Biology subjects

Malik, A. J.

Publications and source records attributed to Malik, A. J..

13 recordsLinked to original sources

bioRxiv2026

L-DOPA Rescues Reach Height but Not Trajectory Area in a Bimanual String-Pulling Behavior in Parkinsonian Rats

While gross motor changes in Parkinson's disease, bradykinesia, hypokinesia, difficulty initiating movement, have been well studied in rodent models of the disease, limb movements, and especially bimanual coordination deficits, are more difficult to study in these animal models. Here we describe a novel bimanual string-pulling task that can be used to generate a rich repertoire of behaviors that require limb and bimanual coordination in rats with unilateral 6-hydroxydopamine (6-OHDA) lesion. We show that 6-OHDA lesion significantly reduces string speed, pulling bout duration, paw speed especially during reaching, and reduces the area of paw trajectories during pulling. However, it does not affect the variability around that movement trajectory. We also show that levodopa (L-DOPA) treatment restores many of these measures but not all. L-DOPA restores paw speed and string-pulling speed and fully restores paw reach height and pulling bout duration, but has no effect on trajectory area. Together, our results demonstrate that dopamine depletion differentially effects features of this bimanual coordination task, that L-DOPA mostly, but not fully, restores string pulling performance, and opens up a new avenue for measuring the effect of Parkinson's disease treatments on motor movement, allowing for better assessment of how effectively treatments restore motor activity.

neuroscience↗

bioRxiv2026

Structural basis of coiled-coil domain-mediated inhibition of A. baumannii AbTir NAD+ nucleosidase activity

TIR (Toll/interleukin-1 receptor) domains are central components of innate immunity and cell-death pathways across diverse forms of life. In bacteria, TIR domain-containing proteins participate in diverse antiphage defence systems, several of which rely on NADase activity carried out by TIR domains. This nucleosidase activity is often regulated by additional domains that modulate activation and specificity in response to infection. AbTir, a TIR domain-containing protein from Acinetobacter baumannii, contains an N-terminal CC domain that negatively regulates its NADase activity, but the structural basis of this regulation remains unknown. Here, we used X-ray crystallography to investigate the molecular basis of CC domain-mediated regulation in AbTir. We determined the crystal structure of full-length AbTir in complex with a nanobody, revealing a symmetric AbTir TIR dimer in which only one of the two symmetry-related CC helices is accommodated at the dimer surface, breaking the overall symmetry of the full-length protein. Structural comparisons with other bacterial TIR domains suggest that such TIR dimers likely represent an inactive state, in which the NAD+-binding site has not yet formed. Phylogenetic analysis places AbTir within a family of bacterial TIR proteins associated with defence-related genomic loci, although phage-spot assays performed in the surrogate host Escherichia coli did not detect measurable antiphage activity. Together, these findings reveal an unusual structural mechanism of CC domain-mediated inhibition of a bacterial TIR NADase and expand our understanding of the mechanistic diversity of bacterial TIR proteins.

biochemistry↗

bioRxiv2026

Know Your Alphabet: Conformational Noise, Latent-Space Encodings, and the Future of Structural Phylogenetics

Structural alphabets have transformed protein phylogenetics by enabling sequence-style alignment and maximum-likelihood inference to be applied directly to structural data. However, a coordinate-explicit alphabet, in which character states are derived from three-dimensional atomic positions, encodes not only evolutionary signal but also the conformational variability inherent to protein structure. This source of noise has not previously been quantified in a phylogenetic context, and no framework exists for comparing alphabets with respect to their conformational sensitivity. Here, we introduce the Normalised Noise Index (NNI), a Shannon entropy-based metric for quantifying conformational sensitivity in structural alphabet encodings, and apply it alongside ensemble-wide Robinson-Foulds (RF) variance as a framework for characterising the impact of conformational noise on phylogenetic inference. Across 3,749 single-chain NMR ensembles from the Protein Data Bank, we show that 3Di character variability is a pervasive feature of experimentally observed conformational spread, with NNI negatively correlated with within-ensemble structural stability. A 100 ns molecular dynamics simulation of myoglobin confirmed that thermal fluctuations alone are sufficient to generate comparable 3Di character variation and, in 2.9% of cases, to redirect maximum-likelihood tree search away from the expected topology in a 4-taxon globin benchmark with independently established relationships. Exhaustive enumeration of 4,800 conformational replicates across three NMR ensembles revealed that topological variance under 3Di encoding is approximately 1.7-fold greater than under structural distance, based on 11,517,600 pairwise RF comparisons, a source of uncertainty invisible to standard bootstrap analysis. By contrast, TEA, a sequence-derived structure-aware alphabet inferred from ESM-2 embeddings rather than directly from atomic coordinates, is insulated from conformational sampling by construction and yields zero topological variance across all conformational replicates, serving here as a noise-insulated reference rather than a proposed replacement for 3Di. Together, these results demonstrate that alphabet choice is a methodological variable in structural phylogenetics, and that the NNI metric and RF variance frame-work introduced here provide a practical basis for principled noise characterisation as new structural alphabets continue to emerge.

bioinformatics↗

bioRxiv2026

The Duplicate Monophyly Criterion: An Empirical Approach to Bootstrapping Distance-Based Structural Phylogenies

Distance-based structural phylogenies summarise relationships between proteins using pairwise structural similarity scores. Unlike character-based methods, however, they lack a natural analogue of the non-parametric bootstrap. Because distances derived from continuous, high-dimensional folds provide no discrete sites to resample, rigorous support estimation would ideally rely on conformational ensembles from Molecular Dynamics or Monte Carlo simulations, which is computationally prohibitive at scale. A practical alternative is parametric bootstrapping in distance space, but this introduces a calibration problem: without an objective estimate of the signal-to-noise ratio, the perturbation magnitude cannot be chosen in a principled way. Here, we introduce the Duplicate Monophyly Criterion (DMC), an empirical strategy for calibrating distance-matrix perturbations using synthetic taxon duplicates as internal controls. We expand the observed distance matrix with virtual copies of each taxon, assign each original-duplicate pair a small baseline distance, and apply a floor-augmented heteroscedastic noise model directly in distance space. The central hypothesis is that loss of duplicate monophyly (i.e., failure of taxon-duplicate pairs to form two-tip cherries) marks a regime in which perturbations overwhelm intrinsic phylogenetic signal, thereby defining a conservative operating range for stability-based (bootstrap-like) support estimation. As a practical use case, we select a dataset-specific perturbation level{lambda} * as the largest noise level that retains a target fraction of duplicate pairings (e.g., [≥] 90%), and report split frequencies across replicate trees generated at{lambda} * as support values. We validate the framework in a geometric toy model where evolving two-dimensional shapes provide a known ground-truth topology, and in an empirical globin benchmark using distances derived from 1 - TM-score. Across both settings, duplicate monophyly tracks the erosion of tree structure under distance-matrix noise, establishing an internally calibrated "resolution limit" for assigning confidence in distance-based structural phylogenetics.

bioinformatics↗

bioRxiv2025

Quantome: A Quantum Surrogate Model for Biophysical Landscapes

Accurately modelling the potential energy landscapes that govern molecular interactions is a central challenge in computational biophysics. While quantum computers promise to solve such problems with high fidelity, a key bottleneck is the encoding of complex spatial information into low-qubit Hamiltonians suitable for near-term devices. Here, we introduce a generalisable framework for creating quantum surrogate models of 2D biophysical landscapes. Our method translates a discrete, classically-derived potential energy grid into a continuous quantum Hamiltonian by fitting it to a high-degree polynomial, where the polynomials coefficients directly define the potential energy operator. We demonstrate this pipeline on a custom-designed landscape featuring two asymmetric potential wells. By systematically varying a kinetic hopping term in the Hamiltonian, our variational quantum eigensolver simulations, averaged over 100 runs, successfully reproduce the physical transition from a localised ground state to a delocalised state governed by tunnelling. The entire framework is made accessible through the Quantome web application, a pedagogical platform that allows users to both design custom landscapes and explore pre-calculated results on EF-hand protein binding sites, which serve as a simplified real-world test bed for this framework. The web app is freely available at: https://biosig.lab.uq.edu.au/quantumlabs/quantome.

biophysics↗

bioRxiv2025

On a quantum-inspired kernel for classifying protein torsion angles

Algorithms grounded in quantum principles need to demonstrate they are at least as expressive as established classical methods before any hardware advantage can be sought. Here we demonstrate that a quantum-inspired kernel reaches the same accuracy and Matthews correlation coefficient as carefully tuned radial basis function and degree-2 polynomial support vector machines when tasked with separating geometric regions in Ramachandran space. On a rigorously curated, balanced dataset of 10,000 torsion angle pairs derived from DSSP-annotated residues, the model achieves 98% accuracy with a Matthews correlation coefficient of 0.96, comparable to the top-performing classical models. By achieving true predictive parity on a well-characterised structural benchmark, the quantum-inspired kernel establishes that quantum-informed similarity measures already match classical baselines, laying a firm groundwork for future quantum-native bioinformatics workflows once suitable hardware becomes available.

bioinformatics↗

bioRxiv2025

Structome-AlignViewer: On Confidence Assessment in Structure-Aware Alignments

Protein structure-based comparison provides a framework for uncovering deep evolutionary relationships that can escape conventional sequence-based approaches. Encoding three-dimensional protein structures using a simplified structure-aware alphabet, can lead to compact, comparable strings that retain key spatial relationships. Although this enables comparison, structure-aware alignments can experience misaligned regions, particularly when comparing proteins with substantial divergence in fold architecture. To address this, a web-based resource, Structome-AlignViewer, is introduced in this work for evaluating the quality of structure-aware alignments through both spatial mapping of alignment columns to protein structures and quantitative confidence scoring. Confidence is computed from pairwise structural substitutions between adjacent inputs and normalized within each alignment to highlight relatively well-supported columns. To provide broader context, thousands of alignments from established structural classification systems were analysed, allowing for an empirical comparative statistic to be derived to assess alignment quality. Option to exclude gap-rich regions enable users to refine alignments and focus on conserved structural cores. This approach provides an interpretable method for assessing structural alignment quality and supports more robust comparative and evolutionary analyses. Structome-AlignViewer is freely available at https://biosig.lab.uq.edu.au/structome_alignviewer/.

bioinformatics↗

bioRxiv2025

On interactive spatial visualisation of pathogenicity predictions

The past few years have revolutionised the utility of protein structural information in assessing disease risk. Such efforts have been pioneered by the DeepMind and Meta teams, who initially developed accurate protein prediction tools AlphaFold and ESMFold, respectively. More recently, both have been readapted to create AlphaMissene and ESM1b, through which, the pathogenicity potential of all possible missense mutations within the human proteome has been predicted. Similarly, VESPA was created as an alignment-agnostic predictor, which infers conservation from protein-language model embeddings to assign an effect for each variant. To facilitate their utility, we present a unified dashboard, MissenseViewer, which permits direct predictor comparison, and visualisation within the protein 3-dimensional structural context. Our interactive resource is freely available at: https://biosig.lab.uq.edu.au/missenseviewer/.

bioinformatics↗

bioRxiv2025

Structome-TM: Complementing dataset assembly for structural phylogenetics by addressing size-based biases

Harnessing the explosion of protein structure data to uncover deep evolutionary relation-ships requires effective comparison methods. While widely used global alignment techniques are powerful, they can fail to identify homologous structures that differ significantly in size or domain architecture. To address this limitation, we introduce Structome-TM, a web resource for assembling datasets for distance-based phylogenetic reconstruction. Making use of the TM-score to prioritize local structural similarity, Structome-TM excels at identifying these otherwise obscured relationships, allowing users to build a comprehensive structural neighbourhood of proteins suitable for comparison. To facilitate this dataset assembly, the resource accepts both RCSB PDB identifiers and protein sequences as inputs. When querying using a protein sequence, protein structures are predicted in real-time and their respective neighbourhoods determined, enabling analysis where experimentally determined structures may not be available. Through its user-friendly interface, Structome-TM provides a powerful and necessary approach for a more comprehensive exploration of protein evolution. This resource is freely available at: https://biosig.lab.uq.edu.au/structome_tm/.

bioinformatics↗

bioRxiv2024

On use of tertiary structure characters in hidden Markov models for protein fold prediction

While advances in protein structure prediction have opened up insights into arcane proteins, weak sequence homology makes functional characterisation challenging. To overcome this challenge, we use structure-based hidden Markov models of groupings in SCOP, CATH and ECOD to predict folds in proteins and thereby infer function. Conservation of structure and ability of hidden Markov models to detect remote signals make this a powerful resource for complete characterisation of arcane proteins.

bioinformatics↗

bioRxiv2023

Tertiary-interaction characters enable fast, model-based structural phylogenetics beyond the twilight zone

Protein structure is more conserved than protein sequence, and therefore may be useful for phylogenetic inference beyond the "twilight zone" where sequence similarity is highly decayed. Until recently, structural phylogenetics was constrained by the lack of solved structures for most proteins, and the reliance on phylogenetic distance methods which made it difficult to treat inference and uncertainty statistically. AlphaFold has mostly overcome the first problem by making structural predictions readily available. We address the second problem by redeploying a structural alphabet recently developed for Foldseek, a highly-efficient deep homology search program. For each residue in a structure, Foldseek identifies a tertiary interaction closest-neighbor residue in the structure, and classifies it into one of twenty "3Di" states. We test the hypothesis that 3Dis can be used as standard phylogenetic characters using a dataset of 53 structures from the ferritin-like superfamily. We performed 60 IQtree Maximum Likelihood runs to compare structure-free, PDB, and AlphaFold analyses, and default versus custom model sets that include a 3DI-specific rate matrix. Analyses that combine amino acids, 3Di characters, partitioning, and custom models produce the closest match to the structural distances tree of Malik et al. (2020), avoiding the long-branch attraction errors of structure-free analyses. Analyses include standard ultrafast bootstrapping confidence measures, and take minutes instead of weeks to run on desktop computers. These results suggest that structural phylogenetics could soon be routine practice in protein phylogenetics, allowing the re-exploration of many fundamental phylogenetic problems.

evolutionary biology↗

bioRxiv2023

On quantum computing and geometry optimization

Quantum computers have demonstrated advantage in tackling problems considered hard for classical computers and hold promise for tackling complex problems in molecular mechanics such as mapping the conformational landscapes of biomolecules. This work attempts to explore a few ways in which classical data, relating to the Cartesian space representation of biomolecules, can be encoded for interaction with empirical quantum circuits not demonstrating quantum advantage. Using the quantum circuit in a variational arrangement together with a classical optimizer, this work deals with the optimization of spatial geometries with potential application to molecular assemblies. Additionally this work uses quantum machine learning for protein side-chain rotamer classification and uses an empirical quantum circuit for random state generation for Monte Carlo simulation for side-chain conformation sampling. Altogether, this novel work suggests ways of bridging the gap between conventional problems in life sciences and how potential solutions can be obtained using quantum computers. It is hoped that this work will provide the necessary impetus for wide-scale adoption of quantum computing in life sciences.

bioinformatics↗

bioRxiv2023

Structome: Exploring the structural neighbourhood of proteins

Protein structures carry signal of common ancestry and can therefore aid in reconstructing their evolutionary histories. To expedite the structure-informed inference process, a web server, Structome, has been developed, that allows users to rapidly identify protein structures similar to a query protein and to assemble datasets useful for structure-based phylogenetics. Structome was created by clustering[~] 94% of the structures in RCSB PDB using 90% sequence identity and representing each cluster by a centroid structure. Structure similarity between centroid proteins was calculated, and annotations from PDB, SCOP and CATH were integrated. To illustrate utility, an H3 histone was used as a query, and results show that the protein structures returned by Structome span both sequence and structural diversity of the histone fold. Additionally, the pre-computed nexus-formated distance matrix, provided by Structome, enables analysis of evolutionary relationships between proteins not identifiable using searches based on sequence similarity alone. Our results demonstrate that, beginning with a single structure, Structome can be used to rapidly generate a dataset of structural neighbours and allows deep evolutionary history of proteins to be studied. Structome is available at: https://structome.bii.a-star.edu.sg

bioinformatics↗