FireProt 2.0: web-based platform for the fully automated design of thermostable proteins.

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

Thermomechanical Properties of Transition Metal Dichalcogenides Predicted by a Machine Learning Parameterized Force Field.

The mechanical and thermal properties of transition metal dichalcogenides (TMDs) are directly relevant to their applications in electronics, thermoelectric devices, and heat management systems. In this study, we use a machine learning (ML) approach to parametrize molecular dynamics (MD) force fields to predict the mechanical and thermal transport properties of a library of monolayered TMDs (MoS2, MoTe2, WSe2, WS2, and ReS2). The ML-trained force fields were then employed in equilibrium MD simulations to calculate the lattice thermal conductivities of the foregoing TMDs and to investigate how they are affected by small and large mechanical strains. Furthermore, using nonequilibrium MD, we studied thermal transport across grain boundaries. The presented approach provides a fast albeit accurate methodology to compute both mechanical and thermal properties of TMDs, especially for relatively large systems and spatially complex structures, where density functional theory computational cost is prohibitive.

The emergence of machine learning force fields in drug design.

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

Atomistic molecular simulations of Aß-Zn conformational ensembles.

The amyloid-forming Aß peptide is able to interact with metal cations to form very stable complexes that influence fibril formation and contribute to the onset of Alzheimer's disease. Multiple structures of peptides derived from Aß in complex with different metals have been resolved experimentally to provide an atomic-level description of the metal-protein interactions. However, Aß is intrinsically disordered, and hence more amenable to an ensemble description. Molecular dynamics simulations can now reach the timescales needed to generate ensembles for these type of complexes. However, this requires accurate force fields both for the protein and the protein-metal interactions. Here we use state-of-the-art methods to generate force field parameters for the Zn(II) cations in a set of complexes formed by different Aß variants and combine them with the Amber99SB*-ILDN optimized force field. Upon comparison of NMR experiments with the simulation results, further optimized with a Bayesian/Maximum entropy approach, we provide an accurate description of the molecular ensembles for most Aß-metal complexes. We find that the resulting conformational ensembles are more heterogeneous than the NMR models deposited in the Protein Data Bank.

sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides.

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

Durability of motor learning by observing.

Information about another person's movement kinematics obtained through visual observation activates brain regions involved in motor learning. Observation-related changes in these brain areas are associated with adaptive changes to feedforward neural control of muscle activation and behavioral improvements in limb movement control. However, little is known about the stability of these observation-related effects over time. Here, we used force channel trials to probe changes in lateral force production at various time points (1 min, 10 min, 30 min, 60 min, 24 h) after participants either physically performed, or observed another individual performing upper limb reaching movements that were perturbed by novel, robot-generated forces (a velocity-dependent force-field). Observers learned to predictively generate directionally and temporally specific compensatory forces during reaching, consistent with the idea that they acquired an internal representation of the novel dynamics. Participants who physically practiced in the force-field showed adaptation that was detectable at all time points, with some decay detected after 24 h. Observation-related adaptation was less temporally stable in comparison, decaying slightly after 1 h and undetectable at 24 h. Observation induced less adaptation overall than physical practice, which could explain differences in temporal stability. Visually acquired representations of movement dynamics are retained and continue to influence behavior for at least 1 h after observation.NEW & NOTEWORTHY We used force channel probes in an upper limb force-field reaching task in humans to compare the durability of learning-related changes that occurred through visual observation to those after physical movement practice. Visually acquired representations of movement dynamics continued to influence behavior for at least 1 h after observation. Our findings point to a 1-h window during which visual observation of another person could play a role in motor learning.

Reward actively engages both implicit and explicit components in dual force field adaptation.

Motor learning occurs through multiple mechanisms, including unsupervised, supervised (error based), and reinforcement (reward based) learning. Although studies have shown that reward leads to an overall better motor adaptation, the specific processes by which reward influences adaptation are still unclear. Here, we examine how the presence of reward affects dual adaptation to novel dynamics and distinguish its influence on implicit and explicit learning. Participants adapted to two opposing force fields in an adaptation/deadaptation/error-clamp paradigm, where five levels of reward (a score and a digital face) were provided as participants reduced their lateral error. Both reward and control (no reward provided) groups simultaneously adapted to both opposing force fields, exhibiting a similar final level of adaptation, which was primarily implicit. Triple-rate models fit to the adaptation process found higher learning rates in the fast and slow processes and a slightly increased fast retention rate for the reward group. Whereas differences in the slow learning rate were only driven by implicit learning, the large difference in the fast learning rate was mainly explicit. Overall, we confirm previous work showing that reward increases learning rates, extending this to dual-adaptation experiments and demonstrating that reward influences both implicit and explicit adaptation. Specifically, we show that reward acts primarily explicitly on the fast learning rate and implicitly on the slow learning rates.NEW & NOTEWORTHY Here we show that rewarding participants' performance during dual force field adaptation primarily affects the initial rate of learning and the early timescales of adaptation, with little effect on the final adaptation level. However, reward affects both explicit and implicit components of adaptation. Whereas the learning rate of the slow process is increased implicitly, the fast learning and retention rates are increased through both implicit components and the use of explicit strategies.

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.

Probing the range of applicability of structure- and energy-adjusted QM/MM link bonds III: QM/MM MD simulations of solid-state systems at the example of layered carbon structures.

The previously introduced workflow to achieve an energetically and structurally optimized description of frontier bonds in quantum mechanical/molecular mechanics (QM/MM)-type applications was extended into the regime of computational material sciences at the example of a layered carbon model systems. Optimized QM/MM link bond parameters at HSEsol/6-311G(d,p) and self-consistent density functional tight binding (SCC-DFTB) were derived for graphitic systems, enabling detailed investigation of specific structure motifs occurring in graphene-derived structures v i a quantum-chemical calculations. Exemplary molecular dynamics (MD) simulations in the isochoric-isothermic (NVT) ensemble were carried out to study the intercalation of lithium and the properties of the Stone-Thrower-Wales defect. The diffusivity of lithium as well as hydrogen and proton adsorption on a defective graphene surface served as additional example. The results of the QM/MM MD simulations provide detailed insight into the applicability of the employed link-bond strategy when studying intercalation and adsorption properties of graphitic materials.

Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions.

Silica nanoparticles (SNPs), one of the most widely researched materials in modern science, are now commonly exploited in surface coatings, biomedicine, catalysis, and engineering of novel self-assembling materials. Theoretical approaches are invaluable to enhancing fundamental understanding of SNP properties and behavior. Tremendous research attention is dedicated to modeling silica structure, the silica-water interface, and functionalization of silica surfaces for tailored applications. In this review, the range of theoretical methodologies are discussed that have been employed to model bare silica and functionalized silica. The evolution of silica modeling approaches is detailed, including classical, quantum mechanical, and hybrid methods and highlight in particular the last decade of theoretical simulation advances. It is started with discussing investigations of bare silica systems, focusing on the fundamental interactions at the silica-water interface, following with a comprehensively review of the modeling studies that examine the interaction of silica with functional ligands, peptides, ions, surfactants, polymers, and carbonaceous species. The review is concluded with the perspective on existing challenges in the field and promising future directions that will further enhance the utility and importance of the theoretical approaches in guiding the rational design of SNPs for applications in engineering and biomedicine.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039 kcal/mol/atom for energy and a root mean square error of 2.57 $\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30 000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

Dimerization of Full-Length Aß-42 Peptide: A Comparison of Different Force Fields and Water Models.

Among the two isoforms of amyloid-ß i. e., Aß-40 and Aß-42, Aß-42 is more toxic due to its increased aggregation propensity. The oligomerization pathways of amyloid-ß may be investigated by studying its dimerization process at an atomic level. Intrinsically disordered proteins (IDPs) lack well-defined structures and are associated with numerous neurodegenerative disorders. Molecular dynamics simulations of these proteins are often limited by the choice of parameters due to inconsistencies in the empirically developed protein force fields and water models. To evaluate the accuracy of recently developed force fields for IDPs, we study the dimerization of full-length Aß-42 in aqueous solution with three different combinations of AMBER force field parameters and water models such as ff14SB/TIP3P, ff19SB/OPC, and ff19SB/TIP3P using classical MD and Umbrella Sampling method. This work may be used as a benchmark to compare the performance of different force fields for the simulations of IDPs.

Vibrational Circular Dichroism Spectroscopy with a Classical Polarizable Force Field: Alanine in the Gas and Condensed Phases.

Polarizable force fields are an essential component for the chemically accurate modeling of complex molecular systems with a significant degree of fluxionality, beyond harmonic or perturbative approximations. In this contribution we examine the performance of such an approach for the vibrational spectroscopy of the alanine amino acid, in the gas and condensed phases, from the Fourier transform of appropriate time correlation functions generated along molecular dynamics (MD) trajectories. While the infrared (IR) spectrum only requires the electric dipole moment, the vibrational circular dichroism (VCD) spectrum further requires knowledge of the magnetic dipole moment, for which we provide relevant expressions to be used with polarizable force fields. The AMOEBA force field was employed here to model alanine in the neutral and zwitterionic isolated forms, solvated by water or nitrogen, and as a crystal. Within this framework, comparison of the electric and magnetic dipole moments to those obtained with nuclear velocity perturbation theory based on density-functional theory for the same MD trajectories are found to agree well with one another. The statistical convergence of the IR and VCD spectra is examined and found to be more demanding in the latter case. Comparisons with experimental frequencies are also provided for the condensed phases.

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.

Bromination of 2D materials.

The adsorption, reaction and thermal stability of bromine on Rh(111)-supported hexagonal boron nitride (h-BN) and graphene were investigated. Synchrotron radiation-based high-resolution x-ray photoelectron spectroscopy (XPS) and temperature-programmed XPS allowed us to follow the adsorption process and the thermal evolutionin situon the molecular scale. Onh-BN/Rh(111), bromine adsorbs exclusively in the pores of the nanomesh while we observe no such selectivity for graphene/Rh(111). Upon heating, bromine undergoes an on-surface reaction onh-BN to form polybromides (170-240 K), which subsequently decompose to bromide (240-640 K). The high thermal stability of Br/h-BN/Rh(111) suggests strong/covalent bonding. Bromine on graphene/Rh(111), on the other hand, reveals no distinct reactivity except for intercalation of small amounts of bromine underneath the 2D layer at high temperatures. In both cases, adsorption is reversible upon heating. Our experiments are supported by a comprehensive theoretical study. DFT calculations were used to describe the nature of theh-BN nanomesh and the graphene moiré in detail and to study the adsorption energetics and substrate interaction of bromine. In addition, the adsorption of bromine onh-BN/Rh(111) was simulated by molecular dynamics using a machine-learning force field.

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. Science 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture.

Upgrading of the general AMBER force field 2 for fluorinated alcohol biosolvents: A validation for water solutions and melittin solvation.

The standard GAFF2 force field parameterization has been refined for the fluorinated alcohols 2,2,2-trifluoroethanol (TFE), 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP), and 1,1,1,3,3,3-hexafluoropropan-2-one (HFA), which are commonly used to study proteins and peptides in biomimetic media. The structural and dynamic properties of both proteins and peptides are significantly influenced by the biomimetic environment created by the presence of these cosolvents in aqueous solutions. Quantum mechanical calculations on stable conformers were used to parameterize the atomic charges. Different systems, such as pure liquids, aqueous solutions, and systems formed by melittin protein and cosolvent/water solutions, have been used to validate the new models. The calculated macroscopic and structural properties are in agreement with experimental findings, supporting the validity of the newly proposed models.

Accurate model of liquid-liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties.

Many intrinsically disordered proteins (IDPs) may undergo liquid-liquid phase separation (LLPS) and participate in the formation of membraneless organelles in the cell, thereby contributing to the regulation and compartmentalization of intracellular biochemical reactions. The phase behavior of IDPs is sequence dependent, and its investigation through molecular simulations requires protein models that combine computational efficiency with an accurate description of intramolecular and intermolecular interactions. We developed a general coarse-grained model of IDPs, with residue-level detail, based on an extensive set of experimental data on single-chain properties. Ensemble-averaged experimental observables are predicted from molecular simulations, and a data-driven parameter-learning procedure is used to identify the residue-specific model parameters that minimize the discrepancy between predictions and experiments. The model accurately reproduces the experimentally observed conformational propensities of a set of IDPs. Through two-body as well as large-scale molecular simulations, we show that the optimization of the intramolecular interactions results in improved predictions of protein self-association and LLPS.

Dynamic Virtual Fixture Generation Based on Intra-Operative 3D Image Feedback in Robot-Assisted Minimally Invasive Thoracic Surgery.

This paper proposes a method for generating dynamic virtual fixtures with real-time 3D image feedback to facilitate human-robot collaboration in medical robotics. Seamless shared control in a dynamic environment, like that of a surgical field, remains challenging despite extensive research on collaborative control and planning. To address this problem, our method dynamically creates virtual fixtures to guide the manipulation of a trocar-placing robot arm using the force field generated by point cloud data from an RGB-D camera. Additionally, the "view scope" concept selectively determines the region for computational points, thereby reducing computational load. In a phantom experiment for robot-assisted port incision in minimally invasive thoracic surgery, our method demonstrates substantially improved accuracy for port placement, reducing error and completion time by 50% (p=1.06×10-2) and 35% (p=3.23×10-2), respectively. These results suggest that our proposed approach is promising in improving surgical human-robot collaboration.

Ultraflat Graphene Oxide Membranes with Newton-Ring Prepared by Vortex Shear Field for Ion Sieving.

The wrinkles on graphene oxide (GO) membranes have unique properties; however, they interfere with the mass transfer of interlayer channels, posing a major challenge in the development of wrinkle-free GO membranes with smooth channels. In this study, the wrinkles on GO were flattened using vortex shear to tightly stack them into ultraflat GO membranes with Newton's ring interference pattern, causing hydrolysis of the lipid bonds in the wrinkles and an increase in the number of oxygen-containing groups. With increasing flatness, the interlayer spacing of the GO membranes decreased, improving the stability of the interlayer structure, the flow resistance of water through the ultraflat interlayer decreased, and the water flux increased 3-fold. Importantly, the selectivity for K+/Mg2+ reached approximately 379.17 in a real salt lake. A novel concept is proposed for the development of new membrane preparation methods. Our findings provide insights into the use of vortex shearing to flatten GO.