TALI: Protein Structure Alignment Using Backbone Torsion Angles

Nonlinear backbone torsional pair correlations in proteins

Protein allostery requires dynamical structural correlations. Physical origin of which, however, remain elusive despite intensive studies during last two decades. Based on analysis of molecular dynamics (MD) simulation trajectories for ten proteins with different sizes and folds, we found that nonlinear backbone torsional pair (BTP) correlations, which are spatially more long-ranged and are mainly executed by loop residues, exist extensively in most analyzed proteins. Examination of torsional motion for correlated BTPs suggested that aharmonic torsional state transitions are essential for such…

扭转空间扩散用于蛋白质主链生成与几何细化

Torsion-Space Diffusion for Protein Backbone Generation with Geometric Refinement

本文提出扭转空间扩散模型，通过去噪扭转角生成物理有效的蛋白质主链，结合几何细化优化结构紧凑性，提升蛋白质生成的准确性与合理性。

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from its sequence of amino acids, a fundamental problem in computational biology. In this work, we demonstrate state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles. We recover 3D coordinat…

Description and Recognition of Regular and Distorted Secondary Structures in Proteins Using the Automated Protein Structure Analysis Method

The Automated Protein Structure Analysis (APSA) method, which describes the protein backbone as a smooth line in 3-dimensional space and characterizes it by curvature kappa and torsion tau as a function of arc length s, was applied on 77 proteins to determine all secondary structural units via specific kappa(s) and tau(s) patterns. A total of 533 alpha-helices and 644 beta-strands were recognized by APSA, whereas DSSP gives 536 and 651 units, respectively. Kinks and distortions were quantified and the boundaries (entry and exit) of secondary structures were classified. Similarity between prote…

离散Hasimoto映射的结构障碍及其在蛋白质主链几何中的应用

Descriptive power and predictive limits of a discrete Hasimoto--DNLS model of protein backbone structure

该研究揭示了离散Hasimoto映射在蛋白质折叠预测中的结构障碍，指出其作为运动学恒等式而非动力学方程的局限性。

Projecting Three-dimensional Protein Structure into a One-dimensional Character Code Utilizing the Automated Protein Structure Analysis Method

The protein backbone is described as a smooth curved and twisted line in three-dimensional (3D) space and characterized by its curvature $κ(s)$ and torsion $τ(s)$ both expressed as a function of arc length s. It is shown that the function $τ(s)$ is sufficient to analyze the contributions of all amino acids to the conformation of the protein backbone. The characteristic peak and trough patterns of the $τ(s)$ diagrams can be translated into a 16-letter code, which provides a rapid identification of helices, strands, and turns, specifies entry and exit points of secondary structural units, and de…