Protein function predictions articles within Nature Communications

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  • Article
    | Open Access

    Bacteria use various defense systems to protect themselves from phage infection, and phages have evolved diverse counter-defense measures to overcome host defenses. Here, the authors use protein structural similarity and gene co-occurrence analyses for identification of new anti-phage and counter-defense systems.

    • Ning Duan
    • , Emily Hand
    •  & Akintunde Emiola

  • Article
    | Open Access

    Energetic local frustration in proteins may have been positively selected by evolution when related to function such as ligand binding, allostery and other. Here the authors present a methodology to analyze local frustration patterns within protein families and superfamilies.

    • Maria I. Freiberger
    • , Victoria Ruiz-Serra
    •  & Alfonso Valencia

  • Article
    | Open Access

    Glycopeptide antibiotics (GPAs) are microbial natural products synthesized by multiple enzymes, including a nonribosomal peptide synthetase for assembly of the peptide core. Here, the authors use computational techniques to infer a gene set for biosynthesis of an ancestral GPA, produce the peptide in a microbial host, and provide insights into the evolution of key enzymatic domains.

    • Mathias H. Hansen
    • , Martina Adamek
    •  & Nadine Ziemert

  • Article
    | Open Access

    Functional annotation of open reading frames in microbial genomes remains substantially incomplete. Here, Kim et al. present a deep learning model that utilizes transformer layers as a neural network architecture to predict specific catalytic functions for enzyme-encoding genes of unknown function.

    • Gi Bae Kim
    • , Ji Yeon Kim
    •  & Sang Yup Lee

  • Article
    | Open Access

    Predicting dynamic RNA-RBP interactions in diverse cell lines is an important challenge in unravelling RNA function and post-transcriptional regulatory mechanisms. Here, authors develop HDRNet, an end-to-end deep-learning-based framework for accurately predicting dynamic RBP binding events across various cellular conditions.

    • Haoran Zhu
    • , Yuning Yang
    •  & Xiangtao Li

  • Article
    | Open Access

    Understanding protein dynamics is a complex scientific challenge. Here, authors construct coarse-grained molecular potentials using artificial neural networks, significantly accelerating protein dynamics simulations while preserving their thermodynamics.

    • Maciej Majewski
    • , Adrià Pérez
    •  & Gianni De Fabritiis

  • Article
    | Open Access

    An important step in understanding and using proteins is to identify the residues that are important for function. The authors present a machine-learning based method to predict functional sites that leverages and combines the information available in protein sequences and structures.

    • Matteo Cagiada
    • , Sandro Bottaro
    •  & Kresten Lindorff-Larsen

  • Article
    | Open Access

    SARS-CoV-2’s rapid evolution threatens public health. Here, authors present a deep learning approach to forecast high-risk mutations that may appear in the future, aiding vaccine development and enhancing preparedness against future variants.

    • Wenkai Han
    • , Ningning Chen
    •  & Xin Gao

  • Article
    | Open Access

    High-affinity antibodies are often identified through directed evolution but deep leaning methods hold great promise. Here the authors report RESP, a pipeline for efficient identification of high affinity antibodies, and apply this to the PD-L1 antibody Atezolizumab.

    • Jonathan Parkinson
    • , Ryan Hard
    •  & Wei Wang

  • Article
    | Open Access

    G protein coupled receptors (GPCRs) can couple to different Gα protein subfamilies either selectively or promiscuously. Here, the authors use computational approach to show that selectivity determinants are at the periphery of the GPCR—G protein interface and that promiscuous GPCRs more frequently sample the common rather than selective contacts.

    • Manbir Sandhu
    • , Aaron Cho
    •  & Nagarajan Vaidehi

  • Article
    | Open Access

    Agonists selectively targeting GPR119 hold promise for treating metabolic disorders. Here, authors reveal that GPR119 adopts a non-canonical consensus structural scaffold with an extended ligand-binding pocket for chemically different agonists.

    • Yuxia Qian
    • , Jiening Wang
    •  & Anna Qiao

  • Article
    | Open Access

    The current work reports the structure of the human organic cation transporter 3 (OCT3 / SLC22A3) and provides the structural basis of its inhibition by two specific inhibitors, decynium-22 and corticosterone.

    • Basavraj Khanppnavar
    • , Julian Maier
    •  & Harald H. Sitte

  • Article
    | Open Access

    The function of many microbial genes is yet unknown. Here the authors repurposed natural language processing algorithms to explore “gene semantics” and infer function for thousands of genes with defense and secretion systems found to have the most discovery potential.

    • Danielle Miller
    • , Adi Stern
    •  & David Burstein

  • Article
    | Open Access

    Kinases are important drug targets, but predicting their activities from phosphoproteomics data remains challenging. While many existing prediction tools rely on phosphosite-specific quantitative data, Crowl et al. develop a kinase activity prediction algorithm that requires no phosphosite quantification.

    • Sam Crowl
    • , Ben T. Jordan
    •  & Kristen M. Naegle

  • Article
    | Open Access

    Identifying determinants of broadly neutralizing antibodies against hepatitis C virus (HCV) may guide HCV vaccine design. Here, the authors discover new anti-HCV antibodies using computational screening and analyze the amino acid composition and sequence-structure relationships in this antibody family.

    • Nina G. Bozhanova
    • , Andrew I. Flyak
    •  & Jens Meiler

  • Article
    | Open Access

    Here, we present TP-DB; a pattern-based search engine based on 1.67 million helices from the Protein Database (PDB). We demonstrate the utility of TP-DB in identifying microbe-specific antigens, as well as the design of antimicrobial peptides and Protein-protein interaction blockers.

    • Cheng-Yu Tsai
    • , Emmanuel Oluwatobi Salawu
    •  & Lee-Wei Yang

  • Article
    | Open Access

    With the rise in number of eukaryotic species being fully sequenced, large scale phylogenetic profiling can give insights on gene function, Here, the authors describe a machine-learning approach that integrates co-evolution across eukaryotic clades to predict gene function and functional interactions among human genes.

    • Doron Stupp
    • , Elad Sharon
    •  & Yuval Tabach

  • Article
    | Open Access

    Finding a biologically-relevant inductive bias for training DNNs on large fitness landscapes is challenging. Here, the authors propose a method called Epistatic Net that improves DNN prediction accuracy and interpretation speed by integrating the knowledge that higher-order epistatic interactions are usually sparse.

    • Amirali Aghazadeh
    • , Hunter Nisonoff
    •  & Kannan Ramchandran

  • Article
    | Open Access

    G protein-coupled receptors (GPCRs) are a critical target in modern drug development across a wide range of indications. Here the authors provide a comprehensive characterization of a typical GPCR, the angiotensin II (AngII) type 1 receptor (AT1R), and provide insight into its activation mechanism that suggest avenues for the design of allosteric GPCR modulators.

    • Shaoyong Lu
    • , Xinheng He
    •  & Jian Zhang

  • 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 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

    The ability to design functional sequences is central to protein engineering and biotherapeutics. Here the authors introduce a deep generative alignment-free model for sequence design applied to highly variable regions and design and test a diverse nanobody library with improved properties for selection experiments.

    • Jung-Eun Shin
    • , Adam J. Riesselman
    •  & Debora S. Marks

  • 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

    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

    Protein-ligand unbinding processes are out of reach for atomistic simulations due to time-scale involved. Here the authors demonstrate an approach relying on dissipation-corrected targeted molecular dynamics that enables to provide binding and unbinding rates with a speed-up of several orders of magnitude.

    • Steffen Wolf
    • , Benjamin Lickert
    •  & Gerhard Stock

  • Article
    | Open Access

    A genetic diagnosis remains unattainable for many individuals with a rare disease because of incomplete knowledge about the genetic basis of many diseases. Here, the authors present the web-based tool GADO (GeneNetwork Assisted Diagnostic Optimization) that uses public RNA-seq data for prioritization of candidate genes.

    • Patrick Deelen
    • , Sipko van Dam
    •  & Lude Franke

  • Article
    | Open Access

    Allostery is a fundamental principle of protein regulation that remains challenging to engineer. Here authors screen human Inward Rectifier K + Channel Kir2.1 for permissibility to domain insertions and propose that differential permissibility is a metric of latent allosteric capacity in Kir2.1.

    • Willow Coyote-Maestas
    • , Yungui He
    •  & Daniel Schmidt

  • Article
    | Open Access

    Chordoid glioma is a slow growing diencephalic tumor whose mutational landscape is poorly characterized. Here, the authors perform whole-exome and RNA-sequencing and find that 15 of 16 chordoid glioma cases studied harbor the same PRKCA mutation which results in enhanced proliferation.

    • Shai Rosenberg
    • , Iva Simeonova
    •  & Marc Sanson

  • Article
    | Open Access

    So far no enzymatic activity has been attributed to flagellin, the major component of bacterial flagella. Here the authors use bioinformatic analysis and identify a metallopeptidase insertion in flagellins from 74 bacterial species and show that recombinant flagellin and flagellar filaments have proteolytic activity.

    • Ulrich Eckhard
    • , Hina Bandukwala
    •  & Andrew C. Doxey

  • Article
    | Open Access

    Protein stability modulation by E3 ubiquitin ligases is an important layer of functional regulation, but screening for E3 ligase-substrate interactions is time-consuming and costly. Here, the authors take an in silico naïve Bayesian classifier approach to integrate multiple lines of evidence for E3-substrate prediction, enabling prediction of the proteome-wide human E3 ligase interaction network.

    • Yang Li
    • , Ping Xie
    •  & Fuchu He

  • Article
    | Open Access

    Plastoquinone (PLQ) shuttles electrons between photosystem II (PSII) and cytochrome b6f. Here the authors perform molecular dynamics simulations and propose that PLQ enters the exchange cavity of PSII by a promiscuous diffusion mechanism whereby three different channels each act as entry and exit points.

    • Floris J. Van Eerden
    • , Manuel N. Melo
    •  & Siewert J. Marrink

  • Article
    | Open Access

    Proteins are sometimes implicated in separate and seemingly unrelated processes, so called moonlighting functions. Here the authors use bioinformatics tools to identify extreme multifunctional proteins and define a signature of extreme multifunctionality.

    • Charles E. Chapple
    • , Benoit Robisson
    •  & Christine Brun

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