Search bioRxiv⌕ Search

bioRxiv · 10.1101/597039

Charge-perturbation dynamics - a new avenue towards in silico protein folding

Abstract

Molecular dynamics (MD) has greatly contributed to understanding and predicting the way proteins fold. However, the time-scale and complexity of folding are not accessible via classical MD. Furthermore, efficient folding pipelines involving enhanced MD techniques are not routinely accessible. We aimed to determine whether perturbing the electrostatic component of the MD force field can help expedite folding simulations. We developed charge-perturbation dynamics (CPD), an MD-based simulation approach that involves periodically perturbing the atomic charges to values non-native to the MD force field. CPD obtains suitable sampling via multiple iterations in which a classical MD segment (with native charges) is followed by a very short segment of perturbed MD (using the same force field and conditions, but with non-native charges); subsequently, partially folded intermediates are refined via a longer segment of classical MD. Among the partially folded structures from low-energy regions of the free-energy landscape sampled, the lowest-energy conformer with high root-mean-square deviation to the starting structure and low radius of gyration is defined as the folded structure. Upon benchmark testing, we found that medium-length peptides such as an alanine-based pentadecapeptide, an amyloid-{beta} peptide, and the tryptophan-cage mini-protein can fold starting from their extended linear structure in under 45 ns of CPD (total simulation time), versus over 100 ns of classical MD. CPD not only achieved folding close to the desired conformation but also sampled key intermediates along the folding pathway without prior knowledge of the folding mechanism or final folded structure. Our findings confirmed that perturbing the electrostatic component of the classical MD force field can help expedite folding simulations without changing the MD algorithm or using expensive computing architectures. CPD can be employed to probe the folding dynamics of known, putative, or planned peptides, as well as to improve sampling in more advanced simulations or to guide further experiments.\n\nAuthor summaryFolding represents the process by which proteins assemble into biologically active conformations. While computational techniques such as molecular dynamics (MD) have provided invaluable insight into protein folding, efficient folding pipelines are not routinely accessible. In MD, the behavior of the studied molecule is simulated under the concerted action of multiple forces described by mathematical functions employing optimized parameters. Using non-native parameters effectively perturbs the MD force field. We show that this can be exploited to help expedite folding simulations. Specifically, we developed charge-perturbation dynamics (CPD), an MD-based simulation approach that involves periodically perturbing the force field by using non-native atomic charges. For folding medium-length peptides such as the tryptophan-cage mini-protein starting from the extended linear structure, CPD is much faster than other MD-based approaches while using the same software, hardware, and know-how required for running classical MD simulations. Furthermore, CPD not only achieves folding close to the desired conformation but also samples key intermediates along the folding pathway without prior knowledge of the folding mechanism or final folded structure. CPD can be employed to probe the folding dynamics of known, putative, or planned peptides, as well as to generate different conformations that can guide further experiments or more advanced simulations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pant, P., Tristao Ramos, R. J., Ionescu, C.-M., Koca, J.. 2019-04-03. Charge-perturbation dynamics - a new avenue towards in silico protein folding. https://doi.org/10.1101/597039

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗