A Second,Druggable Binding Site in UDP‐Galactopyranose Mutase from Mycobacterium tuberculosis?

UDP‐galactopyranose mutase (UGM), a key enzyme in the biosynthesis of mycobacterial cell walls, is a potential target for the treatment of tuberculosis. In this work, we investigate binding models of a non‐substrate‐like inhibitor, MS‐208, with M. tuberculosis UGM. Initial saturation transfer difference (STD) NMR experiments indicated a lack of direct competition between MS‐208 and the enzyme substrate, and subsequent kinetic assays showed mixed inhibition. We thus hypothesized that MS‐208 binds at an allosteric binding site (A‐site) instead of the enzyme active site (S‐site). A candidate A‐site was identified in a subsequent computational study, and the overall hypothesis was supported by ensuing mutagenesis studies of the A‐site. Further molecular dynamics studies led us to propose that MS‐208 inhibition occurs by preventing complete closure of an active site mobile loop that is necessary for productive substrate binding. The results suggest the presence of an A‐site with potential druggability, opening up new opportunities for the development of novel drug candidates against tuberculosis.     

Dynamical Correlations Reveal Allosteric Sites in G Protein-Coupled Receptors

G protein-coupled Receptors (GPCRs) play a central role in many physiological processes and, consequently, constitute important drug targets. In particular, the search for allosteric drugs has recently drawn attention, since they could be more selective and lead to fewer side effects. Accordingly, computational tools have been used to estimate the druggability of allosteric sites in these receptors. In spite of many successful results, the problem is still challenging, particularly the prediction of hydrophobic sites in the interface between the protein and the membrane. In this work, we propose a complementary approach, based on dynamical correlations. Our basic hypothesis was that allosteric sites are strongly coupled to regions of the receptor that undergo important conformational changes upon activation. Therefore, using ensembles of experimental structures, normal mode analysis and molecular dynamics simulations we calculated correlations between internal fluctuations of different sites and a collective variable describing the activation state of the receptor. Then, we ranked the sites based on the strength of their coupling to the collective dynamics. In the β2 adrenergic (β2AR), glucagon (GCGR) and M2 muscarinic receptors, this procedure allowed us to correctly identify known allosteric sites, suggesting it has predictive value. Our results indicate that this dynamics-based approach can be a complementary tool to the existing toolbox to characterize allosteric sites in GPCRs.     

Molecular electronics: insight from first-principles transport simulations

Conduction properties of nanoscale contacts can be studied using first-principles simulations. Such calculations give insight into details behind the conductance that is not readily available in experiments. For example, we may learn how the bonding conditions of a molecule to the electrodes affect the electronic transport. Here we describe key computational ingredients and discuss these in relation to simulations for scanning tunneling microscopy (STM) experiments with C60 molecules where the experimental geometry is well characterized. We then show how molecular dynamics simulations may be combined with transport calculations to study more irregular situations, such as the evolution of a nanoscale contact with the mechanically controllable break-junction technique. Finally we discuss calculations of inelastic electron tunnelling spectroscopy as a characterization technique that reveals information about the atomic arrangement and transport channels.     

Chemical Genetic Approaches for the Investigation of Neutral Lipid Metabolism

The coordinated processes of lipid synthesis, degradation, and transport are mediated by enzymes, cofactors, and transport proteins. Accordingly, lipid‐metabolizing enzymes represent logical targets for the treatment of dyslipidemia, a major risk factor for type 2 diabetes, atherosclerosis, and other disorders. Small‐molecule tool compounds, modulating the functions of such proteins, can substantially facilitate the characterization of target proteins. Such molecules complement genetic studies, and allow time‐ and dose‐dependent control of protein activity in biological systems. This can improve our understanding of physiological processes, give insights into the druggability of target proteins, and might finally result in the development of therapeutic compounds. In this review we summarize the current state of available inhibitors targeting key proteins in neutral lipid metabolism, with a focus on metabolic lipases, acyltransferases, and fatty‐acid‐binding proteins.     

Characterization of Aptamer-Protein Complexes by X-ray Crystallography and Alternative Approaches

Aptamers are oligonucleotide ligands, either RNA or ssDNA, selected for high-affinity binding to molecular targets, such as small organic molecules, proteins or whole microorganisms. While reports of new aptamers are numerous, characterization of their specific interaction is often restricted to the affinity of binding (K(D)). Over the years, crystal structures of aptamer-protein complexes have only scarcely become available. Here we describe some relevant technical issues about the process of crystallizing aptamer-protein complexes and highlight some biochemical details on the molecular basis of selected aptamer-protein interactions. In addition, alternative experimental and computational approaches are discussed to study aptamer-protein interactions.     

Design of new catalysts for ecological high-quality transportation fuels by combinatorial computational chemistry and tight-binding quantum chemical molecular dynamics approaches

Recently, we introduced a concept of combinatorial chemistry to computational chemistry and proposed a new method called “combinatorial computational chemistry”, which enables us to perform a theoretical high-throughput screening of catalysts. In the present paper, we reviewed our recent application of our combinatorial computational chemistry approach to the design of new catalysts for high-quality transportation fuels. By using our combinatorial computational chemistry techniques, we succeeded to predict new catalysts for methanol synthesis and Fischer–Tropsch synthesis. Moreover, we have succeeded in the development of chemical reaction dynamics simulator based on our original tight-binding quantum chemical molecular dynamics method. This program realizes more than 5000 times acceleration compared to the regular first-principles molecular dynamics method. Electronic- and atomic-level information on the catalytic reaction dynamics at reaction temperatures significantly contributes the catalyst design and development. Hence, we also summarized our recent applications of the above quantum chemical molecular dynamics method to the clarification of the methanol synthesis dynamics in this review.     

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.     

Synthesis of Dihydrobenzofuro[3,2-b]chromenes as Potential 3CLpro Inhibitors of SARS-CoV-2: A Molecular Docking and Molecular Dynamics Study

The recent emergence of pandemic of coronavirus (COVID-19) caused by SARS-CoV-2 has raised significant global health concerns. More importantly, there is no specific therapeutics currently available to combat against this deadly infection. The enzyme 3-chymotrypsin-like cysteine protease (3CLpro) is known to be essential for viral life cycle as it controls the coronavirus replication. 3CLpro could be a potential drug target as established before in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). In the current study, we wanted to explore the potential of fused flavonoids as 3CLpro inhibitors. Fused flavonoids (5a,10a-dihydro-11H-benzofuro[3,2-b]chromene) are unexplored for their potential bioactivities due to their low natural occurrences. Their synthetic congeners are also rare due to unavailability of general synthetic methodology. Here we designed a simple strategy to synthesize 5a,10a-dihydro-11H-benzofuro[3,2-b]chromene skeleton and it's four novel derivatives. Our structural bioinformatics study clearly shows excellent potential of the synthesized compounds in comparison to experimentally validated inhibitor N3. Moreover, in-silico ADMET study displays excellent druggability and extremely low level of toxicity of the synthesized molecules. Further, for better understanding, the molecular dynamic approach was implemented to study the change in dynamicity after the compounds bind to the protein. A detailed investigation through clustering analysis and distance calculation gave us sound comprehensive data about their molecular interaction. In summary, we anticipate that the currently synthesized molecules could not only be a potential set of inhibitors against 3CLpro but also the insights acquired from the current study would be instrumental in further developing novel natural flavonoid based anti-COVID therapeutic spectrums.     

Proteome Based Approach Defines Candidates for Designing a Multitope Vaccine against the Nipah Virus

Nipah virus is one of the most harmful emerging viruses with deadly effects on both humans and animals. Because of the severe outbreaks, in 2018, the World Health Organization focused on the urgent need for the development of effective solutions against the virus. However, up to date, there is no effective vaccine against the Nipah virus in the market. In the current study, the complete proteome of the Nipah virus (nine proteins) was analyzed for the antigenicity score and the virulence role of each protein, where we came up with fusion glycoprotein (F), glycoprotein (G), protein (V), and protein (W) as the candidates for epitope prediction. Following that, the multitope vaccine was designed based on top-ranking CTL, HTL, and BCL epitopes from the selected proteins. We used suitable linkers, adjuvant, and PADRE peptides to finalize the constructed vaccine, which was analyzed for its physicochemical features, antigenicity, toxicity, allergenicity, and solubility. The designed vaccine passed these assessments through computational analysis and, as a final step, we ran a docking analysis between the designed vaccine and TLR-³ and validated the docked complex through molecular dynamics simulation, which estimated a strong binding and supported the nomination of the designed vaccine as a putative solution for Nipah virus. Here, we describe the computational approach for design and analysis of this vaccine.     

A Computer-Driven Scaffold-Hopping Approach Generating New PTP1B Inhibitors from the Pyrrolo[1,2-a]quinoxaline Core

Protein tyrosine phosphatase 1B (PTP1B) is a very promising target for the treatment of metabolic disorders such as type II diabetes mellitus. Although it was validated as a promising target for this disease more than 30 years ago, as yet there is no drug in advanced clinical trials, and its biochemical mechanism and functions are still being studied. In the present study, based on our experience generating PTP1B inhibitors, we have developed and implemented a scaffold-hopping approach to vary the pyrrole ring of the pyrrolo[1,2-a]quinoxaline core, supported by extensive computational techniques aimed to explain the molecular interaction with PTP1B. Using a combination of docking, molecular dynamics and end-point free-energy calculations, we have rationally designed a hypothesis for new PTP1B inhibitors, supporting their recognition mechanism at a molecular level. After the design phase, we were able to easily synthesize proposed candidates and their evaluation against PTP1B was found to be in good concordance with our predictions. Moreover, the best candidates exhibited glucose uptake increments in cellulo model, thus confirming their utility for PTP1B inhibition and validating this approach for inhibitors design and molecules thus obtained.     

Surface and bulk properties of glycidyl methacrylate modified polypropylene: Experimental and molecular modeling studies

Modifications of polypropylene (PP) are often carried out to either functionalize them or meet specific property demands. This study considered the process of PP grafting with glycidyl methacrylate (GMA) as an intermediate step to achieve improvements in surface properties of this polymer. Abundant literature is available on this grafting process but little is known about the surface properties of the grafted PP. Present work considered both experimental and computational approaches to attain this goal. Experimentally, it was established that the melting temperature of modified PP changed with the addition of GMA, and at higher concentrations of GMA in the PP matrix, heterogeneous nucleation took place. Experimental results revealed a decrease in the surface energy (SE) as well. To discern the underlying reasons behind these changes, molecular dynamics simulations were undertaken. The computational results revealed that the changes in SE could be associated with the location of the functional group. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Penicillin G acylase purification with the aid of high-throughput screening approach

Penicillin G acylase (PGA) is one of the most important enzymes for the production of semi-synthetic β-lactam antibiotics and their key intermediates. Purification of penicillin G acylase from fermentation broth with the aid of high-throughput screening (HTS) process has been examined in this study. We used a microtiter-plate based on screening method to find appropriate purification conditions for the target protein. The screening method is based on a 96-well plate format, and different matrices and conditions (pH, salt concentration and type) were tested. Through analyses of all pooled fractions (flow-through and elution) we gained appropriate information to choose the best performing matrix and buffer conditions for upscaling. After an upscaled purification step the second unit operation is screened in the similar way and parameters for this operation can be chosen. The purification parameter of purified PGA at the small-screen and upscaling levels were measured, respectively. The results indicate that high-throughput progress based on a 96-well plate is a flexible and efficient paradigm for recombinant protein purification.     

A two‐zone model for fluid catalytic cracking riser with multiple feed injectors

Developments in modeling of the fluid catalytic cracking (FCC) process have progressed along two lines. One emphasizes composition‐based kinetic models based on molecular characterization of feedstocks and reaction products. The other relies on computational fluid dynamics. The aim is to develop an FCC model that strikes a balance between the two approaches. Specifically, we present an FCC riser model consisting of an entrance‐zone and a fully developed zone. The former has four overlapping, fan‐shaped oil sprays. The model predicts the plant data of Derouin et al. and reveals an inherent two‐zone character of the FCC riser. Inside the entrance zone, cracking intensity is highest and changes rapidly, resulting in a steep rise in oil conversion. Outside the entrance zone, cracking intensity is low and varies slowly, leading to a sluggish increase in conversion. The two‐zone model provides a computationally efficient modeling approach for FCC online control, optimization, and molecular management. © 2014 American Institute of Chemical Engineers AIChE J, 61: 610–619, 2015     

Reduction of Empiricism through Flow Visualization and Computational Fluid Dynamics

In chemical process industry, a variety of equipment is used for carrying out different unit operations and unit processes. The design procedures for this equipment have been largely empirical due to the complexity of fluid mechanics. In view of this, a stepwise procedure has been suggested based on experimental fluid dynamics (EFD) and computational fluid dynamics (CFD). One application of EFD and CFD is presented for the prediction of the heat transfer coefficient in the single‐phase turbulent pipe flow. The present capabilities of CFD for the design of different process equipment have been outlined.     

Forward‐osmosis‐aided concentration of fructose sugar through hydrophilized polyamide membrane: Molecular modeling and economic estimation

In this study, a thin‐film composite membrane based on hydrophilized polyamide was synthesized for the concentration of an aqueous fructose solution using a forward‐osmosis (FO) technique. The membrane was prepared by the addition of excess m‐phenylenediamine along with a small quantity of dipolar aprotic dimethyl sulfoxide solvent in the aqueous reaction bath followed by excess trimesoyl chloride in an organic bath with a longer time provided for interfacial polymerization to minimize fructose losses. The effect of operating parameters such as draw NaCl concentration, cross‐flow velocity, and temperature on FO performance was evaluated. Membrane characterization was performed using scanning electron microscopy, Fourier transform infrared spectroscopy, and X‐ray diffraction to study the physicochemical properties. A coupled model based on molecular modeling and computational fluid dynamics was developed to study the diffusion behavior and concentration profiles of the two solutions within the process. A detailed economic estimation for the production of crystalline fructose sugar is presented. The study reveals the significant potential of FO as an economical process for concentration of sugar solutions using brine as the draw solution. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44649.     

A time scale-bridging approach for integrating production scheduling and process control

In this paper, we propose a novel framework for integrating scheduling and nonlinear control of continuous processes. We introduce the time scale-bridging model (SBM) as an explicit, low-order representation of the closed-loop input–output dynamics of the process. The SBM then represents the process dynamics in a scheduling framework geared towards calculating the optimal time-varying setpoint vector for the process control system. The proposed framework accounts for process dynamics at the scheduling stage, while maintaining closed-loop stability and disturbance rejection properties via feedback control during the production cycle. Using two case studies, a CSTR and a polymerization reactor, we show that SBM-based scheduling has significant computational advantages compared to existing integrated scheduling and control formulations. Moreover, we show that the economic performance of our framework is comparable to that of existing approaches when a perfect process model is available, with the added benefit of superior robustness to plant-model mismatch.     

A Molecular-Modeling Toolbox Aimed at Bridging the Gap between Medicinal Chemistry and Computational Sciences

In the current era of high-throughput drug discovery and development, molecular modeling has become an indispensable tool for identifying, optimizing and prioritizing small-molecule drug candidates. The required background in computational chemistry and the knowledge of how to handle the complex underlying protocols, however, might keep medicinal chemists from routinely using in silico technologies. Our objective is to encourage those researchers to exploit existing modeling technologies more frequently through easy-to-use graphical user interfaces. In this account, we present two innovative tools (which we are prepared to share with academic institutions) facilitating computational tasks commonly utilized in drug discovery and development: (1) the VirtualDesignLab estimates the binding affinity of small molecules by simulating and quantifying their binding to the three-dimensional structure of a target protein; and (2) the MD Client launches molecular dynamics simulations aimed at exploring the time-dependent stability of ligand–protein complexes and provides residue-based interaction energies. This allows medicinal chemists to identify sites of potential improvement in their candidate molecule. As a case study, we present the application of our tools towards the design of novel antagonists for the FimH adhesin.     

Parameterization of classical nonpolarizable force field for hydroxide toward the large-scale molecular dynamics simulation of cellulose in pre-cooled alkali/urea aqueous solution

The empirical force fields (FFs) based on molecular dynamics (MD) simulation studying the dissolution mechanism of cellulose in cold alkali solution suffers the lacking of reliable classical FFs for hydroxide. By a simple adjustment, we transferred one available polarizable force field (FF) of hydroxide into a nonpolarizable one and combined it with GORMOS FF. Simulation based on these parameters provided accurate hydration spheres and solution structure of hydroxide that is comparable to the polarizable one, providing an opportunity for the large-scale MD simulation of the long cellulose chain in alkali/urea system for the study of dissolution and regeneration as well as mercerization process.     

Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 Mpro by Using Supervised Molecular Dynamics (SuMD) Simulations

Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low-throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high-throughput supervised molecular dynamics simulations (HT-SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS-CoV-2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD-based posing approaches, a high number of incorrect binding modes still complicate HT-SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in-silico pipeline, allowing us to prioritize only the more reliable predictions.     

Simulation of the Spray Drying of Single Granules: The Correlation Between Microscopic Forces and Granule Morphology

In the ceramic industry, spray drying is an important process transforming fine primary powder into processable granular material. Granule formation at spray drying has been investigated in the past and plausible explanatory models have been established for the governing mechanisms of granule formation. In this study, we use numerical modeling via coupled discrete element method and computational fluid dynamics simulations to investigate this formation process. The focus lies on the evolution of the granule morphology during drying of single droplets. In addition to a demonstration, that the simulation results support the propositions made by the experimentally‐based models, a new correlation was found which leads to an extension of those models for granule formation in terms of the granule stability.