Quantitative Biology

Peroxisome proliferator-activated receptors gamma (PPAR{\gamma}) are ligand-activated controllers of various metabolic actions and insulin sensitivity. PPAR{\gamma} is thus considered as an important target to treat type 2 diabetes. Available PPAR{\gamma} drugs (full agonists) have robust insulin-sensitizing properties but are accompanied by severe side effects leading to complicated health problems. Here, we have used molecular docking and a molecular dynamics simulation study to find a novel PPAR{\gamma} ligand from a natural product. Our study suggests that the inhibition of ceramicine B in the PPAR{\gamma} ligand-binding domain (LBD) could act as a partial agonist and block cdk5-mediated phosphorylation. This result may provide an opportunity for the development of new anti-diabetic drugs by targeting PPAR{\gamma} while avoiding the side effects associated with full agonists.

It has been known that the uptake mechanisms of cell-penetrating peptides(CPPs) depend on the experimental conditions such as concentration of peptides, lipid composition, temperature, etc. In this study we investigate the temperature dependence of the penetration of Arg9s into a DOPC/DOPG(4:1) membrane using molecular dynamics(MD) simulations at two different temperatures, T = 310 K and T = 288 K. Although it is difficult to identify the temperature dependence because of having only one single simulation at each temperature and no evidence of translocation of Arg9s across the membrane at both temperatures, our simulations suggest that followings are strongly correlated with the penetration of Arg9s: a number of water molecules coordinated by Arg9s, electrostatic energy between Arg9s and the lipids molecules. We also present how Arg9s change a bending rigidity of the membrane and how a collective behavior between Arg9s enhances the penetration and the membrane bending. Our analyses can be applicable to any cell-penetrating peptides(CPPs) to investigate their interactions with various membranes using MD simulations.

The hydrogen peroxide is present in the living cell at small concentrations that increase under the action of the heavy ion beams in the process of anticancer therapy. The interactions of hydrogen peroxide with DNA, proteins and other biological molecules are poorly understood. In the present work the competitive binding of the hydrogen peroxide and water molecules with the DNA double helix backbone has been studied using the molecular dynamics method. The simulations have been carried out for the DNA double helix in a water solution with hydrogen peroxide molecules and Na + counterions. The obtained radial distribution functions of counterions, H 2 O 2 and H 2 O molecules with respect to the oxygen atoms of DNA phosphate groups have been used for the analysis of the formation of different complexes. The calculated mean residence times show that a hydrogen peroxide molecule stays at least twice as long near the phosphate group (up to 7 ps) than a water molecule (about 3 ps). The hydrogen peroxide molecules form more stable complexes with the phosphate groups of the DNA backbone than water molecules do.

Despite the huge effort to contain the infection, the novel SARS-CoV-2 coronavirus has rapidly become pandemics, mainly due to its extremely high human-to-human transmission capability, and a surprisingly high viral charge of symptom-less people. While the seek of a vaccine is still ongoing, promising results have been obtained with antiviral compounds. In particular, lactoferrin is found to have beneficial effects both in preventing and soothing the infection. Here, we explore the possible molecular mechanisms with which lactoferrin interferes with SARS-CoV-2 cell invasion, preventing attachment and/or entry of the virus. To this aim, we search for possible interactions lactoferrin may have with virus structural proteins and host receptors. Representing the molecular iso-electron surface of proteins in terms of 2D-Zernike descriptors, we (i) identified putative regions on the lactoferrin surface able to bind sialic acid receptors on the host cell membrane, sheltering the cell from the virus attachment; (ii) showed that no significant shape complementarity is present between lactoferrin and the ACE2 receptor, while (iii) two high complementarity regions are found on the N- and C-terminal domains of the SARS-CoV-2 spike protein, hinting at a possible competition between lactoferrin and ACE2 for the binding to the spike protein.

Studying the conformations involved in the dimerization of cadherins is highly relevant to understand the development of tissue and its failure, which is associated with tumors and metastases. Experimental techniques, like X-ray crystallography, can usually report only the most stable conformations, missing minority states that could nonetheless be important for the recognition mechanism. Computer simulations could be a valid complement to the experimental approach. However, standard all-atom protein models in explicit solvent are computationally too demanding to search thoroughly the conformational space of multiple chains composed of several hundreds of amino acids. To reach this goal, we resorted to a coarse-grained model in implicit solvent. The standard problem with this kind of models is to find a realistic potential to describe their interactions. We used coevolutionary information from cadherin alignments, corrected by a statistical potential, to build an interaction potential which is agnostic of the experimental conformations of the protein. Using this model, we explored the conformational space of multi-chain systems and validated the results comparing with experimental data. We identified dimeric conformations that are sequence-specific and that can be useful to rationalize the mechanism of recognition between cadherins.

Evidence accumulated over the past decade provides support for liquid-liquid phase separation as the mechanism underlying the formation of biomolecular condensates, which include not only membraneless organelles such as nucleoli and RNA granules, but additional assemblies involved in transcription, translation and signaling. Understanding the molecular mechanisms of condensate function requires knowledge of the structures of their constituents. Current knowledge suggests that structures formed via multivalent domain-motif interactions remain largely unchanged within condensates. Two different viewpoints exist regarding structures of disordered low-complexity domains within condensates; one argues that low-complexity domains remain largely disordered in condensates and their multivalency is encoded in short motifs called stickers, while the other argues that the sequences form cross-beta structures resembling amyloid fibrils. We review these viewpoints and highlight outstanding questions that will inform structure-function relationships for biomolecular condensates.

The covalent functionalisation of type I atelocollagen with either 4-vinylbenzyl or methacrylamide residues is presented as a simple synthetic strategy to achieve customisable, cell-friendly UV-cured hydrogel networks with widespread clinical applicability. Molecular parameters, i.e. the type of monomer, degree of atelocollagen functionalisation and UV curing solution, have been systematically varied and their effect on gelation kinetics, swelling behaviour, elastic properties and enzymatic degradability investigated. UV-cured hydrogel networks deriving from atelocollagen precursors functionalised with equivalent molar content of 4-vinylbenzyl and methacrylamide adducts proved to display remarkably-different swelling ratio, storage modulus and collagenase resistance, similarly to the case of UV-cured hydrogel networks obtained with the same type of methacrylamide adduct, but varied degree of functionalisation. UV-induced network formation of 4VBC functionalised atelocollagen molecules yielded hydrogels with increased stiffness and enzymatic stability, attributed to the molecular rigidity of resulting aromatised crosslinking segment, whilst no toxic response was observed with osteosarcoma G292 cells. Although to a lesser extent, the pH of the UV-curing solution also proved to affect macroscopic hydrogel properties, likely due to the altered organisation of atelocollagen molecules during network formation. By leveraging the knowledge gained with classic synthetic networks, this study highlights how the type of monomer can be conveniently exploited to realise customisable atelocollagen hydrogels with controlled structure-property relationships to meet the requirements of unmet clinical applications.

Computation of biological processes creates great promise for everyday life and great challenges for physical scientists. Simulations of molecular dynamics appeal to biologists as a natural extension of structural biology. Once biologists see a structure, they want to see it move. Molecular biology shows that a few atoms, often messenger ions like Ca 2+ , control biological function on the scale of cells, sometimes organisms. Enormously concentrated ions (~20 M) in protein channels and enzymes can control important characteristics of living systems, just as highly concentrated ions near electrodes control important characteristics of electrochemical systems. The scale differences needed to simulate all the atoms of biological cells are 10 7 in linear dimension, 10 21 in three dimensions, 10 9 in resolution, 10 11 in time, and 10 13 in particle number (to deal with concentrations of Ca 2+ ). These scales must be dealt with simultaneously if the simulation is to deal with most biological functions. We suggest a computational approach using explicit multiscale analysis instead of implicit simulation of all scales. Successful computation of ions concentrated in special places may be a significant step to understanding the defining characteristics of biological and electrochemical systems.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is a major concern in coronavirus disease 2019 (COVID-19) prevention and economic reopening. However, rigorous determination of SARS-COV-2 infectivity is essentially impossible owing to its continuous evolution with over 13752 single nucleotide polymorphisms (SNP) variants in six different subtypes. We develop an advanced machine learning algorithm based on the algebraic topology to quantitatively evaluate the binding affinity changes of SARS-CoV-2 spike glycoprotein (S protein) and host angiotensin-converting enzyme 2 (ACE2) receptor following the mutations. Based on mutation-induced binding affinity changes, we reveal that five out of six SARS-CoV-2 subtypes have become either moderately or slightly more infectious, while one subtype has weakened its infectivity. We find that SARS-CoV-2 is slightly more infectious than SARS-CoV according to computed S protein-ACE2 binding affinity changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain (RBD), we show that most likely future mutations will make SARS-CoV-2 more infectious. Combining sequence alignment, probability analysis, and binding affinity calculation, we predict that a few residues on the receptor-binding motif (RBM), i.e., 452, 489, 500, 501, and 505, have very high chances to mutate into significantly more infectious COVID-19 strains.

A Bacillus subtilis derivative was obtained from strain ATCC 6633 by replacement of the native promoter of the mycosubtilin operon by a constitutive promoter originating from the replication gene repU of the Staphylococcus aureus plasmid pUB110. The recombinant strain, designated BBG100, produced up to 15-fold more mycosubtilin than the wild type produced. The overproducing phenotype was related to enhancement of the antagonistic activities against several yeasts and pathogenic fungi. Hemolytic activities were also clearly increased in the modified strain. Mass spectrometry analyses of enriched mycosubtilin extracts showed similar patterns of lipopeptides for BBG100 and the wild type. Interestingly, these analyses also revealed a new form of mycosubtilin which was more easily detected in the BBG100 sample. When tested for its biocontrol potential, wild-type strain ATCC 6633 was almost ineffective for reducing a Pythium infection of tomato seedlings. However, treatment of seeds with the BBG100 overproducing strain resulted in a marked increase in the germination rate of seeds. This protective effect afforded by mycosubtilin overproduction was also visualized by the significantly greater fresh weight of emerging seedlings treated with BBG100 compared to controls or seedlings inoculated with the wild-type strain.