The divergence time of protein structures modelled by Markov matrices and its relation to the divergence of sequences

• URL: http://arxiv.org/abs/2308.06292v1
• Date: Fri, 11 Aug 2023 01:32:05 GMT
• Title: The divergence time of protein structures modelled by Markov matrices and its relation to the divergence of sequences
• Abstract summary: We analyze one million pairs of structures to estimate the Markov time of divergence of tertiary structures. By analyzing one million pairs of structures, we yield a relationship between the Markov divergence time of structures and of sequences. We demonstrate a competitive performance in secondary structure prediction against neural network architectures commonly employed for this task.
• Score: 14.676723972349567
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A complete time-parameterized statistical model quantifying the divergent evolution of protein structures in terms of the patterns of conservation of their secondary structures is inferred from a large collection of protein 3D structure alignments. This provides a better alternative to time-parameterized sequence-based models of protein relatedness, that have clear limitations dealing with twilight and midnight zones of sequence relationships. Since protein structures are far more conserved due to the selection pressure directly placed on their function, divergence time estimates can be more accurate when inferred from structures. We use the Bayesian and information-theoretic framework of Minimum Message Length to infer a time-parameterized stochastic matrix (accounting for perturbed structural states of related residues) and associated Dirichlet models (accounting for insertions and deletions during the evolution of protein domains). These are used in concert to estimate the Markov time of divergence of tertiary structures, a task previously only possible using proxies (like RMSD). By analyzing one million pairs of homologous structures, we yield a relationship between the Markov divergence time of structures and of sequences. Using these inferred models and the relationship between the divergence of sequences and structures, we demonstrate a competitive performance in secondary structure prediction against neural network architectures commonly employed for this task. The source code and supplementary information are downloadable from \url{http://lcb.infotech.monash.edu.au/sstsum}.

Related papers

• ProtAlign: Contrastive learning paradigm for Sequence and structure alignment [27.814501305991]
  We introduce a sequence structure contrastive alignment framework.<n>It learns a shared embedding space where proteins are represented consistently across modalities.<n>Results demonstrate that contrastive learning can serve as a powerful bridge between protein sequences and structures.
  arXiv  Detail & Related papers  (2026-03-06T00:36:41Z)
• S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening [72.89086338778098]
  We propose a two-stage framework for protein-ligand contrastive representation learning.<n>In the first stage, we perform protein sequence pretraining on ChemBL using an ESM2-based backbone.<n>In the second stage, we fine-tune on PDBBind by fusing sequence and structure information through a residue-level gating module.<n>This auxiliary task guides the model to accurately localize binding residues within the protein sequence and capture their 3D spatial arrangement.
  arXiv  Detail & Related papers  (2025-11-10T11:57:47Z)
• Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure [0.0]
  We introduce a unified machine-learning framework designed to tackle the temporal evolution of alloy microstructures under the influence of an elastic field.<n>This approach allows for the simultaneous extraction of elastic parameters from a short trajectory and for the prediction of further microstructure evolution under their influence.
  arXiv  Detail & Related papers  (2025-07-29T12:43:04Z)
• DISPROTBENCH: A Disorder-Aware, Task-Rich Benchmark for Evaluating Protein Structure Prediction in Realistic Biological Contexts [76.59606029593085]
  DisProtBench is a benchmark for evaluating protein structure prediction models (PSPMs) under structural disorder and complex biological conditions.<n>DisProtBench spans three key axes: data complexity, task diversity, and Interpretability.<n>Results reveal significant variability in model robustness under disorder, with low-confidence regions linked to functional prediction failures.
  arXiv  Detail & Related papers  (2025-06-18T23:58:22Z)
• Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression [45.49904590474368]
  ConfRover is an autoregressive model that simultaneously learns protein conformation and dynamics from MD trajectories.<n>It supports both time-dependent and time-independent sampling.<n>Experiments on ATLAS, a large-scale protein MD dataset, demonstrate the effectiveness of our model.
  arXiv  Detail & Related papers  (2025-05-23T05:00:15Z)
• SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation [97.99658944212675]
  We introduce a novel pre-training strategy for protein foundation models. It emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features. Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability.
  arXiv  Detail & Related papers  (2024-10-31T15:22:03Z)
• DPLM-2: A Multimodal Diffusion Protein Language Model [75.98083311705182]
  We introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures. DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures.
  arXiv  Detail & Related papers  (2024-10-17T17:20:24Z)
• Approximate learning of parsimonious Bayesian context trees [0.0]
  The proposed framework is tested on synthetic and real-world data examples. It outperforms existing sequence models when fitted to real protein sequences and honeypot computer terminal sessions.
  arXiv  Detail & Related papers  (2024-07-27T11:50:40Z)
• FoldToken: Learning Protein Language via Vector Quantization and Beyond [56.19308144551836]
  We introduce textbfFoldTokenizer to represent protein sequence-structure as discrete symbols. We refer to the learned symbols as textbfFoldToken, and the sequence of FoldTokens serves as a new protein language.
  arXiv  Detail & Related papers  (2024-02-04T12:18:51Z)
• Neural Structure Learning with Stochastic Differential Equations [9.076396370870423]
  We introduce a novel structure learning method, SCOTCH, which combines neural differential equations with variational inference to infer a posterior distribution over possible structures. This continuous-time approach can naturally handle both learning from and predicting observations at arbitrary time points.
  arXiv  Detail & Related papers  (2023-11-06T17:58:47Z)
• Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders [1.8835495377767553]
  Three-dimensional native states of natural proteins display recurring and hierarchical patterns. Traditional graph-based modeling of protein structures is often limited to operate within a single fine-grained resolution. We introduce Ophiuchus, an SO(3)-equivariant coarse-graining model that efficiently operates on all-atom protein structures.
  arXiv  Detail & Related papers  (2023-10-04T01:01:11Z)
• Protein Sequence and Structure Co-Design with Equivariant Translation [19.816174223173494]
  Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models. We propose a new approach capable of protein sequence and structure co-design, which iteratively translates both protein sequence and structure into the desired state. Our model consists of a trigonometry-aware encoder that reasons geometrical constraints and interactions from context features. All protein amino acids are updated in one shot in each translation step, which significantly accelerates the inference process.
  arXiv  Detail & Related papers  (2022-10-17T06:00:12Z)
• Unsupervised Learning of Equivariant Structure from Sequences [30.974508897223124]
  We present an unsupervised framework to learn the symmetry from the time sequence of length at least three. We will demonstrate that, with our framework, the hidden disentangled structure of the dataset naturally emerges as a by-product.
  arXiv  Detail & Related papers  (2022-10-12T07:29:18Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting [50.901984244738806]
  We show how to model the evolutionary and multi-scale interactions of time series. In particular, we first provide a hierarchical graph structure cooperated with the dilated convolution to capture the scale-specific correlations. A unified neural network is provided to integrate the components above to get the final prediction.
  arXiv  Detail & Related papers  (2022-06-28T08:11:12Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.