Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

The retinal chromophore environment in an inward light-driven proton pump studied by solid-state NMR and hydrogen-bond network analysis

Inward proton pumping is a relatively new function for microbial rhodopsins, retinal-binding light-driven membrane proteins. So far, it has been demonstrated for two unrelated subgroups of microbial rhodopsins, xenorhodopsins and schizorhodopsins. A number of recent studies suggest unique retinal-protein interactions as being responsible for the reversed direction of proton transport in the latter group. Here, we use solid-state NMR to analyze the retinal chromophore environment and configuration in an inward proton-pumping Antarctic schizorhodopsin. Using fully 13C-labeled retinal, we have assigned chemical shifts for every carbon atom and, assisted by structure modelling and molecular dynamics simulations, made a comparison with well-studied outward proton pumps, identifying locations of the unique protein-chromophore interactions for this functional subclass of microbial rhodopsins. Both the NMR results and molecular dynamics simulations point to the distinctive polar environment in the proximal part of the retinal, which may result in a hydration pattern dramatically different from that of the outward proton pumps, causing the reversed proton transport.

Computational Insights into the Non-Heme Diiron Alkane Monooxygenase Enzyme AlkB: Electronic Structures, Dioxygen Activation, and Hydroxylation Mechanism of Liquid Alkanes

Alkane monooxygenase (AlkB) is a membrane-spanning metalloenzyme that catalyzes the terminal hydroxylation of straight-chain alkanes involved in the microbially mediated degradation of liquid alkanes. According to the cryoEM structures, AlkB features a unique multihistidine ligand coordination environment with a long Fe-Fe distance in its active center. Up to now, how AlkB employs the diiron center to activate dioxygen and which species is responsible for triggering the hydroxylation are still elusive. In this work, we constructed computational models and performed quantum mechanics/molecular mechanics (QM/MM) calculations to illuminate the electronic characteristics of the diiron active center and how AlkB carries out the terminal hydroxylation. Our calculations revealed that the spin-spin interaction between two irons is rather weak. The dioxygen may ligate to either the Fe1 or Fe2 atom and prefers to act as a linker to increase the spin-spin interaction of two irons, facilitating the dioxygen cleavage to generate the highly reactive Fe(IV)═O. Thus, AlkB employs Fe(IV)═O to trigger the hydrogen abstraction. In addition, the previously suggested mechanism that AlkB uses both the dioxygen and Fe-coordinated water to perform hydroxylation was calculated to be unlikely. Besides, our results indicate that AlkB cannot use the Fe-coordinated dioxygen to directly trigger hydrogen abstraction.

Kernel-Based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations

A kernel-based method (kernelized minimal distributed charge model (kMDCM)) to represent the molecular electrostatic potential (ESP) in terms of off-center point charges is introduced. The positions of the charges adapt to the molecular geometry and allow the description of intramolecular charge flow. Using Gaussian kernels and atom-atom distances as the features, the ESPs for water and methanol are shown to improve by at least a factor of 2 compared with point charge models fit to an ensemble of structures. The conformationally fluctuating molecular dipole moment of water is reproduced almost twice as accurately using kMDCM compared with static PCs, despite not fitting to the dipole directly. The role of hyperparameters in the kernelization is investigated and their implication on model performance and simulation stability is discussed. Combining kMDCM for the electrostatics and reproducing kernels for the bonded terms allows energy-conserving simulations of 2000 water molecules with periodic boundary conditions on the nanosecond time scale. These MD simulations sample geometries outside the training set but remain stable, which demonstrates the robustness of the model and its implementation.

Direct evidence for a deprotonated lysine serving as a H-bond "acceptor" in a photoreceptor protein

Deprotonation or suppression of the pKa of the amino group of a lysine sidechain is a widely recognized phenomenon whereby the sidechain amino group transiently can act as a nucleophile at the active site of enzymatic reactions. However, a deprotonated lysine and its molecular interactions have not been directly experimentally detected. Here, we demonstrate a deprotonated lysine stably serving as an "acceptor" in a H-bond between the photosensor protein RcaE and its chromophore. Signal splitting and trans-H-bond J coupling observed by NMR spectroscopy provide direct evidence that Lys261 is deprotonated and serves as a H-bond acceptor for the chromophore NH group. Quantum mechanical/molecular mechanical calculations also indicate that this H-bond exists stably. Interestingly, the sidechain amino group of the lysine can act as both donor and acceptor. The remarkable shift in the H-bond characteristics arises from a decrease in solvation, triggered by photoisomerization. Our results provide insights into the dual role of this lysine. This mechanism has broad implications for other biological reactions in which lysine plays a role.

Advancing rational pesticide development against Drosophila suzukii: bioinformatics tools and applications-a systematic review

CONTEXT

Drosophila suzukii (Matsumura, 1931) is a widespread agricultural pest responsible for significant damage to various soft-skinned fruit hosts. The revolutionary potential of bioinformatics in agriculture emerges from its ability to provide extensive information on pests, fungi, chemical resistance, implications of non-target species, and other critical aspects. This wealth of information allows researchers to engage in projects and applied research in diverse agricultural domains that face these challenges. In this context, bioinformatics tools play a fundamental role. The negative impact of pests on crops, resulting in substantial economic losses, has highlighted the importance of in silico methods.

METHODS

To achieve this, we conducted a systematic search in scientific databases using as keywords "Drosophila suzukii," "biopesticides," "simulations computational," and "in-silico." After applying the filters of relevance and publication date, we organized the articles and prioritized those that directly addressed that matched the keywords and the use of bioinformatics tools. Additionally, we included studies focusing on in silico assays of biopesticides, such as molecular docking. Our review aimed to present a collection of recent literature on biopesticides against Drosophila suzukii, emphasizing bioinformatics methods. Through this work, we strive to contribute to the literature of new perspectives on the development and efficiency of biopesticides, along with to advance research that may improve pest control strategies.

RESULTS

In the results of the systematic review, we found 2734 articles related to the selected keywords. Six of these articles directly address Drosophila suzukii and the use of bioinformatics tools in the search for alternatives in pest control. In the selected studies, we observed that two articles tend to focus on phylogenetic approaches, searching for gene sequences, amino acids, and constructing phylogenetic trees. The other three articles used molecular modeling and docking of receptors such as GABA and TRP with plant-derived and synthetic compounds to study intermolecular interactions. However, we identified gaps in these studies that could lead to further research in the biorational development of biopesticides using bioinformatics tools.

pyGROMODS: a Python package for the generation of input files for molecular dynamic simulation with GROMACS

The pyGROMODS, an easy-to-use cross-platform python-based package, with a graphical user interface, for the generation of molecular dynamic (MD) input files and running MD simulation (MDS) of proteins, peptides, and protein-ligand complex using GROMACS, is here presented. Four routes, with underlining Python scripts, are implemented in pyGROMODS for the generation of MD input files. They are 'RLmulti' for processing multi-ligand protein complex, 'RLmany' for processing multiple ligands against a single protein target, 'RLsingle' for processing multiple pairs of proteins and ligands, and 'PPmore' for processing peptides or proteins without ligands or non-standard residues. In addition, using the package, the generated input files or appropriate input files from other sources can be uploaded to run MDS with GROMACS. The pyGROMODS is implemented with a unique ability to search the host machine systems for the installation of the required software, update and/or install required Python packages, allow the user to pre-define working directory, and generate unique workflow organization with well-defined folders and files in a well-organized manner. The pyGROMODS, which is released under the MIT License, is freely available for download via the GitHub (https://github.com/Dankem/pyGROMODS) and Zenodo (https://doi.org/10.5281/zenodo.7912747) repositories. The precompiled executables can also be downloaded from Zenodo (https://doi.org/10.5281/zenodo.8087090), and a video tutorial can be downloaded from https://youtu.be/I4OKc6uVx1M.Communicated by Ramaswamy H. Sarma.

Experimental and computational study of binary mixture ethanolamine and 2-amino-2-methyl-1-propanol

The present work involves experimental and computational investigations into the density of pure and mixed states of ethanolamine (ET) and 2-amino-2-methyl-1-propanol (AMP) under a pressure of 1 atm and temperatures ranging from 293.15 K to 333.15 K The density data were used to derive the excess molar volume, thermal expansion coefficient, and isothermal coefficient of pressure excess molar enthalpy. The Redlich-Kister equation was employed to calculate the excess molar and its accompanying coefficients. In the gas phase, density functional theory (DFT) was utilized to explore the most stable structures of ET … ET, AMP … AMP, and the ET … AMP mixture. Molecular dynamics simulation (MD) was used to calculate the structural properties of these mixtures in the liquid phase. Radial distribution function (RDFs) combined distribution function (CDF) and spatial distribution function (SDF) in different mole fractions calculated in the liquid phase. The intramolecular and intermolecular interactions of ethanolamine and AMP were obtained using the radial distribution function in different molar fractions. It was found that the ethanolamine molecule has a greater tendency to form intramolecular hydrogen bonds, while the AMP molecule has a greater tendency to form intermolecular hydrogen bonds.

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

D1-Tyr246 and D2-Tyr244 in photosystem II: Insights into bicarbonate binding and electron transfer from QA•- to QB

In photosystem II (PSII), D1-Tyr246 and D2-Tyr244 are symmetrically located at the binding site of the bicarbonate ligand of the non-heme Fe complex. Here, we investigated the role of the symmetrically arranged tyrosine pair, D1-Tyr246 and D2-Tyr244, in the function of PSII, by generating four chloroplast mutants of PSII from Chlamydomonas reinhardtii: D1-Y246F, D1-Y246T, D2-Y244F, and D2-Y244T. The mutants exhibited altered photoautotrophic growth, reduced PSII protein accumulation, and impaired O2-evolving activity. Flash-induced fluorescence yield decay kinetics indicated a significant slowdown in electron transfer from QA•- to QB in all mutants. Bicarbonate reconstitution resulted in enhanced O2-evolving activity, suggesting destabilization of bicarbonate binding in the mutants. Structural analyses based on a quantum mechanical/molecular mechanical approach identified the existence of a water channel that leads to incorporation of bulk water molecules and destabilization of the bicarbonate binding site. The water intake channels, crucial for bicarbonate stability, exhibited distinct paths in the mutants. These findings shed light on the essential role of the tyrosine pair in maintaining bicarbonate stability and facilitating efficient electron transfer in native PSII.

Superexchange Electron Transfer and Protein Matrix in the Charge-Separation Process of Photosynthetic Reaction Centers

In type-II reaction centers, such as photosystem II (PSII) and reaction centers from purple bacteria (PbRC), light-induced charge separation involves electron transfer from pheophytin (PheoD1) to quinone (QA), occurring near a conserved tryptophan residue (D2-Trp253 in PSII and Trp-M252 in PbRC). This study investigates the route of the PheoD1-to-QA electron transfer, focusing on the superexchange coupling (|HPheoD1···QA|) in the PSII protein environment. |HPheoD1···QA| is significantly larger for the PheoD1-to-QA electron transfer via the unoccupied molecular orbitals of D2-Trp253 ([Trp]•--like intermediate state, 0.73 meV) compared to direct electron transfer (0.13 meV), suggesting that superexchange is the dominant mechanism in the PSII protein environment. While the overall impact of the protein environment is limited, local interactions, particularly H-bonds, enhance superexchange electron transfer by directly affecting the delocalization of molecular orbitals. The D2-W253F mutation significantly decreases the electron transfer rate. The conservation of D2-Trp253/D1-Phe255 (Trp-M252/Phe-L216 in PbRC) in the two branches appears to differentiate superexchange coupling, contributing to the branches being either active or inactive in electron transfer.

Absence of a link between stabilized charge-separated state and structural changes proposed from crystal structures of a photosynthetic reaction center

Structural differences between illuminated and unilluminated crystal structures led to the proposal that the charge-separated state was stabilized by structural changes in its membrane extrinsic protein subunit H in a bacterial photosynthetic reaction center [Katona, G. et al. Nat. Struct. Mol. Biol. 2005, 12, 630-631]. Here, we explored the proposal by titrating all titratable sites and calculating the redox potential (Em) values in these crystal structures. Contrary to the expected charge-separated states, Em for quinone, Em(QA/QA•-), is even lower in the proposed charge-separated structure than in the ground-state structure. The subunit-H residues, which were proposed to exhibit electron-density changes in the two crystal structures, contribute to an Em(QA/QA•-) difference of only <0.5 mV. Furthermore, the protonation states of the titratable residues in the entire reaction center are practically identical in the two structures. These findings indicate that the proposed structural differences are irrelevant to explaining the significant prolongation of the charge-separated-state lifetime.

Computational insights into the aggregation mechanism and amyloidogenic core of aortic amyloid medin polypeptide

Medin amyloid, prevalent in the vessel walls of 97 % of individuals over 50, contributes to arterial stiffening and cerebrovascular dysfunction, yet our understanding of its aggregation mechanism remains limited. Dividing the full-length 50-amino-acid medin peptide into five 10-residue segments, we conducted individual investigations on each segment's self-assembly dynamics via microsecond-timescale atomistic discrete molecular dynamics (DMD) simulations. Our findings showed that medin1-10 and medin11-20 segments predominantly existed as isolated unstructured monomers, unable to form stable oligomers. Medin31-40 exhibited moderate aggregation, forming dynamic β-sheet oligomers with frequent association and dissociation. Conversely, medin21-30 and medin41-50 segments demonstrated significant self-assembly capability, readily forming stable β-sheet-rich oligomers. Residue pairwise contact frequency analysis highlighted the critical roles of residues 22-26 and 43-49 in driving the self-assembly of medin21-30 and medin41-50, acting as the β-sheet core and facilitating β-strand formation in other regions within medin monomers, expecting to extend to oligomers and fibrils. Regions containing residues 22-26 and 43-49, with substantial self-assembly abilities and assistance in β-sheet formation, represent crucial targets for amyloid inhibitor drug design against aortic medial amyloidosis (AMA). In summary, our study not only offers deep insights into the mechanism of medin amyloid formation but also provides crucial theoretical and practical guidance for future treatments of AMA.

Discovery of New Highly Potent Histamine H3 Receptor Antagonists, Calcium Channel Blockers, and Acetylcholinesterase Inhibitors

At present, one of the most promising strategies to tackle the complex challenges posed by Alzheimer's disease (AD) involves the development of novel multitarget-directed ligands (MTDLs). To this end, we designed and synthesized nine new MTDLs using a straightforward and cost-efficient one-pot Biginelli three-component reaction. Among these newly developed compounds, one particular small molecule, named 3e has emerged as a promising MTDL. This compound effectively targets critical biological factors associated with AD, including the simultaneous inhibition of cholinesterases (ChEs), selective antagonism of H3 receptors, and blocking voltage-gated calcium channels. Additionally, compound 3e exhibited remarkable neuroprotective activity against H2O2 and Aβ1-40, and effectively restored cognitive function in AD mice treated with scopolamine in the novel object recognition task, confirming that this compound could provide a novel and innovative therapeutic approach for the effective treatment of AD.

A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning

Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective training set. In this work, we implement and train a MLP to obtain an accurate description of the potential energy surface and property predictions for organic compounds, as both single molecules and in the condensed phase. We devise a dual descriptor, based on the atomic cluster expansion (ACE), that couples an information-rich short-range description with a coarser long-range description that captures weak intermolecular interactions. We employ uncertainty-guided active learning for the training set generation, creating a dataset that is comparatively small for the breadth of application and consists of alcohols, alkanes, and an adipate. Utilizing that MLP, we calculate densities of those systems of varying chain lengths as a function of temperature, obtaining a discrepancy of less than 4% compared with experiment. Vibrational frequencies calculated with the MLP have a root mean square error of less than 1 THz compared to DFT. The heat capacities of condensed systems are within 11% of experimental findings, which is strong evidence that the dual descriptor provides an accurate framework for the prediction of both short-range intramolecular and long-range intermolecular interactions.

Integrating Newton's equations of motion in the reciprocal space

We here present the normal dynamics technique, which recasts the Newton's equations of motion in terms of phonon normal modes by exploiting a proper sampling of the reciprocal space. After introducing the theoretical background, we discuss how the reciprocal space sampling enables us to (i) obtain a computational speedup by selecting which and how many wave vectors of the Brillouin zone will be considered and (ii) account for distortions realized across large atomic distances without the use of large simulation cells. We implemented the approach into an open-source code, which we used to present three case studies: in the first one, we elucidate the general strategy for the sampling of the reciprocal space; in the second one, we illustrate the potential of the approach by studying the stabilization effect of temperature in α-uranium; and in the last one, we investigate the characterization of Raman spectra at different temperatures in MoS2/MX2 transition metal dichalcogenide heterostructures. Finally, we discuss how the procedure is general and can be used to simulate periodic, semiperiodic, and finite systems such as crystals, slabs, nanoclusters, or molecules.

Islet amyloid polypeptide fibril catalyzes amyloid-β aggregation by promoting fibril nucleation rather than direct axial growth

Aberrant aggregation of amyloid-β (Aβ) and islet amyloid polypeptide (IAPP) into amyloid fibrils underlies the pathogenesis of Alzheimer's disease (AD) and type 2 diabetes (T2D), respectively. T2D significantly increases AD risk, with evidence suggesting that IAPP and Aβ co-aggregation and cross-seeding might contribute to the cross-talk between two diseases. Experimentally, preformed IAPP fibril seeds can accelerate Aβ aggregation, though the cross-seeding mechanism remains elusive. Here, we computationally demonstrated that Aβ monomer preferred to bind to the elongation ends of preformed IAPP fibrils. However, due to sequence mismatch, the Aβ monomer could not directly grow onto IAPP fibrils by forming multiple stable β-sheets with the exposed IAPP peptides. Conversely, in our control simulations of self-seeding, the Aβ monomer could axially grow on the Aβ fibril, forming parallel in-register β-sheets. Additionally, we showed that the IAPP fibril could catalyze Aβ fibril nucleation by promoting the formation of parallel in-register β-sheets in the C-terminus between bound Aβ peptides. This study enhances our understanding of the molecular interplay between Aβ and IAPP, shedding light on the cross-seeding mechanisms potentially linking T2D and AD. Our findings also underscore the importance of clearing IAPP deposits in T2D patients to mitigate AD risk.

Comparative analysis of the LARP1 C-terminal DM15 region through Coelomate evolution

TOR (target of rapamycin), a ubiquitous protein kinase central to cellular homeostasis maintenance, fundamentally regulates ribosome biogenesis in part by its target La-related protein 1 (LARP1). Among other target transcripts, LARP1 specifically binds TOP (terminal oligopyrimidine) mRNAs encoding all 80 ribosomal proteins in a TOR-dependent manner through its C-terminal region containing the DM15 module. Though the functional implications of the LARP1 interaction with target mRNAs is controversial, it is clear that the TOP-LARP1-TOR axis is critical to cellular health in humans. Its existence and role in evolutionarily divergent animals remain less understood. We focused our work on expanding our knowledge of the first arm of the axis: the connection between LARP1-DM15 and the 5' TOP motif. We show that the overall DM15 architecture observed in humans is conserved in fruit fly and zebrafish. Both adopt familiar curved arrangements of HEAT-like repeats that bind 5' TOP mRNAs on the same conserved surface, although molecular dynamics simulations suggest that the N-terminal fold of the fruit fly DM15 is predicted to be unstable and unfold. We demonstrate that each ortholog interacts with TOP sequences with varying affinities. Importantly, we determine that the ability of the DM15 region to bind some TOP sequences but not others might amount to the context of the RNA structure, rather than the ability of the module to recognize some sequences but not others. We propose that TOP mRNAs may retain similar secondary structures to regulate LARP1 DM15 recognition.

High-entropy non-covalent cyclic peptide glass

Biomolecule-based non-covalent glasses are biocompatible and biodegradable, and offer a sustainable alternative to conventional glass. Cyclic peptides (CPs) can serve as promising glass formers owing to their structural rigidity and resistance to enzymatic degradation. However, their potent crystallization tendency hinders their potential in glass construction. Here we engineered a series of CP glasses with tunable glass transition behaviours by modulating the conformational complexity of CP clusters. By incorporating multicomponent CPs, the formation of high-entropy CP glass is facilitated, which-in turn-inhibits the crystallization of individual CPs. The high-entropy CP glass demonstrates enhanced mechanical properties and enzyme tolerance compared with individual CP glass and a unique biorecycling capability that is unattainable by traditional glasses. These findings provide a promising paradigm for the design and development of stable non-covalent glasses based on naturally derived biomolecules, and advance their application in pharmaceutical formulations and smart functional materials.

Methods for Classical-Mechanical Molecular Simulation in Chemistry: Achievements, Limitations, Perspectives

More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.

In-silico assessment of novel peptidomimetics inhibitor targeting STAT3 and STAT4 N-terminal domain dimerization: A comprehensive study using molecular docking, molecular dynamics simulation, and binding free energy analysis

Dysregulation in Janus kinase-Signal Transducer and Activation of Transcription (JAK-STAT) pathway is closely linked to various cancer types. The N-terminal domain (NTD) of STAT proteins, upon dimerization, assumes a multifaceted role with remarkable adaptability in mediating interactions between proteins. Consequently, the strategic targeting of the N-terminal domain of STATs has emerged as a promising tactic for disrupting dimerization and impeding the translocation of STAT proteins. In this study, we have deployed an integrated in-silico methodology to rationally design Peptidomimetic foldamers as inhibitors of the N-terminal domains of STAT3 and STAT4, with the objective of disrupting protein dimerization. Consequently, we have judiciously designed a series of peptidomimetics that encompass β3-amino acids, bearing side chains that mimic the residues within interface II of the dimeric structures of the NTDs. Employing molecular docking techniques; we have assessed the binding affinity of these designed peptidomimetics toward both the NTDs. Furthermore, we have conducted an evaluation of the stability and conformational alterations within the docked complexes over an extensive Molecular Dynamics, subsequently computing the binding free energy utilizing MM/PBSA calculations. Our findings unequivocally demonstrate that the peptidomimetic foldamers we have devised (Peptide-A, Peptide-B, and Peptide-C) exhibit a propensity to bind to and impede the dimerization process of the NTDs of both STAT3 and STAT4. These outcomes serve to underscore the potential of these meticulously designed peptidomimetics as potential candidates meriting further exploration in the realm of cancer prevention and management.

Systematic improvement of empirical energy functions in the era of machine learning

The impact of targeted replacement of individual terms in empirical force fields is quantitatively assessed for pure water, dichloromethane (CH  2 Cl  2 ), and solvated K  + and Cl  - ions. For the electrostatic interactions, point charges (PCs) and machine learning (ML)-based minimally distributed charges (MDCM) fitted to the molecular electrostatic potential are evaluated together with electrostatics based on the Coulomb integral. The impact of explicitly including second-order terms is investigated by adding a fragment molecular orbital (FMO)-derived polarization energy to an existing force field, in this case CHARMM. It is demonstrated that anisotropic electrostatics reduce the RMSE for water (by 1.4 kcal/mol), CH  2 Cl  2 (by 0.8 kcal/mol) and for solvated Cl  - clusters (by 0.4 kcal/mol). An additional polarization term can be neglected for CH  2 Cl  2 but further improves the models for pure water (by ∼ 1.0 kcal/mol) and hydrated Cl  - (by 0.4 kcal/mol), and is key for solvated K  + , reducing the RMSE by 2.3 kcal/mol. A 12-6 Lennard-Jones functional form performs satisfactorily with PC and MDCM electrostatics, but is not appropriate for descriptions that account for the electrostatic penetration energy. The importance of many-body contributions is assessed by comparing a strictly 2-body approach with self-consistent reference data. Two-body interactions suffice for CH  2 Cl  2 whereas water and solvated K  + and Cl  - ions require explicit many-body corrections. Finally, a many-body-corrected dimer potential energy surface exceeds the accuracy attained using a conventional empirical force field, potentially reaching that of an FMO calculation. The present work systematically quantifies which terms improve the performance of an existing force field and what reference data to use for parametrizing these terms in a tractable fashion for ML fitting of pure and heterogeneous systems.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Getting the intermolecular forces correct: introducing the ASTA strategy for a water model

Having a force field for water providing good bulk properties is paramount for modern studies of most biological systems. Some of the most common three-site force fields are TIP3, SPC/ε or OPC3, providing a decent range of bulk properties. That does not mean though, that they have realistic inter-atomic forces. These force fields have been parameterized with a top-down approach, meaning, by fitting the force field parameters to the experimental bulk properties. This approach has been the governing strategy also for many variants of four- and more-site models. We test a bottom-up approach, in which the force field is parameterized by optimizing the non-bonded inter-atomic forces. Our philosophy is that correct inter-atomic forces lead to correct geometrical and dynamical properties. The first system we try to optimize with the accurately system tailored atomic (ASTA) approach is water, but we aim to eventually probe other systems in the future as well. We applied our ASTA strategy to find a good set of parameters providing accurate bulk properties for the simple three-site force field forms, and also for AMOEBA, a more detailed and polarizable force field. Even though our bottom-up approach did not provide satisfactory results for the simple three-site force fields (with fixed charges), for the case of the AMOEBA force field it led to a modification of the original strategy, giving very good intra- and inter-molecular forces, as compared to accurate quantum chemically calculated reference forces. At the same time, important bulk properties, in this study restricted to the density and diffusion, were accurately reproduced with respect to the experimental values.

Antioxidant Active Phytochemicals in Ternstroemia lineata Explained by Aquaporin Mechanisms

The antioxidant action of terngymnoside C (1) and hydroxytyrosol-1-glucoside (2), isolated for the first time from the flower buds of Ternstroemia lineata, as well as katsumadin (3), obtained from the seedless fruits, was evaluated using ABTS•+ and H2O2-Saccharomyces cerevisiae models. In silico docking analysis of 1, 2, and 3 determined their affinity forces to the aquaporin monomers of the modeled S. cerevisiae protein 3 (AQP3) and human protein 7 (AQP7) channels that regulate the H2O2 cell transport. The ABTS•+ antiradical capacity of these compounds showed IC50 values of 22.00 μM (1), 47.64 μM (2), and 73.93 μM (3). The S. cerevisiae antioxidant assay showed that at 25 µM (1) and 50 µM (2 and 3), the cells were protected from H2O2-oxidative stress. These compounds, together with quercetin and vitamin C, were explored through the modeled S. cerevisiae AQP3 and human AQP7 by molecular docking analysis. To explain these results, an antioxidant mechanism for the isolated compounds was proposed through blocking H2O2 passage mediated by aquaporin transport. On the other hand, 1, 2, and 3 were not cytotoxic in a panel of three cancer cell lines.

Molecular origins of absorption wavelength variation among phycocyanobilin-binding proteins

Phycocyanobilin (PCB)-binding proteins, including cyanobacteriochromes and phytochromes, function as photoreceptors and exhibit a wide range of absorption maximum wavelengths. To elucidate the color-tuning mechanisms among these proteins, we investigated seven crystal structures of six PCB-binding proteins: Anacy_2551g3, AnPixJg2, phosphorylation-responsive photosensitive histidine kinase, RcaE, Sb.phyB(PG)-PCB, and Slr1393g3. Employing a quantum chemical/molecular mechanical approach combined with a polarizable continuum model, our analysis revealed that differences in absorption wavelengths among PCB-binding proteins primarily arise from variations in the shape of the PCB molecule itself, accounting for a ∼150 nm difference. Remarkably, calculated excitation energies sufficiently reproduced the absorption wavelengths of these proteins spanning ∼200 nm, including 728 nm for Anacy_2551g3. However, assuming the hypothesized lactim conformation resulted in a significant deviation from the experimentally measured absorption wavelength for Anacy_2551g3. The significantly red-shifted absorption wavelength of Anacy_2551g3 can unambiguously be explained by the significant overlap of molecular orbitals between the two pyrrole rings at both edges of the PCB chromophore without the need to hypothesize lactim formation.

Multiscale Modeling of Vinyl-Addition Polynorbornenes: The Effect of Stereochemistry

Vinyl-addition polynorbornenes are candidates for designing high-performance polymers due to unique characteristics, which include a high glass transition temperature associated with a rigid backbone. Recent studies have established that the processability and properties of these polymers can be fine-tuned by using targeted substitutions. However, synthesis with different catalysts results in materials with distinct properties, potentially due to the presence of various stereoisomers that are difficult to quantify experimentally. Herein, we develop all-atom models of polynorbornene oligomers based on classical force fields and density functional theory. To establish the relationship between chemical architecture, chain conformations, and melt structure, we perform detailed molecular dynamics simulations with the fine-tuned atomistic force field and propose simpler coarse-grained descriptions to address the high molecular weight limit. All-atom simulations of oligomers suggest high glass transition temperatures in the range of 550-600 K. In the melt state (800 K), meso chains form highly rigid extended coils (C∞≈11) with amorphous structural characteristics similar to the X-ray diffraction data observed in the literature. In contrast, simulations with racemo chains predict highly helical tubular chain conformations that could promote assembly into crystalline structures.

TDP-43 Promotes Amyloid-Beta Toxicity by Delaying Fibril Maturation via Direct Molecular Interaction

Amyloid-β (Aβ) is a peptide that undergoes self-assembly into amyloid fibrils, which compose the hallmark plaques observed in Alzheimer's disease (AD). TAR DNA-binding protein 43 (TDP-43) is a protein with mislocalization and aggregation implicated in amyotrophic lateral sclerosis and other neurodegenerative diseases. Recent work suggests that TDP-43 may interact with Aβ, inhibiting the formation of amyloid fibrils and worsening AD pathology, but the molecular details of their interaction remain unknown. Using all-atom discrete molecular dynamics simulations, we systematically investigated the direct molecular interaction between Aβ and TDP-43. We found that Aβ monomers were able to bind near the flexible nuclear localization sequence of the N-terminal domain (NTD) of TDP-43, adopting β-sheet rich conformations that were promoted by the interaction. Furthermore, Aβ associated with the nucleic acid binding interface of the tandem RNA recognition motifs of TDP-43 via electrostatic interactions. Using the computational peptide array method, we found the strongest C-terminal domain interaction with Aβ to be within the amyloidogenic core region of TDP-43. With experimental evidence suggesting that the NTD is necessary for inhibiting Aβ fibril growth, we also simulated the NTD with an Aβ40 fibril seed. We found that the NTD was able to strongly bind the elongation surface of the fibril seed via extensive hydrogen bonding and could also diffuse along the lateral surface via electrostatic interactions. Our results suggest that TDP-43 binding to the elongation surface, thereby sterically blocking Aβ monomer addition, is responsible for the experimentally observed inhibition of fibril growth. We conclude that TDP-43 may promote Aβ toxicity by stabilizing the oligomeric state and kinetically delaying fibril maturation.

The Pathway of a Transmembrane Helix Insertion into the Membrane Assisted by Sec61α Channel

The significant inconsistency between the experimental and simulation results of the free energy for the translocon-assisted insertion of the transmembrane helix (TMH) has not been reasonably explained. Understanding the mechanism of TMH insertion through the translocon is the key to solving this problem. In this study, we performed a series of coarse-grained molecular dynamics simulations and calculated the potential mean forces (PMFs) for three insertion processes of a hydrophobic TMH. The simulations reveal the pathway of the TMH insertion assisted by a translocon. The results indicate that the TMH contacts the top of the lateral gate first and then inserts down the lateral gate, which agrees with the sliding model. The TMH begins to transfer laterally to the bilayer when it is blocked by the plug and reaches the exit of the lateral gate, where there is a free energy minimum point. We also found that the connecting section between TM2 and TM3 of Sec61α prevented TMH from leaving the lateral gate and directly transitioning to the surface-bound state. These findings provide insight into the mechanism of the insertion of TMH through the translocon.

Regioselective enzymatic depolymerization of aromatic-aliphatic polyester revealed by computational modelling

Poly(butylene adipate-co-terephthalate) (PBAT) is widely utilized in the production of food packaging and mulch films. Its extensive application has contributed significantly to global solid waste, posing numerous environmental challenges. Recently, enzymatic recycling has emerged as a promising eco-friendly solution for the management of plastic waste. Here, we systematically investigate the depolymerization mechanism of PBAT catalyzed by cutinase TfCutSI with molecular docking, molecular dynamics simulations, and quantum mechanics/molecular mechanics calculations. Although the binding affinities for acid ester and terephthalic acid ester bonds are similar, a regioselective depolymerization mechanism and a "chain-length" effect on regioselectivity were proposed and evidenced. The regioselectivity is highly associated with specific structural parameters, namely Substrate@O4-Met@H7 and Substrate@C1-Ser@O1 distances. Notably, the binding mode of BTa captured by X-ray crystallography does not facilitate subsequent depolymerization. Instead, a previously unanticipated binding mode, predicted through computational analysis, is confirmed to play a crucial role in BTa depolymerization. This finding proves the critical role of computational modelling in refining experimental results. Furthermore, our results revealed that both the hydrogen bond network and enzyme's intrinsic electric field are instrumental in the formation of the final product. In summary, these novel molecular insights into the PBAT depolymerization mechanism offer a fundamental basis for enzyme engineering to enhance industrial plastic recycling.

Competitive Interaction of the SGFRKMAF Peptide with 3CLpro Dimerization Intermediates: A Brownian Dynamics Investigation

The SGFRKMAF peptide is known to inhibit the dimerization of 3CLpro monomers, which is essential for SARS-CoV-2 replication. The mechanism behind this, however, is largely unknown. In this work, we used Brownian dynamics simulations to compare and contrast 3CLpro monomer-monomer interactions and 3CLpro monomer-SGFRKMAF peptide interactions. We found that formation of the 3CLpro wild-type dimer could potentially involve formation of three intermediates that are primarily stabilized by G11-G124, S1-S301, and T118-G278 interactions. Analysis of 3CLpro monomer interaction with the SGFRKMAF peptide, however, revealed the presence of eight basins of interactions where the peptide assumes the highest local densities at the 3CLPro monomer surface. The second highest-density basin was found to coincide with the interface region of the wild-type 3CLpro dimer, thereby directly blocking the 3CLpro dimer-dimer interactions. The other basins, however, were found to lie far from the interface region. Notably, we found that only 6% of the BD trajectories end up directly into the basin at the interface region and ∼39% of the trajectories end up into those basins lying away from the interface region, indicating a greater role for peptide binding at sites away from the dimer interface region. Importantly, the locations of the basins lying away from the interface were found to coincide with the 3CLpro residues involved in stabilization of the 3CLpro monomer-monomer intermediates. Given that the rate constant of the peptide reaching the monomer surface was found to be almost an order of magnitude higher than the rate constant of monomer-monomer association, the SGFRKMAF peptide has the potential to inhibit dimerization of 3CLpro monomers not only through blocking the interface region but also through blocking the formation of the intermediates involved in the dimerization process. This could potentially open new avenues for 3CLpro dimerization inhibitors that transcend traditional X-ray-based discovery approaches.

Hydrogen-Bonding and Hydrophobic Interaction Networks as Structural Determinants of Microbial Rhodopsin Function

Microbial pump rhodopsins are highly versatile light-driven membrane proteins that couple protein conformational dynamics with ion translocation across the cell membranes. Understanding how microbial pump rhodopsins use specific amino acid residues at key functional sites to control ion selectivity and ion pumping direction is of general interest for membrane transporters, and could guide site-directed mutagenesis for optogenetics applications. To enable direct comparisons between proteins with different sequences we implement, for the first time, a unique numbering scheme for the microbial pump rhodopsin residues, NS-mrho. We use NS-mrho to show that distinct microbial pump rhodopsins typically have hydrogen-bond networks that are less conserved than anticipated from the amino acid residue conservation, whereas their hydrophobic interaction networks are largely conserved. To illustrate the role of the hydrogen-bond networks as structural elements that determine the functionality of microbial pump rhodopsins, we performed experiments, atomic-level simulations, and hydrogen bond network analyses on GR, the outward proton pump from Gloeobacter violaceus, and KR2, the outward sodium pump from Krokinobacter eikastus. The experiments indicate that multiple mutations that recover KR2 amino acid residues in GR not only fail to convert it into a sodium pump, but completely inactivate GR by abolishing photoisomerization of the retinal chromophore. This observation could be attributed to the drastically altered hydrogen-bond interaction network identified with simulations and network analyses. Taken together, our findings suggest that functional specificity could be encoded in the collective hydrogen-bond network of microbial pump rhodopsins.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

MMC: A Monte Carlo laboratory

This Note describes features of the program MMC, several of which are unique to MMC, developed over the past five decades. These include sampling in three different ensembles, biased moves, and some non-conventional analysis techniques.

The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling

Force fields are a key component of physics-based molecular modeling, describing the energies and forces in a molecular system as a function of the positions of the atoms and molecules involved. Here, we provide a review and scientific status report on the work of the Open Force Field (OpenFF) Initiative, which focuses on the science, infrastructure and data required to build the next generation of biomolecular force fields. We introduce the OpenFF Initiative and the related OpenFF Consortium, describe its approach to force field development and software, and discuss accomplishments to date as well as future plans. OpenFF releases both software and data under open and permissive licensing agreements to enable rapid application, validation, extension, and modification of its force fields and software tools. We discuss lessons learned to date in this new approach to force field development. We also highlight ways that other force field researchers can get involved, as well as some recent successes of outside researchers taking advantage of OpenFF tools and data.

How to Sample Dozens of Substitutions per Site with λ Dynamics

Alchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.

Iodine Clusters in the Atmosphere I: Computational Benchmark and Dimer Formation of Oxyacids and Oxides

The contribution of iodine-containing compounds to atmospheric new particle formation is still not fully understood, but iodic acid and iodous acid are thought to be significant contributors. While several quantum chemical studies have been carried out on clusters containing iodine, there is no comprehensive benchmark study quantifying the accuracy of the applied methods. Here, we present the first study in a series that investigate the role of iodine species in atmospheric cluster formation. In this work, we have studied the iodic acid, iodous acid, iodine tetroxide, and iodine pentoxide monomers and their dimers formed with common atmospheric precursors. We have tested the accuracy of commonly applied methods for calculating the geometry of the monomers, thermal corrections of monomers and dimers, the contribution of spin-orbit coupling to monomers and dimers, and finally, the accuracy of the electronic energy correction calculated at different levels of theory. We find that optimizing the structures either at the ωB97X-D3BJ/aug-cc-pVTZ-PP or the M06-2X/aug-cc-pVTZ-PP level achieves the best thermal contribution to the binding free energy. The electronic energy correction can then be calculated at the ZORA-DLPNO-CCSD(T0) level with the SARC-ZORA-TZVPP basis for iodine and ma-ZORA-def2-TZVPP for non-iodine atoms. We applied this methodology to calculate the binding free energies of iodine-containing dimer clusters, where we confirm the qualitative trends observed in previous studies. However, we identify that previous studies overestimate the stability of the clusters by several kcal/mol due to the neglect of relativistic effects. This means that their contributions to the currently studied nucleation pathways of new particle formation are likely overestimated.

Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path?

Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.

Identification of potent inhibitors of HDAC2 from herbal products for the treatment of colon cancer: Molecular docking, molecular dynamics simulation, MM/GBSA calculations, DFT studies, and pharmacokinetic analysis

The histone deacetylase 2 (HDAC2), an enzyme involved in gene regulation, is a potent drug target for the treatment of colon cancer. Phytocompounds having anticancer properties show the ability to interact with HDAC2 enzyme. Among the compounds, docking scores of caffeic acid (CA) and p-coumaric acid (pCA) with HDAC2 showed good binding efficacy of -5.46 kcal/mol and -5.16 kcal/mol, respectively, with small inhibition constants. The higher binding efficacy of CA compared to pCA can be credited to the presence of an extra oxygen atom in the CA molecule, which forms an additional hydrogen bond with Tyr297. The HDAC2 in complex with these molecules was found to be stable by analyzing RMSD, RMSF, Rg, and SASA values obtained through MD simulations. Furthermore, CA and pCA exhibited low MM/GBSA free energies of -16.32 ± 2.62 kcal/mol and -17.01 ± 2.87 kcal/mol, respectively. The HOMO and LUMO energy gaps, dipole moments, global reactivity descriptor values, and MEP surfaces showed the reactivity of the molecules. The favourable physicochemical and pharmacokinetic properties, along with absence of toxicity of the molecules determined using ADMET analysis, suggested both the acids to be regarded as effective drugs in the treatment of colon cancer.

A λ-Dynamics Investigation of Insulin Wakayama and Other A3 Variant Binding Affinities to the Insulin Receptor

Insulin Wakayama is a clinical insulin variant where a conserved valine at the third residue on insulin's A chain (ValA3) is replaced with a leucine (LeuA3), weakening insulin receptor (IR) binding by 140-500-fold. This severe impact on binding from a subtle modification has posed an intriguing problem for decades. Although experimental investigations of natural and unnatural A3 mutations have highlighted the sensitivity of insulin-IR binding at this site, atomistic explanations of these binding trends have remained elusive. We investigate this problem computationally using λ-dynamics free energy calculations to model structural changes in response to perturbations of the ValA3 side chain and to calculate associated relative changes in binding free energy (ΔΔGbind). The Wakayama LeuA3 mutation and seven other A3 substitutions were studied in this work. The calculated ΔΔGbind results showed high agreement compared to experimental binding potencies with a Pearson correlation of 0.88 and a mean unsigned error of 0.68 kcal/mol. Extensive structural analyses of λ-dynamics trajectories revealed that critical interactions were disrupted between insulin and the insulin receptor as a result of the A3 mutations. This investigation also quantifies the effect that adding an A3 Cδ atom or losing an A3 Cγ atom has on insulin's binding affinity to the IR. Thus, λ-dynamics was able to successfully model the effects of mutations to insulin's A3 side chain on its protein-protein interactions with the IR and shed new light on a decades-old mystery: the exquisite sensitivity of hormone-receptor binding to a subtle modification of an invariant insulin residue.

Substrate Turnover Dynamics Guide Ketol-Acid Reductoisomerase Redesign for Increased Specific Activity

The task of adapting enzymes for specific applications is often hampered by our incomplete ability to tune and tailor catalytic functions, particularly when seeking increased activity. Here, we develop and demonstrate a rational approach to address this challenge, applied to ketol-acid reductoisomerase (KARI), which has uses in industrial-scale isobutanol production. While traditional structure-based computational enzyme redesign strategies typically focus on the enzyme-bound ground state (GS) and transition state (TS), we postulated that additionally treating the underlying dynamics of complete turnover events that connect and pass through both states could further elucidate the structural properties affecting catalysis and help identify mutations that lead to increased catalytic activity. To examine the dynamics of substrate conversion with atomistic detail, we adapted and applied computational methods based on path sampling techniques to gather thousands of QM/MM simulations of attempted substrate turnover events by KARI: both productive (reactive) and unproductive (nonreactive) attempts. From these data, machine learning models were constructed and used to identify specific conformational features (interatomic distances, angles, and torsions) associated with successful, productive catalysis. Multistate protein redesign techniques were then used to select mutations that stabilized reactive-like structures over nonreactive-like ones while also meeting additional criteria consistent with enhanced specific activity. This procedure resulted in eight high-confidence enzyme mutants with a significant improvement in calculated specific activity relative to wild type (WT), with the fastest variant's increase in calculated k cat being (2 ± 1) × 104-fold. Collectively, these results suggest that introducing mutations designed to increase the population of reaction-promoting conformations of the enzyme-substrate complex before it reaches the barrier can provide an effective approach to engineering improved enzyme catalysts.

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Combined Theoretical and Experimental Investigations: Design, Synthesis, Characterization, and In Vitro Cytotoxic Activity Assessment of a Complex of a Novel Ureacellobiose Drug Carrier with the Anticancer Drug Carmustine

Drug delivery systems (DDSs) are used to transport drugs which are characterized by some pharmaceutical problems to the specific target site, enhancing therapeutic efficacy and reducing off-target accumulation in the body. In this work, one of the recently synthesized molecules, 1,10-N,N'-bis-(β-ᴅ-ureidocellobiosyl)-4,7,13,16-tetraoxa-1,10-diazacyclooctadecane (TN), was tested as a potential drug carrier towards the anticancer drug carmustine. For this purpose, different techniques were used, from synthesis and calculations to cytotoxicity assessment. Our results showed that TN is characterized by a very compact geometry, which significantly impacts its complexation properties. Although it forms a very stable complex with carmustine, it adopts a non-inclusion geometry, as verified by both experimental and theoretical NMR analyses. The cytotoxicity study performed for all analyzed molecules (TN; carmustine; TN:carmustine complex) towards normal and cancer (breast and colon) cells revealed that TN is not toxic and that the formation of complexes with carmustine reduces the toxicity of carmustine to normal cells.

Antimalarial Delivery with a Ferritin-Based Protein Cage: A Step toward Developing Smart Therapeutics against Malaria

Over the past two decades, the utilization of protein cages has witnessed exponential growth driven by their extensive applications in biotechnology and therapeutics. In the context of the recent Covid-19 pandemic, protein-cage-based scaffolds played a pivotal role in vaccine development. Beyond vaccines, these protein cages have proven valuable in diverse drug delivery applications thanks to their distinctive architecture and structural stability. Among the various types of protein cages, ferritin-based cages have taken the lead in drug delivery applications. This is primarily attributed to their ease of production, exceptional thermal stability, and nontoxic nature. While ferritin-based cages are commonly employed in anticancer drug delivery and contrast agent delivery, their efficacy in malarial drug delivery had not been explored until this study. In this investigation, several antimalarial drugs were encapsulated within horse spleen ferritin, and the binding and loading processes were validated through both experimental and computational techniques. The data unequivocally demonstrate the facile incorporation of antimalarial drugs into ferritin without disrupting its three-dimensional structure. Computational docking and molecular dynamics simulations were employed to pinpoint the precise location of the drug binding site within ferritin. Subsequent efficacy testing on Plasmodium revealed that the developed nanoconjugate, comprising the drug-ferritin conjugate, exhibited significant effectiveness in eradicating the parasite. In conclusion, the findings strongly indicate that ferritin-based carrier systems hold tremendous promise for the future of antimalarial drug delivery, offering high selectivity and limited side effects.

Computational Insights into the Intramolecular Aromatic C-C Coupling Catalyzed by the Cytochrome P450 Enzyme CYP121 from Mycobacterium tuberculosis

CYP121 is a P450 enzyme that catalyzes the intramolecular C-C coupling of its native substrate, dicyclotyrosine (cYY). According to previous suggestions, when the cosubstrate peracetic acid was used to generate Cpd I, the substrate cYY was suggested to participate in the cleavage of the O-O bond; however, whether cYY is involved in the formation of Cpd I and how two distant aromatic carbon atoms are activated are still unclear. Here, we constructed computational models and performed QM/MM calculations to clarify the reaction mechanism. On the basis of our calculation results, cYY is not involved in the formation of Cpd I, and the C-C coupling reaction starts from hydrogen abstraction. In the second stage, the substrate should first undergo a complex conformational change, leading to two phenolic hydroxyls of cYY close to each other. In the subsequent reaction, the resultant Cpd II again abstracts a hydrogen atom from the proximal tyrosine to generate the diradical intermediate. In addition, the C-C coupling occurs in the active site, but the final aromatization may be a nonenzymatic reaction. In general, the intramolecular C-C coupling requires two basic conditions, including the active site having good flexibility and the substrate itself having a suitable and rotatable skeleton.

An Unfitted Finite Element Poisson-Boltzmann Solver with Automatic Resolving of Curved Molecular Surface

So far, the existing Poisson-Boltzmann (PB) solvers that accurately take into account the interface jump conditions need a pregenerated body-fitted mesh (molecular surface mesh). However, qualified biomolecular surface meshing and its implementation into numerical methods remains a challenging and laborious issue, which practically hinders the progress of further developments and applications of a bunch of numerical methods in this field. In addition, even with a molecular surface mesh, it is only a low-order approximation of the original curved surface. In this article, an interface-penalty finite element method (IPFEM), which is a typical unfitted finite element method, is proposed to solve the Poisson-Boltzmann equation (PBE) without requiring the user to generate a molecular surface mesh. The Gaussian molecular surface is used to represent the molecular surface and can be automatically resolved with a high-order approximation within our method. Theoretical convergence rates of the IPFEM for the linear PB equation have been provided and are well validated on a benchmark problem with an analytical solution (we also noticed from numerical examples that the IPFEM has similar convergence rates for the nonlinear PBE). Numerical results on a set of different-sized biomolecules demonstrate that the IPFEM is numerically stable and accurate in the calculation of biomolecular electrostatic solvation energy.

Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment

This roadmap reviews the new, highly interdisciplinary research field studying the behavior of condensed matter systems exposed to radiation. The Review highlights several recent advances in the field and provides a roadmap for the development of the field over the next decade. Condensed matter systems exposed to radiation can be inorganic, organic, or biological, finite or infinite, composed of different molecular species or materials, exist in different phases, and operate under different thermodynamic conditions. Many of the key phenomena related to the behavior of irradiated systems are very similar and can be understood based on the same fundamental theoretical principles and computational approaches. The multiscale nature of such phenomena requires the quantitative description of the radiation-induced effects occurring at different spatial and temporal scales, ranging from the atomic to the macroscopic, and the interlinks between such descriptions. The multiscale nature of the effects and the similarity of their manifestation in systems of different origins necessarily bring together different disciplines, such as physics, chemistry, biology, materials science, nanoscience, and biomedical research, demonstrating the numerous interlinks and commonalities between them. This research field is highly relevant to many novel and emerging technologies and medical applications.

Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach

Force fields (FFs) are an established tool for simulating large and complex molecular systems. However, parametrizing FFs is a challenging and time-consuming task that relies on empirical heuristics, experimental data, and computational data. Recent efforts aim to automate the assignment of FF parameters using pre-existing databases and on-the-fly ab initio data. In this study, we propose a graph-based force field (GB-FFs) model to directly derive parameters for the Generalized Amber Force Field (GAFF) from chemical environments and research into the influence of functional forms. Our end-to-end parametrization approach predicts parameters by aggregating the basic information in directed molecular graphs, eliminating the need for expert-defined procedures and enhances the accuracy and transferability of GAFF across a broader range of molecular complexes. Simulation results are compared to the original GAFF parametrization. In practice, our results demonstrate an improved transferability of the model, showcasing its improved accuracy in modeling intermolecular and torsional interactions, as well as improved solvation free energies. The optimization approach developed in this work is fully applicable to other nonpolarizable FFs as well as to polarizable ones.

Computational Investigation of Coaggregation and Cross-Seeding between Aβ and hIAPP Underpinning the Cross-Talk in Alzheimer's Disease and Type 2 Diabetes

The coexistence of amyloid-β (Aβ) and human islet amyloid polypeptide (hIAPP) in the brain and pancreas is associated with an increased risk of Alzheimer's disease (AD) and type 2 diabetes (T2D) due to their coaggregation and cross-seeding. Despite this, the molecular mechanisms underlying their interaction remain elusive. Here, we systematically investigated the cross-talk between Aβ and hIAPP using atomistic discrete molecular dynamics (DMD) simulations. Our results revealed that the amyloidogenic core regions of both Aβ (Aβ10-21 and Aβ30-41) and hIAPP (hIAPP8-20 and hIAPP22-29), driving their self-aggregation, also exhibited a strong tendency for cross-interaction. This propensity led to the formation of β-sheet-rich heterocomplexes, including potentially toxic β-barrel oligomers. The formation of Aβ and hIAPP heteroaggregates did not impede the recruitment of additional peptides to grow into larger aggregates. Our cross-seeding simulations demonstrated that both Aβ and hIAPP fibrils could mutually act as seeds, assisting each other's monomers in converting into β-sheets at the exposed fibril elongation ends. The amyloidogenic core regions of Aβ and hIAPP, in both oligomeric and fibrillar states, exhibited the ability to recruit isolated peptides, thereby extending the β-sheet edges, with limited sensitivity to the amino acid sequence. These findings suggest that targeting these regions by capping them with amyloid-resistant peptide drugs may hold potential as a therapeutic approach for addressing AD, T2D, and their copathologies.