Computational investigations of structure probing experiments for RNA structure prediction

Ribonucleic acids (RNA) transcripts, and in particular non-coding RNAs, play fundamental roles in cellular metabolism, as they are involved in protein synthesis, catalysis, and regulation of gene expression. In some cases, an RNA\u2019s biological function is mostly dependent on a specific active conformation, making the identification of this single stable structure crucial to identify the role of the RNA and the relationships between its mutations and diseases. On the other hand, RNAs are often found in a dynamic equilibrium of multiple interconverting conformations, that is necessary to regulate their functional activity. In these cases it becomes fundamental to gain knowledge of RNA\u2019s structural ensembles, in order to fully determine its mechanism of action. The current structure determination techniques, both for single-state models such as X-ray crystallography, and for multi-state models such as nuclear magnetic resonance and single-molecule methods, despite proving accurate and reliable in many cases, are extremely slow and costly. In contrast, chemical probing is a class of experimental techniques that provide structural information at single-nucleotide resolution at significantly lower costs in terms of time and required infrastructures. In particular, selective 2\u2032 hydroxyl acylation analyzed via primer extension (SHAPE) has proved a valid chemical mapping technique to probe RNA structure even in vivo. This thesis reports a systematic investi- gation of chemical probing experiments based on two different approaches. The first approach, presented in Chapter 2, relies on machine-learning techniques to optimize a model for mapping experimental data into structural information. The model relies also on co-evolutionary data, in the form of direct coupling analysis (DCA) couplings. The inclusion of this kind of data is chosen in the same spirit of reducing the costs of structure probing, as co-evolutionary analysis relies only on sequencing techniques. The resulting model is proposed as a candidate standard tool for prediction of RNA secondary structure, and some insight in the mechanism of chemical probing is gained by interpreting back its features. Importantly, this work has been developed in the per- spective of building a framework for future refinement and improvement. In this spirit, all the used data and scripts are available at https://github.com/bussilab/shape-dca-data, and the model can be easily retrained and adapted to incorporate arbitrary experimental informa- tion. As the interpretation of the model features suggests the possible emergence of cooperative effects involving RNA nucleotides interacting with SHAPE reagents, a second approach based on Molecular Dynamics simulations is proposed to investigate this hypothesis. The results, along with an originally developed methodology to analyse Molecular Dynamics simulations at variable number of particles, are presented in Chapter 3

Elasticity and yielding in model polymer glasses

The goal of this thesis is to characterize the mechanical response to deforma- tions of a coarse-grained polymer model in the glassy state. In particular, we want to relate the mechanical behaviour to connectivity. To this aim MD simulations are performed with systematic variation of bond length and chain stiffness, which are the interaction parameters determining the connectivity of the model. Mechanical deformation is simulated via the Athermal Quasi-Static (AQS) procedure at zero temperature. We show that connectivity does not directly affect the elastic shear modulus. However, since the latter depend on the morphology of the solid state, con- nectivity still plays a role as it determines the solidification behaviour of the model. In the plastic regime, we show that the stress at yielding depends on the connectivity- related parameters of the model. Moreover, we find a correlation between the elastic shear modulus and the stress at yielding, compatible with experimental data reported in the literature

Toward empirical force fields that match experimental observables

Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics simulations on larger systems achieving ergodic sampling is paving the way to directly using such simulations along with solution experiments obtained on macromolecular systems. Recently, a number of methods have been introduced to automatize this approach. Here, we review these methods, highlight their relationship with machine learning methods, and discuss the open challenges in the field

Effect of nematic ordering on the elasticity and yielding in disordered polymeric solids

The relation between elasticity and yielding is investigated in a model polymer solid by Molecular-Dynamics simulations. By changing the bending stiffness of the chain and the bond length, semicrystalline and disordered glassy polymers — both with bond disorder — as well as nematic glassy polymers with bond ordering are obtained. It is found that in systems with bond disorder the ratio tau_Y/G between the shear yield strength tau_Y and the shear modulus G is close to the universal value of the atomic metallic glasses. The increase of the local nematic order in glasses leads to the increase of the shear modulus and the decrease of the shear yield strength, as observed in experiments on nematic thermosets. A tentative explanation of the subsequent reduction of the ratio tau_Y/G in terms of the distributions of the per-monomer stress is offered

Machine learning a model for RNA structure 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 and SHAPE) and co-evolutionary data (direct coupling analysis) 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

Preprint: Integrated quality control of allele-specific copy numbers, mutations and tumour purity from cancer whole genome sequencing assays

Cancer genomes contain thousands of somatic point mutations, chromosome copy alterations and more complex structural variants, which contribute to tumour growth and therapy response. Whole genome sequencing is a well established approach for somatic variant identification, but its broad application comes with complications, particularly in how proposed calls are quality assessed. To address this issue, we present CNAqc, a quantitative framework to quality control somatic mutations and allele-specific copy numbers, both in clonal and subclonal settings while accounting for variations in tumour purity, as commonly seen in bulk sampling. We test the model via extensive simulations, validate it using low-pass single-cell data, and apply it to 2778 single-sample PCAWG whole-genomes, 10 in-house multi-region whole-genomes and 48 TCGA whole-exomes. CNAqc is compatible with common bioinformatic pipelines and designed to support automated parameterization processes that are crucial in the era of large-scale whole genome sequencing

Clonal KEAP1 mutations with loss of heterozygosity share reduced immunotherapy efficacy and low immune cell infiltration in lung adenocarcinoma

KEAP1 mutations have been associated with reduced survival in lung adenocarcinoma (LUAD) patients treated with immune checkpoint inhibitors (ICIs), particularly in the presence of STK11/KRAS alterations. We hypothesized that, beyond co-occurring genomic events, clonality prediction may help identify deleterious KEAP1 mutations and their counterparts with retained sensitivity to ICIs