Faculty Opinions recommendation of Accurate prediction of protein structures and interactions using a three-track neural network.

By G. Makhatadze. 2021

Subfields: Computational Biology & Bioinformatics

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TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, background): “These works have been shown great potential in various applications like protein structure modeling Jumper et al. [2021], Baek et al. [2021], Jing et al. [2021], Ganea et al. [2021], molecular low-energy generation prediction Shi et al. [2021], Xu et al. [2022], Méndez-Lucio et al. [2021],…”
High-resolution de novo structure prediction from primary sequence (2022, methodology): “Both these methods need as input evolutionary data in the form of multiple sequence alignments (MSAs) of homologous sequences aligned to the primary one, a technique which has been a staple of structure prediction methods ( 1 , 2 , 4 – 15 ).”
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, background): “However, very recent DL models have produced a breakthrough in computational protein folding (Jumper et al., 2021b; Baek et al., 2021) and fast generation of small molecule low-energy conformation ensembles (Ganea et al., 2021b; Luo et al., 2021; Xu et al., 2020; Shi et al., 2021; Du et al., 2022).”
Learning inverse folding from millions of predicted structures (2022, background): “With progress in protein structure prediction (Jumper et al., 2021; Evans et al., 2022; Baek et al., 2021), it is now possible to consider learning from predicted structures at scale.”
PDBe-KB: collaboratively defining the biological context of structural data (2021, methodology): “Researchers increasingly deploy Artificial Intelligence (AI) techniques to predict a protein’s structure computationally from its amino-acid sequence alone (12,13,48).”
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery (2022, methodology): “Recently, machine learning methods have made signiﬁcant progress in many drug discovery tasks, such as protein structure prediction (Baek et al., 2021; Jumper et al., 2021), molecular property prediction (Duvenaud et al., 2015; Hu et al., 2019), de novo molecular design and optimization (You et…”
De novo protein design by inversion of the AlphaFold structure prediction network (2022, methodology): “This method allows the network to “ hallucinate ” a sequence and structure different from those in nature (Anishchenko et al., 2021).”
Single-sequence protein structure prediction using language models and deep learning (2022, methodology): “8 ) and RoseTTAFold (RF) 27 , even though AF2/RF can achieve higher absolute GDT_TS scores than RGN2 on naturally occurring orphan proteins without known homologs and de novo designed proteins.”
Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022, background): “…prediction of protein-ligand structures that are coupled to receptor conformational responses is still hampered by the prohibitive cost of physically simulating slow protein state transitions [7, 8], as well as the static nature of existing protein folding prediction algorithms [9, 10].”
Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2022, background): “Similarly, recent advances in protein structure prediction have made a large number of protein structures available for structure-based drug design 20–22 .”
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, methodology): “A LPHA F OLD 2 (Jumper et al., 2021) and Rosetta Fold (Baek et al., 2021) are state-of-the-art approaches, and directly predict protein structures from co-evolution information embedded in homologous sequences, using geometric deep learning and E(3)-NNs. Protein-Protein Docking and Interaction.”
Deep learning methods for designing proteins scaffolding functional sites (2021, methodology): “We found following the development of RosettaFold ( 15 ) that using it, rather than trRosetta, to guide motif-constrained hallucination resulted in designed protein sequences that more strongly encoded their structures (Fig.”
Distinct structural bases for sequence-specific DNA binding by mammalian BEN domain proteins (2022, unknown): “Recent monumental advances in protein structural prediction provide valuable tools to explore 3D structure with unprecedented efficacy (Baek et al. 2021; Tunyasuvunakool et al. 2021).”