Biology subjects

Will, S.

Publications and source records attributed to Will, S..

6 recordsLinked to original sources

bioRxiv2026

Reversible peptide self-assembly enables sustained drug delivery with tuneable pharmacokinetics

Therapeutic peptides combine high target specificity with potent biological activity.1 However, treatment success is often limited by rapid clearance and the need for frequent injections.2, 3 This challenge is particularly acute for therapeutic peptides used in obesity, where clinical benefit must be balanced against dose-dependent adverse effects. In nature, these constraints are overcome by storing hormones as reversible fibrils,4 but pharmacokinetic control is essential for widespread adoption of bio-inspired self-assembled depots for therapeutic peptides. Here, we show that tuneable pharmacokinetics can be achieved and modelled by mapping the fundamental chemical parameters of reversibly self-assembly in vitro. We demonstrate this approach for the amylin analogue pramlintide. Amylin analogues are under development for the next generation of diabetes and obesity treatments, with improved mechanism of action e.g. preserving lean body mass.5-8 Pramlintide is an approved drug with a well-established safety profile, however, it has a comparable half-life to native amylin.8-12 In a pilot study, we achieve in vitro-in vivo correlation, increasing the half-life of pramlintide 20-82-fold in rats, while controlling burst release. These findings demonstrate that the optimisation of pharmacokinetics can be decoupled from peptide engineering, establishing a generalisable framework for generating long-acting peptide formulations by emulating native storage mechanisms.

biophysics↗

bioRxiv2023

Modeling Kinetics of RNA RNA Interactions on Direct Paths

MotivationRNA-RNA interactions play essential roles in gene regulation and are controlled by both thermodynamics and kinetics. State-of-the-art tools predict thermodynamically optimal RNA-RNA interactions, but they neglect kinetic effects. While folding kinetics of single RNAs have been successfully modeled using transition systems between conformations, analogous approaches for RNA-RNA interactions quickly lead to infeasibly large systems. Novel models with controlled system size are required to overcome these limitations and enable computational analysis of RNA- RNA interaction kinetics. Such methods have the potential to improve our understanding of the governing principles of RNA-RNA interaction formation and improve target prediction tools. ResultsWe propose a targeted interaction kinetics model that focuses on a given candidate interaction structure, and further limits the state space by considering only direct paths. This allows us to describe interaction formation as a Markov process on the limited state space and to study which properties are relevant for interaction formation. By comparing experimentally confirmed sRNA-mRNA interactions in E. coli with a randomized background, we show that native interactions are indeed kinetically favored and identify key features, such as seed accessibility and folding energy barrier. Using a machine learning classifier, we identified most-informative combinations of interaction features with respect to the kinetic behavior of native RNA-RNA interactions. Our kinetics model enables the efficient computation of various features that can be used to evaluate genome-wide target predictions by kinetic criteria. Beyond immediate practical improvements, our results contribute to long-standing general questions such as the influence of initial contact site accessibility. Availability and implementationRRIkinDP is available as free software on GitHub at https://github.com/mwaldl/RRIkinDP. Contactmaria@tbi.univie.ac.at

bioinformatics↗

bioRxiv2023

SparseRNAFolD: Sparse RNA pseudoknot-free Folding including Dangles

MotivationComputational RNA secondary structure prediction by free energy minimization is indispensable for analyzing structural RNAs and their interactions. These methods find the structure with the minimum free energy (MFE) among exponentially many possible structures and have a restrictive time and space complexity (O(n3) time and O(n2) space for pseudoknot-free structures) for longer RNA sequences. Furthermore, accurate free energy calculations including dangles contributions can be difficult and costly to implement, particularly when optimizing for time and space requirements. ResultsHere we introduce a fast and efficient sparsified MFE pseudoknot-free structure prediction algorithm, SparseRNAFolD, that utilizes an accurate energy model that accounts for dangles contributions. While sparsification technique was previously employed to improve time and space complexity of a pseudoknot-free structure prediction method with a realistic energy model, SparseMFEFold, it was not extended to include dangles contributions due to complexity of computation. This may be at the cost of prediction accuracy. In this work, we compare three different sparsified implementations for dangles contributions and provide pros and cons of each method. As well, we compare our algorithm to LinearFold, a linear time and space algorithm, where we find in practice, SparseRNAFolD has lower memory consumption across all lengths of sequence and a faster time for lengths up to 1000 bases. ConclusionOur SparseRNAFolD algorithm is an MFE-based algorithm that guarantees optimality of result and employs the most general energy model including dangles contributions. We provide basis for applying dangles to sparsified recursion in a pseudoknot-free model which has the ability to be extended to pseudoknots. AvailabilitySparseRNAFolDs algorithm and detailed results are available at https://github.com/mateog4712/SparseRNAFolD.

bioinformatics↗

bioRxiv2023

CParty: Conditional partition function for density-2RNA pseudoknots

Biologically relevant RNA secondary structures are routinely predicted by efficient dynamic programming algorithms that minimize their free energy. Starting from such algorithms, one can devise partition function algorithms, which enable stochastic perspectives on RNA structure ensembles. As most prominent example McCaskills partition function algorithm is derived from pseudoknot-free energy minimization. While this algorithm became hugely successful for the stochastic analysis of pseudoknot-free RNA structure, as of yet there exists only one pseudoknotted partition function implementation, which covers only simple pseudoknots and comes with a borderline-prohibitive complexity of O(n5) in the RNA length n. In this article, we develop a partition function algorithm corresponding to the hierarchical pseudoknot prediction of HFold, which performs exact optimization in a realistic pseudoknot energy model. In consequence, our algorithm CParty carries over HFolds advantages over classical pseudoknot prediction to stochastic analysis. In only cubic time, it computes the hierarchically constrained partition function over pseudoknotted density-2 structures G {cup} G', composed of pseudoknot-free parts G and G', where G is given. Thus, it follows the common hypothesis of hierarchical pseudoknot formation, where pseudoknots form as tertiary contacts only after a first pseudoknot-free core G. Like HFold, CParty is very efficient, achieving the low complexity of the pseudoknot-free algorithm. Finally, by computing pseudoknotted ensemble energies, we unveil kinetics features of a therapeutic target in SARS-CoV-2. AvailibilityCParty is available at https://github.com/HosnaJabbari/CParty.

bioinformatics↗

bioRxiv2019

Bi-Alignments as Models of Incongruent Evolution of RNA Sequence and Structure

RNA molecules may experience independent selection pressures on their sequence and (secondary) structure. Structural features then may be preserved without maintaining their exact position along the sequence. In such cases, corresponding base pairs are no longer formed by homologous bases, leading to the incongruent evolutionary conservation of sequence and structure. In order to model this phenomenon, we introduce bi-alignments as a superposition of two alignments: one modeling sequence homology; the other, structural homology. We show that under natural assumptions on the scoring functions, bi-alignments form a special case of 4-way alignments, in which the incongruencies are measured as indels in the pairwise alignment of the two alignment copies. A preliminary survey of the Rfam database suggests that incongruent evolution of RNAs is not a very rare phenomenon.\n\nAvailabilityOur software is freely available at https://github.com/s-will/BiAlign

bioinformatics↗

bioRxiv2019

Renal and Renal Sinus Fat Volumes as Quantified by Magnetic Resonance Imaging in Subjects with Prediabetes, Diabetes, and Normal Glucose Tolerance.

PurposeThe aim of this study was to assess the volume of the respective kidney compartments with particular interest in renal sinus fat as an early biomarker and to compare the distribution between individuals with normal glucose levels and individuals with prediabetes and diabetes.\n\nMaterial and MethodsThe sample comprised N = 366 participants who were either normoglycemic (N = 230), had prediabetes (N = 87) or diabetes (N =49), as determined by Oral Glucose Tolerance Test. Other covariates were obtained by standardized measurements and interviews. Whole-body MR measurements were performed on a 3 Tesla scanner. For assessment of the kidneys, a coronal T1w dual-echo Dixon and a coronal T2w single shot fast spin echo sequence were employed. Stepwise semi-automated segmentation of the kidneys on the Dixon-sequences was based on thresholding and geometric assumptions generating volumes for the kidneys and sinus fat. Inter- and intra-reader variability were determined on a subset of 40 subjects. Associations between glycemic status and renal volumes were evaluated by linear regression models, adjusted for other potential confounding variables. Furthermore, the association of renal volumes with visceral adipose tissue was assessed by linear regression models and Pearsons correlation coefficient.\n\nResultsRenal volume, renal sinus volume and renal sinus fat increased gradually from normoglycemic controls to individuals with prediabetes to individuals with diabetes (renal volume: 280.3{+/-}64.7 ml vs 303.7{+/-}67.4 ml vs 320.6{+/-}77.7ml, respectively, p < 0.001). After adjustment for age and sex, prediabetes and diabetes were significantly associated to increased renal volume, sinus volume (e.g. {beta}prediabetes = 10.1, 95% CI: [6.5, 13.7]; p<0.01, {beta}Diabetes = 11.86, 95% CI: [7.2, 16.5]; p<0.01) and sinus fat (e.g. {beta}prediabetes = 7.13, 95% CI: [4.5, 9.8]; p<0.001, {beta}Diabetes = 7.34, 95% CI: [4.0, 10.7]; p<0.001). Associations attenuated after adjustment for additional confounders were only significant for prediabetes and sinus volume ({beta} =4.0 95% CI [0.4, 7.6]; p<0.05). Hypertension was significantly associated with increased sinus volume ({beta} = 3.7, 95% CI: [0.4, 6.9; p<0.05]) and absolute sinus fat volume ({beta} = 3.0, 95%CI: [0.7, 5.2]; p<0.05). GFR and all renal volumes were significantly associated as well as urine albumin levels and renal sinus volume ({beta} = 1.6, 95% CI: [0.2, 3.0]; p<0.05). There was a highly significant association between VAT and the absolute sinus fat volume ({beta} = 2.75, 95% CI: [2.3, 3.2]; p<0.01).\n\nConclusionRenal volume and particularly renal sinus fat volume already increases significantly in prediabetic subjects. There is a significant association between VAT and renal sinus fat, suggesting that there are metabolic interactions between these perivascular fat compartments.

epidemiology↗