Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity.

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib's mechanism of Mpro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1-M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.

Improving collective variables: The case of crystallization.

Several enhanced sampling methods, such as umbrella sampling or metadynamics, rely on the identification of an appropriate set of collective variables. Recently two methods have been proposed to alleviate the task of determining efficient collective variables. One is based on linear discriminant analysis; the other is based on a variational approach to conformational dynamics and uses time-lagged independent component analysis. In this paper, we compare the performance of these two approaches in the study of the homogeneous crystallization of two simple metals. We focus on Na and Al and search for the most efficient collective variables that can be expressed as a linear combination of X-ray diffraction peak intensities. We find that the performances of the two methods are very similar. Wherever the different metastable states are well-separated, the method based on linear discriminant analysis, based on its harmonic version, is to be preferred because simpler to implement and less computationally demanding. The variational approach, however, has the potential to discover the existence of different metastable states.

Folding a small protein using harmonic linear discriminant analysis.

Many processes of scientific importance are characterized by time scales that extend far beyond the reach of standard simulation techniques. To circumvent this impediment, a plethora of enhanced sampling methods has been developed. One important class of such methods relies on the application of a bias that is a function of a set of collective variables specially designed for the problem under consideration. The design of good collective variables can be challenging and thereby constitutes the main bottle neck in the application of these methods. To address this problem, recently we have introduced Harmonic Linear Discriminant Analysis, a method to systematically construct collective variables as linear combinations of a set of descriptors. The method uses input information that can be gathered in short unbiased molecular dynamics simulations in which the system is trapped in the metastable states. Here, to scale up our examination of the method's efficiency, we applied it to the folding of chignolin in water. Interestingly, already before any biased simulations were run, the constructed one-dimensional collective variable revealed much of the physics that underlies the folding process. In addition, using it in metadynamics, we were able to run simulations in which the system goes from the folded state to the unfolded one and back, where to get fully converged results, we combined metadynamics with parallel tempering. Finally, we examined how the collective variable performs when different sets of descriptors are used in its construction.

Rigorous analysis of image force barrier lowering in bounded geometries: application to semiconducting nanowires.

Bounded geometries introduce a fundamental problem in calculating the image force barrier lowering of metal-wrapped semiconductor systems. In bounded geometries, the derivation of the barrier lowering requires calculating the reference energy of the system, when the charge is at the geometry center. In the following, we formulate and rigorously solve this problem; this allows combining the image force electrostatic potential with the band diagram of the bounded geometry. The suggested approach is applied to spheres as well as cylinders. Furthermore, although the expressions governing cylindrical systems are complex and can only be evaluated numerically, we present analytical approximations for the solution, which allow easy implementation in calculated band diagrams. The results are further used to calculate the image force barrier lowering of metal-wrapped cylindrical nanowires; calculations show that although the image force potential is stronger than that of planar systems, taking the complete band-structure into account results in a weaker effect of barrier lowering. Moreover, when considering small diameter nanowires, we find that the electrostatic effects of the image force exceed the barrier region, and influence the electronic properties of the nanowire core. This study is of interest to the nanowire community, and in particular for the analysis of nanowire I-V measurements where wrapped or omega-shaped metallic contacts are used.

Systematic modification of functionality in disordered elastic networks through free energy surface tailoring.

A combined machine learning-physics-based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.

Collective Variables for Free Energy Surface Tailoring: Understanding and Modifying Functionality in Systems Dominated by Rare Events.

We introduce a method for elucidating and modifying the functionality of systems dominated by rare events that relies on the semiautomated tuning of their underlying free energy surface. The proposed approach seeks to construct collective variables (CVs) that encode the essential information regarding the rare events of the system of interest. The appropriate CVs are identified using harmonic linear discriminant analysis (HLDA), a machine-learning-based method that is trained solely on data collected from short ordinary simulations in the relevant metastable states of the system. Utilizing the interpretable form of the resulting CVs, the critical interaction potentials that determine the system's rare transitions are identified and purposely modified to tailor the free energy surface in a manner that alters functionality as desired. The applicability of the method is illustrated in the context of three different systems, thereby demonstrating that thermodynamic and kinetic properties can be tractably modified with little to no prior knowledge or intuition.

Solubility of Organic Salts in Solvent-Antisolvent Mixtures: A Combined Experimental and Molecular Dynamics Simulations Approach.

We combine molecular dynamics simulations with experiments to estimate solubilities of an organic salt in complex growth environments. We predict the solubility by simulations of the growth and dissolution of ions at the crystal surface kink sites at different solution concentrations. Thereby, the solubility is identified as the solution's salt concentration, where the energy of the ion pair dissolved in solution equals the energy of the ion pair crystallized at the kink sites. The simulation methodology is demonstrated for the case of anhydrous sodium acetate crystallized from various solvent-antisolvent mixtures. To validate the predicted solubilities, we have measured the solubilities of sodium acetate in-house, using an experimental setup and measurement protocol that guarantees moisture-free conditions, which is key for a hygroscopic compound like sodium acetate. We observe excellent agreement between the experimental and the computationally evaluated solubilities for sodium acetate in different solvent-antisolvent mixtures. Given the agreement and the rich data the simulations produce, we can use them to complement experimental tasks, which in turn will reduce time and capital in the design of complicated industrial crystallization processes of organic salts.

Blind Search for Complex Chemical Pathways Using Harmonic Linear Discriminant Analysis.

Disentangling the mechanistic details of a chemical reaction pathway is a hard problem that often requires a considerable amount of chemical intuition and a component of luck. Experiments struggle in observing short-life metastable intermediates, while computer simulations often rely upon a good initial guess. In this work, we propose a method that, from the simulations of a reactant and a product state, searches for reaction mechanisms connecting the two by exploring the configuration space through metadynamics, a well-known enhanced molecular dynamics method. The key quantity underlying this search is based on the use of an approach called harmonic linear discriminant analysis which allows a systematic construction of collective variables. Given the reactant and product states, we choose a set of descriptors capable of discriminating between the two states. In order to not prejudge the results, generic descriptors are introduced. The fluctuations of the descriptors in the two states are used to construct collective variables. We use metadynamics in an exploratory mode to discover the intermediates and the transition states that lead from reactant to product. The search is at first conducted at a low theory level. The calculation is then refined, and the energy of the intermediates and transition states discovered during metadynamics is computed again using a higher level of theory. The method's aim is to offer a simple reaction mechanism search procedure that helps in saving time and is able to find unexpected mechanisms that defy well established chemical paradigms. We apply it to two reactions, showing that a high level of complexity can be hidden even in seemingly trivial and small systems. The method can be applied to larger systems, such as reactions in solution or catalysis.

Metadynamics with Discriminants: A Tool for Understanding Chemistry.

We introduce an extension of a recently published method [ Mendels et al. J. Phys. Chem. Lett. 2018 , 9 , 2776 - 2781 ] to obtain low-dimensional collective variables for studying multiple states free energy processes in chemical reactions. The only information needed is a collection of simple statistics of the equilibrium properties of the reactants and product states. No information on the reaction mechanism has to be given. The method allows studying a large variety of chemical reactivity problems including multiple reaction pathways, isomerization, and stereo- and regiospecificity. We applied the method to two fundamental organic chemical reactions. First we study the SN2 nucleophilic substitution reaction of a Cl in CH2Cl2 leading to an understanding of the kinetic origin of the chirality inversion in such processes. Subsequently, we tackle the problem of regioselectivity in the hydrobromination of propene revealing that the nature of empirical observations such as Markovnikov's rule lies in the chemical kinetics rather than the thermodynamic stability of the products.

Collective Variables from Local Fluctuations.

We introduce a method to obtain one-dimensional collective variables for studying rarely occurring transitions between two metastable states separated by a high free energy barrier. No previous information, not even approximated, on the path followed during the transition is needed. The only requirement is to know the fluctuations of the system while in the two metastable states. With this information in hand, we build the collective variable using a modified version of Fisher's linear discriminant analysis. The usefulness of this approach is tested on the metadynamics simulation of two representative systems. The first is the freezing of silver iodide into the superionic α-phase, and the second is the study of a classical Diels-Alder reaction. The collective variable works very well in these two diverse cases.

A Comprehensive study of the Effects of Chain Morphology on the Transport Properties of Amorphous Polymer Films.

Organic semiconductors constitute one of the main components underlying present-day paradigm shifting optoelectronic applications. Among them, polymer based semiconductors are deemed particularly favorable due to their natural compatibility with low-cost device fabrication techniques. In light of recent advances in the syntheses of these classes of materials, yielding systems exhibiting charge mobilities comparable with those found in organic crystals, a comprehensive study of their charge transport properties is presented. Among a plethora of effects arising from these systems morphological and non morphological attributes, it is shown that a favorable presence of several of these attributes, including that of rapid on-chain carrier propagation and the presence of elongated conjugation segments, can lead to an enhancement of the system's mobility by more than 5 orders of magnitude with respect to 'standard' amorphous organic semiconductors. New insight for the formulation of new engineering strategies for next generation polymer based semiconductors is thus gathered.

Thermoelectricity in Disordered Organic Semiconductors under the Premise of the Gaussian Disorder Model and Its Variants.

Using Monte Carlo simulations, we investigate the thermoelectric properties of disordered organic semiconductors under the premise of the Gaussian disorder model and its variants. In doing so, we provide much needed additional dimensions for comparison between these theoretical frameworks and real systems beyond those based on extensively studied charge-transport properties and aim to provide a frame-of-reference for rising interest in these systems for thermoelectric-based applications. To illustrate the potential existing in the implementation of combined transport and thermoelectric investigation, we discuss strategies to experimentally deduce a system's DOS shape and the temperature dependence of its transport energy (which can discern hopping transport from multiple trapping transport), infer whether a system's activation energy originates from inherent energetic disorder or a polaron activation energy (while deducing the given polaron activation energy), and discerning whether a system's energetic disorder is spatially correlated or accompanied by off-diagonal disorder.