Hashing for Protein Structure Similarity Search

• URL: http://arxiv.org/abs/2411.08286v1
• Date: Wed, 13 Nov 2024 02:02:52 GMT
• Title: Hashing for Protein Structure Similarity Search
• Authors: Jin Han, Wu-Jun Li,
• Abstract summary: We propose a novel method for protein structure similarity search (PSSS) Underlinetextp$runderlinetexto$tein $underlinetexts$tructure $underlinetexth$ashing (POSH) for PSSS. POSH learns a binary vector representation for each protein structure, which can dramatically reduce the time and memory cost for PSSS.
• Score: 19.352125515561287
• License:
• Abstract: Protein structure similarity search (PSSS), which tries to search proteins with similar structures, plays a crucial role across diverse domains from drug design to protein function prediction and molecular evolution. Traditional alignment-based PSSS methods, which directly calculate alignment on the protein structures, are highly time-consuming with high memory cost. Recently, alignment-free methods, which represent protein structures as fixed-length real-valued vectors, are proposed for PSSS. Although these methods have lower time and memory cost than alignment-based methods, their time and memory cost is still too high for large-scale PSSS, and their accuracy is unsatisfactory. In this paper, we propose a novel method, called $\underline{\text{p}}$r$\underline{\text{o}}$tein $\underline{\text{s}}$tructure $\underline{\text{h}}$ashing (POSH), for PSSS. POSH learns a binary vector representation for each protein structure, which can dramatically reduce the time and memory cost for PSSS compared with real-valued vector representation based methods. Furthermore, in POSH we also propose expressive hand-crafted features and a structure encoder to well model both node and edge interactions in proteins. Experimental results on real datasets show that POSH can outperform other methods to achieve state-of-the-art accuracy. Furthermore, POSH achieves a memory saving of more than six times and speed improvement of more than four times, compared with other methods.

Related papers

• A PLMs based protein retrieval framework [4.110243520064533]
  We propose a novel protein retrieval framework that mitigates the bias towards sequence similarity. Our framework initiatively harnesses protein language models (PLMs) to embed protein sequences within a high-dimensional feature space. Extensive experiments demonstrate that our framework can equally retrieve both similar and dissimilar proteins.
  arXiv  Detail & Related papers  (2024-07-16T09:52:42Z)
• Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance? [4.7077642423577775]
  We propose ProtLOCA, a local geometry alignment method based solely on amino acid structure representation. Our method outperforms existing sequence- and structure-based representation learning methods by more quickly and accurately matching structurally consistent protein domains.
  arXiv  Detail & Related papers  (2024-06-28T08:54:37Z)
• PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design [19.324059406159325]
  We introduce two novel metrics: refoldability-based metric and stability-based metric. ByProt, ProteinMPNN, and ESM-IF perform exceptionally well on our benchmark, while ESM-Design and AF-Design fall short. Our proposed benchmark paves the way for a fair and comprehensive evaluation of protein design methods.
  arXiv  Detail & Related papers  (2023-11-30T02:37:55Z)
• FABind: Fast and Accurate Protein-Ligand Binding [127.7790493202716]
  $mathbfFABind$ is an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. Our proposed model demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods.
  arXiv  Detail & Related papers  (2023-10-10T16:39:47Z)
• DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models [47.73386438748902]
  DiffDock-PP is a diffusion generative model that learns to translate and rotate unbound protein structures into their bound conformations. We achieve state-of-the-art performance on DIPS with a median C-RMSD of 4.85, outperforming all considered baselines.
  arXiv  Detail & Related papers  (2023-04-08T02:10:44Z)
• Structure-informed Language Models Are Protein Designers [69.70134899296912]
  We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs) We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. Experiments show that our approach outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-03T10:49:52Z)
• HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative [61.984700682903096]
  HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2. Our proposed method pre-trains a large-scale protein language model with thousands of millions of primary sequences. We obtain an end-to-end differentiable model to predict the 3D coordinates of atoms from only the primary sequence.
  arXiv  Detail & Related papers  (2022-07-28T07:30:33Z)
• Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model [93.9943278892735]
  Key problem in protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. We propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM) Our result shows that the proposed method can effectively capture the interresidue correlations and improves the performance of contact prediction by up to 9% compared to the baseline.
  arXiv  Detail & Related papers  (2021-10-29T04:01:32Z)
• DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction [2.697420611471228]
  We present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for geometric deep learning of protein interfaces. The previous version of DIPS contains only the Cartesian coordinates and types of the atoms comprising a given protein complex. DIPS-Plus now includes a plethora of new residue-level features including protrusion indices, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid.
  arXiv  Detail & Related papers  (2021-06-06T23:56:27Z)
• Transfer Learning for Protein Structure Classification at Low Resolution [124.5573289131546]
  We show that it is possible to make accurate ($geq$80%) predictions of protein class and architecture from structures determined at low ($leq$3A) resolution. We provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function.
  arXiv  Detail & Related papers  (2020-08-11T15:01:32Z)

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.