Quantitative Biology

RNA interference, particularly siRNA induced gene silencing is becoming an important avenue of modern therapeutics. The siRNA is delivered to the cells as short double helical RNA which becomes single stranded for forming the RISC complex. Significant experimental evidence is available for most of the steps except the process of the separation of the two strands. We have attempted to understand the pathway for double stranded siRNA (dsRNA) to single stranded (ssRNA) molecules using steered molecular dynamics simulations. As the process is completely unexplored we have applied force from all possible directions restraining all possible residues to convert dsRNA to ssRNA. We found pulling one strand along helical axis direction restraining far end of the other strand demands excessive force for ssRNA formation. Pulling a central residue of one strand, in a direction perpendicular to the helix axis, while keeping the base paired residue fixed requires intermediate force for strand separation. Moreover we found that in this process the force requirement is quite high for the first bubble formation (nucleation energy) and the bubble propagation energies are quite small. We hypothesize this could be the mode of action adopted by the proteins in the cells.

The primary building block of the body is collagen, which is found in the extracellular matrix and in many stress-bearing tissues such as tendon and cartilage. It provides elasticity and support to cells and tissues while influencing biological pathways including cell signaling, motility and differentiation. Collagen's unique triple helical structure is thought to impart mechanical stability. However, detailed experimental studies on its molecular mechanics have been only recently emerging. Here, we review the treatment of the triple helix as a homogeneous flexible rod, including bend (standard worm-like chain model), twist, and stretch deformations, and the assumption of backbone linearity. Additionally, we discuss protein-specific properties of the triple helix including sequence dependence, and relate single-molecule mechanics to collagen's physiological context.

The ability to switch on the activity of an enzyme through its spontaneous reconstitution has proven to be a valuable tool in fundamental studies of enzyme structure/reactivity relationships or in the design of artificial signal transduction systems in bioelectronics, synthetic biology, or bioanalytical applications. In particular, those based on the spontaneous reconstitution/activation of the apo-PQQ-dependent soluble glucose dehydrogenase (sGDH) from Acinetobacter calcoaceticus were widely developed. However, the reconstitution mechanism of sGDH with its two cofactors, i.e. the pyrroloquinoline quinone (PQQ) and Ca2+, remains unknown. The objective here is to elucidate this mechanism by stopped-flow kinetics under single-turnover conditions. The reconstitution of sGDH exhibited biphasic kinetics, characteristic of a square reaction scheme associated to two activation pathways. From a complete kinetic analysis, we were able to fully predict the reconstitution dynamic, but also to demonstrate that when PQQ first binds to the apo-sGDH, it strongly impedes the access of Ca2+ to its enclosed position at the bottom of the enzyme binding site, thereby greatly slowing down the reconstitution rate of sGDH. This slow calcium insertion may purposely be accelerated by providing more flexibility to the Ca2+ binding loop through the specific mutation of the calcium coordinating P248 proline residue, reducing thus the kinetic barrier to calcium ion insertion. The dynamic nature of the reconstitution process is also supported by the observation of a clear loop shift and a reorganization of the hydrogen bonding network and van der Waals interactions observed in both active sites of the apo and holo forms, a structural change modulation that was revealed from the refined X-ray structure of apo-sGDH (PDB:5MIN).

Self-assembly and force generation are two central processes in biological systems that usually are considered in separation. However, the signals that activate non-muscle myosin II molecular motors simultaneously lead to self-assembly into myosin II minifilaments as well as progression of the motor heads through the crossbridge cycle. Here we investigate theoretically the possible effects of coupling these two processes. Our assembly model, which builds upon a consensus architecture of the minifilament, predicts a critical aggregation concentration at which the assembly kinetics slows down dramatically. The combined model predicts that increasing actin filament concentration and force both lead to a decrease in the critical aggregation concentration. We suggest that due to these effects, myosin II minifilaments in a filamentous context might be in a critical state that reacts faster to varying conditions than in solution. We finally compare our model to experiments by simulating fluorescence recovery after photobleaching.

The relationship between sequence variation and phenotype is poorly understood. Here we use metabolomic analysis to elucidate the molecular mechanism underlying the filamentous phenotype of E. coli strains that carry destabilizing mutations in the Dihydrofolate Reductase (DHFR). We find that partial loss of DHFR activity causes SOS response indicative of DNA damage and cell filamentation. This phenotype is triggered by an imbalance in deoxy nucleotide levels, most prominently a disproportionate drop in the intracellular dTTP. We show that a highly cooperative (Hill coefficient 2.5) in vivo activity of Thymidylate Kinase (Tmk), a downstream enzyme that phosphorylates dTMP to dTDP, is the cause of suboptimal dTTP levels. dTMP supplementation in the media rescues filamentation and restores in vivo Tmk kinetics to almost perfect Michaelis-Menten, like its kinetics in vitro. Overall, this study highlights the important role of cellular environment in sculpting enzymatic kinetics with system level implications for bacterial phenotype.

It is a common belief that metamorphic proteins challenge the Anfinsen thermodynamic hypothesis (or dogma). Here we argue against this view aims to show that metamorphic proteins not just fulfill the Anfinsen dogma but also exhibit marginal stability comparable to that seen on macromolecules and macromolecular complexes. This work contributes to our general understanding of protein classification and may spur significant progress in our effort to analyze protein evolvability.

The deadly coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has gone out of control globally. Despite much effort by scientists, medical experts, and society in general, the slow progress on drug discovery and antibody therapeutic development, the unknown possible side effects of the existing vaccines, and the high transmission rate of the SARS-CoV-2, remind us of the sad reality that our current understanding of the transmission, infectivity, and evolution of SARS-CoV-2 is unfortunately very limited. The major limitation is the lack of mechanistic understanding of viral-host cell interactions, the viral regulation, protein-protein interactions, including antibody-antigen binding, protein-drug binding, host immune response, etc. This limitation will likely haunt the scientific community for a long time and have a devastating consequence in combating COVID-19 and other pathogens. Notably, compared to the long-cycle, highly cost, and safety-demanding molecular-level experiments, the theoretical and computational studies are economical, speedy, and easy to perform. There exists a tsunami of the literature on molecular modeling, simulation, and prediction of SARS-CoV-2 that has become impossible to fully be covered in a review. To provide the reader a quick update about the status of molecular modeling, simulation, and prediction of SARS-CoV-2, we present a comprehensive and systematic methodology-centered narrative in the nick of time. Aspects such as molecular modeling, Monte Carlo (MC) methods, structural bioinformatics, machine learning, deep learning, and mathematical approaches are included in this review. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are assessing the current status in the field.

Engineering simple, artificial models of living cells allows synthetic biologists to study cellular functions under well-controlled conditions. Reconstituting multicellular behaviors with synthetic cell-mimics is still a challenge because it requires efficient communication between individual compartments in large populations. This protocol presents a microfluidic method to produce large quantities of cell-mimics with highly porous, stable and chemically modifiable polymer membranes that can be programmed on demand with nucleus-like DNA-hydrogel compartments for gene expression. We describe expression of genes encoded in the hydrogel compartment and communication between neighboring cell-mimics through diffusive protein signals.

Giant unilamellar vesicles (GUVs), are a convenient tool to study membrane-bound processes using optical microscopy. An increasing number of studies highlights the potential of these model membranes when addressing questions in membrane biophysics and cell biology. Among them, phase transitions and domain formation, dynamics and stability in raft-like mixtures are probably some of the most intensively investigated. In doing so, many research teams rely on standard protocols for GUV preparation and handling involving the use of sugar solutions. Here, we demonstrate that following such a standard approach can lead to abnormal formation of micron-sized domains in GUVs grown from only a single phospholipid. The membrane heterogeneity is visualized by means of a small fraction (0.1 mol%) of a fluorescent lipid dye. For dipalmitoylphosphatidylcholine GUVs, different types of membrane heterogeneities were detected. First, an unexpected formation of micron-sized dye-depleted domains was observed upon cooling. These domains nucleated about 10 K above the lipid main phase transition temperature, TM. In addition, upon further cooling of the GUVs down to the immediate vicinity of TM, stripe-like dye-enriched structures around the domains are detected. The micron-sized domains in quasi single-component GUVs were observed also when using two other lipids. Whereas the stripe structures are related to the phase transition of the lipid, the dye-excluding domains seem to be caused by traces of impurities present in the glucose. Supplementing glucose solutions with nm-sized liposomes at millimolar lipid concentration suppresses the formation of the micron-sized domains, presumably by providing competitive binding of the impurities to the liposome membrane in excess. It is likely that such traces of impurities can significantly alter lipid phase diagrams and cause differences among reported ones.

Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be improved, given recent architectures for protein folding.