Biology subjects

Flamm, C.

Publications and source records attributed to Flamm, C..

6 recordsLinked to original sources

bioRxiv2025

Computational Approaches in Chemical Space Exploration for Carbon Fixation Pathways

Chemical space exploration is an important part of chemistry and biology, enabling the discovery and optimization of metabolic pathways, advancing synthetic metabolic functions, and understanding biochemical network evolution. We use a graph-based computational approach implemented in the cheminformatics software MOD, integrated with Integer Linear Programming (ILP) optimization, to systematically search chemical spaces. This approach allows for flexible and targeted queries, including identification of autocatalytic cycles, thermodynamic considerations, and discovery of novel enzymatic cascades. Specifically, we explore the chemical space of natural and artificial carbon fixation pathways defined from relevant enzyme reactions. By applying different optimization criteria, we identify new varieties and recombinations of natural autocatalytic pathways, and compare the properties of the pathways. This work highlights the versatility of graph-based cheminformatics for the purpose of chemical space exploration and artificial pathway design. Potential applications of this framework extend to carbon capture technologies, improved agricultural yields, and value-added chemical production, advancing efforts to address global sustainability challenges.

systems biology↗

bioRxiv2024

KinPFN: Bayesian Approximation of RNA FoldingKinetics using Prior-Data Fitted Networks

AO_SCPLOWBSTRACTC_SCPLOWRNA is a dynamic biomolecule crucial for cellular regulation, with its function largely determined by its folding into complex structures, while misfolding can lead to multifaceted biological sequelae. During the folding process, RNA traverses through a series of intermediate structural states, with each transition occurring at variable rates that collectively influence the time required to reach the functional form. Understanding these folding kinetics is vital for predicting RNA behavior and optimizing applications in synthetic biology and drug discovery. While in silico kinetic RNA folding simulators are often computationally intensive and time-consuming, accurate approximations of the folding times can already be very informative to assess the efficiency of the folding process. In this work, we present KinPFN, a novel approach that leverages prior-data fitted networks to directly model the posterior predictive distribution of RNA folding times. By training on synthetic data representing arbitrary prior folding times, KinPFN efficiently approximates the cumulative distribution function of RNA folding times in a single forward pass, given only a few initial folding time examples. Our method offers a modular extension to existing RNA kinetics algorithms, promising significant computational speed-ups orders of magnitude faster, while achieving comparable results. We showcase the effectiveness of KinPFN through extensive evaluations and real-world case studies, demonstrating its potential for RNA folding kinetics analysis, its practical relevance, and generalization to other biological data.

bioinformatics↗

bioRxiv2021

Caveats to deep learning approaches to RNA secondary structure prediction

Machine learning (ML) and in particular deep learning techniques have gained popularity for predicting structures from biopolymer sequences. An interesting case is the prediction of RNA secondary structures, where well established biophysics based methods exist. The accuracy of these classical methods is limited due to lack of experimental parameters and certain simplifying assumptions and has seen little improvement over the last decade. This makes RNA folding an attractive target for machine learning and consequently several deep learning models have been proposed in recent years. However, for ML approaches to be competitive for de-novo structure prediction, the models must not just demonstrate good phenomenological fits, but be able to learn a (complex) biophysical model. In this contribution we discuss limitations of current approaches, in particular due to biases in the training data. Furthermore, we propose to study capabilities and limitations of ML models by first applying them on synthetic data (obtained from a simplified biophysical model) that can be generated in arbitrary amounts and where all biases can be controlled. We assume that a deep learning model that performs well on these synthetic, would also perform well on real data, and vice versa. We apply this idea by testing several ML models of varying complexity. Finally, we show that the best models are capable of capturing many, but not all, properties of RNA secondary structures. Most severely, the number of predicted base pairs scales quadratically with sequence length, even though a secondary structure can only accommodate a linear number of pairs.

bioinformatics↗

bioRxiv2021

4-phenylbutyrate restored GABA uptake and reduced seizures in SLC6A1 variants-mediated disorders

We have previously studied the molecular mechanisms of solute carrier family 6 member 1 (SLC6A1) associated with a continuum of neurodevelopmental disorders, including various epilepsy syndromes, autism, and intellectual disability. Based on functional assays of variants in a large cohort with heterogenous clinical phenotypes, we conclude that partial or complete loss of GABA uptake function in the mutant GAT-1 is the primary etiology as identified in GABAA receptor mutation-mediated epilepsy and in cystic fibrosis. Importantly, we identified that there are common patterns of the mutant protein trafficking from biogenesis, oligomerization, glycosylation, and translocation to the cell membrane across variants with the conservation of this process across cell types. Conversely any approach to facilitate membrane trafficking would increase presence of the functional protein in the targeted destination in all involved cells. PBA is an FDA-approved drug for pediatric use and is orally bioavailable so it can be quickly translated to patient use. It has been demonstrated that PBA can correct protein misfolding, reduce ER stress, and attenuate unfolded protein response in neurodegenerative diseases, it has also showed promise in treatment of cystic fibrosis. The common cellular mechanisms shared by the mutant GAT-1 and the mutant cystic fibrosis transmembrane conductance regulator led us to test if PBA and other pharmaco-chaperones could be a potential treatment option for SLC6A1 mutations. We examined the impact of PBA and other small molecules in a library of variants and in cell and knockin mouse models. Because of the critical role of astrocytic GAT-1 deficit in seizures, we focused on astrocytes, and demonstrated that the existence of the mutant GAT-1 retained the wildtype GAT-1, suggesting aberrant protein oligomerization and trafficking caused by the mutant GAT-1. PBA increased GABA uptake in both mouse and human astrocytes bearing the mutations. Importantly, PBA increased GAT-1 expression and suppressed spike wave discharges (SWDS) in the heterozygous knockin mice. Although the detailed mechanisms of action for PBA are ambiguous, it is likely that PBA can facilitate the forward trafficking of the wildtype GAT-1 favoring over the mutant GAT-1, thus increasing GABA uptake. Since all patients with SLC6A1 mutations are heterozygous and carry one wildtype functional allele, this suggests a great opportunity for treatment development by leveraging the endogenous protein trafficking pathway to promote forward trafficking of the wildtype in combination with enhancing the disposal of the mutant allele as treatment mode. The study opens a novel avenue of treatment development for genetic epilepsy via drug repurposing.

neuroscience↗

bioRxiv2018

Efficient computation of co-transcriptional RNA-ligand interaction dynamics

Riboswitches form an abundant class of cis-regulatory RNA elements that mediate gene expression by binding a small metabolite. For synthetic biology applications, they are becoming cheap and accessible systems for selectively triggering transcription or translation of downstream genes. Many riboswitches are kinetically controlled, hence knowledge of their co-transcriptional mechanisms is essential. We present here an efficient implementation for analyzing co-transcriptional RNA-ligand interaction dynamics. This approach allows for the first time to model concentration-dependent metabolite binding/unbinding kinetics. We exemplify this novel approach by means of the recently studied I-A 2-deoxyguanosine (2dG)-sensing riboswitch from Mesoplasma florum.

bioinformatics↗

bioRxiv2018

In silico design of ligand triggered RNA switches

This contribution sketches a work flow to design an RNA switch that is able to adapt two structural conformations in a ligand-dependent way. A well characterized RNA aptamer, i. e., knowing its Kd and adaptive structural features, is an essential ingredient of the described design process. We exemplify the principles using the well-known theophylline aptamer throughout this work. The aptamer in its ligand-binding competent structure represents one structural conformation of the switch while an alternative fold that disrupts the binding-competent structure forms the other conformation. To keep it simple we do not incorporate any regulatory mechanism to control transcription or translation. We elucidate a commonly used design process by explicitly dissecting and explaining the necessary steps in detail. We developed a novel objective function which specifies the mechanistics of this simple, ligand-triggered riboswitch and describe an extensive in silico analysis pipeline to evaluate important kinetic properties of the designed sequences. This protocol and the developed software can be easily extended or adapted to fit novel design scenarios and thus can serve as a template for future needs.

bioinformatics↗