A global map of the protein shape universe

Abstract

Proteins are involved in almost all functions in a living cell, and functions of proteins are realized by their tertiary structures. Obtaining a global perspective of the variety and distribution of protein structures lays a foundation for our understanding of the building principle of protein structures. In light of the rapid accumulation of low-resolution structure data from electron tomography and cryo-electron microscopy, here we map and classify three-dimensional (3D) surface shapes of proteins into a similarity space. Surface shapes of proteins were represented with 3D Zernike descriptors, mathematical moment-based invariants, which have previously been demonstrated effective for biomolecular structure similarity search. In addition to single chains of proteins, we have also analyzed the shape space occupied by protein complexes. From the mapping, we have obtained various new insights into the relationship between shapes, main-chain folds, and complex formation. The unique view obtained from shape mapping opens up new ways to understand design principles, functions, and evolution of proteins.

Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics

“…Protein structures have been classified based on their backbone conformation and evolutionary history ( Chandonia et al , 2019 ; Dawson et al , 2017 ). Proteins interact through their molecular surface that is considered as a higher-level representation of the protein structure ( Guzenko et al , 2020 ; Han et al , 2019 ; Pawlowski and Godzik, 2001 ). Molecular surface abstracts the underlying protein sequence, structure and fold into a continuous shape with geometric and chemical features that fingerprint their interactions ( Gainza et al , 2020 ; Shulman-Peleg et al , 2004 ).…”

“…Molecular surface abstracts the underlying protein sequence, structure and fold into a continuous shape with geometric and chemical features that fingerprint their interactions ( Gainza et al , 2020 ; Shulman-Peleg et al , 2004 ). Functionally related proteins often share similar molecular surface properties despite a potentially low sequence and/or backbone conformation similarity ( Han et al , 2019 ; Sael et al , 2008a ). Different categories of methods based on protein surface shape have been developed over time such as protein–protein docking ( Ruiz Echartea et al , 2019 ; Schneidman-Duhovny et al , 2005 ), protein structure alignment ( Mavridis and Ritchie, 2010 ) or protein surface shape comparison methods ( Craciun et al , 2017 ; Gainza et al , 2020 ; Gao et al , 2016 ; Gramada and Bourne, 2006 ; Guzenko et al , 2020 ; Han et al , 2019 ; Mak et al , 2008 ; Sael et al , 2008b ).…”

“…Functionally related proteins often share similar molecular surface properties despite a potentially low sequence and/or backbone conformation similarity ( Han et al , 2019 ; Sael et al , 2008a ). Different categories of methods based on protein surface shape have been developed over time such as protein–protein docking ( Ruiz Echartea et al , 2019 ; Schneidman-Duhovny et al , 2005 ), protein structure alignment ( Mavridis and Ritchie, 2010 ) or protein surface shape comparison methods ( Craciun et al , 2017 ; Gainza et al , 2020 ; Gao et al , 2016 ; Gramada and Bourne, 2006 ; Guzenko et al , 2020 ; Han et al , 2019 ; Mak et al , 2008 ; Sael et al , 2008b ). Shape comparison and retrieval methods have been extensively developed in the computer vision field, notably for military, civil security or medical imaging applications ( Bustos et al , 2007 ).…”

Ten simple rules for designing analogue science games

“…Rather than text, then, consider how you can use the mechanics of the game itself to convey this information. For example, if you are designing a game about a global map of the protein shape universe [ 43 ], then, instead of having several different cards with lots of flavour text (that is unrelated to the ways in which the cards are used in the game), you might create a game in which players create different 3D shapes and assemblies of proteins. Such a game would likely give the players a clearer understanding of the processes that are being discussed, presenting them with a kinaesthetic experience for discovery.…”

2DKD: a toolkit for content-based local image search

“…Moment-based approaches are very useful for representing biological and medical images as they are pixelized [1] or voxelized data [2][3][4]. In medical imaging, such as computerized tomography (CT) scan and magnetic resonance imaging (MRI), objects are observed at different viewpoints and local images need to be extracted and examined.…”