Improved Generalized Born Solvent Model Parameters for Protein Simulations

In silico study on graphene quantum dots modified with various functional groups inhibiting α‑synuclein dimerization

HYPOTHESIS

Graphene quantum dots (GQDs) with various functional groups are hypothesized to inhibit the α-synuclein (αS) dimerization, a crucial step in Parkinson's disease pathogenesis. The potential of differently functionalized GQDs is systematically explored.

EXPERIMENTS

All-atom replica-exchange molecular dynamics simulations (accumulating to 75.6 μs) in explicit water were performed to study the dimerization of the αS non-amyloid component region and the influence of GQDs modified with various functional groups. Conformation ensemble, binding behavior, and free energy analysis were conducted.

FINDINGS

All studied GQDs inhibit β-sheet and backbone hydrogen bond formation in αS dimers, leading to looser oligomeric conformations. Charged GQDs severely impede the growth of extended β-sheets by providing extra contact surface. GQD binding primarily disrupts αS inter-peptide interactions through π-π stacking, CH-π interactions, and for charged GQDs, additionally through salt-bridge and hydrogen bonding interactions. GQD-COO- showed the most optimal inhibitory effect, binding mode, and intensity, which holds promise for the development of nanomedicines targeting amyloid aggregation in neurodegenerative diseases.

Exact Analytical Algorithm for the Solvent-Accessible Surface Area and Derivatives in Implicit Solvent Molecular Simulations on GPUs

In this paper, we present differentiable solvent-accessible surface area (dSASA), an exact geometric method to calculate SASA analytically along with atomic derivatives on GPUs. The atoms in a molecule are first assigned to tetrahedra in groups of four atoms by Delaunay tetrahedralization adapted for efficient GPU implementation, and the SASA values for atoms and molecules are calculated based on the tetrahedralization information and inclusion-exclusion method. The SASA values from the numerical icosahedral-based method can be reproduced with >98% accuracy for both proteins and RNAs. Having been implemented on GPUs and incorporated into AMBER, we can apply dSASA to implicit solvent molecular dynamics simulations with the inclusion of this nonpolar term. The current GPU version of GB/SA simulations has been accelerated up to nearly 20-fold compared to the CPU version, outperforming LCPO, a commonly used, fast algorithm for calculating SASA, as the system size increases. While we focus on the accuracy of the SASA calculations for proteins and nucleic acids, we also demonstrate stable GB/SA MD mini-protein simulations.

Identification and Validation of Compounds Targeting Leishmania major Leucyl-Aminopeptidase M17

Leishmaniasis is a neglected tropical disease; there is currently no vaccine and treatment is reliant upon a handful of drugs suffering from multiple issues including toxicity and resistance. There is a critical need for development of new fit-for-purpose therapeutics, with reduced toxicity and targeting new mechanisms to overcome resistance. One enzyme meriting investigation as a potential drug target in Leishmania is M17 leucyl-aminopeptidase (LAP). Here, we aimed to chemically validate LAP as a drug target in L. major through identification of potent and selective inhibitors. Using RapidFire mass spectrometry, the compounds DDD00057570 and DDD00097924 were identified as selective inhibitors of recombinant Leishmania major LAP activity. Both compounds inhibited in vitro growth of L. major and L. donovani intracellular amastigotes, and overexpression of LmLAP in L. major led to reduced susceptibility to DDD00057570 and DDD00097924, suggesting that these compounds specifically target LmLAP. Thermal proteome profiling revealed that these inhibitors thermally stabilized two M17 LAPs, indicating that these compounds selectively bind to enzymes of this class. Additionally, the selectivity of the inhibitors to act on LmLAP and not against the human ortholog was demonstrated, despite the high sequence similarities LAPs of this family share. Collectively, these data confirm LmLAP as a promising therapeutic target for Leishmania spp. that can be selectively inhibited by drug-like small molecules.

The search for an antiviral lead molecule to combat the neglected emerging Oropouche virus

Oropouche virus (OROV) is a member of the Peribunyaviridae family and the causative agent of a dengue-like febrile illness transmitted by mosquitoes. Although mild symptoms generally occur, complications such as encephalitis and meningitis may develop. A lack of proper diagnosis, makes it a potential candidate for new epidemics and outbreaks like other known arboviruses such as Dengue, Yellow Fever and Zika virus. The study of natural molecules as potential antiviral compounds is a promising alternative for antiviral therapies. Wedelolactone (WDL) has been demonstrated to inhibit some viral proteins and virus replication, making it useful to target a wide range of viruses. In this study, we report the in silico effects of WDL on the OROV N-terminal polymerase and its potential inhibitory effects on several steps of viral infection in mammalian cells in vitro, which revealed that WDL indeed acts as a potential inhibitor molecule against OROV infection.

Exact analytical algorithm for solvent accessible surface area and derivatives in implicit solvent molecular simulations on GPUs

In this paper, we present dSASA (differentiable SASA), an exact geometric method to calculate solvent accessible surface area (SASA) analytically along with atomic derivatives on GPUs. The atoms in a molecule are first assigned to tetrahedra in groups of four atoms by Delaunay tetrahedrization adapted for efficient GPU implementation and the SASA values for atoms and molecules are calculated based on the tetrahedrization information and inclusion-exclusion method. The SASA values from the numerical icosahedral-based method can be reproduced with more than 98% accuracy for both proteins and RNAs. Having been implemented on GPUs and incorporated into the software Amber, we can apply dSASA to implicit solvent molecular dynamics simulations with inclusion of this nonpolar term. The current GPU version of GB/SA simulations has been accelerated up to nearly 20-fold compared to the CPU version, outperforming LCPO, a commonly used, fast algorithm for calculating SASA, as the system size increases. While we focus on the accuracy of the SASA calculations for proteins and nucleic acids, we also demonstrate stable GB/SA MD mini-protein simulations.

Integrating Explicit and Implicit Fullerene Models into UNRES Force Field for Protein Interaction Studies

Fullerenes, particularly C60, exhibit unique properties that make them promising candidates for various applications, including drug delivery and nanomedicine. However, their interactions with biomolecules, especially proteins, remain not fully understood. This study implements both explicit and implicit C60 models into the UNRES coarse-grained force field, enabling the investigation of fullerene-protein interactions without the need for restraints to stabilize protein structures. The UNRES force field offers computational efficiency, allowing for longer timescale simulations while maintaining accuracy. Five model proteins were studied: FK506 binding protein, HIV-1 protease, intestinal fatty acid binding protein, PCB-binding protein, and hen egg-white lysozyme. Molecular dynamics simulations were performed with and without C60 to assess protein stability and investigate the impact of fullerene interactions. Analysis of contact probabilities reveals distinct interaction patterns for each protein. FK506 binding protein (1FKF) shows specific binding sites, while intestinal fatty acid binding protein (1ICN) and uteroglobin (1UTR) exhibit more generalized interactions. The explicit C60 model shows good agreement with all-atom simulations in predicting protein flexibility, the position of C60 in the binding pocket, and the estimation of effective binding energies. The integration of explicit and implicit C60 models into the UNRES force field, coupled with recent advances in coarse-grained modeling and multiscale approaches, provides a powerful framework for investigating protein-nanoparticle interactions at biologically relevant scales without the need to use restraints stabilizing the protein, thus allowing for large conformational changes to occur. These computational tools, in synergy with experimental techniques, can aid in understanding the mechanisms and consequences of nanoparticle-biomolecule interactions, guiding the design of nanomaterials for biomedical applications.

Bioactive α-Pyrone Analogs from the Endophytic Fungus Diaporthe sp. CB10100: α-Glucosidase Inhibitory Activity, Molecular Docking, and Molecular Dynamics Studies

Two α-pyrone analogs were isolated from the endophytic fungus Diaporthe sp. CB10100, which is derived from the medicinal plant Sinomenium acutum. These analogs included a new compound, diaporpyrone F (3), and a known compound, diaporpyrone D (4). The structure of 3 was identified by a comprehensive examination of HRESIMS, 1D and 2D NMR spectroscopic data. Bioinformatics analysis revealed that biosynthetic gene clusters for α-pyrone analogs are common in fungi of Diaporthe species. The in vitro α-glucosidase inhibitory activity and antibacterial assay of 4 revealed that it has a 46.40% inhibitory effect on α-glucosidase at 800 μM, while no antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA), Mycolicibacterium (Mycobacterium) smegmatis or Klebsiella pneumoniae at 64 μg/mL. Molecular docking and molecular dynamics simulations of 4 with α-glucosidase further suggested that the compounds are potential α-glucosidase inhibitors. Therefore, α-pyrone analogs can be used as lead compounds for α-glucosidase inhibitors in more in-depth studies.

Structural mechanism underlying variations in DNA binding by the androgen receptor

The androgen receptor (AR) is a steroid activated transcription factor which recognizes DNA motifs resembling inverted repeats of a conserved 5'-AGAACA-3'-like hexanucleotides separated by a three-nucleotide spacer from a similar, but less conserved hexanucleotide. Here, we report the structures of the human AR DNA binding domain (DBD) bound to two natural AREs (C3 and MTV) in head-to-head dimer conformations, diffracting at 2.05 Å and 2.25 Å, respectively. These structures help to explain the impact of androgen insensitivity mutations on the structure integrity, DNA binding and DBD dimerization. The binding affinity of the AR DBD to different DNA motifs were measured by the BioLayer Interferometry (BLI) and further validated by Molecular Dynamics (MD) simulations. This shows that the high binding affinity of the first DBD to the upstream 5'-AGAACA-3' motif induces the cooperative binding of the second DBD to the second hexanucleotide. Our data indicate identical interaction of the DBDs to the upstream hexanucleotides, while forming an induced closer contact of the second DBD on the non-canonical hexanucleotides. The variation in binding between the DBD monomers are the result of differences in DNA occupancy, protein-protein interactions, DNA binding affinity, and DNA binding energy profiles. We propose this has functional consequences.

Peptide-based inhibitors targeting the PD-1/PD-L1 axis: potential immunotherapeutics for cancer

The PD-1/PD-L1 complex belongs to the group of inhibitory immune checkpoints and plays a critical role in immune regulation. The PD-1/PD-L1 axis is also responsible for immune evasion of cancer cells, and this complex is one of the main targets of immunotherapies used in oncology. Treatment using immune checkpoint inhibitors is mainly based on antibodies. This approach has great therapeutic potential; however, it also has major drawbacks and can induce immune-related adverse events. Thus, there is a strong need for alternative, non-antibody-based therapies using small molecules, peptides, or peptidomimetics. In the present study, we designed, synthesized, and evaluated a set of PD-1-targeting peptides based on the sequence and structure of PD-L1. The binding of these peptides to PD-1 was investigated using SPR and ELISA. We also assessed their ability to compete with PD-L1 for binding to PD-1 and their inhibitory properties against the PD-1/PD-L1 complex at the cellular level. The best results were obtained for the peptide PD-L1(111-127)(Y112C-I126C), named (L11), which displaced PD-L1 from binding to PD-1 in the competitive assay and inhibited the formation of the PD-1/PD-L1 complex. The (L11) peptide also exhibited strong affinity for PD-1. NMR studies revealed that (L11) does not form a well-defined secondary structure; however, MD simulation indicated that (L11) binds to PD-1 at the same place as PD-L1. After further optimization of the structure, the peptide inhibitor obtained in this study could also be used as a potential therapeutic compound targeting the PD-1/PD-L1 axis.

Myomerger Induces Membrane Hemifusion and Regulates Fusion Pore Expansion

Membrane fusion is a crucial mechanism in a wide variety of important events in cell biology from viral infection to exocytosis. However, despite many efforts and much progress, cell-cell fusion has remained elusive to our understanding. Along the life of the fusion pore, large conformational changes take place from the initial lipid bilayer bending, passing through the hemifusion intermediates, and ending with the formation of the first nascent fusion pore. In this sense, computer simulations are an ideal technique for describing such complex lipid remodeling at the molecular level. In this work, we studied the role played by the muscle-specific membrane protein Myomerger during the formation of the fusion pore. We have conducted μs length atomistic and coarse-grained molecular dynamics, together with free-energy calculations using ad hoc collective variables. Our results show that Myomerger favors the hemifusion diaphragm-stalk transition, reduces the nucleation-expansion energy difference, and promotes the formation of nonenlarging fusion pores.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity.

Oligo-PROTAC strategy for cell-selective and targeted degradation of activated STAT3

Decoy oligodeoxynucleotides (ODNs) allow targeting undruggable transcription factors, such as STAT3, but their limited potency and lack of delivery methods hampered translation. To overcome these challenges, we conjugated a STAT3-specific decoy to thalidomide, a ligand to cereblon in E3 ubiquitin ligase complex, to generate a proteolysis-targeting chimera (STAT3DPROTAC). STAT3DPROTAC downregulated STAT3 in target cells, but not STAT1 or STAT5. Computational modeling of the STAT3DPROTAC ternary complex predicted two surface lysines, K601 and K626, in STAT3 as potential ubiquitination sites. Accordingly, K601/K626 point mutations in STAT3, as well as proteasome inhibition or cereblon deletion, alleviated STAT3DPROTAC effect. Next, we conjugated STAT3DPROTAC to a CpG oligonucleotide targeting Toll-like receptor 9 (TLR9) to generate myeloid/B cell-selective C-STAT3DPROTAC. Naked C-STAT3DPROTAC was spontaneously internalized by TLR9+ myeloid cells, B cells, and human and mouse lymphoma cells but not by T cells. C-STAT3DPROTAC effectively decreased STAT3 protein levels and also STAT3-regulated target genes critical for lymphoma cell proliferation and/or survival (BCL2L1, CCND2, and MYC). Finally, local C-STAT3DPROTAC administration to human Ly3 lymphoma-bearing mice triggered tumor regression, while control C-STAT3D and C-SCR treatments had limited effects. Our results underscore the feasibility of using a PROTAC strategy for cell-selective, decoy oligonucleotide-based STAT3 targeting of and potentially other tumorigenic transcription factors for cancer therapy.

Benchmarking Water Models in Molecular Dynamics of Protein-Glycosaminoglycan Complexes

Glycosaminoglycans (GAGs) made of repeating disaccharide units intricately engage with proteins, playing a crucial role in the spatial organization of the extracellular matrix (ECM) and the transduction of biological signals in cells to modulate a number of biochemical processes. Exploring protein-GAG interactions reveals several challenges for their analysis, namely, the highly charged and periodic nature of GAGs, their multipose binding, and the abundance of the interfacial water molecules in the protein-GAG complexes. Most of the studies on protein-GAG interactions are conducted using the TIP3P water model, and there are no data on the effect of various water models on the results obtained in molecular dynamics (MD) simulations of protein-GAG complexes. Hence, it is essential to perform a systematic analysis of different water models in MD simulations for these systems. In this work, we aim to evaluate the properties of the protein-GAG complexes in MD simulations using different explicit: TIP3P, SPC/E, TIP4P, TIP4PEw, OPC, and TIP5P and implicit: IGB = 1, 2, 5, 7, and 8 water models to find out which of them are best suited to study the dynamics of protein-GAG complexes. The FF14SB and GLYCAM06 force fields were used for the proteins and GAGs, respectively. The interactions of several GAG types, such as heparin, chondroitin sulfate, and hyaluronic acid with basic fibroblast growth factor, cathepsin K, and CD44 receptor, respectively, are investigated. The observed variations in different descriptors used to study the binding in these complexes emphasize the relevance of the choice of water models for the MD simulation of these complexes.

Accurate estimates of dynamical statistics using memory

Many chemical reactions and molecular processes occur on time scales that are significantly longer than those accessible by direct simulations. One successful approach to estimating dynamical statistics for such processes is to use many short time series of observations of the system to construct a Markov state model, which approximates the dynamics of the system as memoryless transitions between a set of discrete states. The dynamical Galerkin approximation (DGA) is a closely related framework for estimating dynamical statistics, such as committors and mean first passage times, by approximating solutions to their equations with a projection onto a basis. Because the projected dynamics are generally not memoryless, the Markov approximation can result in significant systematic errors. Inspired by quasi-Markov state models, which employ the generalized master equation to encode memory resulting from the projection, we reformulate DGA to account for memory and analyze its performance on two systems: a two-dimensional triple well and the AIB9 peptide. We demonstrate that our method is robust to the choice of basis and can decrease the time series length required to obtain accurate kinetics by an order of magnitude.

Targeting the hSSB1-INTS3 Interface: A Computational Screening Driven Approach to Identify Potential Modulators

Human single-stranded DNA binding protein 1 (hSSB1) forms a heterotrimeric complex, known as a sensor of single-stranded DNA binding protein 1 (SOSS1), in conjunction with integrator complex subunit 3 (INTS3) and C9ORF80. This sensory protein plays an important role in homologous recombination repair of double-strand breaks in DNA to efficiently recruit other repair proteins at the damaged sites. Previous studies have identified elevated hSSB1-mediated DNA repair activities in various cancers, highlighting its potential as an anticancer target. While prior efforts have focused on inhibiting hSSB1 by targeting its DNA binding domain, this study seeks to explore the inhibition of the hSSB1 function by disrupting its interaction with the key partner protein INTS3 in the SOSS1 complex. The investigative strategy entails a molecular docking-based screening of a specific compound library against the three-dimensional structure of INTS3 at the hSSB1 binding interface. Subsequent assessments involve in vitro analyses of protein-protein interaction (PPI) disruption and cellular effects through co-immunoprecipitation and immunofluorescence assays, respectively. Moreover, the study includes an evaluation of the structural stability of ligands at the INTS3 hot-spot site using molecular dynamics simulations. The results indicate a potential in vitro disruption of the INTS3-hSSB1 interaction by three of the tested compounds obtained from the virtual screening with one impacting the recruitment of hSSB1 and INTS3 to chromatin following DNA damage. To our knowledge, our results identify the first set of drug-like compounds that functionally target INTS3-hSSB1 interaction, and this provides the basis for further biophysical investigations that should help to speed up PPI inhibitor discovery.

Molecular Modeling of Self-Assembling Peptides

Peptide epitopes mediate as many as 40% of protein-protein interactions and fulfill signaling, inhibition, and activation roles within the cell. Beyond protein recognition, some peptides can self- or coassemble into stable hydrogels, making them a readily available source of biomaterials. While these 3D assemblies are routinely characterized at the fiber level, there are missing atomistic details about the assembly scaffold. Such atomistic detail can be useful in the rational design of more stable scaffold structures and with improved accessibility to functional motifs. Computational approaches can in principle reduce the experimental cost of such an endeavor by predicting the assembly scaffold and identifying novel sequences that adopt said structure. Yet, inaccuracies in physical models and inefficient sampling have limited atomistic studies to short (two or three amino acid) peptides. Given recent developments in machine learning and advances in sampling strategies, we revisit the suitability of physical models for this task. We use the MELD (Modeling Employing Limited Data) approach to drive self-assembly in combination with generic data in cases where conventional MD is unsuccessful. Finally, despite recent developments in machine learning algorithms for protein structure and sequence predictions, we find the algorithms are not yet suited for studying the assembly of short peptides.

Analysis and interpretation of first passage time distributions featuring rare events

In this contribution we consider theory and associated computational tools to treat the kinetics associated with competing pathways on multifunnel energy landscapes. Multifunnel landscapes are associated with molecular switches and multifunctional materials, and are expected to exhibit multiple relaxation time scales and associated thermodynamic signatures in the heat capacity. Our focus here is on the first passage time distribution, which is encoded in a kinetic transition network containing all the locally stable states and the pathways between them. This network can be renormalised to reduce the dimensionality, while exactly conserving the mean first passage time and approximately conserving the full distribution. The structure of the reduced network can be visualised using disconnectivity graphs. We show how features in the first passage time distribution can be associated with specific kinetic traps, and how the appearance of competing relaxation time scales depends on the starting conditions. The theory is tested for two model landscapes and applied to an atomic cluster and a disordered peptide. Our most important contribution is probably the reconstruction of the full distribution for long time scales, where numerical problems prevent direct calculations. Here we combine accurate treatment of the mean first passage time with the reliable part of the distribution corresponding to faster time scales. Hence we now have a fundamental understanding of both thermodynamic and kinetic signatures of multifunnel landscapes.

Cryo-EM structure and B-factor refinement with ensemble representation

Cryo-EM experiments produce images of macromolecular assemblies that are combined to produce three-dimensional density maps. Typically, atomic models of the constituent molecules are fitted into these maps, followed by a density-guided refinement. We introduce TEMPy-ReFF, a method for atomic structure refinement in cryo-EM density maps. Our method represents atomic positions as components of a Gaussian mixture model, utilising their variances as B-factors, which are used to derive an ensemble description. Extensively tested on a substantial dataset of 229 cryo-EM maps from EMDB ranging in resolution from 2.1-4.9 Å with corresponding PDB and CERES atomic models, our results demonstrate that TEMPy-ReFF ensembles provide a superior representation of cryo-EM maps. On a single-model basis, it performs similarly to the CERES re-refinement protocol, although there are cases where it provides a better fit to the map. Furthermore, our method enables the creation of composite maps free of boundary artefacts. TEMPy-ReFF is useful for better interpretation of flexible structures, such as those involving RNA, DNA or ligands.

Network Pharmacology and Molecular Modeling Techniques in Unraveling the Underlying Mechanism of Citri Reticulatae Pericarpium aganist Type 2 Diabetic Osteoporosis

Type 2 diabetic osteoporosis (T2DOP) is a common complication in diabetic patients that seriously affects their health and quality of life. The pathogenesis of T2DOP is complex, and there are no targeted governance means in modern medicine. Citri Reticulatae Pericarpium (CRP) is a traditional Chinese medicine that has a long history and has been used in the treatment of osteoporosis diseases. However, the molecular mechanism for the CRP treatment of T2DOP is not clear. Therefore, this study aimed to explore the underlying mechanisms of CRP for the treatment of T2DOP by using network pharmacology and molecular modeling techniques. By retrieving multiple databases, we obtained 5 bioactive compounds and 63 common targets of bioactive compounds with T2DOP, and identified AKT 1, TP 53, JUN, BCL 2, MAPK 1, NFKB 1, and ESR 1 as the core targets of their PPI network. Enrichment analysis revealed that these targets were mainly enriched in the estrogen signaling pathway, TNF signaling pathway, and AGE-RAGE signaling pathway in diabetics, which were mainly related to oxidative stress and hormonal regulation. Molecular docking and molecular dynamics simulations have shown the excellent binding effect of the bioactive compounds of CRP and the core targets. These findings reveal that CRP may ameliorate T2DOP through multiple multicomponent and multitarget pathways.

Tuning Potential Functions to Host-Guest Binding Data

Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.

A Universal Strategy for Enhancing the Circulating miRNAs' Detection Performance of Rolling Circle Amplification by Using a Dual-Terminal Stem-Loop Padlock

Rolling circle amplification (RCA) is one of the most promising nucleic acid detection technologies and has been widely used in the molecular diagnosis of disease. Padlock probes are often used to form circular templates, which are the core of RCA. However, RCA often suffers from insufficient specificity and sensitivity. Here we report a reconstruction strategy for conventional padlock probes to promote their overall performance in nucleic acid detection while maintaining probe functions uncompromised. When two rationally designed stem-loops were strategically placed at the two terminals of linear padlock probes, the specificity of target recognition was enhanced and the negative signal was significantly delayed. Our design achieved the best single-base discrimination compared with other structures and over a 1000-fold higher sensitivity than that of the conventional padlock probe, validating the effectiveness of this reconstruction. In addition, the underlying mechanisms of our design were elucidated through molecular dynamics simulations, and the versatility was validated with longer and shorter padlocks targeting the same target, as well as five additional targets (four miRNAs and dengue virus - 2 RNA mimic (DENV-2)). Finally, clinical applicability in multiplex detection was demonstrated by testing real plasma samples. Our exploration of the structures of nucleic acids provided another perspective for developing high-performance detection systems, improving the efficacy of practical detection strategies, and advancing clinical diagnostic research.

Binding Affinity and Mechanism of Six PFAS with Human Serum Albumin: Insights from Multi-Spectroscopy, DFT and Molecular Dynamics Approaches

Per- and Polyfluoroalkyl Substances (PFAS) bioaccumulate in the human body, presenting potential health risks and cellular toxicity. Their transport mechanisms and interactions with tissues and the circulatory system require further investigation. This study investigates the interaction mechanisms of six PFAS with Human Serum Albumin (HSA) using multi-spectroscopy, DFT and a molecular dynamics approach. Multi-spectral analysis shows that perfluorononanoic acid (PFNA) has the best binding capabilities with HSA. The order of binding constants (298 K) is as follows: "Perfluorononanoic Acid (PFNA, 7.81 × 106 L·mol-1) > Perfluoro-2,5-dimethyl-3,6-dioxanonanoic Acid (HFPO-TA, 3.70 × 106 L·mol-1) > Perfluorooctanoic Acid (PFOA, 2.27 × 105 L·mol-1) > Perfluoro-3,6,9-trioxadecanoic Acid (PFO3DA, 1.59 × 105 L·mol-1) > Perfluoroheptanoic Acid (PFHpA, 4.53 × 103 L·mol-1) > Dodecafluorosuberic Acid (DFSA, 1.52 × 103 L·mol-1)". Thermodynamic analysis suggests that PFNA and PFO3DA's interactions with HSA are exothermic, driven primarily by hydrogen bonds or van der Waals interactions. PFHpA, DFSA, PFOA, and HFPO-TA's interactions with HSA, on the other hand, are endothermic processes primarily driven by hydrophobic interactions. Competitive probe results show that the main HSA-PFAS binding site is in the HSA structure's subdomain IIA. These findings are also consistent with the findings of molecular docking. Molecular dynamics simulation (MD) analysis further shows that the lowest binding energy (-38.83 kcal/mol) is fund in the HSA-PFNA complex, indicating that PFNA binds more readily with HSA. Energy decomposition analysis also indicates that van der Waals and electrostatic interactions are the main forces for the HSA-PFAS complexes. Correlation analysis reveals that DFT quantum chemical descriptors related to electrostatic distribution and characteristics like ESP and ALIE are more representative in characterizing HSA-PFAS binding. This study sheds light on the interactions between HSA and PFAS. It guides health risk assessments and control strategies against PFAS, serving as a critical starting point for further public health research.

Network pharmacology to unveil the mechanism of suanzaoren decoction in the treatment of alzheimer's with diabetes

BACKGROUND

Suanzaoren Decoction (SZRD), a well-known formula from traditional Chinese medicine, has been shown to have reasonable cognitive effects while relaxing and alleviating insomnia. Several studies have demonstrated significant therapeutic effects of SZRD on diabetes and Alzheimer's disease (AD). However, the active ingredients and probable processes of SZRD in treating Alzheimer's with diabetes are unknown. This study aims to preliminarily elucidate the potential mechanisms and potential active ingredients of SZRD in the treatment of Alzheimer's with diabetes.

METHODS

The main components and corresponding protein targets of SZRD were searched on the TCMSP database. Differential gene expression analysis for diabetes and Alzheimer's disease was conducted using the Gene Expression Omnibus database, with supplementation from OMIM and genecards databases for differentially expressed genes. The drug-compound-target-disease network was constructed using Cytoscape 3.8.0. Disease and SZRD targets were imported into the STRING database to construct a protein-protein interaction network. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed on the intersection of genes. Molecular docking and molecular dynamics simulations were conducted on the Hub gene and active compounds. Gene Set Enrichment Analysis was performed to further analyze key genes.

RESULTS

Through the Gene Expression Omnibus database, we obtained 1977 diabetes related genes and 622 AD related genes. Among drugs, diabetes and AD, 97 genes were identified. The drug-compound-target-disease network revealed that quercetin, kaempferol, licochalcone a, isorhamnetin, formononetin, and naringenin may be the core components exerting effects. PPI network analysis identified hub genes such as IL6, TNF, IL1B, CXCL8, IL10, CCL2, ICAM1, STAT3, and IL4. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that SZRD in the treatment of Alzheimer's with diabetes is mainly involved in biological processes such as response to drug, aging, response to xenobiotic, and enzyme binding; as well as signaling pathways such as Pathways in cancer, Chemical carcinogenesis - receptor activation, and Fluid shear stress and atherosclerosis. Molecular docking results showed that licochalcone a, isorhamnetin, kaempferol, quercetin, and formononetin have high affinity with CXCL8, IL1B, and CCL2. Molecular dynamics simulations also confirmed a strong interaction between CXCL8 and licochalcone a, isorhamnetin, and kaempferol. Gene Set Enrichment Analysis revealed that CXCL8, IL1B, and CCL2 have significant potential in diabetes.

CONCLUSION

This study provides, for the first time, insights into the active ingredients and potential molecular mechanisms of SZRD in the treatment of Alzheimer's with diabetes, laying a theoretical foundation for future basic research.

Using NMR diffusion data to validate MD models of disordered proteins: Test case of N-terminal tail of histone H4

MD simulations can provide uniquely detailed models of intrinsically disordered proteins (IDPs). However, these models need careful experimental validation. The coefficient of translational diffusion Dtr, measurable by pulsed field gradient NMR, offers a potentially useful piece of experimental information related to the compactness of the IDP's conformational ensemble. Here, we investigate, both experimentally and via the MD modeling, the translational diffusion of a 25-residue N-terminal fragment from histone H4 (N-H4). We found that the predicted values of Dtr, as obtained from mean-square displacement of the peptide in the MD simulations, are largely determined by the viscosity of the MD water (which has been reinvestigated as a part of our study). Beyond that, our analysis of the diffusion data indicates that MD simulations of N-H4 in the TIP4P-Ew water give rise to an overly compact conformational ensemble for this peptide. In contrast, TIP4P-D and OPC simulations produce the ensembles that are consistent with the experimental Dtr result. These observations are supported by the analyses of the 15N spin relaxation rates. We also tested a number of empirical methods to predict Dtr based on IDP's coordinates extracted from the MD snapshots. In particular, we show that the popular approach involving the program HYDROPRO can produce misleading results. This happens because HYDROPRO is not intended to predict the diffusion properties of highly flexible biopolymers such as IDPs. Likewise, recent empirical schemes that exploit the relationship between the small-angle x-ray scattering-informed conformational ensembles of IDPs and the respective experimental Dtr values also prove to be problematic. In this sense, the first-principle calculations of Dtr from the MD simulations, such as demonstrated in this work, should provide a useful benchmark for future efforts in this area.

Molecular Dynamics Simulation-Based Prediction of Glycosaminoglycan Interactions with Drug Molecules

Glycosaminoglycans (GAGs) are a class of long linear anionic periodic polysaccharides. Their biological activities are very broad including tissue remodeling, regulation of cell proliferation, cell migration, cell differentiation, participation in bacterial/viral infections, and immune response. They can interact with many important biomolecular partners in the extracellular matrix of the cell including small drug molecules. Recently, several GAG-bioactive small molecule complexes have been experimentally and theoretically studied. Some of these compounds in complexes with GAGs may potentially interfere with protein-GAG or peptide-GAG multimolecular systems affecting the processes of cellular differentiation or have anti-inflammatory, antiviral as well as antithrombotic effects. Although many studies have been conducted on GAG-drug complexes, the molecular mechanisms of the formation of such complexes are still poorly understood. At the same time, the complexity of their physicochemical properties renders the use of both experimental and computational methods to study these molecular systems challenging. Here, we present the molecular dynamics-based protocols successfully employed to in silico analyze GAG-small molecule interactions.

Identification of peanut skin components for treating hepatocellular carcinoma via network pharmacology and in vitro experiments

Peanut skin (PS) contains various flavonoids and phenols that have antitumor and antioxidant effects. However, no research has been conducted on PS and hepatocellular carcinoma (HCC). Therefore, this study sought to explore the potential mechanism of PS in treating HCC. PS was searched for in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and SYMMAP databases. HCC targets were searched for in five major databases. Protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were performed. Molecular docking and molecular dynamics simulation were used for verification. Furthermore, in vitro experiments were used to verify the regulation of PS on human HCC (HepG2) cells. Ten ingredients and 95 common targets were identified for PS and HCC, respectively. The key targets of ingredients mainly relate to pathways such as hepatitis B, lipid and atherosclerosis, advanced glycation end products (AGEs)-AGE receptors (RAGEs) signaling pathway in diabetic complications, interleukin-17 (IL-17) signaling pathway, mitogen activated kinase-like protein (MAPK) signaling pathway, the PI3K-Akt signaling pathway. In addition, the molecular docking and molecular dynamics simulation analysis indicated the ingredients had strong binding ability with the targets. Moreover, in vitro experiments confirmed that luteolin can promote the apoptosis of HepG2 cells by controlling the expression of phosphorylated protein-tyrosine kinase (p-AKT). This study provides preliminary evidence that PS produces a marked effect in regulating multiple signaling pathways in HCC through multiple ingredients acting on multiple core genes, including AKT serine/threonine kinase 1 (AKT1), MYC, caspase 3 (CASP3), estrogen receptor 1 (ESR1), epidermal growth factor receptor (EGFR), jun proto-oncogene(JUN), and provides the basis for follow-up research to verify the mechanism of action of PS in treating HCC.

Molecular docking and dynamics of a dextranase derived from Penicillium cyclopium CICC-4022

This study aimed to investigate the recognition mechanism of dextranase (PC-Edex) produced by Penicillium cyclopium CICC-4022 on dextran. Whole genome information of P. cyclopium CICC-4022 was obtained through genome sequencing technology. The coding information of PC-Edex was determined based on the annotation of the protein-coding genes using protein databases. The three-dimensional structure of PC-Edex was obtained via homology modelling. The active site and binding free energy between PC-Edex and dextran were calculated by molecular docking and molecular dynamics techniques. The results showed that the total sequence length and GC content of P. cyclopium CICC-4022 were 29,710,801 bp and 47.02 %, respectively. The annotation of protein-encoding genes showed that P. cyclopium CICC-4022 is highly active and has many carbohydrate transport and metabolic functions, and most of its proteases are glycolytic anhydrases. Furthermore, the gene encoding PC-Edex was successfully annotated. Molecular dynamics simulations indicated that van der Waals interaction was the main driving force of interaction. Residues Ile114, Asp115, Tyr332, Lys344, and Gln403 significantly promoted the binding between dextran and PC-Edex. In summary, this study explored the active site catalyzed by PC-Edex based on the binding pattern of PC-Edex and dextran. Therefore, this study provides genomic information on dextranase and data supporting the rational modification and enhancement of PC-Edex.

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks

Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.

Acridones as promising drug candidates against Oropouche virus

Oropouche virus (OROV) is an emerging vector-borne arbovirus found in South America that causes Oropouche fever, a febrile infection similar to dengue fever. It has a high epidemic potential, causing illness in over 500,000 cases diagnosed since the virus was first discovered in 1955. Currently, the prevention of human viral infection depends on vaccination, but availability for many viruses is limited, and they are classified as neglected viruses. At present, there are no vaccines or antiviral treatments available. An alternative approach to limiting the spread of the virus is to selectively disrupt viral replication mechanisms. Here, we demonstrate the inhibitory effect of acridones, which efficiently inhibited viral replication by 99.9 % in vitro. To evaluate possible mechanisms of action, we conducted tests with dsRNA, an intermediate in virus replication, as well as MD simulations, docking, and binding free energy analysis. The results showed a strong interaction between FAC21 and the OROV endonuclease, which possibly limits the interaction of viral RNA with other proteins. Therefore, our results suggest a dual mechanism of antiviral action, possibly caused by ds-RNA intercalation. In summary, our findings demonstrate that a new generation of antiviral drugs could be developed based on the selective optimization of molecules.

When MELD Meets GaMD: Accelerating Biomolecular Landscape Exploration

We introduce Gaussian accelerated MELD (GaMELD) as a new method for exploring the energy landscape of biomolecules. GaMELD combines the strengths of Gaussian accelerated molecular dynamics (GaMD) and modeling employing limited data (MELD) to navigate complex energy landscapes. MELD uses replica-exchange molecular simulations to integrate limited and uncertain data into simulations via Bayesian inference. MELD has been successfully applied to problems of structure prediction like protein folding and complex structure prediction. However, the computational cost for MELD simulations has limited its broader applicability. The synergy of GaMD and MELD surmounts this limitation efficiently sampling the energy landscape at a lower computational cost (reducing the computational cost by a factor of 2 to six). Effectively, GaMD is used to shift energy distributions along replicas to increase the overlap in energy distributions across replicas, facilitating a random walk in replica space. We tested GaMELD on a benchmark set of 12 small proteins that have been previously studied through MELD and conventional MD. GaMELD consistently achieves accurate predictions with fewer replicas. By increasing the efficacy of replica exchange, GaMELD effectively accelerates convergence in the conformational space, enabling improved sampling across a diverse set of systems.

Mapping the configurational landscape and aggregation phase behavior of the tau protein fragment PHF6

The PHF6 (Val-Gln-Ile-Val-Tyr-Lys) motif, found in all isoforms of the microtubule-associated protein tau, forms an integral part of ordered cores of amyloid fibrils formed in tauopathies and is thought to play a fundamental role in tau aggregation. Because PHF6 as an isolated hexapeptide assembles into ordered fibrils on its own, it is investigated as a minimal model for insight into the initial stages of aggregation of larger tau fragments. Even for this small peptide, however, the large length and time scales associated with fibrillization pose challenges for simulation studies of its dynamic assembly, equilibrium configurational landscape, and phase behavior. Here, we develop an accurate, bottom-up coarse-grained model of PHF6 for large-scale simulations of its aggregation, which we use to uncover molecular interactions and thermodynamic driving forces governing its assembly. The model, not trained on any explicit information about fibrillar structure, predicts coexistence of formed fibrils with monomers in solution, and we calculate a putative equilibrium phase diagram in concentration-temperature space. We also characterize the configurational and free energetic landscape of PHF6 oligomers. Importantly, we demonstrate with a model of heparin that this widely studied cofactor enhances the aggregation propensity of PHF6 by ordering monomers during nucleation and remaining associated with growing fibrils, consistent with experimentally characterized heparin-tau interactions. Overall, this effort provides detailed molecular insight into PHF6 aggregation thermodynamics and pathways and, furthermore, demonstrates the potential of modern multiscale modeling techniques to produce predictive models of amyloidogenic peptides simultaneously capturing sequence-specific effects and emergent aggregate structures.

Differentiable rotamer sampling with molecular force fields

Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

Rapid Rescoring and Refinement of Ligand-Receptor Complexes Using Replica Exchange Molecular Dynamics with a Monte Carlo Pose Reservoir

Virtual screening (VS) involves generation of poses for a library of ligands and ranking using simplified energy functions and limited flexibility. Top-scored poses are used to rank and prioritize ligands. Here, we adapt the reservoir replica exchange molecular dynamics (res-REMD) method to rerank poses generated through VS. REMD simulations are carried out but with occasional Monte Carlo jumps to alternate VS-generated poses using a Metropolis criterion. The simulations converge within 10 ns for all systems, generating populations of alternate poses in the context of fully flexible ligand and protein side chains. The protocol is applied to four model protein-ligand complexes, where DOCK resulted in two successes and two scoring failures. In all four systems, the most populated cluster from the final ensemble exhibits high similarity to the crystallographic pose with ligand RMSD values under 2.0 Å. Both DOCK failures were rescued. For one DOCK success, the protocol identified the correct pose but also sampled an alternate pose at equal probability. Opportunities for future improvements and extensions are discussed.

PPARG is a potential target of Tanshinone IIA in prostate cancer treatment: a combination study of molecular docking and dynamic simulation based on transcriptomic bioinformatics

Tanshinone IIA is a lipophilic organic compound from the root of Danshen (Salvia miltiorrhiza) and is one of the most well-known Tanshinone molecules by pharmacologists. In recent years, in addition to effects of anti-cardiovascular and neurological diseases, Tanshinone IIA has also shown some degrees of anti-prostate cancer potential. Although they do have some studies focusing on the molecular mechanism of Tanshinone IIA's anti-prostate cancer effects, a further understanding on the transcriptomic and structural level is still lacking. In this study, transcriptomic sequencing technology and computer technology were employed to illustrate the effects of Tanshinone IIA on prostate cancer through bioinformatic analysis and molecular dynamics simulation, and PPARG was considered to be one of the targets for Tanshinone IIA according to docking scoring and dynamic calculation. Our study provides a novel direction to further understand the mechanism of the effects of Tanshinone IIA on prostate cancer, and further molecular biological studies need to be carried on to further investigate the molecular mechanism of Tanshinone IIA's anti-prostate cancer effect through PPARG.

Identification of key mutations responsible for the enhancement of receptor-binding affinity and immune escape of SARS-CoV-2 Omicron variant

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised concerns worldwide due to its enhanced transmissibility and immune escapability. The first dominant Omicron BA.1 subvariant harbors more than 30 mutations in the spike protein from the prototype virus, of which 15 mutations are located at the receptor binding domain (RBD). These mutations in the RBD region attracted significant attention, which potentially enhance the binding of the receptor human angiotensin-converting enzyme 2 (hACE2) and decrease the potency of neutralizing antibodies/nanobodies. This study applied the molecular dynamics simulations combined with the molecular mechanics-generalized Born surface area (MMGBSA) method, to investigate the molecular mechanism behind the impact of the mutations acquired by Omicron on the binding affinity between RBD and hACE2. Our results indicate that five key mutations, i.e., N440K, T478K, E484A, Q493R, and G496S, contributed significantly to the enhancement of the binding affinity by increasing the electrostatic interactions of the RBD-hACE2 complex. Moreover, fourteen neutralizing antibodies/nanobodies complexed with RBD were used to explore the effects of the mutations in Omicron RBD on their binding affinities. The calculation results indicate that the key mutations E484A and Y505H reduce the binding affinities to RBD for most of the studied neutralizing antibodies/nanobodies, mainly attributed to the elimination of the original favorable gas-phase electrostatic and hydrophobic interactions between them, respectively. Our results provide valuable information for developing effective vaccines and antibody/nanobody drugs.

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

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

Intrinsic disorder and conformational coexistence in auxin coreceptors

AUXIN/INDOLE 3-ACETIC ACID (Aux/IAA) transcriptional repressor proteins and the TRANSPORT INHIBITOR RESISTANT 1/AUXIN SIGNALING F-BOX (TIR1/AFB) proteins to which they bind act as auxin coreceptors. While the structure of TIR1 has been solved, structural characterization of the regions of the Aux/IAA protein responsible for auxin perception has been complicated by their predicted disorder. Here, we use NMR, CD and molecular dynamics simulation to investigate the N-terminal domains of the Aux/IAA protein IAA17/AXR3. We show that despite the conformational flexibility of the region, a critical W-P bond in the core of the Aux/IAA degron motif occurs at a strikingly high (1:1) ratio of cis to trans isomers, consistent with the requirement of the cis conformer for the formation of the fully-docked receptor complex. We show that the N-terminal half of AXR3 is a mixture of multiple transiently structured conformations with a propensity for two predominant and distinct conformational subpopulations within the overall ensemble. These two states were modeled together with the C-terminal PB1 domain to provide the first complete simulation of an Aux/IAA. Using MD to recreate the assembly of each complex in the presence of auxin, both structural arrangements were shown to engage with the TIR1 receptor, and contact maps from the simulations match closely observations of NMR signal-decreases. Together, our results and approach provide a platform for exploring the functional significance of variation in the Aux/IAA coreceptor family and for understanding the role of intrinsic disorder in auxin signal transduction and other signaling systems.

Triazolothiadiazoles and triazolothiadiazines as potent α-glucosidase inhibitors: Mechanistic insights from kinetics studies, molecular docking and dynamics simulations

Diabetes mellitus has been considered as a serious health problem worldwide due its high prevalence rate and associated complications. In this context, the current research work aims at exploring new structural leads for the treatment of a major metabolic disorder, diabetes mellitus type 2. The outcomes of our prior studies on a diverse set of triazolothiadiazole and triazolothiadiazine derivatives and their therapeutic potential, encouraged us to explore their anti-diabetic competency by targeting the key carbohydrate catabolic enzyme, α-glucosidase. Therefore, all these analogues were examined to reveal their contribution towards this severe metabolic issue. Interestingly, all the tested compounds (2a-2l and 3a-3p) exhibited several times more potent α-glucosidase inhibitory activities (IC50 in the range of 2.44-219.93 μM) as compared to marketed drug, acarbose (IC50 = 873.34 ± 1.67 μM). Furthermore, their mechanism of action was investigated through in vitro kinetics studies which revealed compounds 3a, 3d, 3o, and 2k as competitive inhibitors, and 3f as a mixed type inhibitor of α-glucosidase. In addition, in silico molecular docking and molecular dynamics simulations were applied to observe the mode of interaction of the active hits within the binding pocket of α-glucosidase. Both docking and simulation results favored our in vitro mechanistic analysis.

The avidin-theophylline complex: A structural and computational study

The interaction between avidin and its counterpart biotin is one of central importance in biology and has been reproposed and studied at length. However, the binding pocket of avidin is prone to promiscuous binding, able to accommodate even non-biotinylated ligands. Comprehending the factors that distinguish the extremely strong interaction with biotin to other ligands is an important step to fully picture the thermodynamics of these low-affinity complexes. Here, we present the complex between chicken white egg avidin and theophylline (TEP), the xanthine derivative used in the therapy of asthma. In the crystal structure, TEP lies in the biotin-binding pocket with the same orientation and planarity of the aromatic ring of 8-oxodeoxyguanosine. Indeed, its affinity for avidin measured by isothermal titration calorimetry is in the same μM range as those obtained for the previously characterized nucleoside derivatives. By the use of molecular dynamic simulations, we have investigated the most important intermolecular interactions occurring in the avidin-TEP binding pocket and compared them with those obtained for the avidin 8-oxodeoxyguanosine and avidin-biotin complexes. These results testify the capability of avidin to complex purely aromatic molecules.

Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks

Coarse-grained (CG) force fields are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. These simulations employ simplified models grouping atoms into interaction sites, enabling the study of complex biomolecular systems over biologically relevant timescales. Efforts are underway to develop accurate and transferable CG force fields, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to many-body effects, lack of analytical expressions for the PMF, and limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent CG force fields and potential contrasting for parameterization from atomistic simulation data. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside the training data. Our study offers valuable insights for building accurate coarse-grained models bottom-up.

BTLA-derived peptides as inhibitors of BTLA/HVEM complex formation - design, synthesis and biological evaluation

Immune checkpoints can be divided into co-stimulatory and co-inhibitory molecules that regulate the activation and effector functions of T cells. The co-inhibitory pathways mediated by ICPs are used by cancer cells to escape from immune surveillance, and therefore the blockade of these receptor/ligand interactions is one of the strategies used in the treatment of cancer. The two main pathways currently under investigation are CTLA-4/CD80/CD86 and PD-1/PD-L1, and the monoclonal Abs targeting them have shown potent immunomodulatory effects and activity in clinical environments. Another interesting target in cancer treatment is the BTLA/HVEM complex. Binding of BTLA protein on T cells to HVEM on cancer cells leads to inhibition of T cell proliferation and cytokine production. In the presented work, we focused on blocking the HVEM protein using BTLA-derived peptides. Based on the crystal structure of the BTLA/HVEM complex and MM/GBSA analysis performed here, we designed and synthesized peptides, specifically fragments of BTLA protein. We subsequently checked the inhibitory capacities of these compounds using ELISA and a cellular reporter platform. Two of these peptides, namely BTLA(35-43) and BTLA(33-64)C58Abu displayed the most promising properties, and we therefore performed further studies to evaluate their affinity to HVEM protein, their stability in plasma and their effect on viability of human PBMCs. In addition, the 3D structure for the peptide BTLA(33-64)C58Abu was determined using NMR. Obtained data confirmed that the BTLA-derived peptides could be the basis for future drugs and their immunomodulatory potential merits further examination.

The protective mechanism of Dehydromiltirone in diabetic kidney disease is revealed through network pharmacology and experimental validation

Background: Salvia miltiorrhiza (SM) is an effective traditional Chinese medicine for treating DKD, but the exact mechanism is elusive. In this study, we aimed to investigate and confirm the method underlying the action of the active components of SM in the treatment of DKD. Methods: Renal tissue transcriptomics and network pharmacology of DKD patients was performed to identify the active components of SM and the disease targets of DKD. Next, the point of convergence among these three groups was studied. Potential candidate genes were identified and analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The component-target networks were modelled and visualized with Cytoscape. In addition, docking studies were performed to validate our potential target predictions. Lastly, in vitro and in vivo experiments were performed to understand the role of Dehydromiltirone (DHT), the active component of SM, in the phenotypic switching of mesangial cells. Results: Transcriptomics of DKD patients' renal tissues screened 4,864 differentially expressed genes. Eighty-nine active components of SM and 161 common targets were found. Functional enrichment analysis indicated that 161 genes were enriched in apoptosis, the PI3K-AKT signaling pathway, and the AGE-RAGE signaling pathway in diabetes complications. Molecular docking and molecular dynamic simulations show that DHT can bind to functional PIK3CA pockets, thereby becoming a possible inhibitor of PIK3CA. In vitro study demonstrated that DHT reduced the expression of phenotypic switching markers α-SMA, Col-I, and FN in HMCs by downregulating the over-activation of the PI3K-AKT signaling pathway through the inhibition of PIK3CA. Furthermore, the DKD mouse model confirmed that DHT could reduce proteinuria and improve glomerular hypertrophy in vivo. Conclusion: DHT was identified as the key active component of SM, and its therapeutic effect on DKD was achieved by inhibiting the phenotypic switching of mesangial cells via the PIK3CA signaling pathway.

SurfPB: A GPU-Accelerated Electrostatic Calculation and Visualization Tool for Biomolecules

In this work, we present SurfPB as a useful tool for the study of biomolecules. It can do many typical calculations, including the molecular surface, electrostatic potential, solvation free energy, entropy, and binding free energy. Among all of the calculations, the entropy calculation is the most time-consuming one. In SurfPB, the calculation can be performed in a vacuum or implicit solvent and accelerated on GPU. The Poisson-Boltzmann equation solver is accelerated on GPU as well. Moreover, we developed a graphical user interface for SurfPB. It allows users to input the parameters and complete the whole calculation in a visual way. The calculated electrostatic potentials are shown on the molecular surface in a three-dimensional scene.

Oligo-PROTAC strategy for cell-selective and targeted degradation of activated STAT3

Decoy-oligodeoxynucleotides (D-ODNs) can target undruggable transcription factors, such as STAT3. However, challenges in D-ODN delivery and potency hampered their translation. To overcome these limitations, we conjugated STAT3-specific D-ODN to thalidomide (Tha), a known ligand to cereblon (CRBN, a component of E3 ubiquitin ligase) to generate a proteolysis-targeting chimera (STAT3D PROTAC ). STAT3D PROTAC downregulated STAT3, but not STAT1 or STAT5, in target cells. Computational modeling of the STAT3D PROTAC ternary complex predicted two surface lysines on STAT3, K601 and K626 as potential ubiquitination sites for the PROTAC bound E3 ligase. Accordingly, K601/K626 point mutations in STAT3, as well as proteasome inhibitors, and CRBN deletion alleviated STAT3D PROTAC effect. Next, we conjugated STAT3D PROTAC to a CpG ligand targeting Toll-like receptor 9 (TLR9) to generate myeloid/B-cell-selective C-STAT3D PROTAC conjugate. Naked C-STAT3D PROTAC was spontaneously internalized by TLR9 + myeloid cells, B cells as well as human Ly18 and mouse A20 lymphoma cells, but not by T cells. C-STAT3D PROTAC decreased STAT3 levels to 50% at 250 nM and over 85% at 2 µM dosing in myeloid cells. We also observed significantly improved downregulation of STAT3 target genes involved in lymphoma cell proliferation and/or survival ( BCL2L1, CCND2, MYC ). Finally, we assessed the antitumor efficacy of C-STAT3D PROTAC compared to C-STAT3D or scrambled control (C-SCR) against human lymphoma xenotransplants. Local C-STAT3D PROTAC administration triggered lymphoma regression while control treatments had limited effects. Our results underscore feasibility of using PROTAC strategy for cell-selective, decoy oligonucleotide-based targeting of STAT3 and potentially other tumorigenic transcription factors for cancer therapy.

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the "feature importance" approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.

Modulation of the substrate specificity of the kinase PDK1 by distinct conformations of the full-length protein

The activation of at least 23 different mammalian kinases requires the phosphorylation of their hydrophobic motifs by the kinase PDK1. A linker connects the phosphoinositide-binding PH domain to the catalytic domain, which contains a docking site for substrates called the PIF pocket. Here, we used a chemical biology approach to show that PDK1 existed in equilibrium between at least three distinct conformations with differing substrate specificities. The inositol polyphosphate derivative HYG8 bound to the PH domain and disrupted PDK1 dimerization by stabilizing a monomeric conformation in which the PH domain associated with the catalytic domain and the PIF pocket was accessible. In the absence of lipids, HYG8 potently inhibited the phosphorylation of Akt (also termed PKB) but did not affect the intrinsic activity of PDK1 or the phosphorylation of SGK, which requires docking to the PIF pocket. In contrast, the small-molecule valsartan bound to the PIF pocket and stabilized a second distinct monomeric conformation. Our study reveals dynamic conformations of full-length PDK1 in which the location of the linker and the PH domain relative to the catalytic domain determines the selective phosphorylation of PDK1 substrates. The study further suggests new approaches for the design of drugs to selectively modulate signaling downstream of PDK1.

Computational Study of Driving Forces in ATSP, PDIQ, and P53 Peptide Binding: C═O···C═O Tetrel Bonding Interactions at Work

Understanding the molecular interactions that drive peptide folding is crucial to chemistry and biology. In this study, we analyzed the role of CO···CO tetrel bonding (TtB) interactions in the folding mechanism of three different peptides (ATSP, pDIQ, and p53), which exhibit a different propensity to fold in an α helix motif. To achieve this goal, we used both a recently developed Bayesian inference approach (MELDxMD) and Quantum Mechanics (QM) calculations at the RI-MP2/def2-TZVP level of theory. These techniques allowed us to study the folding process and to evaluate the strength of the CO···CO TtBs as well as the synergies between TtBs and hydrogen-bonding (HB) interactions. We believe that the results derived from our study will be helpful for those scientists working in computational biology, peptide chemistry, and structural biology.

A novel insight on SARS-CoV-2 S-derived fragments in the control of the host immunity

Despite all efforts to combat the pandemic of COVID-19, we are still living with high numbers of infected persons, an overburdened health care system, and the lack of an effective and definitive treatment. Understanding the pathophysiology of the disease is crucial for the development of new technologies and therapies for the best clinical management of patients. Since the manipulation of the whole virus requires a structure with an adequate level of biosafety, the development of alternative technologies, such as the synthesis of peptides from viral proteins, is a possible solution to circumvent this problem. In addition, the use and validation of animal models is of extreme importance to screen new drugs and to compress the organism's response to the disease. Peptides derived from recombinant S protein from SARS-CoV-2 were synthesized and validated by in silico, in vitro and in vivo methodologies. Macrophages and neutrophils were challenged with the peptides and the production of inflammatory mediators and activation profile were evaluated. These peptides were also inoculated into the swim bladder of transgenic zebrafish larvae at 6 days post fertilization (dpf) to mimic the inflammatory process triggered by the virus, which was evaluated by confocal microscopy. In addition, toxicity and oxidative stress assays were also developed. In silico and molecular dynamics assays revealed that the peptides bind to the ACE2 receptor stably and interact with receptors and adhesion molecules, such as MHC and TCR, from humans and zebrafish. Macrophages stimulated with one of the peptides showed increased production of NO, TNF-α and CXCL2. Inoculation of the peptides in zebrafish larvae triggered an inflammatory process marked by macrophage recruitment and increased mortality, as well as histopathological changes, similarly to what is observed in individuals with COVID-19. The use of peptides is a valuable alternative for the study of host immune response in the context of COVID-19. The use of zebrafish as an animal model also proved to be appropriate and effective in evaluating the inflammatory process, comparable to humans.

Enhanced sampling in explicit solvent by deep learning module in FSATOOL

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

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking

The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.