24 papers · ranked by Valyu relevance
Ştefan-Bogdan Marcu, Sabin Tăbîrcă, Mark Tangney
This paper presents a short summary of the protein folding problem, what it is and why it is significant. Introduces the CASP competition and how accuracy is measured. Looks at different approaches for solving the problem followed by a review of the current breakthroughs in the field introduced by AlphaFold 1 and…
Xeerak Agha, Nihang Fu, Jianjun Hu
Protein structures and functions are determined by a contiguous arrangement of amino acid sequences. Designing novel protein sequences and structures with desired geometry and functions is a complex task with large state spaces. Here we develop a novel protein design pipeline consisting of two deep learning algorithms…
Flavia Maria Galeazzi, Gabriel Monteiro da Silva, Pablo Arantes, Iz Varghese + 2 more
Deep learning approaches like AlphaFold 2 (AF2) have revolutionized structural biology by accurately predicting the ground state structures of proteins. Recently, clustering and subsampling techniques that manipulate multiple sequence alignment (MSA) inputs into AlphaFold to generate conformational ensembles of…
Douglas V. Laurents
The artificial intelligence program AlphaFold 2 is revolutionizing the field of protein structure determination as it accurately predicts the 3D structure of two thirds of the human proteome. Its predictions can be used directly as structural models or indirectly as aids for experimental structure determination using…
Kaustav Mehta
AlphaFold2's 93 million parameters, shaped by the evolutionary record of protein structure encoded in the Protein Data Bank and in sequence alignments, are conventionally treated only as machinery for converting sequence to structure. We propose they are also a scientific object that can be analyzed directly: a learned…
Authors not listed
We introduce AlphaFold2-RAVE (af2rave), an open-source Python package that integrates machine learning-based structure prediction with physics-driven sampling to generate alternative protein conformations efficiently. Protein structures are not static but exist as ensembles of conformations, many of which are…
Victor Daniel Aldas-Bulos, Fabien Plisson
Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles. Nevertheless, they are primarily trained on sequences where the tridimensional structures of peptides and proteins are often overlooked. We need a fast and reliable approach to…
Jannik Adrian Gut, Thomas Lemmin
Protein structure prediction, a fundamental challenge in computational biology, aims to predict a protein’s 3D structure from its amino acid sequence. This structure is pivotal for elucidating protein functions, interactions, and driving innovations in drug discovery and enzyme engineering. AlphaFold2, a powerful deep…
Arne Elofsson
In Dec 2020, the results of AlphaFold2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of single protein domains. The code of AlphaFold2 was released in the…
Ragousandirane Radjasandirane, Alexandre G. de Brevern
analyses Authors: ['Ragousandirane Radjasandirane' 'Alexandre G. de Brevern'] AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design and the elucidation of disease mechanisms. Many scientists now use…
Vojtěch Spiwok, Martin Kurečka, Aleš Křenek
AlphaFold is a neural network-based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of…
Sophia M. Hartley, Kelly A. Tiernan, Gjina Ametaj, Adriana Cretu + 2 more
AlphaFold2 and RoseTTAfold are able to predict, based solely on their sequence whether GFP-like proteins will post-translationally form a chromophore or not. Their training has not only taught them protein structure and folding, but also chemistry. The structures of 21 sequences of GFP-like fluorescent proteins that…
Devlina Chakravarty, Lauren L. Porter
AlphaFold2 has revolutionized protein structure prediction by leveraging sequence information to rapidly model protein folds with atomic-level accuracy. Nevertheless, previous work has shown that these predictions tend to be inaccurate for structurally heterogeneous proteins. To systematically assess factors that…
Authors not listed
Bi-Specific T-Cell Engager (BiTE) Therapy is a type of immunotherapy that redirects cytotoxic T-cells to target tumor cells. Traditionally, a BiTE structure dual binds to the CD3 receptor on the T-cell surface and to the mutated antigen presented on the surface of the cancer cell as an extracellular protein. An example…
Daria Gutnik, Peter Evseev, Konstantin Miroshnikov, Mikhail Shneider + 1 more
'Quan Zou'] Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine.…
Alicja W. Wojciechowska, Jakub W. Wojciechowski, Malgorzata Kotulska
The recent release of AlphaFold3 raises a question about its powers and limitations. Here, we analyze the potential of AlphaFold3 in correct reproduction of amyloid structures, which are an example of multimeric proteins characterized by polymorphism and low representation in protein structure databases. We show that…
Priscila S. F. C. Gomes, Diego E. B. Gomes, Rafael C. Bernardi
Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how mechanoactive proteins sense and respond to mechanical forces. However, even SMFS has its…
Pranshu Jahagirdar
Database Authors: ['Pranshu Jahagirdar'] AlphaFold, a groundbreaking protein prediction model, has revolutionized protein structure prediction, populating the AlphaFold Protein Database (AFDB) with millions of predicted structures. However, AlphaFold's accuracy in predicting proteins with intricate topologies, such as…
Anastassis Perrakis, Titia K Sixma
AlphaFold is the most ground-breaking application of AI in science so far; it will revolutionize structural biology, but caution is warranted.
Authors not listed
Relative binding free energy (RBFE) calculations have emerged as a powerful tool in drug discovery, capable of achieving experimental-level accuracy. However, the accuracy is compromised by a multitude of factors, including the initial structure modelling. The current study contributes to the quantification of the…
Myeongsang Lee, Lauren L. Porter
A protein's function depends critically on its conformational ensemble, a collection of energy weighted structures whose balance depends on temperature and environment. Though recent deep learning (DL) methods have substantially advanced predictions of single protein structures, computationally modeling conformational…
Oleg Kovalevskiy, Juan Mateos-García, Kathryn Tunyasuvunakool
Two years on from the initial release of AlphaFold2 we have seen its widespread adoption as a structure prediction tool. Here we discuss some of the latest work based on AlphaFold2, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure…
Theresa Ramelot, Roberto Tejero, Gaetano Montelione
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges…
Marina A. Pak, Karina A. Markhieva, Mariia S. Novikova, Dmitry S. Petrov + 4 more
AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is “solved”. However, protein folding problem is more than just structure prediction from…