Advances in protein structure prediction and design

By Brian Kuhlman, Philip Bradley. 2019

718 citations recorded by the source corpus.

The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem — designing an amino acid sequence that will fold into a specified three-dimensional structure — has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein–protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled. Predicting how proteins fold enables inferring their function. Conversely, rational protein design allows for engineering novel protein functionalities. Recent improvements in computational algorithms and technological advances have dramatically increased the accuracy and speed of protein structure modelling, providing novel opportunities for controlling protein function, with potential applications in biomedicine, industry and research.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, neutral): “Thus, the computational approaches for predicting protein structures purely from residue sequences are highly desirable [4, 5].”
Deep Learning in Protein Structural Modeling and Design (2020, background): “Problem Definition The prediction of protein 3D structure from amino acid sequence has been a grand challenge in computational biophysics for decades.(33,34) Folding of peptide chains is a fundamental concept in biophysics, and atomic-level structures of proteins and complexes are often the starting”
Deep learning methods in protein structure prediction (2020, methodology): “In fact, most of the recent progress in Protein Structure Prediction has been driven by Deep Learning methods applied to the prediction of contact or distance maps [87,88].”
AlphaDesign: A de novo protein design framework based on AlphaFold (2021, background): “This has fuelled a plethora of diverse computational protein structure prediction approaches across decades of eﬀort [6–14].”
Perturbing the energy landscape for improved packing during computational protein design (2020, positive): “protein structures and complexes, and introduce new functional sites into proteins.(1) Most protein design programs use rotamer-based side chain optimization to identify amino acid sequences that will stabilize the desired protein structure or complex.”
Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “Although most current contributions to the de novo design focus on new structures, efforts in the ﬁ eld are increasingly directed toward designing new biological functions and their applications (1, 2).”
Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development (2020, methodology): “If no structure is available, one must resort to modeling (predicting) the structure for which several techniques are available, such as homology or comparative modeling [4].”
Artificial intelligence in cancer target identification and drug discovery (2022, methodology): “The druggability evaluating approach requires a long development cycle and high ﬁ nancial cost for the 3D structures of protein analysis 211 , while network-based biology analysis application provides an alternative methods to accelerate the evaluation procedure for the druggability of potential…”
Ins and outs of AlphaFold2 transmembrane protein structure predictions (2022, background): “Although enormous resources were devoted to predict protein structures for many decades, building a protein structure from its sequence remained a challenging task [1].”
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design (2021, background): “The computational design of proteins provides a route to create new protein structures and functions (Kuhlman and Bradley, 2019), with applications such as therapeutics (Chevalier et al.”
CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome (2022, background): “Fortu-nately, protein structure prediction approaches have made great progress in the past several years [24–26].”