Protein structure predictions

  • Article
    | Open Access

    Covalent labeling (CL) from mass spectrometry experiments provides structural information of higher-order protein structure. Here, the authors develop an algorithm which integrates experimental CL data to predict protein complexes in the Rosetta molecular modeling suite using AlphaFold models.

    • Zachary C. Drake
    • , Justin T. Seffernick
    •  & Steffen Lindert
  • Article
    | Open Access

    The accuracy of AlphaFold decreases with the number of protein chains and the available GPU memory limits the size of protein complexes that can be predicted. Here, the authors show that complexes with 10–30 chains can be assembled from predicted subcomponents using Monte Carlo tree search.

    • Patrick Bryant
    • , Gabriele Pozzati
    •  & Arne Elofsson
  • Article
    | Open Access

    The formation of ternary degrader-protein complexes is a key step in the targeted degradation of proteins of interest. Here, the authors explore the structure and dynamics of such complexes applying high-performance computer simulations augmented with experimental data.

    • Tom Dixon
    • , Derek MacPherson
    •  & Jesus A. Izaguirre
  • Article
    | Open Access

    PAPP-A substrate selectivity underlies the tight regulation of IGF signaling. Here, the authors report cryo-EM structures of dimeric PAPP-A in its substrate-free form and in complex with a peptide substrate, which combined with biochemical assays provide a mechanism for PAPP-A substrate binding and selectivity.

    • Russell A. Judge
    • , Janani Sridar
    •  & Qi Hao
  • Article
    | Open Access

    Collision cross sections (CCS) from ion mobility mass spectrometry provide information about protein shape and size. Here, the authors develop an algorithm to predict CCS and integrate experimental ion mobility data into Rosetta-based molecular modelling to predict protein structures from sequence.

    • SM Bargeen Alam Turzo
    • , Justin T. Seffernick
    •  & Steffen Lindert
  • Article
    | Open Access

    Folded proteins are composed of secondary structures, α-helices and β-sheets, that are generally assumed to be stable. Here, the authors combine computational prediction with experimental validation to show that many sequence-diverse NusG protein domains switch completely from α-helix to β-sheet folds.

    • Lauren L. Porter
    • , Allen K. Kim
    •  & Marie-Paule Strub
  • Article
    | Open Access

    Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Here the authors present AF2Complex and show that application to the E. coli cytochrome biogenesis system I yields confident computational models for three sought-after assemblies.

    • Mu Gao
    • , Davi Nakajima An
    •  & Jeffrey Skolnick
  • Article
    | Open Access

    Predicting the structure of protein complexes is extremely difficult. Here, authors apply AlphaFold2 with optimized multiple sequence alignments to model complexes of interacting proteins, enabling prediction of both if and how proteins interact with state-of-art accuracy.

    • Patrick Bryant
    • , Gabriele Pozzati
    •  & Arne Elofsson
  • Article
    | Open Access

    AlphaFold2 has originally been developed to provide highly accurate predictions of protein monomer structures. Here, the authors present a simple adaptation of AlphaFold2 that enables structural modeling of peptide–protein complexes, and explore the underlying mechanisms and limitations of this approach.

    • Tomer Tsaban
    • , Julia K. Varga
    •  & Ora Schueler-Furman
  • Article
    | Open Access

    The authors present DeepRank, a deep learning framework for the data mining of large sets of 3D protein-protein interfaces (PPI). They use DeepRank to address two challenges in structural biology: distinguishing biological versus crystallographic PPIs in crystal structures, and secondly the ranking of docking models.

    • Nicolas Renaud
    • , Cunliang Geng
    •  & Li C. Xue
  • Article
    | Open Access

    Glycosyltransferases (GT) are proteins that display extensive sequence and functional variation on a subset of 3D folds. Here, the authors use interpretable deep learning to predict 3D folds from sequence without the need for sequence alignment, which also enables the prediction of GTs with new folds.

    • Rahil Taujale
    • , Zhongliang Zhou
    •  & Natarajan Kannan
  • Article
    | Open Access

    Vilazodone (VLZ) is a drug for the treatment of major depressive disorders that targets the serotonin transporter (SERT). Here, the authors combine pharmacology measurements and cryo-EM structural analysis to characterize VLZ binding to SERT and observe that VLZ exhibits non-competitive inhibition of serotonin transport and binds with nanomolar affinity to an allosteric site in SERT.

    • Per Plenge
    • , Dongxue Yang
    •  & Claus J. Loland
  • Article
    | Open Access

    The authors present flDPnn, a computational tool for disorder and disorder function predictions from protein sequences. flDPnn was assessed with the data from the “Critical Assessment of Protein Intrinsic Disorder Prediction” experiment and on an independent and low-similarity test dataset, which show that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions.

    • Gang Hu
    • , Akila Katuwawala
    •  & Lukasz Kurgan
  • Article
    | Open Access

    The class Frizzled of G protein-coupled receptors (GPCRs) consist of ten Frizzled (FZD1-10) subtypes and Smoothened (SMO). Here the Schulte laboratory demonstrates that FZDs differ substantially from SMO in receptor activation-associated conformational changes, while SMO manifests a preference for a straight TM6, the TM6 of FZDs is kinked upon activation.

    • Ainoleena Turku
    • , Hannes Schihada
    •  & Gunnar Schulte
  • Article
    | Open Access

    The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, the authors introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures.

    • Vladimir Gligorijević
    • , P. Douglas Renfrew
    •  & Richard Bonneau
  • Article
    | Open Access

    Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures.

    • Peng Xiong
    • , Ruibo Wu
    •  & Yaoqi Zhou
  • Article
    | Open Access

    Previous work identified goddard as a putative de novo evolved gene in Drosophila melanogaster. Here, the authors characterize the structure and function of the Goddard protein in D. melanogaster, and they infer its ancestral and extant structures across the Drosophila genus.

    • Andreas Lange
    • , Prajal H. Patel
    •  & Erich Bornberg-Bauer
  • Article
    | Open Access

    Our understanding of the residue-level details of protein interactions remains incomplete. Here, the authors show sequence coevolution can be used to infer interacting proteins with residue-level details, including predicting 467 interactions de novo in the Escherichia coli cell envelope proteome.

    • Anna G. Green
    • , Hadeer Elhabashy
    •  & Debora S. Marks
  • Article
    | Open Access

    An EGFR mutant with kinase domain duplication (EGFR-KDD) was previously identified in an index patient, but the functional and therapeutic implications remain unclear. Here, the authors show that KDD occurs in other ErbB receptors in multiple cancers, and characterize the mechanism and inhibition of EGFR-KDD.

    • Zhenfang Du
    • , Benjamin P. Brown
    •  & Christine M. Lovly
  • Article
    | Open Access

    Here the authors present DeepAccNet, a deep learning framework that estimates per-residue accuracy and residue-residue distance signed error in protein models, which are used to guide Rosetta protein structure refinement. Benchmarking suggests an improvement of accuracy prediction and refinement compared to other related state of the art methods.

    • Naozumi Hiranuma
    • , Hahnbeom Park
    •  & David Baker
  • Article
    | Open Access

    Family 1 glycosidases (GH1) are present in the three domains of life and share classical TIM-barrel fold. Structural and biochemical analyses of a resurrected ancestral GH1 enzyme reveal heme binding, not known in its modern descendants. Heme rigidifies the TIM-barrel and allosterically enhances catalysis.

    • Gloria Gamiz-Arco
    • , Luis I. Gutierrez-Rus
    •  & Jose M. Sanchez-Ruiz
  • Article
    | Open Access

    Most approaches for modeling the membrane protein complexes are not capable of incorporating the topological information provided by the membrane. Here authors present an integrative computational protocol for the modeling of membrane-associated protein assemblies, specifically complexes consisting of a membrane-embedded protein and a soluble partner.

    • Jorge Roel-Touris
    • , Brian Jiménez-García
    •  & Alexandre M. J. J. Bonvin
  • Article
    | Open Access

    Identifying variants capable of causing genetic disease is challenging. The authors use semisupervised learning to predict pathogenic missense variants and their impacts on protein structure and function, enabling a molecular mechanism-driven approach to studying different types of human disease.

    • Vikas Pejaver
    • , Jorge Urresti
    •  & Predrag Radivojac
  • Article
    | Open Access

    Therapeutic proteins are often conjugated with polymers, but separating the conjugate from unconjugated protein and free polymer is a major challenge. Here, the authors discover that proteins conjugated to charged or zwitterionic polymers maintain solubility in 100% ammonium sulfate, greatly simplifying purification.

    • Stefanie L. Baker
    • , Aravinda Munasinghe
    •  & Alan J. Russell
  • Article
    | Open Access

    Prediction of protein structures on the scale of genomes remains a challenge. Here the authors introduce a protein structure prediction method that uses deep learning to predict inter-atomic distances, torsion angles and hydrogen bonds, and apply it to predict the structures of 1475 Pfam domains.

    • Joe G. Greener
    • , Shaun M. Kandathil
    •  & David T. Jones
  • Article
    | Open Access

    Protein structure determination in complex biological samples is still challenging. Here, the authors develop a computational modeling-guided cross-linking mass spectrometry method, obtaining a high-resolution model of a 1.8 MDa protein assembly from cross-links detected in a mixture of human plasma and bacteria.

    • Simon Hauri
    • , Hamed Khakzad
    •  & Lars Malmström
  • Article
    | Open Access

    Further automation of NMR structure determination is needed to increase the throughput and accessibility of this method. Here the authors present 4D-CHAINS/autoNOE-Rosetta, a complete pipeline that allows rapid and fully automated structure determination from two highly complementary NMR datasets.

    • Thomas Evangelidis
    • , Santrupti Nerli
    •  & Konstantinos Tripsianes
  • Article
    | Open Access

    MHCII proteins bind and present both foreign and self-antigens to potentially activate CD4+ T cells via cognate T cell receptors (TCRs) during the adaptive immune response. Here, the authors combine NMR-detected H/D exchange with Markov modelling analysis to shed light on the dynamics of MHCII peptide exchange.

    • Marek Wieczorek
    • , Jana Sticht
    •  & Christian Freund
  • Article
    | Open Access

    Retinitis pigmentosa is often caused by mutations that affect the activity or transport of rhodopsin, but some mutations cause disease even though an apparently functional protein is produced. Here the authors show that three such enigmatic mutants retain scramblase activity but are unable to dimerize.

    • Birgit Ploier
    • , Lydia N. Caro
    •  & Anant K. Menon