Cryoprotective activities of FK20, a human genome-derived intrinsically disordered peptide against cryosensitive enzymes without a stereospecific molecular interaction

Background Intrinsically disordered proteins (IDPs) have been shown to exhibit cryoprotective activity toward other cellular enzymes without any obvious conserved sequence motifs. This study investigated relationships between the physical properties of several human genome-derived IDPs and their cryoprotective activities. Methods Cryoprotective activity of three human-genome derived IDPs and their truncated peptides toward lactate dehydrogenase (LDH) and glutathione S-transferase (GST) was examined. After the shortest cryoprotective peptide was defined (named FK20), cryoprotective activity of all-D-enantiomeric isoform of FK20 (FK20-D) as well as a racemic mixture of FK20 and FK20-D was examined. In order to examine the lack of increase of thermal stability of the target enzyme, the CD spectra of GST and LDH in the presence of a racemic mixture of FK20 and FK20-D at varying temperatures were measured and used to estimate Tm. Results Cryoprotective activity of IDPs longer than 20 amino acids was nearly independent of the amino acid length. The shortest IDP-derived 20 amino acid length peptide with sufficient cryoprotective activity was developed from a series of TNFRSF11B fragments (named FK20). FK20, FK20-D, and an equimolar mixture of FK20 and FK20-D also showed similar cryoprotective activity toward LDH and GST. Tm of GST in the presence and absence of an equimolar mixture of FK20 and FK20-D are similar, suggesting that IDPs’ cryoprotection mechanism seems partly from a molecular shielding effect rather than a direct interaction with the target enzymes.

Theoretical insights into the molecular mechanism of I117V mutation in neuraminidase mediated reduction of oseltamivir drug susceptibility in A/H5N1 influenza virus

The substitution of Ile to Val at residue 117 (I117V) of neuraminidase (NA) reduces the susceptibility of the A/H5N1 influenza virus to oseltamivir (OTV). However, the molecular mechanism by which the I117V mutation affects the intermolecular interactions between NA and OTV has not been fully elucidated. In this study, we performed molecular dynamics (MD) simulations to analyze the characteristic conformational changes that contribute to the reduced binding affinity of NA to OTV after the I117V mutation. The results of MD simulations revealed that after the I117V mutation in NA, the changes in the secondary structure around the mutation site had a noticeable effect on the residue interactions in the OTV-binding site. In the case of the WT NA-OTV complex, the positively charged side chain of R118, located in the β-sheet region, frequently interacted with the negatively charged side chain of E119, which is an amino acid residue in the OTV-binding site. This can reduce the electrostatic repulsion of E119 toward D151, which is also a negatively charged residue in the OTV-binding site, so that both E119 and D151 simultaneously form hydrogen bonds with OTV more frequently, which greatly contributes to the binding affinity of NA to OTV. After the I117V mutation in NA, the side chain of R118 interacted with the side chain of E119 less frequently, likely because of the decreased tendency of R118 to form a β-sheet structure. As a result, the electrostatic repulsion of E119 toward D151 is greater than that of the WT case, making it difficult for both E119 and D151 to simultaneously form hydrogen bonds with OTV, which in turn reduces the binding affinity of NA to OTV. Hence, after the I117V mutation in NA, influenza viruses are less susceptible to OTV because of conformational changes in residues of R118, E119, and D151 around the mutation site and in the binding site.

Using a genetic algorithm to find molecules with good docking scores

A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, starting from randomly chosen molecules from the ZINC database, for four different targets: Bacillus subtilis chorismate mutase (CM), human β2-adrenergic G protein-coupled receptor (β2AR), the DDR1 kinase domain (DDR1), and β-cyclodextrin (BCD). By the combined use of functional group filters and a score modifier based on a heuristic synthetic accessibility (SA) score our approach identifies between ca 500 and 6,000 structurally diverse molecules with scores better than known binders by screening a total of 400,000 molecules starting from 8,000 randomly selected molecules from the ZINC database. Screening 250,000 molecules from the ZINC database identifies significantly more molecules with better docking scores than known binders, with the exception of CM, where the conventional screening approach only identifies 60 compounds compared to 511 with GA+Filter+SA. In the case of β2AR and DDR1, the GA+Filter+SA approach finds significantly more molecules with docking scores lower than −9.0 and −10.0. The GA+Filters+SA docking methodology is thus effective in generating a large and diverse set of synthetically accessible molecules with very good docking scores for a particular target. An early incarnation of the GA+Filter+SA approach was used to identify potential binders to the COVID-19 main protease and submitted to the early stages of the COVID Moonshot project, a crowd-sourced initiative to accelerate the development of a COVID antiviral.

Adsorption of phenol over bio-based silica/calcium carbonate (CS-SiO2/CaCO3) nanocomposite synthesized from waste eggshells and rice husks

A bio-based Silica/Calcium Carbonate (CS–SiO2/CaCO3) nanocomposite was synthesized in this study using waste eggshells (ES) and rice husks (RH). The adsorbents (ESCaCO3, RHSiO2 and, CS-SiO2/CaCO3) characterized using XRD show crystallinity associated with the calcite and quartz phase. The FTIR of ESCaCO3 shows the CO−23 group of CaCO3, while the spectra of RHSiO2 majorly show the siloxane bonds (Si–O–Si) in addition to the asymmetric and symmetric bending mode of SiO2. The spectra for Chitosan (CS) show peaks corresponding to the C=O vibration mode of amides, C–N stretching, and C–O stretching. The CS–SiO2/CaCO3 nanocomposite shows the spectra pattern associated with ESCaCO3 and RHSiO2. The FESEM micrograph shows a near monodispersed and spherical CS–SiO2/CaCO3 nanocomposite morphology, with an average size distribution of 32.15 ± 6.20 nm. The corresponding EDX showed the representative peaks for Ca, C, Si, and O. The highest removal efficiency of phenol over the adsorbents was observed over CS–SiO2/CaCO3 nanocomposite compared to other adsorbents. Adsorbing 84–89% of phenol in 60–90 min at a pH of 5.4, and a dose of 0.15 g in 20 ml of 25 mg/L phenol concentration. The result of the kinetic model shows the adsorption processes to be best described by pseudo-second-order. The highest correlation coefficient (R2) of 0.99 was observed in CS-SiO2/CaCO3 nanocomposite, followed by RHSiO2 and ESCaCO3. The result shows the equilibrium data for all the adsorbents fitting well to the Langmuir isotherm model, and follow the trend CS-SiO2/CaCO3 > ESCaCO3 > RHSiO2. The Langmuir equation and Freundlich model in this study show a higher correlation coefficient (R2 = 0.9912 and 0.9905) for phenol adsorption onto the CS–SiO2/CaCO3 nanocomposite with a maximum adsorption capacity (qm ) of 14.06 mg/g compared to RHSiO2 (10.64 mg/g) and ESCaCO3 (10.33 mg/g). The results suggest good monolayer coverage on the adsorbent’s surface (Langmuir) and heterogeneous surfaces with available binding sites (Freundlich).

High throughput virtual screening of 230 billion molecular solar heat battery candidates

The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising candidate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and lifetime of the high energy form (i.e., the free energy barrier to the back reaction) of the canonical parent compound must be increased significantly to be of practical use. We use semiempirical quantum chemical methods, machine learning, and density functional theory to virtually screen over 230 billion substituted DHA molecules to identify promising candidates. We identify a molecule with a predicted energy density of 0.38 kJ/g, which is significantly larger than the 0.14 kJ/g computed for the parent compound. The free energy barrier to the back reaction is 11 kJ/mol higher than the parent compound, which should correspond to a half-life of about 10 days—4 months. This is considerably longer than the 3–39 h (depending on solvent) observed for the parent compound and sufficiently long for many practical applications. Our paper makes two main important contributions: (1) a novel and generally applicable methodological approach that makes screening of huge libraries for properties involving chemical reactivity with modest computational resources, and (2) a clear demonstration that the storage capacity of the DHA/VHF thermocouple cannot be increased to >0.5 kJ/g by combining simple substituents.

Fast and automatic estimation of transition state structures using tight binding quantum chemical calculations

We present a method for the automatic determination of transition states (TSs) that is based on Grimme’s RMSD-PP semiempirical tight binding reaction path method (J. Chem. Theory Comput. 2019, 15, 2847–2862), where the maximum energy structure along the path serves as an initial guess for DFT TS searches. The method is tested on 100 elementary reactions and located a total of 89 TSs correctly. Of the 11 remaining reactions, nine are shown not to be elementary reactions after all and for one of the two true failures the problem is shown to be the semiempirical tight binding model itself. Furthermore, we show that the GFN2-xTB RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol. Thus, GFN2-xTB RMSD-PP barrier heights, which can be estimated at the cost of a single energy minimisation, can be used to quickly identify reactions with low barriers, although it will also produce some false positives.

The SIR dynamic model of infectious disease transmission and its analogy with chemical kinetics

Mathematical models of the dynamics of infectious disease transmission are used to forecast epidemics and assess mitigation strategies. In this article, we highlight the analogy between the dynamics of disease transmission and chemical reaction kinetics while providing an exposition on the classic Susceptible–Infectious–Removed (SIR) epidemic model. Particularly, the SIR model resembles a dynamic model of a batch reactor carrying out an autocatalytic reaction with catalyst deactivation. This analogy between disease transmission and chemical reaction enables the exchange of ideas between epidemic and chemical kinetic modeling communities.

Aggregation kinetics of binary systems containing kaolinite and Pseudomonas putida induced by different 1:1 electrolytes: specific ion effects

Background The interactions between colloidal particles in the binary systems or mixture colloids containing clay minerals and bacteria have important influences on formations and stabilities of soil aggregates, transportations of soil water, as well as biological activities of microorganisms. How the interfacial reaction of metal ions affects their interaction therefore becomes an important scientific issue. Methods Dynamic light scattering studies on the aggregation kinetics of mixture colloids containing kaolinite and Pseudomonas putida (P. putida) were conducted in this study. Results Aggregation could be observed between kaolinite and kaolinite, between kaolinite and P. putida when P. putida content was less than 33.3%. Additionally, aggregation rates decreased with increasing P. putida content. The critical coagulation concentrations and activation energies indicated that there were strong specific ion effects on the aggregation of mixture colloids. Most importantly, the activation energy increased sharply with increasing P. putida content, which might result from the lower Hamaker constant of P. putida compared with that of kaolinite. Contributions (1) Strong specific ion effects on mixture colloids aggregation of kaolinite-P. putida were observed; (2) the aggregation behavior of mixture colloids was determined by the average effects of mixture colloids, rather than the specific component. This finding provides an important methodological guide for further studies on the colloidal aggregation behavior of mixture systems with organic and inorganic materials.

Molecular dynamics simulations of the interactions between triose phosphate isomerase and sulfonamides

Malaria is a disease with debilitating health and negative economic impacts in regions at high risk of infection. Parasitic resistance and side effects of current antimalarial drugs are major setbacks to the successful campaigns that have reduced malaria incidence by 40% in the last decade. The parasite’s dependence on glycolysis for energy requirements makes pathway enzymes suitable targets for drug development. Specifically, triose phosphate isomerase (TPI) from Plasmodium falciparum (pTPI) and human (hTPI) cells show striking structural features that can be used in development of new antimalarial agents. In this study MD simulations were used to characterize binding sites on hTPI and pTPI interactions with sulfonamides. The molecular mechanics Poisson–Boltzmann surface area (MM–PBSA) method was used to estimate the interaction energies of four sulfonamide-TPI docked complexes. A unique combination of key residues at the dimer interface of pTPI is responsible for the observed selective affinity to pTPI compared to hTPI. The representative sulfonamide; 4-amino-N-(3,5-dimethylphenyl)-3-fluorbenzenesulfonamide (sulfaE) shows a strong affinity with pTPI (dimer interface, −42.91 kJ/mol and active site region, −71.62 kJ/mol), hTPI (dimer interface, −41.32 kJ/mol and active site region, −84.40 kJ/mol). Strong and favorable Van der Waals interactions and increases in non-polar solvation energies explain the difference in affinity between pTPI with sulfaE compared to hTPI at the dimer interface. This is an indication that the dimer interface of TPI glycolytic enzyme is vital for development of sulfonamide based antimalarial drugs.

Chemical space exploration: how genetic algorithms find the needle in the haystack

We explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 1060 molecules) by considering only a tiny subset (typically 103−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom- or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem.