Quantitative Biology

Antibodies with high titer and affinity to small molecule are critical in the field for the development of vaccines against drugs of abuse, antidotes to toxins and immunoassays for compounds. However, little is known regarding how properties of small molecule influence and which chemical descriptor could indicate the degree of the antibody response. Based on our previous study, we designed and synthesized two groups of small molecules, called haptens, with varied hydrophobicities to investigate the relationship between properties of small molecules and antibody response in term of titer and affinity. We found that the magnitude of the antibody response is positively correlated with the degree of molecular hydrophobicity and related chemical descriptors. This study provides insight into the immunological characteristics of small molecules themselves and useful clues to produce high quality antibodies against small molecules.

Prevention of protein aggregation and thus stabilization of proteins has large biological and biotechnological implications. Here, we show that inhibition of amyloid-like aggregates is possible in stoichiometric conjugates of polymer surfactant and bovine serum albumin (BSA) chosen as a model protein. We investigate using a combination of Thioflavin-T fluorescence spectroscopy, dynamic light scattering and FTIR spectroscopy the aggregation behavior in polymer surfactant modified and unmodified (native) BSA solutions. The BSA-polymer surfactant conjugates are stable up to 5 days under aggregation conditions, while native BSA forms amyloid fibrillar structures. Further, DLS-based micro-rheology studies performed with heat-treated 100 to 200 {\mu}M native BSA aggregates provided understanding of the equilibrium elastic and viscous moduli over a very large frequency range, reaching MHz, which are inaccessible using bulk rheology. Our results indicate that after 6 days of aggregation conditions, elastic moduli showed values between 1.2 to 3.6 Pa corresponding to an entanglement length ({\xi}) of 105 nm. Interestingly, heating 200 {\mu}M native BSA solution at 65 degree C for 2 days in a plastic Eppendorf resulted in self-standing films. These films exhibited strong ThT-fluorescence intensity and a predominant \b{eta}-sheet secondary structure from the FTIR studies, suggesting that self-standing microstructure resulted from hierarchical self-assembly of amyloid fibrils.

Dynamics of nucleosomes, the building blocks of the chromatin, has crucial effects on expression, replication and repair of genomes in eukaryotes. Beside constant movements of nucleosomes by thermal fluctuations, ATP-dependent chromatin remodelling complexes cause their active displacements. Here we propose a theoretical analysis of dinucleosome wrapping and unwrapping dynamics in the presence of an external force. We explore the energy landscape and configurations of dinucleosome in different unwrapped states. Moreover, using a dynamical Monte-Carlo simulation algorithm, we demonstrate the dynamical features of the system such as the unwrapping force for partial and full wrapping processes. Furthermore, we show that in the short length of linker DNA ( ∼10−90 bp), the asymmetric unwrapping occurs. These findings could shed some light on chromatin dynamics and gene accessibility.

Interacting proteins coevolve at multiple but interconnected scales, from the residue-residue over the protein-protein up to the family-family level. The recent accumulation of enormous amounts of sequence data allows for the development of novel, data-driven computational approaches. Notably, these approaches can bridge scales within a single statistical framework. While being currently applied mostly to isolated problems on single scales, their immense potential for an evolutionary informed, structural systems biology is steadily emerging.

Expanding the scope of graph-based, deep-learning models to noncovalent protein-ligand interactions has earned increasing attention in structure-based drug design. Modeling the protein-ligand interactions with graph neural networks (GNNs) has experienced difficulties in the conversion of protein-ligand complex structures into the graph representation and left questions regarding whether the trained models properly learn the appropriate noncovalent interactions. Here, we proposed a GNN architecture, denoted as InteractionNet, which learns two separated molecular graphs, being covalent and noncovalent, through distinct convolution layers. We also analyzed the InteractionNet model with an explainability technique, i.e., layer-wise relevance propagation, for examination of the chemical relevance of the model's predictions. Separation of the covalent and noncovalent convolutional steps made it possible to evaluate the contribution of each step independently and analyze the graph-building strategy for noncovalent interactions. We applied InteractionNet to the prediction of protein-ligand binding affinity and showed that our model successfully predicted the noncovalent interactions in both performance and relevance in chemical interpretation.

Often the development of novel functional peptides is not amenable to high throughput or purely computational screening methods. Peptides must be synthesized one at a time in a process that does not generate large amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both peptide properties and predictive modeling accuracy. Here, we study the effectiveness of active learning, optimizing experiment order, and meta-learning, transferring knowledge between contexts, to reduce the number of experiments necessary to build a predictive model. We present a multi-task benchmark database of peptides designed to advance these methods for experimental design. Each task is binary classification of peptides represented as a sequence string. We find neither active learning method tested to be better than random choice. The meta-learning method Reptile was found to improve average accuracy across datasets. Combining meta-learning with active learning offers inconsistent benefits.

Atomic Force Microscopy was utilized to study the morphology of Gag, {\Psi}RNA, and their binding complexes with lipids in a solution environment with 0.1Å vertical and 1nm lateral resolution. TARpolyA RNA was used as a RNA control. The lipid used was phospha-tidylinositol-(4,5)-bisphosphate (PI(4,5)P2). The morphology of specific complexes Gag-{\Psi}RNA, Gag-TARpolyA RNA, Gag-PI(4,5)P2 and PI(4,5)P2-{\Psi}RNA-Gag were studied. They were imaged on either positively or negatively charged mica substrates depending on the net charges carried. Gag and its complexes consist of monomers, dimers and tetramers, which was confirmed by gel electrophoresis. The addition of specific {\Psi}RNA to Gag is found to increase Gag multimerization. Non-specific TARpolyA RNA was found not to lead to an increase in Gag multimerization. The addition PI(4,5)P2 to Gag increases Gag multimerization, but to a lesser extent than {\Psi}RNA. When both {\Psi}RNA and PI(4,5)P2 are present Gag undergoes comformational changes and an even higher degree of multimerization.

The TAZ1 domain of CREB binding protein is crucial for transcriptional regulation and recognizes multiple targets. The interactions between TAZ1 and its specific targets are related to the cellular hypoxic negative feedback regulation. Previous experiments reported that one of the TAZ1 targets CITED2 is an efficient competitor of another target HIF-1{\alpha}. Here by developing the structure-based models of TAZ1 complexes we have uncovered the underlying mechanisms of the competitions between HIF-1{\alpha} and CITED2 binding to TAZ1. Our results are consistent with the experimental hypothesis on the competition mechanisms and the apparent affinity. In addition, the simulations prove the dominant position of forming TAZ1-CITED2 complex in both thermodynamics and kinetics. For thermodynamics, TAZ1-CITED2 is the lowest basin located on the free energy surface of binding in the ternary system. For kinetics, the results suggest that CITED2 binds to TAZ1 faster than HIF-1{\alpha}. Besides, the analysis of contact map and f values in this study will be helpful for further experiments on TAZ1 systems.

Since the largest 2014-2016 Ebola virus disease outbreak in West Africa, understanding of Ebola virus infection has improved, notably the involvement of innate immune mediators. Amongst them, collectins are important players in the antiviral innate immune defense. A screening of Ebola glycoprotein (GP)-collectins interactions revealed the specific interaction of human surfactant protein D (hSP-D), a lectin expressed in lung and liver, two compartments where Ebola was found in vivo. Further analyses have demonstrated an involvement of hSP-D in the enhancement of virus infection in several in vitro models. Similar effects were observed for porcine SP-D (pSP-D). In addition, both hSP-D and pSP-D interacted with Reston virus (RESTV) GP and enhanced pseudoviral infection in pulmonary cells. Thus, our study reveals a novel partner of Ebola GP that may participate to enhance viral spread.

The highly charged RNA molecules, with each phosphate carrying a single negative charge, cannot fold into well-defined architectures with tertiary interactions, in the absence of ions. For ribozymes, divalent cations are known to be more efficient than monovalent ions in driving them to a compact state although Mg 2+ ions are needed for catalytic activities. Therefore, how ions interact with RNA is relevant in understanding RNA folding. It is often thought that most of the ions are territorially and non-specifically bound to the RNA, as predicted by the counterion condensation (CIC) theory. Here, we show using simulations of Azoarcus ribozyme, based on an accurate coarse-grained Three Site Interaction (TIS) model, with explicit divalent and monovalent cations, that ion condensation is highly specific and depends on the nucleotide position. The regions with high coordination between the phosphate groups and the divalent cations are discernible even at very low Mg 2+ concentrations when the ribozyme does not form tertiary interactions. Surprisingly, these regions also contain the secondary structural elements that nucleate subsequently in the self-assembly of RNA, implying that ion condensation is determined by the architecture of the folded state. These results are in sharp contrast to interactions of ions (monovalent and divalent) with rigid charged rods in which ion condensation is uniform and position independent. The differences are explained in terms of the dramatic non-monotonic shape fluctuations in the ribozyme as it folds with increasing Mg 2+ or Ca 2+ concentration.