Biology subjects

Tamiola, K.

Publications and source records attributed to Tamiola, K..

6 recordsLinked to original sources

bioRxiv2026

Transient tertiary structure in intrinsically disordered proteins revealed by multithermal enhanced sampling

Intrinsically disordered proteins populate heterogeneous conformational ensembles that are challenging to characterise. While all-atom molecular dynamics simulations can provide highly detailed insights into dynamic ensembles, achieving sufficient sampling remains difficult. Here, we show that On-the-fly Probability Enhanced Sampling (OPES) in the multithermal ensemble enables efficient generation of atomistic ensembles for disordered peptides and proteins ranging from 15 to 71 residues in length. Using the potential energy as a collective variable, OPES achieves multithermal sampling within a single simulation replica, without replica exchange or extensive parameter tuning. Across multiple systems, OPES yields reweighted ensembles broadly consistent with replica-exchange with solute tempering (REST2) and unbiased simulations, while accelerating convergence and enabling broader exploration of low-population conformational states. Applied to the intrinsically disordered transcriptional coactivator ACTR, OPES reveals transiently structured states in which multiple -helices involved in partner binding fold cooperatively and form tertiary contacts. These rare, partially structured conformations are reversibly sampled during the simulations, consistent with extensive NMR and SAXS data, and could facilitate folding-upon-binding through conformational selection. They may also represent viable targets for drug design or for engineering disordered proteins with customised conformational landscapes. More broadly, our results establish OPES multithermal sampling as a robust and accessible approach for uncovering rare, functionally relevant conformations in intrinsically disordered proteins.

biophysics↗

bioRxiv2025

Advancing Protein Ensemble Predictions Across the Order-Disorder Continuum

While deep learning has transformed structure prediction for ordered proteins, intrinsically disordered proteins remain poorly predicted due to systematic underrepresentation in training data, despite constituting approximately 30% of eukaryotic proteomes. We introduce PeptoneBench, the first benchmark to enable systematic assessment of ensemble generators for both ordered and disordered proteins, integrating diverse experimental observables. Our analysis reveals that existing evaluation metrics exhibit systematic bias toward the structured spectrum of the proteome. Assessment of popular predictors (AlphaFold2, ESMFlow, Boltz2) confirms high accuracy on ordered proteins but shows performance degradation with increasing disorder. We further present PepTron, a flow-matching ensemble generator trained on data augmented with synthetic disordered protein ensembles. On our benchmark PepTron matches BioEmu on disordered regions while maintaining competitive accuracy on ordered protein benchmarks. Our data augmentation approach demonstrates that targeted training strategies can approach the performance of computationally expensive simulation-based methods, establishing a generalizable framework applicable to other protein generative models. All datasets, models, and code are openly available.

biophysics↗

bioRxiv2024

Improving Inverse Folding models at Protein Stability Prediction without additional Training or Data

Deep learning protein sequence models have shown outstanding performance at de novo protein design and variant effect prediction. We substantially improve performance without further training or use of additional experimental data by introducing a second term derived from the models themselves which align outputs for the task of stability prediction. On a task to predict variants which increase protein stability the absolute success probabilities of PO_SCPLOWROTEINC_SCPLOWMPNN and ESMO_SCPLOWIFC_SCPLOW are improved by 11% and 5% respectively. We term these models PO_SCPLOWROTEINC_SCPLOWMPNN-O_SCPLOWDDC_SCPLOWG and ESMO_SCPLOWIFC_SCPLOW-O_SCPLOWDDC_SCPLOWG.

biophysics↗

bioRxiv2022

NeRFax: An efficient and scalable conversion from the internal representation to Cartesian space

MotivationAccurate modelling of protein ensembles requires sampling of a large number of 3D conformations. A number of sampling approaches that use internal coordinates have been proposed, yet poor performance in the conversion from internal to Cartesian coordinates limits their applicability. ResultsWe describe here NeRFax, an efficient method for the conversion from internal to Cartesian coordinates that utilizes the platform-agnostic JAX Python library. The relative benefit of NeRFax is demonstrated here, on peptide chain reconstruction tasks. Our novel approach offers 35-175x times performance gains compared to previous state-of-the-art methods, whereas >10,000x speedup is reported in a reconstruction of a biomolecular condensate of 1,000 chains. AvailabilityNeRFax has purely open-source dependencies and is available at https://github.com/PeptoneLtd/nerfax. Contactoliver@peptone.io

bioinformatics↗

bioRxiv2022

ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers

Intrinsically disordered proteins (IDP) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT, a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue level representations from Facebooks Evolutionary Scale Modeling (ESM) library. The latter uses a database of NMR chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features which are relevant for the prediction performance and show that good performance can already gained with less than 100 features. ADOPT is available as a standalone package at https://github.com/PeptoneLtd/ADOPT.

bioinformatics↗

bioRxiv2017

Structural Propensity Database Of Proteins

An overwhelming amount of experimental evidence suggests that elucidations of protein function, interactions, and pathology are incomplete without inclusion of intrinsic protein disorder and structural dynamics. Thus, to expand our understanding of intrinsic protein disorder, we have created a database of secondary structure (SS) propensities for proteins (dSPP) as a reference resource for experimental research and computational biophysics. The dSPP comprises SS propensities of 7,094 unrelated proteins, as gauged from NMR chemical shift measurements in solution and solid state. Here, we explain the concept of SS propensity and analyze dSPP entries of therapeutic relevance, -synuclein, MOAG-4, and the ZIKA NS2B-NS3 complex to show: (1) how propensity mapping generates novel structural insights into intrinsically disordered regions of pathologically relevant proteins, (2) how computational biophysics tools can benefit from propensity mapping, and (3) how the residual disorder estimation based on NMR chemical shifts compares with sequence-based disorder predictors. This work demonstrates the benefit of propensity estimation as a method that reports both on protein structure, lability, and disorder.

biophysics↗