Biology subjects

Ahnert, S. E.

Publications and source records attributed to Ahnert, S. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Exploring the large-scale properties of a protein secondary structure genotype-to-phenotype map

We perform a large-scale computational characterization of the map of protein primary to secondary structure using an AVR3a class protein effector domain from the plant pathogen P. palmivora as a case study. We formulate a modified site-scanning approach for exploring the neutral component of secondary structure phenotypes based on predictions from the machine-learning algorithm Porter 5 and apply it to the AVR3a phenotype. We predict a set of sensitive sites within the effector domain that are generally located at or near the boundaries of structured regions, with restrictions on the possible amino acid residues at these sites dictated by the secondary structure type that they participate in within the WT. We characterize a set of mutated phenotypes derived through the exploration of the neutral component of the WT effector domain, selecting them so that they span a range including both very rarely and very commonly seen secondary structures, and that they include both secondary structures nearly identical to the WT and ones far removed from it. We find that all these diverse phenotypes have an estimated robustness of the same order as that of the WT, and that the robustness scales logarithmically phenotype frequency, as seen in other genotype-to-phenotype maps. Furthermore, we observe that the dependence of the estimated phenotype frequency on the Kolmogorov complexity indicates simplicity bias in the protein secondary structure map.

biophysics↗

bioRxiv2026

FlowFI: an interactive graphical software package for bespoke design of imaging parameters in flow cytometry to explore morphological diversity in bone marrow megakaryocytes

BackgroundImaging flow cytometry (IFC) provides a high quantity of single-cell morphological data, yet the field lacks open access tools for designing interpretable, bespoke parameters. In particular, rare and atypical cell populations where well annotated data is limited, are negatively affected. ResultsWe present Flow cytometry Feature Importance (FlowFI), an open-source graphical software for bespoke image parameter design and analysis. FlowFI provides a suite of image parameter options combining data across multiple channels and markers, tailored digital noise reduction (reducing noise resulting from common flow cytometry ultra-high image acquisition modalities), and a scalable, unsupervised feature selection pipeline that allows experimentalists to refine image-derived parameters iteratively, with a novel ensemble subsampling approach that provides robust feature importance scoring. We validated FlowFI using data from a rare and heterogenous bone marrow cell type, megakaryocytes, demonstrating that the tool can successfully identify novel, discriminatory morphological features to improve the purity of selected cell populations and gating strategy. ConclusionFlowFIs core functionalities are interacted with through an intuitive user interface for researchers with options to export data directly to common image and flow cytometry software formats. With this in mind, FlowFI offers a scalable way to both feature design, and feature refinement using a range of approaches to manifold learning, augmented by a data efficient bootstrap subsampling approach for unsupervised parameter recommendations in the big data regime. The software also introduces a new feature selection measures based on common manifold learning methods in the space inspired by the Uniform Manifold Approximation and Projection (UMAP) algorithm and finds performance comparable to existing methods. FlowFI provides a versatile testing ground for future developments in broad and dynamically developing areas of research including single cell analysis, label-free sorting and intra- and inter-cellular interaction analysis, while ensuring interoperability with current research workflows. Desktop installation options as well as detailed documentation can be found at https://github.com/EarlhamInst/FlowFI

cell biology↗

bioRxiv2026

Comparing Random and Natural RNA Boltzmann Ensembles

A morphospace is an abstract space of theoretically possible biological traits, shapes, or property values. It is interesting to explore which parts of a morphospace life occupies, as compared to those parts which could be occupied, but are not. Comparing random and natural non-coding (nc) RNA secondary structures is an established approach to studying morphospace occupation for RNA structures. Most earlier studies have focused on the minimum free energy (MFE) structure, while relatively few have looked at the Boltzmann distribution, describing the ensemble of energetically suboptimal RNA folds. These suboptimal structures may have important roles and functions, and hence should be examined carefully. Here we compare random and natural ncRNA in terms of their Boltzmann distributions, finding that natural RNA tend to have very similar profiles to random RNA, with the main difference being that natural RNA are slightly more energetically stable, except for very short sequences (20 to 30 nucleotides) which appear to be equally or slightly less stable. We infer that natural ncRNA and random RNA occupy similar parts of the morphospace, indicating that the biophysics of the genotype-phenotype map largely determines the ensemble properties of ncRNA.

biophysics↗

bioRxiv2026

Decoupling Topology from Geometry: Detecting Large-Scale Conformational Changes via Conformational Scanning

Protein function is fundamentally driven by structural dynamics, yet the majority of structural bioinformatics treats proteins as static rigid bodies. While Molecular Dynamics (MD) simulations attempt to capture these motions, they are computationally prohibitive for exploring large-scale conformational changes, such as domain movements or allostery, which occur on timescales often inaccessible to standard simulation. However, the Protein Data Bank (PDB) contains a latent wealth of dynamic information in the form of redundant entries proteins solved in multiple distinct conformational states. Detecting these "shape-shifting" pairs remains challenging because standard structural alignment algorithms (e.g., TM-align) rely on rigid-body superposition, which fails when substantial geometric rearrangement occurs. In this study, we introduce a high-throughput method to systematically mine the PDB for proteins that share identical topology but exhibit divergent tertiary conformations. By utilizing a coarse-grained Secondary Structure Element (SSE) representation, we decouple topological connectivity from geometric rigidity, allowing for the detection of conformational homologues that share low global structural similarity despite high predicted structural similarity. We applied this "conformational scanning" across the entire RCSB database, identifying a curated dataset of proteins undergoing significant structural rearrangements. This work bridges the gap between static structural data and dynamic function, providing a critical "ground truth" dataset for benchmarking data-driven protein design and checking the plausibility of generative structure models.

bioinformatics↗

bioRxiv2026

Millisecond Prediction of Protein Contact Maps from Amino AcidSequences

Protein structure prediction typically outputs static coordinates, often obscuring the underlying physical principles and conformational flexibility. In this work, we present a coarse-grained generative framework to recover the Circuit Topology (CT) of proteins using Generative Flow Matching. We represent protein architecture using highly compressed Secondary Structure Elements (SSEs), reducing the sequence length to roughly 1/13 of the original amino acid sequence. We show that this minimal representation captures the essential "topological fingerprint" required to determine the global fold. By employing a joint-prediction head, our model simultaneously generates contact probabilities and asymmetric topological features, achieving a mean F1 score of 0.822 at the SSE level. Notably, our results demonstrate a counter-intuitive robustness in capturing long-range interactions, suggesting that global topology acts as a stable constraint compared to local residue packing. Furthermore, we show that these coarse-grained predictions can be mapped back to residue-level contact maps with sub-helical precision, yielding a mean alignment error of 2.69 residues. The probabilistic nature of the flow model effectively separates the stable structural signal of the folding core from flexible regions, providing a physically interpretable view of the proteins conformational ensemble. This pipeline is extremely fast, capable of completing a contact map prediction from amino acid sequence in an average of 110 milliseconds on a single GPU. These ultra-fast and accurate predictions provide a valuable tool for identifying conserved protein folding cores, facilitating the exploration of the protein structural genotype-phenotype (GP) map through large-scale sampling of mutants with highly similar folding cores.

bioinformatics↗

bioRxiv2026

Mapping protein neutral networks from predicted secondary structure

Protein evolution can be understood as movement through a genotype- phenotype (GP) map, where genetic variation is constrained by the mapping from sequence to structure. While RNA GP maps have been extensively characterised, equivalent maps for proteins remain largely unexplored due to the complexity of folding. Here, we construct an operational GP map for influenza hemagglutinin (HA) using predicted secondary structure as a coarse-grained phenotype. Combining site-scanning with exhaustive local neighbourhood enumeration, we estimate neutral component (NC) sizes, mutational robustness, and local connectivity patterns across representative HA variants. We observe strong phenotypic bias, with NC sizes spanning orders of magnitude. Robustness increases with estimated NC size, but far more weakly than in RNA, consistent with each NC occupying a vanishing fraction of amino acid sequence space. Local neutral neighbourhood graphs reveal dense, position-centric clusters with limited overlap, suggesting heterogeneous and weakly percolating connectivity. Single-step structural transitions are biased toward structurally similar phenotypes, so that accessible novelty is largely incremental and redundant. Together, these results provide a tractable empirical framework for protein GP map analysis and suggest that, in HA, neutrality is locally structured yet globally constrained, limiting long-range structural evolvability relative to RNA systems.

evolutionary biology↗

bioRxiv2025

Phenotypic cliffs in the RNA genotype-phenotype map

Point mutations of a genotype can leave the phenotype unchanged, or change it, in some cases radically. The extent of this phenotypic change can critically impact fitness. To investigate the range of possible phenotypic changes that result from point mutations, we analyse the structure of the well-established RNA genotype-phenotype (GP) map for sequences of length 12 by developing a general phenotypic distance framework. Our analysis reveals that phenotypes are more likely to be surrounded by similar phenotypes than would be expected by chance. Furthermore, we see that generalised versions of the GP map metrics of frequency, robustness, and evolvability that take phenotypic distance into account still exhibit the same fundamental structural relationships that are observed in many GP maps. We also develop site-specific quantities for robustness, evolvability, and accessible phenotypic distance, which reveal a key insight: RNA secondary structure (SS) sites that are more likely to be neutral and access fewer new phenotypes tend to produce larger changes in phenotype when a change does occur. Robust sites therefore produce cliffs in the landscape (flat in some directions, steep in others) whereas non-robust sites give rise to more gently undulating landscapes.

biophysics↗

bioRxiv2025

Detection of protein symmetry and structural rearrangements using secondary structure elements

Many proteins exhibit a degree of internal symmetry in their tertiary structure, including circular permutations. These characteristics play an important role in terms of the functional robustness of proteins against mutations, and are pivotal for the study of protein function and evolution. Proteins exhibiting internal symmetry often demonstrate enhanced functional benefits, such as increased binding affinity due to repeated structural motifs, and evolutionary advantages that may result from gene duplication or fusion events, leading to more robust and adaptable molecular architectures. Similarly, circular permutations have been linked to improved catalytic activity and enhanced thermostability, opening new avenues in the design of enzymes and other functional proteins. In this study, we introduce a novel computational pipeline that leverages secondary structure elements (SSEs) as a compressed yet informative representation of protein structure to detect both symmetry and structural rearrangements effectively. Our method shows strong advantages against the Combinatorial Extension with Circular Permutations (CECP) for computational cost and identifies 17,130 circularly related proteins. Additionally, it detects 26,739 proteins associated with indel mutations. Notably, 8,855 of these exhibit both circular permutation and indel mutations-highlighting a potential co-evolutionary relationship between these two types of structural variation. We also recovered most symmetric proteins identified by the CE-symm tool and revealed more than 2.5 times as many symmetric proteins in the same dataset. By integrating precise boundary detection, clustering analysis, and rigorous cross-validation against established tools, our framework robustly maps internal structural features. These insights deepen our understanding of protein architecture and evolution, and can also inform de novo protein design and engineering.

bioinformatics↗

bioRxiv2025

Compression of protein secondary structures enables ultra-fast and accurate structure searching

Protein structure prediction has undergone a revolution with the advent of AI-based algorithms, such as AlphaFold and RoseTTAFold. As a result, over 200 million predicted protein structures have been published. This wealth of structural data has created a need for rapid structure comparison algorithms, such as Foldseek, which enable efficient searches across this vast space of protein structures. Here we introduce a new ultra-compact representation of protein structure in the form of Secondary Structure Elements (SSEs). These are short sequences around 8% of the length and with 10% of the information content of full amino acid sequences and 3Di sequences. We show that, despite this compression factor, SSEs can be used as a highly effective tertiary structure comparison tool, with accuracy that approaches that of Foldseek, while offering a 200-fold speedup. In addition SSEs offer comparable performance to Foldseek in domain boundary retrieval. Furthermore we show that the particular way in which SSEs encode structure can also be used to specifically detect proteins that differ due to conformational change. These findings demonstrate that SSEs offer a valuable complementary approach for protein structure characterisation.

bioinformatics↗

bioRxiv2025

An entropic measure of diverse specialisation highlights multifunctional neurons in annotated connectomes

The creation and curation of synaptic-level neuronal networks, or connectomes, enables the study of the relationship between neuronal structure and function. Topological characteristics of neuronal networks have been studied extensively. Separately, there have been considerable efforts to classify the morphology, cell types, and lineages of neurons. Here, we introduce a network metric that combines topological analysis with node metadata. This entropic quantity measures the diversity of incoming or outgoing connections to a node in terms of the metadata distribution. We find that in Caenorhabditis elegans, the top-scoring neurons (PVR, RMGL/R, DVA, CEP, ADE, URXR, RIGL, BAG, SMBDL) have known functions that integrate and disseminate multi-modal information involved in sensorimotor functions. In the nerve cord of Drosophila melanogaster, we find that top-scoring neurons are embryonic neurons located in the abdominal neuropil, where sensorimotor coordination is required for complex innate behaviour such as mating.

neuroscience↗

bioRxiv2024

Phenotypic plasticity can be an evolutionary response to fluctuating environments

Phenotypic plasticity refers to the ability of a single genotype to produce multiple distinct phenotypes. Using the computationally tractable genotype-phenotype (GP) map of RNA secondary structures, we model RNA phenotypic plasticity using the Boltzmann distribution of secondary structures for each genotype. Through evolutionary simulations that involve periodic environmental switching on the GP map, we reveal that RNA phenotypes can adapt to these fluctuations towards an optimal plasticity. The optimal phenotypes exhibit dominant near-equal Boltzmann probabilities of distinct structures, each representing the fittest structure for each alternating environment. Our findings demonstrate that phenotypic plasticity, a widespread biological phenomenon, is a fundamental evolutionary response to changing environments for RNA secondary structure. We also find naturally evolved functional RNAs that exhibit optimal plasticity unlikely to arise by neutral drift alone, suggesting functional relevance in fluctuating environments.

biophysics↗

bioRxiv2024

Changes in miRNA secondary structure can predict mutations associated with cancer and other diseases

MicroRNAs (miRNAs) are ubiquitous short RNAs regulating gene expression in many organisms, including humans. How the secondary structure (SS) of a mature miRNA affects its regulatory function remains an open question. Here we investigate this question through computational SS predictions of miRNA point mutants. We explore the mutational neighborhoods of miRNAs with association to human diseases, including cancer. We focus on possible SS changes independent of target-site complementarity, by leaving the seed region unchanged. We formulate metrics of the SS differences between such mutants and their wild types (WTs), and test whether these metrics predict disease association by comparing our results with the miRNASNP-v3 database. We find that disease-related mutants tend to have a higher probability of being fully unfolded than their WT; this and other SS-related measures are statistically significant at the database level. With the same approach, we identify a subset of individual miRNAs for which SS changes are most likely to predict disease-related mutations. These are hsa-miR-1269b, hsa-miR-4537, hsa-miR-4477b, hsa-miR-4641, and hsa-miR-6821-3p. In addition, we show that there are pairs of known miRNA WTs differing only by disease-related point mutations outside the seed region and exhibit very different SS. These pairs include hsa-miR-1269a--hsa-miR-1269b, and hsa-miR-3689a-3p--hsa-miR-3689b-3p.

bioinformatics↗

bioRxiv2023

Maximum Mutational Robustness in Genotype-Phenotype Maps Follows a Self-similar Blancmange-like Curve

Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organised as bricklayers graphs, so called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype (GP) maps for RNA secondary structure and the HP model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayers graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.

evolutionary biology↗

bioRxiv2023

The non-deterministic genotype-phenotype map of RNA secondary structure

Selection and variation are both key aspects in the evolutionary process. Previous research on the mapping between molecular sequence (genotype) and molecular fold (phenotype) has shown the presence of several structural properties in different biological contexts, implying that these might be universal in evolutionary spaces. The deterministic genotype-phenotype (GP) map that links short RNA sequences to minimum free energy secondary structures has been studied extensively because of its computational tractability and biologically realistic nature. However, this mapping ignores the phenotypic plasticity of RNA. We define a GP map that incorporates non-deterministic phenotypes, and take RNA as a case study; we use the Boltzmann probability distribution of folded structures and examine the structural properties of non-deterministic (ND) GP maps for RNA sequences of length 12 and coarse-grained RNA structures of length 30 (RNAshapes30). A framework is presented to study robustness, evolvability and neutral spaces in the non-deterministic map. This framework is validated by demonstrating close correspondence between the non-deterministic quantities and sample averages of their deterministic counterparts. When using the non-deterministic framework we observe the same structural properties as in the deterministic GP map, such as bias, negative correlation between genotypic robustness and evolvability, and positive correlation between phenotypic robustness and evolvability.

biophysics↗

bioRxiv2023

The Boltzmann distributions of folded molecular structures predict likely changes through random mutations

New folded molecular structures can only evolve after arising through mutations. This aspect is modelled using genotype-phenotype (GP) maps, which connect sequence changes through mutations to changes in molecular structures. Previous work has shown that the likelihood of appearing through mutations can differ by orders of magnitude from structure to structure and that this can affect the outcomes of evolutionary processes. Thus, we focus on the phenotypic mutation probabilities{phi} qp, i.e. the likelihood that a random mutation changes structure p into structure q. For both RNA secondary structures and the HP protein model, we show that a simple biophysical principle can explain and predict how this likelihood depends on the new structure q:{phi} qp is high if sequences that fold into p as the minimum-free-energy structure are likely to have q as an alternative structure with high Boltzmann frequency. This generalises the existing concept of plastogenetic congruence from individual sequences to the entire neutral spaces of structures. Our result helps us understand why some structural changes are more likely than others, can be used as a basis for estimating these likelihoods via sampling and makes a connection to alternative structures with high Boltzmann frequency, which could be relevant in evolutionary processes.

biophysics↗

bioRxiv2022

Predicting phenotype transition probabilities via conditional algorithmic probability approximations

Unravelling the structure of genotype-phenotype (GP) maps is an important problem in biology. Recently, arguments inspired by algorithmic information theory (AIT) and Kolmogorov complexity have been invoked to uncover simplicity bias in GP maps, an exponentially decaying upper bound in phenotype probability with increasing phenotype descriptional complexity. This means that phenotypes with very many genotypes assigned via the GP map must be simple, while complex phenotypes must have few genotypes assigned. Here we use similar arguments to bound the probability P (x [->] y) that phenotype x, upon random genetic mutation, transitions to phenotype y. The bound is [Formula], where [Formula] is the estimated conditional complexity of y given x, quantifying how much extra information is required to make y given access to x. This upper bound is related to the conditional form of algorithmic probability from AIT. We demonstrate the practical applicability of our derived bound by predicting phenotype transition probabilities (and other related quantities) in simulations of RNA and protein secondary structures. Our work contributes to a general mathematical understanding of GP maps, and may facilitate the prediction of transition probabilities directly from examining phenotype themselves, without utilising detailed knowledge of the GP map.

evolutionary biology↗

bioRxiv2021

The structure of genotype-phenotype maps makes fitness landscapes navigable

Fitness landscapes are often described in terms of peaks and valleys, implying an intuitive low-dimensional landscape of the kind encountered in everyday experience. The space of genotypes, however, is extremely high-dimensional, which results in counter-intuitive properties of genotype-phenotype maps, such as the close proximity of one phenotype to many others. Here we investigate how common structural properties of high-dimensional genotype-phenotype maps, such as the presence of neutral networks, affect the navigability of fitness landscapes. For three biologically realistic genotype-phenotype map models--RNA secondary structure, protein tertiary structure and protein complexes--we find that, even under random fitness assignment, fitness maxima can be reached from almost any other phenotype without passing through a fitness valley. This in turn implies that true fitness valleys are very rare. By considering evolutionary simulations between pairs of real examples of functional RNA sequences, we show that accessible paths are also likely to be utilised under evolutionary dynamics.

evolutionary biology↗

bioRxiv2021

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution

Engineers routinely design systems to be modular and symmetric in order to increase robustness to perturbations and to facilitate alterations at a later date. Biological structures also frequently exhibit modularity and symmetry, but the origin of such trends is much less well understood. It can be tempting to assume - by analogy to engineering design - that symmetry and modularity arise from natural selection. But evolution, unlike engineers, cannot plan ahead, and so these traits must also afford some immediate selective advantage which is hard to reconcile with the breadth of systems where symmetry is observed. Here we introduce an alternative non-adaptive hypothesis based on an algorithmic picture of evolution. It suggests that symmetric structures preferentially arise not just due to natural selection, but also because they require less specific information to encode, and are therefore much more likely to appear as phenotypic variation through random mutations. Arguments from algorithmic information theory can formalise this intuition, leading to the prediction that many genotype-phenotype maps are exponentially biased towards phenotypes with low descriptional complexity. A preference for symmetry is a special case of this bias towards compressible descriptions. We test these predictions with extensive biological data, showing that that protein complexes, RNA secondary structures, and a model gene-regulatory network all exhibit the expected exponential bias towards simpler (and more symmetric) phenotypes. Lower descriptional complexity also correlates with higher mutational robustness, which may aid the evolution of complex modular assemblies of multiple components.

evolutionary biology↗