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 determines their regularity in terms of distortions, kinks or breaks. Via computer encoding, 3D protein structure is projected into a 1D string of conformational letters. The 3D-1D-projection procedure represents an extension of the Automated Protein Structure Analysis (APSA) method. APSA has been applied to describe 155 super secondary structures from 94 proteins and to compare results with Efimov's classification system of super secondary structure. The applicability of the latter is demonstrated.
Protein splicing is a post-translational autocatalystic excision of internal protein sequence (intein) with the subsequent ligation of the flanking polypeptides (exteins). The high specificity of excision ensured by intein makes it possible to use a phenomenon of protein splicing for the biotechnology purposes. The aim of this work was optimization of obtaining and purification of the recombinant human growth hormone using the protein splicing. It was experimentally demonstrated that the use of modified intein as auto-removal affine marker makes it possible to perform the rapid and cheap isola…
Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in efficiency and accuracy of binding mode prediction on a large and diverse set of protein systems compared to standard docking. Whereas the first graph convolutional network is used for re-ranking poses the second approach aims to generate and rank poses independent of standard docking approaches. This novel approach relies on the prediction of distance matric…
Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized data sets that facilitate fair assessment of new methods and lower the barrier to entry for non-domain experts. While data sets of protein sequence and structure exist, they lack certain components critical for machine learning, including high-quality multiple sequence alignments and insulated training / validation splits that account for deep but only weakly …
Massachusetts Institute of Technology（麻省理工学院）；Dartmouth College（达特茅斯学院）
Computational protein design has the potential to deliver novel molecular structures, binders, and catalysts for myriad applications. Recent neural graph-based models that use backbone coordinate-derived features show exceptional performance on native sequence recovery tasks and are promising frameworks for design. A statistical framework for modeling protein sequence landscapes using Tertiary Motifs (TERMs), compact units of recurring structure in proteins, has also demonstrated good performance on protein design tasks. In this work, we investigate the use of TERM-derived data as features in …
Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protein quaternary structures using predicted inter-chain contacts. Here, we present an agent-based self-learning method based on deep reinforcement learning (DRLComplex) to build protein complex structures using inter-chain contacts as distance constraints. We rigorously tested DRLComplex on two standard datasets of homodimeric and heterodimeric protein complexes (i.e., the CASP-CAPRI homodime…
The Chinese University of Hong Kong (CUHK)（香港中文大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；The CUHK Shenzhen Research Institute（香港中文大学深圳研究院）
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Due to the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in …
Recent computational advances in the accurate prediction of protein three-dimensional (3D) structures from amino acid sequences now present a unique opportunity to decipher the interrelationships between proteins. This task entails--but is not equivalent to--a problem of 3D structure comparison and classification. Historically, protein domain classification has been a largely manual and subjective activity, relying upon various heuristics. Databases such as CATH represent significant steps towards a more systematic (and automatable) approach, yet there still remains much room for the developme…
Enhancing Multimodal Protein Function Prediction Through Dual-Branch Dynamic Selection with Reconstructive Pre-Training
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；College of Applied Technology, Shenzhen University（深圳大学应用技术学院）；Laboratory for Artificial Intelligence in Design, Hong Kong（人工智能设计实验室（香港））；College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳学院计算机科学与技术学院）
Traditional approaches to elucidation of protein structures by NMR spectroscopy rely on distance restraints also know as nuclear Overhauser effects (NOEs). The use of NOEs as the primary source of structure determination by NMR spectroscopy is time consuming and expensive. Residual Dipolar Couplings (RDCs) have become an alternate approach for structure calculation by NMR spectroscopy. In this work we report our results for structure calculation of the novel protein PF2048.1 from RDC data and establish the minimum data requirement for successful structure calculation using the software package…
Among all the proteins of Periplasmic C type cytochrome A (PPCA) family obtained from cytochrome C7 found in Geobacter sulfurreducens, PPCA protein can interact with Deoxycholate (DXCA), while its other homologs do not, as observed from the crystal structures. Utilizing the concept of 'structure-function relationship', an effort has been initiated towards understanding the driving force for recognition of DXCA exclusively by PPCA among its homologs. Further, a combinatorial analysis of the binding sequences (contiguous sequence of amino acid residues of binding locations) is performed to build…
Argonne National Laboratory（阿贡国家实验室）；Brookhaven National Laboratory（布鲁克海文国家实验室）
We propose a benchmark to study surrogate model accuracy for protein-ligand docking. We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. Our work shows surrogate docking models have six orders of magnitude more throughput than standard docking protocols on the same supercomputer node types. We demonstrate the power of high-speed surrogate models by running each target against 1 billion molecules in under a day (50k predictions pe…
