Advances in protein structure prediction and design

Abstract

The stunning diversity of molecular functions performed by naturally evolved proteins is made possible by their finely tuned three-dimensional structures, which are in turn determined by their genetically encoded amino acid sequences. A predictive understanding of the relationship between amino acid sequence and protein structure would therefore open up new avenues, both for the prediction of function from genome sequence data and also for the rational engineering of novel protein functions through the design of amino acid sequences with specific structures. The past decade has seen dramatic improvements in our ability to predict and design the three-dimensional structures of proteins, with potentially far-reaching implications for medicine and our understanding of biology. New machine-learning algorithms have been developed that analyse the patterns of correlated mutations in protein families, to predict structurally interacting residues from sequence information alone 1,2. Improved protein energy functions 3,4 have for the first time made it possible to start with an approximate structure prediction model and move it closer to the experimentally determined structure by an energy-guided refinement process 5,6. Advances in protein conformational sampling and sequence optimization have permitted the design of novel protein structures and complexes 7,8 , some of which show promise as therapeutics 9. These advances in protein structure prediction and design have been fuelled by technological breakthroughs as well as by a rapid growth in biological databases. Protein-modelling algorithms (Box 1) are computationally demanding both to develop and to apply. The rapid increase in computing power available to researchers (both CPU-based and, increasingly, GPU-based computing power) facilitates rapid benchmarking of new algorithms and enables their application to larger molecules and molecular assemblies. At the same time, next-generation sequencing has fuelled a dramatic increase in protein sequence databases as genomic and metagenomic sequencing efforts have expanded 10. Advances in software and automation have increased the pace of experimental structure determination, speeding the growth of the database of experimentally determined protein structures (the Protein Data Bank (PDB)) 11 , which now contains close to 150,000 macromolecular structures. Deep-learning algorithms 12 that have revolutionized image processing and speech recognition are now being adopted by protein modellers seeking to take advantage of these expanded sequence and structural databases. In this Review, we highlight a selection of recent breakthroughs that these technological advances have enabled. We describe current approaches to the prediction and design of protein structures, focusing primarily on template-free methods that do not require an

Machine Learning Approaches for Quality Assessment of Protein Structures

“…To overcome these problems, researchers have developed computational methods for protein-structure prediction. Popular methods include Modeller [3], SWISS-MODEL [4], Rosetta [5,6], I-TASSER [7], FALCON [8], Raptor/RaptorX [9,10], and IntFOLD [11] (see [12,13] for recent comprehensive reviews of the prediction theory and methods). Prediction functions are also available in some commercial software packages such as Internal Coordinate Mechanics, Molecular Operating Environment, and Schrödinger.…”

FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

“…41,42 The increase in the accuracy of predicted residue-residue contacts has fueled the substantial improvements in de novo protein structure prediction. 43 The question then becomes whether residue-residue contacts can be used to predict more than one conformation. Current techniques can only predict one single structure from residue-residue contacts due to the challenge of deconvoluting coevolutionary signals coming from different conformations.…”

A Robust Method for Collecting X-ray Diffraction Data from Protein Crystals across Physiological Temperatures

“…It is now routine to visualize the fold, intramolecular interactions, and binding sites of proteins-information with profound implications for the understanding of protein structure, function, and evolution (e.g. Berg et al, 2002;Brändén & Tooze, 1999;Fersht, 2017;Ufimtsev & Levitt, 2019;Wlodawer et al, 2008); and the thousands of examples of protein structures, along with simplified energetic rules, have led to our current ability to predict structure from sequence for many proteins and to design proteins that form specified folds in many cases (Huang et al, 2016;Kuhlman & Bradley, 2019;Marks et al, 2012).…”