAK3 inhibition, a systematic computational method, including 3D-QSAR, molecular dynamics simulation, and free energy calculations, was carried out on a series of JAK3 isoform-selective inhibitors. Necessary pharmacodynamic structures and key residues involved in efficient JAK3-inhibition were then highlighted. Finally, 10 novel JAK3 inhibitors were designed, the satisfactory predicted binding affinity to JAK3 of these analogous demonstrated that this study may facilitate the rational design of novel and selective JAK3 inhibitors.

Microscopic Characterization of GRP1 PH Domain Interaction with Anionic Membranes

The pleckstrin homology (PH) domain of general receptor for phosphoionositides 1 (GRP1-PHD) binds specifically to phosphatidylinositol (3,4,5)-triphosphate (PIP3 ), and acts as a second messenger. Using an extensive array of molecular dynamics (MD) simulations employing highly mobile membrane mimetic (HMMM) model as well as complementary full membrane simulations, we capture differentiable binding and dynamics of GRP1-PHD in the presence of membranes containing PC, PS, and PIP3 lipids in varying compositions. While GRP1-PHD forms only transient interactions with pure PC membranes, incorporation of anionic lipids resulted in stable membrane-bound configurations. We report the first observation of two distinct PIP3 binding modes on GRP1-PHD, involving PIP3 interactions at a "canonical" and at an "alternate" site, suggesting the possibility of simultaneous binding of multiple anionic lipids. The full membrane simulations confirmed the stability of the membrane bound pose of GRP1-PHD as captured from our HMMM membrane binding simulations. By performing additional steered membrane unbinding simulations and calculating nonequilibrium work associated with the process, as well as metadynamics simulations, on the protein bound to full membranes, allowing for more quantitative examination of the binding strength of the GRP1-PHD to the membrane, we demonstrate that along with the bound PIP3 , surrounding anionic PS lipids increase the energetic cost of unbinding of GRP1-PHD from the canonical mode, causing them to dissociate more slowly than the alternate mode. Our results demonstrate that concurrent binding of multiple anionic lipids by GRP1-PHD contributes to its membrane affinity, which in turn control its signaling activity. © 2019 Wiley Periodicals, Inc.

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.

Parallel Strategy Increases the Thermostability and Activity of Glutamate Decarboxylase

Glutamate decarboxylase (GAD; EC 4.1.1.15) is a unique pyridoxal 5-phosphate (PLP)-dependent enzyme that specifically catalyzes the decarboxylation of L-glutamic acid to produce γ-aminobutyric acid (GABA), which exhibits several well-known physiological functions. However, glutamate decarboxylase from different sources has the common problem of poor thermostability that affects its application in industry. In this study, a parallel strategy comprising sequential analysis and free energy calculation was applied to identify critical amino acid sites affecting thermostability of GAD and select proper mutation contributing to improve structure rigidity of the enzyme. Two mutant enzymes, D203E and S325A, with higher thermostability were obtained, and their semi-inactivation temperature (T₅₀¹⁵) values were 2.3 °C and 1.4 °C higher than the corresponding value of the wild-type enzyme (WT), respectively. Moreover, the mutant, S325A, exhibited enhanced activity compared to the wild type, with a 1.67-fold increase. The parallel strategy presented in this work proved to be an efficient tool for the reinforcement of protein thermostability.

Characterizing the Binding Sites for GK Domain of DLG1 and DLG4 via Molecular Dynamics Simulation

Discs-large (DLG) is a member that belongs to the membrane-associated guanylate kinase (MAGUK) family. The GK domain of DLGs has evolved into a protein–protein interaction module that could bind with kinds of proteins to regulate diverse cellular functions. Previous reports have demonstrated the GK domain of DLGs functioned as a phosphor-peptide-binding module by resolving the crystal structures. Here we investigated into the interactions of DLG1 and DLG4 with their reported phosphor-peptides by molecular dynamics simulations. Post-dynamics analysis showed that DLG1/4 formed extensive interactions with phosphorylated ligands, including hydrophobic and hydrogen bonding interactions. Among them, the highly conserved residues among the DLGs in phosphor-site and β5 sheet were crucial for the binding according to the energy decomposition calculations. Additionally, the binding interactions between DLG4 and reported unphosphorylated peptides including MAP1A and designed GK inhibitory (GKI-QSF) peptides were analyzed. We found the key residues that played important roles in DLG4/unphosphorylated peptide systems were very similar as in DLG4/phosphor-peptide systems. Moreover, the molecular dynamic simulation for the complex of DLG1 and GKI-QSF was carried out and predicted that the GKI-QSF could bind with DLG1 with similar Kd value compared to DLG4/GKI-QSF, which was verified by using ITC assay (Kd = 1.20 ± 0.29 μM). Our study might be helpful for the better understanding of the structural and biological function of DLGs GK domain and encourage the discovery of new binders.

Molecular modeling studies of pyrrolo[2,3-d]pyrimidin-4-amine derivatives as JAK1 inhibitors based on 3D-QSAR, molecular docking, molecular dynamics (MD) and MM-PBSA calculations

Rheumatoid Arthritis (RA) is an autoimmune disease caused by overproduction of pro-inflammatory cytokines. Janus Kinases (JAKs) mediate cytokines signaling through the Janus Kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathways. Clinical studies have shown that Janus kinase 1 (JAK1) mediated signaling plays a key role in synovial response in rheumatoid arthritis. Hence, the inhibition JAK1 is considered as an important therapeutic route for treatment of rheumatoid arthritis. In this study, we have performed three-dimensional quantitative structure-activity relationship (3 D-QSAR), molecular docking, molecular dynamics (MD) and free energy calculations on a series of pyrrolo[2,3-d]pyrimidin-4-amine JAK1 inhibitors. Molecular docking studies of the compounds 03, 13, 36 and 49 with JAK1 were performed to study the binding interactions. The binding conformations of the compounds from docking studies were selected based on binding energy and H-bond interactions and were used as initial structure for MD simulations. Using 3 D-QSAR techniques, a ligand-based comparative molecular field analysis (CoMFA) model (q² = 0.5, r² = 0.96) and a receptor-based CoMFA model (q² = 0.78, r² = 0.98) were developed. Analysis of the MD results of the most active compound (compound 49) with JAK1 showed the formation of H-bond interactions with residues Glu957, Leu959 and Gly887 and water-mediated H-bond interaction with Gly887 and His885. Based on the contour map analyses of the receptor-based CoMFA, a design strategy was proposed and was used for designing new JAK1 inhibitors. Four of the designed compounds (D57, D58, D98 and D99) showed predicted activity values (pIC₅₀> 8.8) greater than the most active compound for JAK1. MM-PBSA based free energy calculations indicated that the designed compounds were able to form stable binding with JAK1 primarily through electrostatic interactions and van der Waal interactions. Collectively, the outcome of this study can be used to further the progress of JAK1 inhibition for the treatment of rheumatoid arthritis. Communicated by Ramaswamy H. Sarma.

FSATOOL: A useful tool to do the conformational sampling and trajectory analysis work for biomolecules

Reliable conformational sampling and trajectory analysis are always important to the study of the folding or binding mechanisms of biomolecules. Generally, one has to prepare many complicated parameters and follow a lot of steps to obtain the final data. The whole process is too complicated to new users. In this article, we provide a convenient and user-friendly tool that is compatible to AMBER, called fast sampling and analysis tool (FSATOOL). FSATOOL has some useful features. First and the most important, the whole work is extremely simplified into two steps, one is the fast sampling procedure and the other is the trajectory analysis procedure. Second, it contains several powerful sampling methods for the simulation on graphics process unit, including our previous mixing replica exchange molecular dynamics method. The method combines the advantages of the biased and unbiased simulations. Finally, it extracts the dominant transition pathways automatically from the folding network by Markov state model. Users do not need to do the tedious intermediate steps by hand. To illustrate the usage of FSATOOL in practice, we perform one simulation for a RNA hairpin in explicit solvent. All the results are presented. © 2019 Wiley Periodicals, Inc.

Potential of Matrix Metalloproteinase Inhibitors for the Treatment of Local Tissue Damage Induced by a Type P-I Snake Venom Metalloproteinase

Snake bite envenoming is a public health problem that was recently included in the list of neglected tropical diseases of the World Health Organization. In the search of new therapies for the treatment of local tissue damage induced by snake venom metalloproteinases (SVMPs), we tested the inhibitory activity of peptidomimetic compounds designed as inhibitors of matrix metalloproteinases on the activities of the SVMP Batx-I, from Bothrops atrox venom. The evaluated compounds show great potential for the inhibition of Batx-I proteolytic, hemorrhagic and edema-forming activities, especially the compound CP471474, a peptidomimetic including a hydroxamate zinc binding group. Molecular dynamics simulations suggest that binding of this compound to the enzyme is mediated by the electrostatic interaction between the hydroxamate group and the zinc cofactor, as well as contacts, mainly hydrophobic, between the side chain of the compound and amino acids located in the substrate binding subsites S1 and S1′. These results show that CP471474 constitutes a promising compound for the development of co-adjuvants to neutralize local tissue damage induced by snake venom metalloproteinases.

Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides

There is interest in peptide drug design, especially for targeting intracellular protein–protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.

Passive membrane transport of lignin-related compounds

Lignin is an abundant aromatic polymer found in plant secondary cell walls. In recent years, lignin has attracted renewed interest as a feedstock for bio-based chemicals via catalytic and biological approaches and has emerged as a target for genetic engineering to improve lignocellulose digestibility by altering its composition. In lignin biosynthesis and microbial conversion, small phenolic lignin precursors or degradation products cross membrane bilayers through an unidentified translocation mechanism prior to incorporation into lignin polymers (synthesis) or catabolism (bioconversion), with both passive and transporter-assisted mechanisms postulated. To test the passive permeation potential of these phenolics, we performed molecular dynamics simulations for 69 monomeric and dimeric lignin-related phenolics with 3 model membranes to determine the membrane partitioning and permeability coefficients for each compound. The results support an accessible passive permeation mechanism for most compounds, including monolignols, dimeric phenolics, and the flavonoid, tricin. Computed lignin partition coefficients are consistent with concentration enrichment near lipid carbonyl groups, and permeability coefficients are sufficient to keep pace with cellular metabolism. Interactions between methoxy and hydroxy groups are found to reduce membrane partitioning and improve permeability. Only carboxylate-modified or glycosylated lignin phenolics are predicted to require transporters for membrane translocation. Overall, the results suggest that most lignin-related compounds can passively traverse plant and microbial membranes on timescales commensurate with required biological activities, with any potential transport regulation mechanism in lignin synthesis, catabolism, or bioconversion requiring compound functionalization.

Combining the biased and unbiased sampling strategy into one convenient free energy calculation method

Constructing a free energy landscape for a large molecule is difficult. One has to use either a high temperature or a strong driving force to enhance the sampling on the free energy barriers. In this work, we propose a mixed method that combines these two kinds of acceleration strategies into one simulation. First, it applies an adaptive biasing potential to some replicas of the molecule. These replicas are particularly accelerated in a collective variable space. Second, it places some unbiased and exchangeable replicas at various temperature levels. These replicas generate unbiased sampling data in the canonical ensemble. To improve the sampling efficiency, biased replicas transfer their state variables to the unbiased replicas after equilibrium by Monte Carlo trial moves. In comparison to previous integrated methods, it is more convenient for users. It does not need an initial reference biasing potential to guide the sampling of the molecule. And it is also unnecessary to insert many replicas for the requirement of passing the free energy barriers. The free energy calculation is accomplished in a single stage. It samples the data as fast as a biased simulation and it processes the data as simple as an unbiased simulation. The method provides a minimalist approach to the construction of the free energy landscape. © 2019 Wiley Periodicals, Inc.

Molecular Dynamics Simulations of Wild Type and Mutants of SAPAP in Complexed with Shank3

Specific interactions between scaffold protein SH3 and multiple ankyrin repeat domains protein 3 (Shank3) and synapse-associated protein 90/postsynaptic density-95–associated protein (SAPAP) are essential for excitatory synapse development and plasticity. In a bunch of human neurological diseases, mutations on Shank3 or SAPAP are detected. To investigate the dynamical and thermodynamic properties of the specific binding between the N-terminal extended PDZ (Post-synaptic density-95/Discs large/Zonaoccludens-1) domain (N-PDZ) of Shank3 and the extended PDZ binding motif (E-PBM) of SAPAP, molecular dynamics simulation approaches were used to study the complex of N-PDZ with wild type and mutated E-PBM peptides. To compare with the experimental data, ₉₇₄QTRL₉₇₇ and ₉₆₆IEIYI₉₇₀ of E-PBM peptide were mutated to prolines to obtain the M4P and M5P system, respectively. Conformational analysis shows that the canonical PDZ domain is stable while the βN extension presents high flexibility in all systems, especially for M5P. The high flexibility of βN extension seems to set up a barrier for the non-specific binding in this area and provide the basis for specific molecular recognition between Shank3 and SAPAP. The wild type E-PBM tightly binds to N-PDZ during the simulation while loss of binding is observed in different segments of the mutated E-PBM peptides. Energy decomposition and hydrogen bonds analysis show that M4P mutations only disrupt the interactions with canonical PDZ domain, but the interactions with βN1′ remain. In M5P system, although the interactions with βN1′ are abolished, the binding between peptide and the canonical PDZ domain is not affected. The results indicate that the interactions in the two-binding site, the canonical PDZ domain and the βN1′ extension, contribute to the binding between E-PBM and N-PDZ independently. The binding free energies calculated by MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) are in agreement with the experimental binding affinities. Most of the residues on E-PBM contribute considerably favorable energies to the binding except A963 and D964 in the N-terminal. The study provides information to understand the molecular basis of specific binding between Shank3 and SAPAP, as well as clues for design of peptide inhibitors.

Rapid and accurate structure-based therapeutic peptide design using GPU accelerated thermodynamic integration

Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.

3D-QSAR-aided design of potent c-Met inhibitors using molecular dynamics simulation and binding free energy calculation

Mesenchymal-epithelial transition factor (c-Met) is a member of receptor tyrosine kinase. It involves in various cellular signaling pathways which includes proliferation, motility, migration, and invasion. Over-expression of c-Met has been reported in various cancers. Hence, it is an ideal therapeutic target for cancer. The main objective of the study is to identify crucial residues involved in the inhibition of c-Met kinase and to design a series of potent imidazo [4,5-b] pyrazine derivatives as c-Met inhibitors. Docking was used to identify important active site residues involved in the inhibition of c-Met kinase which was further validated by 100 ns of molecular dynamics simulation and free energy calculation using molecular mechanics generalized born surface area. Furthermore, binding energy decomposition identified that residues Tyr1230, Met1211, Asp1222, Tyr1159, Met1160, Val1092, Ala1108, and Leu1157 contributed favorably to the binding stability of compound 32. Receptor-guided Comparative Molecular Field Analysis (CoMFA) (q² = 0.751, NOC = 6, r² = 0.933) and Comparative Molecular Similarity Indices Analysis (COMSIA) (q² = 0.744, NOC = 6, r² = 0.950) models were generated based on the docked conformation of the most active compound 32. The robustness of these models was tested using various validation techniques and found to be predictive. The results of CoMFA and CoMSIA contour maps exposed the regions favorable to enhance the activity. Based on this information, 27 novel c-Met inhibitors were designed. These designed compounds exhibited potent activity than the most active compound of the existing dataset. Communicated by Ramaswamy H. Sarma.

Interactions between Triterpenes and a P-I Type Snake Venom Metalloproteinase: Molecular Simulations and Experiments

Small molecule inhibitors of snake venom metalloproteinases (SVMPs) could provide a means to rapidly halt the progression of local tissue damage following viperid snake envenomations. In this study, we examine the ability of candidate compounds based on a pentacyclic triterpene skeleton to inhibit SVMPs. We leverage molecular dynamics simulations to estimate the free energies of the candidate compounds for binding to BaP1, a P-I type SVMP, and compare these results with experimental assays of proteolytic activity inhibition in a homologous enzyme (Batx-I). Both simulation and experiment suggest that betulinic acid is the most active candidate, with the simulations predicting a standard binding free energy of ΔG∘=−11.0±1.4 kcal/mol. The simulations also reveal the atomic interactions that underlie binding between the triterpenic acids and BaP1, most notably the electrostatic interaction between carboxylate groups of the compounds and the zinc cofactor of BaP1. Together, our simulations and experiments suggest that occlusion of the S1′ subsite is essential for inhibition of proteolytic activity. While all active compounds make hydrophobic contacts in the S1′ site, β-boswellic acid, with its distinct carboxylate position, does not occlude the S1′ site in simulation and exhibits negligible activity in experiment.

Influence of gag and RRE Sequences on HIV-1 RNA Packaging Signal Structure and Function

The packaging signal (Ψ) and Rev-responsive element (RRE) enable unspliced HIV-1 RNAs' export from the nucleus and packaging into virions. For some retroviruses, engrafting Ψ onto a heterologous RNA is sufficient to direct encapsidation. In contrast, HIV-1 RNA packaging requires 5' leader Ψ elements plus poorly defined additional features. We previously defined minimal 5' leader sequences competitive with intact Ψ for HIV-1 packaging, and here examined the potential roles of additional downstream elements. The findings confirmed that together, HIV-1 5' leader Ψ sequences plus a nuclear export element are sufficient to specify packaging. However, RNAs trafficked using a heterologous export element did not compete well with RNAs using HIV-1's RRE. Furthermore, some RNA additions to well-packaged minimal vectors rendered them packaging-defective. These defects were rescued by extending gag sequences in their native context. To understand these packaging defects' causes, in vitro dimerization properties of RNAs containing minimal packaging elements were compared to RNAs with sequence extensions that were or were not compatible with packaging. In vitro dimerization was found to correlate with packaging phenotypes, suggesting that HIV-1 evolved to prevent 5' leader residues' base pairing with downstream residues and misfolding of the packaging signal. Our findings explain why gag sequences have been implicated in packaging and show that RRE's packaging contributions appear more specific than nuclear export alone. Paired with recent work showing that sequences upstream of Ψ can dictate RNA folds, the current work explains how genetic context of minimal packaging elements contributes to HIV-1 RNA fate determination.

Insight into tetrodotoxin blockade and resistance mechanisms of Nav 1.2 sodium channel by theoretical approaches

Na_v 1.2, a member of voltage-gated sodium channels (Na_v s) that are responsible for the generation and propagation of action potentials along the cell membrane, and play a vital role in the process of information transmission within the nervous system and muscle contraction, is preferentially expressed in the central nervous system. As a potent and selective blocker of Na_v s, tetrodotoxin (TTX) has been extensively studied in biological and chemical sciences, whereas the detailed mechanism by which it blocks nine Na_v 1 channel subtypes remain elusive. Despite the high structural similarity, the TTX metabolite 4,9-anhydro-TTX is 161 times less effective toward the mammalian Na_v 1.2, which puzzled us to ask a question why such a subtle structural variation results in the largely binding affinity difference. In the current work, an integrated computational strategy, including homology modeling, induced fit docking, explicit-solvent MD simulations, and free energy calculations, was employed to investigate the binding mechanism and conformational determinants of TTX analogs. Based on the computational results, the H-bond interactions between C4-OH and C9-OH of TTX and the outer ring carboxylates of the selectivity-filter residues, and the cation-π interaction between the primary amine of guanidinium of TTX and Phe385 determine the difference of their binding affinities. Moreover, the computationally simulations were carried out for the D384N and E945K mutants of hNa_v 1.2-TTX, and the rank of the predicted binding free energies is in accordance with the experimental data. These observations provide a valuable model to design potent and selective neurotoxins of Na_v 1.2 and shed light on the blocking mechanism of TTX to sodium channels.