pwc-archive/papers-with-abstracts huggingface dataset:
Homozygous GRN mutations: unexpected phenotypes and new insights into pathological and molecular mechanisms
Authors: Vincent Huin, Mathieu Barbier, Armand Bottani, Johannes Lobrinus, Fabienne Clot, Foudil Lamari, Laureen Chat, Benoît Rucheton, Frédérique Fluchère, Stéphane Auvin, Peter Myers, Antoinette Gelot, Agnès Camuzat, Catherine Caillaud, Ludmila Jornéa, Sylvie Forlani, Dario Saracino, Charles Duyckaerts, Alexis Brice, Alexandra Durr, Isabelle Le Ber
Date: 2020-11-20 00:00:00

Homozygous mutations in the progranulin gene (GRN) are associated with neuronal ceroid lipofuscinosis 11 (CLN11), a rare lysosomal-storage disorder characterized by cerebellar ataxia, seizures, retinitis pigmentosa, and cognitive disorders, usually beginning between 13 and 25 years of age. This is a rare condition, previously reported in only four families. In contrast, heterozygous GRN mutations are a major cause of frontotemporal dementia associated with neuronal cytoplasmic TDP-43 inclusions. We identified homozygous GRN mutations in six new patients. The phenotypic spectrum is much broader than previously reported, with two remarkably distinct presentations, depending on the age of onset. A childhood/juvenile form is characterized by classical CLN11 symptoms at an early age at onset. Unexpectedly, other homozygous patients presented a distinct delayed phenotype of frontotemporal dementia and parkinsonism after 50 years; none had epilepsy or cerebellar ataxia. Another major finding of this study is that all GRN mutations may not have the same impact on progranulin protein synthesis. A hypomorphic effect of some mutations is supported by the presence of residual levels of plasma progranulin and low levels of normal transcript detected in one case with a homozygous splice-site mutation and late onset frontotemporal dementia. This is a new critical finding that must be considered in therapeutic trials based on replacement strategies. The first neuropathological study in a homozygous carrier provides new insights into the pathological mechanisms of the disease. Hallmarks of neuronal ceroid lipofuscinosis were present. The absence of TDP-43 cytoplasmic inclusions markedly differs from observations of heterozygous mutations, suggesting a pathological shift between lysosomal and TDP-43 pathologies depending on the mono or bi-allelic status. An intriguing observation was the loss of normal TDP-43 staining in the nucleus of some neurons, which could be the first stage of the TDP-43 pathological process preceding the formation of typical cytoplasmic inclusions. Finally, this study has important implications for genetic counselling and molecular diagnosis. Semi-dominant inheritance of GRN mutations implies that specific genetic counseling should be delivered to children and parents of CLN11 patients, as they are heterozygous carriers with a high risk of developing dementia. More broadly, this study illustrates the fact that genetic variants can lead to different phenotypes according to their mono- or bi-allelic state, which is a challenge for genetic diagnosis.
The TMEM240 Protein, Mutated in SCA21, Is Expressed in Purkinje Cells and Synaptic Terminals
Authors: Sabiha Eddarkaoui, Mégane Homa, Anne Loyens, Emilie Faivre, Vincent Deramecourt, Claude-Alain Maurage, Luc Buée, Vincent Huin, Bernard Sablonnière
Date: 2020-11-20 00:00:00
Tasks: Hippocampus

A variety of missense mutations and a stop mutation in the gene coding for transmembrane protein 240 (TMEM240) have been reported to be the causative mutations of spinocerebellar ataxia 21 (SCA21). We aimed to investigate the expression of TMEM240 protein in mouse brain at the tissue, cellular, and subcellular levels. Immunofluorescence labeling showed TMEM240 to be expressed in various areas of the brain, with the highest levels in the hippocampus, isocortex, and cerebellum. In the cerebellum, TMEM240 was detected in the deep nuclei and the cerebellar cortex. The protein was expressed in all three layers of the cortex and various cerebellar neurons. TMEM240 was localized to climbing, mossy, and parallel fiber afferents projecting to Purkinje cells, as shown by coimmunostaining with VGLUT1 and VGLUT2. Co-immunostaining with synaptophysin, post-synaptic fractionation, and confirmatory electron microscopy showed TMEM240 to be localized to the post-synaptic side of synapses near the Purkinje-cell soma. Similar results were obtained in human cerebellar sections. These data suggest that TMEM240 may be involved in the organization of the cerebellar network, particularly in synaptic inputs converging on Purkinje cells. This study is the first to describe TMEM240 expression in the normal mouse brain.
Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design
Authors: JunJie Wee, Kelin Xia
Date: 2020-11-20 00:00:00
Tasks: BIG-bench Machine Learning, Drug Design, Feature Engineering, molecular representation

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.
The Budapest Amyloid Predictor and its Applications
Authors: Laszlo Keresztes, Evelin Szogi, Balint Varga, Viktor Farkas, Andras Perczel, Vince Grolmusz
Date: 2020-11-07 00:00:00

The amyloid state of proteins is widely studied with relevancy in neurology, biochemistry, and biotechnology. In contrast with amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and anti-parallel $\beta$-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed by using mostly artificial neural networks (ANNs) as the underlying computational techniques. From a good neural network-based predictor, it is a very difficult task to identify those attributes of the input amino acid sequence, which implied the decision of the network. Here we present a Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84\%, i.e., it is at least as good as the published ANN-based tools. Unlike the artificial neural networks, the decision of the SVMs are much easier to analyze, and from a good predictor, we can infer rich biochemical knowledge. Availability and Implementation: The Budapest Amyloid Predictor webserver is freely available at https://pitgroup.org/bap.
Design Of Drug-Like Protein-Protein Interaction Stabilizers Guided By Chelation-Controlled Bioactive Conformation Stabilization
Authors: Francesco Bosica, Sebastian Andrei, João Filipe Neves, Peter Brandt, Anders Gunnarsson, Isabelle Landrieu, Christian Ottmann, Gavin O'Mahony
Date: 2020-11-06 00:00:00

The protein-protein interactions (PPIs) of 14-3-3 proteins are a model system for studying PPI stabilization. The complex natural product Fusicoccin A stabilizes many 14-3-3 PPIs but is not amenable for use in SAR studies, motivating the search for more drug-like chemical matter. However, drug-like 14-3-3 PPI stabilizers enabling such study have remained elusive. An X-ray crystal structure of a PPI in complex with an extremely low potency stabilizer uncovered an unexpected non-protein interacting, ligand-chelated Mg 2+ leading to the discovery of metal ion-dependent 14-3-3 PPI stabilization potency. This originates from a novel chelation-controlled bioactive conformation stabilization effect. Metal chelation has been associated with pan-assay interference compounds (PAINS) and frequent hitter behavior, but chelation can evidently also lead to true potency gains and find use as a medicinal chemistry strategy to guide compound optimization. To demonstrate this, we exploited the effect to design the first potent, selective and drug-like 14-3-3 PPI stabilizers.
Development of antibacterial compounds that block evolutionary pathways to resistance
Authors: Yanmin Zhang, Sourav Choudhury, Joao Rodrigues, Eugene Shakhnovich
Date: 2020-11-02 00:00:00

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC50 50-75 micromoles against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3 resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC50) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which block evolutionary escape routes in pathogens.
Computation of single-cell metabolite distributions using mixture models
Authors: Mona K Tonn, Philipp Thomas, Mauricio Barahona, Diego A Oyarzún
Date: 2020-10-07 00:00:00

Metabolic heterogeneity is widely recognised as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.
Molecular dynamics studies of interactions between Arg9(nona-arginine) and a DOPC/DOPG(4:1) membrane
Authors: Seungho Choe
Date: 2020-06-03 00:00:00

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.
Comparative Roles of Charge, $π$ and Hydrophobic Interactions in Sequence-Dependent Phase Separation of Intrinsically Disordered Proteins
Authors: Suman Das, Yi-Hsuan Lin, Robert M. Vernon, Julie D. Forman-Kay, Hue Sun Chan
Date: 2020-05-14 00:00:00

Endeavoring toward a transferable, predictive coarse-grained explicit-chain model for biomolecular condensates underlain by liquid-liquid phase separation (LLPS), we conducted multiple-chain simulations of the N-terminal intrinsically disordered region (IDR) of DEAD-box helicase Ddx4, as a test case, to assess the roles of electrostatic, hydrophobic, cation-$\pi$, and aromatic interactions in amino acid sequence-dependent LLPS. We evaluated 3 residue-residue interaction schemes with a shared electrostatic potential. Neither a common hydrophobicity scheme nor one augmented with arginine/lysine-aromatic cation-$\pi$ interactions consistently accounted for the experimental LLPS data on the wildtype, a charge-scrambled, an FtoA, and an RtoK mutant of Ddx4 IDR. In contrast, interactions based on contact statistics among folded globular protein structures reproduce the overall experimental trend, including that the RtoK mutant has a much diminished LLPS propensity. Consistency between simulation and LLPS experiment was also found for RtoK mutants of P-granule protein LAF-1, underscoring that, to a degree, the important LLPS-driving $\pi$-related interactions are embodied in classical statistical potentials. Further elucidation will be necessary, however, especially of phenylalanine's role in condensate assembly because experiments on FtoA and YtoF mutants suggest that LLPS-driving phenylalanine interactions are significantly weaker than those posited by common statistical potentials. Protein-protein electrostatic interactions are modulated by relative permittivity, which depends on protein concentration. Analytical theory suggests that this dependence entails enhanced inter-protein interactions in the condensed phase but more favorable protein-solvent interactions in the dilute phase. The opposing trends lead to a modest overall impact on LLPS.
Computational tools for drawing, building and displaying carbohydrates: a visual guide
Authors: Kanhaya Lal, Rafael Bermeo, Serge Pérez
Date: 2020-10-06 00:00:00

Drawing and visualisation of molecular structures are some of the most common tasks carried out in structural glycobiology, typically using various software. In this perspective article, we outline developments in the computational tools for the sketching, visualisation and modelling of glycans. The article also provides details on the standard representation of glycans, and glycoconjugates, which helps the communication of structure details within the scientific community. We highlight the comparative analysis of the available tools which could help researchers to perform various tasks related to structure representation and model building of glycans. These tools can be useful for glycobiologists or any researcher looking for a ready to use, simple program for the sketching or building of glycans.
Machine learning a model for RNA structure prediction
Authors: Nicola Calonaci, Alisha Jones, Francesca Cuturello, Michael Sattler, Giovanni Bussi
Date: 2020-04-01 00:00:00
Tasks: BIG-bench Machine Learning, model, Prediction

RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on computing the partition function of folding ensembles, and can thus estimate minimum free-energy structures and ensemble populations. These models sometimes fail in identifying native structures unless complemented by auxiliary experimental data. Here, we build a set of models that combine thermodynamic parameters, chemical probing data (DMS, SHAPE), and co-evolutionary data (Direct Coupling Analysis, DCA) through a network that outputs perturbations to the ensemble free energy. Perturbations are trained to increase the ensemble populations of a representative set of known native RNA structures. In the chemical probing nodes of the network, a convolutional window combines neighboring reactivities, enlightening their structural information content and the contribution of local conformational ensembles. Regularization is used to limit overfitting and improve transferability. The most transferable model is selected through a cross-validation strategy that estimates the performance of models on systems on which they are not trained. With the selected model we obtain increased ensemble populations for native structures and more accurate predictions in an independent validation set. The flexibility of the approach allows the model to be easily retrained and adapted to incorporate arbitrary experimental information.
Transients generate memory and break hyperbolicity in stochastic enzymatic networks
Authors: Ashutosh Kumar, R. Adhikari, Arti Dua
Date: 2020-10-02 00:00:00

The hyperbolic dependence of catalytic rate on substrate concentration is a classical result in enzyme kinetics, quantified by the celebrated Michaelis-Menten equation. The ubiquity of this relation in diverse chemical and biological contexts has recently been rationalized by a graph-theoretic analysis of deterministic reaction networks. Experiments, however, have revealed that "molecular noise" - intrinsic stochasticity at the molecular scale - leads to significant deviations from classical results and to unexpected effects like "molecular memory", i.e., the breakdown of statistical independence between turnover events. Here we show, through a new method of analysis, that memory and non-hyperbolicity have a common source in an initial, and observably long, transient peculiar to stochastic reaction networks of multiple enzymes. Networks of single enzymes do not admit such transients. The transient yields, asymptotically, to a steady-state in which memory vanishes and hyperbolicity is recovered. We propose new statistical measures, defined in terms of turnover times, to distinguish between the transient and steady states and apply these to experimental data from a landmark experiment that first observed molecular memory in a single enzyme with multiple binding sites. Our study shows that catalysis at the molecular level with more than one enzyme always contains a non-classical regime and provides insight on how the classical limit is attained.
Review on DNA Strand Algebra and its Application
Authors: Mandrita Mondal, Kumar S. Ray
Date: 2019-03-04 00:00:00
Tasks: Automated Theorem Proving

Several technological limitations of traditional silicon based computing are leading towards the paradigm shift, from silicon to carbon, in computational world. Among the unconventional modes of computing evolved in past several decades, DNA computing has been considered to be quite promising in solving computational and reasoning problems by using DNA strands. Along with the sequential operations, the huge parallelism of DNA computing methodologies engaging numerous numbers of DNA strands induce the consideration of concurrent high-level formalisms. In this paper we have reviewed the algebraic explanation of concurrent DNA processes using DNA strand algebra, process calculus and DNA strand graph. We have demonstrated the application of syntax and semantics of the illustrated methodologies in the domains of reasoning and theorem proving with resolution refutation. Finally, we have presented DNA cryptography as one of the prominent areas for the future scope of research work where DNA strand algebra can be used as formal modelling tool to authenticate the security, logic and reasoning of the existing protocols.
Protein Folding/Unfolding Phenomenon is originated by Synchronization/Desynchronization of Instantaneously Induced Oscillating Dipoles. A Hypothesis
Authors: German Mino-Galaz
Date: 2020-09-04 00:00:00
Tasks: Protein Folding

Protein folding is a phenomenon that has been studied for about 50 years and still remains as an unsolved problem. The main feature of this process is that it occurs as an all or none process, so a protein, can jump directly between folded and unfolded states. It is proposed in this manuscript that the cooperative phenomena associated to protein folding has its origin in the synchronization oscillating phases of the instantaneously induced dipoles that gives rise to the van der Waals London dispersion interaction. When the oscillation of the induced dipoles is synchronized an enhanced interaction regime is triggered and the protein enters a into a folding process. The propagation of this regime throughout the molecular structure complete the folding process. If a desynchronizing strong enough perturbation is introduced at any component cooperative network the system enters in a weakened -- or even a repulsive -- van der Waals dispersion interaction regime that propagate itself throughout the structure, that triggers a phase transition that finally unfolds the protein.
Effective Model of Loop Extrusion Predicts Chromosomal Domains
Authors: M. Crippa, Y. Zhan, G. Tiana
Date: 2020-05-14 00:00:00
Tasks: Blocking, model, Position

An active loop-extrusion mechanism is regarded as the main out--of--equilibrium mechanism responsible for the structuring of megabase-sized domains in chromosomes. We developed a model to study the dynamics of the chromosome fibre by solving the kinetic equations associated with the motion of the extruder. By averaging out the position of the extruder along the chain, we build an effective equilibrium model capable of reproducing experimental contact maps based solely on the positions of extrusion--blocking proteins. We assessed the quality of the effective model using numerical simulations of chromosomal segments and comparing the results with explicit-extruder models and experimental data.
$αβ$ DCA method identifies unspecific binding but specific disruption of the group I intron by the StpA chaperone
Authors: Vladimir Reinharz, Tsvi Tlusty
Date: 2019-11-08 00:00:00

Chaperone protein - the most disordered among all protein groups - help RNAs fold into their functional structure by destabilizing misfolded configurations or stabilizing the functional ones. But disentangling the mechanism underlying RNA chaperoning is challenging, mostly due to inherent disorder of the chaperones and the transient nature of their interactions with RNA. In particular, it is unclear how specific the interactions are and what role is played by amino acid charge and polarity patterns. Here, we address these questions in the RNA chaperone StpA. We adapted direct coupling analysis (DCA) into the $\alpha\beta$DCA method that can treat in tandem sequences written in two alphabets, nucleotides and amino acids. With $\alpha\beta$DCA, we could analyze StpA-RNA interactions and show consistency with a previously proposed two-pronged mechanism: StpA disrupts specific positions in the group I intron while globally and loosely binding to the entire structure. Moreover, the interactions are strongly associated with the charge pattern: negatively charged regions in the destabilizing StpA N-terminal affect a few specific positions in the RNA, located in stems and in the pseudoknot. In contrast, positive regions in the C-terminal contain strongly coupled amino acids that promote non-specific or weakly-specific binding to the RNA. The present study opens new avenues to examine the functions of disordered proteins and to design disruptive proteins based on their charge patterns.