Quantitative Biology

AGel amyloidosis, formerly known as familial amyloidosis of the Finnish-type, is caused by pathological aggregation of proteolytic fragments of plasma gelsolin. So far, four mutations in the gelsolin gene have been reported as responsible for the disease. Although D187N is the first identified variant and the best characterized, its structure has been hitherto elusive. Exploiting a recently-developed nanobody targeting gelsolin, we were able to stabilize the G2 domain of the D187N protein and obtained, for the first time, its high-resolution crystal structure. In the nanobody-stabilized conformation, the main effect of the D187N substitution is the impairment of the calcium binding capability, leading to a destabilization of the C-terminal tail of G2. However, molecular dynamics simulations show that in the absence of the nanobody, D187N-mutated G2 further misfolds, ultimately exposing its hydrophobic core and the furin cleavage site. The nanobody's protective effect is based on the enhancement of the thermodynamic stability of different G2 mutants (D187N, G167R and N184K). In particular, the nanobody reduces the flexibility of dynamic stretches, and most notably decreases the conformational entropy of the C-terminal tail, otherwise stabilized by the presence of the Ca2+ ion. A Caenorhabditis elegans-based assay was also applied to quantify the proteotoxic potential of the mutants and determine whether nanobody stabilization translates into a biologically relevant effect. Successful protection from G2 toxicity in vivo points to the use of C. elegans as a tool for investigating the mechanisms underlying AGel amyloidosis and rapidly screen new therapeutics.

Protein tertiary structure prediction has improved dramatically in recent years. A considerable fraction of various proteomes can be modelled in the absence of structural templates. We ask whether our DMPfold method can model all the proteins without templates in the JCVI-syn3.0 minimal genome, which contains 438 proteins. We find that a useful tertiary structure annotation can be provided for all but 10 proteins. The models may help annotate function in cases where it is unknown, and provide coverage for 29 predicted protein-protein interactions which lacked monomer models. We also show that DMPfold performs well on proteins with structures released since initial publication. It is likely that the minimal genome will have complete structural coverage within a few years.

Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities and functions. The design of large scale and complex biological structures spurs dedicated graph-based deep generative modeling techniques, which represents a key but underappreciated aspect of computational drug discovery. In this work, we investigate the principles behind representing and generating different RNA structural modalities, and propose a flexible framework to jointly embed and generate these molecular structures along with their sequence in a meaningful latent space. Equipped with a deep understanding of RNA molecular structures, our most sophisticated encoding and decoding methods operate on the molecular graph as well as the junction tree hierarchy, integrating strong inductive bias about RNA structural regularity and folding mechanism such that high structural validity, stability and diversity of generated RNAs are achieved. Also, we seek to adequately organize the latent space of RNA molecular embeddings with regard to the interaction with proteins, and targeted optimization is used to navigate in this latent space to search for desired novel RNA molecules.

Computational procedures to foresee the 3D structure of aptamers are in continuous progress. They constitute a crucial input to research, mainly when the crystallographic counterpart of the structures in silico produced is not present. At now, many codes are able to perform structure and binding prediction, although their ability in scoring the results remains rather weak. In this paper, we propose a novel procedure to complement the ranking outcomes of free docking code, by applying it to a set of anti-angiopoietin aptamers, whose performances are known. We rank the in silico produced configurations, adopting a maximum likelihood estimate, based on their topological and electrical properties. From the analysis, two principal kinds of conformers are identified, whose ability to mimick the binding features of the natural receptor is discussed. The procedure is easily generalizable to many biological biomolecules, useful for increasing chances of success in designing high-specificity biosensors (aptasensors).

Properties of non-canonical GC Base Pairs and their relation with mechanochemical cleavage of DNA are analyzed. A hypothesis of the involvement of the Transient GC Wobble Base Pairs in the mechanisms of the mechanochemical cleavage of DNA and epigenetic mechanisms with participation of 5-methylcytosine is proposed. The hypothesis explains the increase in the frequency of the breaks of the sugar-phosphate backbone of DNA after cytosines, asymmetric character of these breaks, and an increase in the frequency of breaks in CpG after cytosine methylation.

G protein-coupled receptors (GPCRs) are a large superfamily of membrane proteins that are activated by extracellular small molecules or photons. Neurotensin receptor 1 (NTSR1) is a GPCR that is activated by neurotensin, i.e. a 13 amino acid peptide. Binding of neurotensin induces conformational changes in the receptor that trigger the intracellular signaling processes. While recent single-molecule studies have reported a dynamic monomer - dimer equilibrium of NTSR1 in vitro, a biophysical characterization of the oligomerization status of NTSR1 in living mammalian cells is complicated. Here we report on the oligomerization state of the human NTSR1 tagged with mRuby3 by dissolving the plasma membranes of living HEK293T cells into 10 nm-sized soluble lipid nanoparticles by addition of styrene-maleic acid copolymers (SMALPs). Single SMALPs were analyzed one after another in solution by multi-parameter single molecule spectroscopy including brightness, fluorescence lifetime and anisotropy for homoFRET. Brightness analysis was improved using single SMALP detection in a confocal ABELtrap for extended observation times in solution. A bimodal brightness distribution indicated a significant fraction of dimeric NTSR1 in SMALPs or in the plasma membrane, respectively, before addition of neurotensin.

Coronavirus (COVID-19) outbreak in late 2019 and 2020 comprises a serious and more likely a pandemic threat worldwide. Given that the disease has not approved vaccines or drugs up to now, any efforts for drug design and or clinical trails of old drugs based on their mechanism of action are worthy and creditable in such circumstances. Experienced docking experiments using the newly released coordinate structure for COVID-19 protease as a receptor and thoughtfully selected chemicals among antiviral and antibiotics drugs as ligands may be leading in this context. We selected nine drugs from HIV-1 protease inhibitors and twenty-one candidates from anti bronchitis drugs based on their chemical structures and enrolled them in blind and active site-directed dockings in different modes and in native-like conditions of interactions. Our findings suggest the binding capacity and the inhibitory potency of candidates are as follows Tipranavir>Indinavir>Atazanavir>Darunavir>Ritonavir>Amprenavir for HIV-1 protease inhibitors and Cefditoren>Cefixime>Erythromycin>Clarithromycin for anti bronchitis medicines. The drugs bioavailability, their hydrophobicity and the hydrophobic properties of their binding sites and also the rates of their metabolisms and deactivations in the human body are the next determinants for their overall effects on viral infections, the net results that should survey by clinical trials to assess their therapeutic usefulness for coronavirus infections.

Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here we propose persistent Ricci curvature (PRC), in particular Ollivier persistent Ricci curvature (OPRC), for the molecular featurization and feature engineering, for the first time. Filtration process proposed in persistent homology is employed to generate a series of nested molecular graphs. Persistence and variation of Ollivier Ricci curvatures on these nested graphs are defined as Ollivier persistent Ricci curvature. Moreover, persistent attributes, which are statistical and combinatorial properties of OPRCs during the filtration process, are used as molecular descriptors, and further combined with machine learning models, in particular, gradient boosting tree (GBT). Our OPRC-GBT model is used in the prediction of protein-ligand binding affinity, which is one of key steps in drug design. Based on three most-commonly used datasets from the well-established protein-ligand binding databank, i.e., PDBbind, we intensively test our model and compare with existing models. It has been found that our model are better than all machine learning models with traditional molecular descriptors.

Identifying the secondary structure of an RNA is crucial for understanding its diverse regulatory functions. This paper focuses on how to enhance target identification in a Boltzmann ensemble of structures via chemical probing data. We employ an information-theoretic approach to solve the problem, via considering a variant of the Rényi-Ulam game. Our framework is centered around the ensemble tree, a hierarchical bi-partition of the input ensemble, that is constructed by recursively querying about whether or not a base pair of maximum information entropy is contained in the target. These queries are answered via relating local with global probing data, employing the modularity in RNA secondary structures. We present that leaves of the tree are comprised of sub-samples exhibiting a distinguished structure with high probability. In particular, for a Boltzmann ensemble incorporating probing data, which is well established in the literature, the probability of our framework correctly identifying the target in the leaf is greater than 90% .

The Protein Data Bank (PDB) today contains more than 153,000 entries with the 3-dimensional structures of biological macromolecules. Using the rich resources of this repository, it is possible identifying subsets with specific, interesting properties for different applications. Our research group prepared an automatically updated list of amyloid- and probably amyloidogenic molecules, the PDB\_Amyloid collection, which is freely available at the address \url{this http URL}. This resource applies exclusively the geometric properties of the steric structures for identifying amyloids. In the present contribution, we analyze the starting (i.e., prefix) subsequences of the characteristic, parallel beta-sheets of the structures in the PDB\_Amyloid collection, and identify further appearances of these length-5 prefix subsequences in the whole PDB data set. We have identified this way numerous proteins, whose normal or irregular functions involve amyloid formation, structural misfolding, or anti-coagulant properties, simply by containing these prefixes: including the T-cell receptor (TCR), bound with the major histocompatibility complexes MHC-1 and MHC-2; the p53 tumor suppressor protein; a mycobacterial RNA polymerase transcription initialization complex; the human bridging integrator protein BIN-1; and the tick anti-coagulant peptide TAP.