40,736 research outputs found

    From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

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    ©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein

    Kernel-based machine learning protocol for predicting DNA-binding proteins

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    DNA-binding proteins (DNA-BPs) play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Attempts have been made to identify DNA-BPs based on their sequence and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines (SVMs). Information used for classification is derived from characteristics that include surface and overall composition, overall charge and positive potential patches on the protein surface. In total 121 DNA-BPs and 238 non-binding proteins are used to build and evaluate the protocol. In self-consistency, accuracy value of 100% has been achieved. For cross-validation (CV) optimization over entire dataset, we report an accuracy of 90%. Using leave 1-pair holdout evaluation, the accuracy of 86.3% has been achieved. When we restrict the dataset to less than 20% sequence identity amongst the proteins, the holdout accuracy is achieved at 85.8%. Furthermore, seven DNA-BPs with unbounded structures are all correctly predicted. The current performances are better than results published previously. The higher accuracy value achieved here originates from two factors: the ability of the SVM to handle features that demonstrate a wide range of discriminatory power and, a different definition of the positive patch. Since our protocol does not lean on sequence or structural homology, it can be used to identify or predict proteins with DNA-binding function(s) regardless of their homology to the known ones

    A creature with a hundred waggly tails: intrinsically disordered proteins in the ribosome

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    This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Intrinsic disorder (i.e., lack of a unique 3-D structure) is a common phenomenon, and many biologically active proteins are disordered as a whole, or contain long disordered regions. These intrinsically disordered proteins/regions constitute a significant part of all proteomes, and their functional repertoire is complementary to functions of ordered proteins. In fact, intrinsic disorder represents an important driving force for many specific functions. An illustrative example of such disorder-centric functional class is RNA-binding proteins. In this study, we present the results of comprehensive bioinformatics analyses of the abundance and roles of intrinsic disorder in 3,411 ribosomal proteins from 32 species. We show that many ribosomal proteins are intrinsically disordered or hybrid proteins that contain ordered and disordered domains. Predicted globular domains of many ribosomal proteins contain noticeable regions of intrinsic disorder. We also show that disorder in ribosomal proteins has different characteristics compared to other proteins that interact with RNA and DNA including overall abundance, evolutionary conservation, and involvement in protein–protein interactions. Furthermore, intrinsic disorder is not only abundant in the ribosomal proteins, but we demonstrate that it is absolutely necessary for their various functions

    Frustration in Biomolecules

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    Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

    Unique features of Plasmids among different Citrobacter species

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    The _Citrobacter_ plasmids are supposed to represent the host genetic association within the living bacterial cell. The plasmids impart various beneficial characteristics to the host, helping it to retain suitable characteristics for adaptation as well as evolution. The study aims at understanding the role of prophage in influencing host functional characteristics by horizontal gene transfer or as whole plasmids. The _Citrobacter_ plasmid can be understood by analyzing many hypothetical protein sequences within its genome. Our study included 82 hypothetical proteins in 5 _Citrobacter_ plasmids genomes. The function predictions in 31 hypothetical proteins and 3-D structures were predicted for 11 protein sequences using PS2 server. The probable function prediction was done by using Bioinformatics web tools like CDD-BLAST, INTERPROSCAN, PFAM and COGs by searching sequence databases for the presence of orthologous enzymatic conserved domains in the hypothetical sequences. This study identified many uncharacterized proteins, whose roles are yet to be discovered in _Citrobacter_ plasmids. These results for unknown proteins within plasmids can be used in linking the genetic interactions of _Citrobacter_ species and their functions in different environmental conditions

    Functional Diversity and Structural Disorder in the Human Ubiquitination Pathway

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    The ubiquitin-proteasome system plays a central role in cellular regulation and protein quality control (PQC). The system is built as a pyramid of increasing complexity, with two E1 (ubiquitin activating), few dozen E2 (ubiquitin conjugating) and several hundred E3 (ubiquitin ligase) enzymes. By collecting and analyzing E3 sequences from the KEGG BRITE database and literature, we assembled a coherent dataset of 563 human E3s and analyzed their various physical features. We found an increase in structural disorder of the system with multiple disorder predictors (IUPred - E1: 5.97%, E2: 17.74%, E3: 20.03%). E3s that can bind E2 and substrate simultaneously (single subunit E3, ssE3) have significantly higher disorder (22.98%) than E3s in which E2 binding (multi RING-finger, mRF, 0.62%), scaffolding (6.01%) and substrate binding (adaptor/substrate recognition subunits, 17.33%) functions are separated. In ssE3s, the disorder was localized in the substrate/adaptor binding domains, whereas the E2-binding RING/HECT-domains were structured. To demonstrate the involvement of disorder in E3 function, we applied normal modes and molecular dynamics analyses to show how a disordered and highly flexible linker in human CBL (an E3 that acts as a regulator of several tyrosine kinase-mediated signalling pathways) facilitates long-range conformational changes bringing substrate and E2-binding domains towards each other and thus assisting in ubiquitin transfer. E3s with multiple interaction partners (as evidenced by data in STRING) also possess elevated levels of disorder (hubs, 22.90% vs. non-hubs, 18.36%). Furthermore, a search in PDB uncovered 21 distinct human E3 interactions, in 7 of which the disordered region of E3s undergoes induced folding (or mutual induced folding) in the presence of the partner. In conclusion, our data highlights the primary role of structural disorder in the functions of E3 ligases that manifests itself in the substrate/adaptor binding functions as well as the mechanism of ubiquitin transfer by long-range conformational transitions. © 2013 Bhowmick et al

    Abundance of intrinsic disorder in SV-IV, a multifunctional androgen-dependent protein secreted from rat seminal vesicle

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    The potent immunomodulatory, anti-inflammatory and procoagulant properties of the
protein no. 4 secreted from the rat seminal vesicle epithelium (SV-IV) have been
previously found to be modulated by a supramolecular monomer-trimer equilibrium.
More structural details that integrate experimental data into a predictive framework
have recently been reported. Unfortunately, homology modelling and fold-recognition
strategies were not successful in creating a theoretical model of the structural
organization of SV-IV. It was inferred that the global structure of SV-IV is not similar
to any protein of known three-dimensional structure. Reversing the classical approach
to the sequence-structure-function paradigm, in this paper we report on novel
information obtained by comparing physicochemical parameters of SV-IV with two
datasets made of intrinsically unfolded and ideally globular proteins. In addition, we
have analysed the SV-IV sequence by several publicly available disorder-oriented
predictors. Overall, disorder predictions and a re-examination of existing experimental
data strongly suggest that SV-IV needs large plasticity to efficiently interact with the
different targets that characterize its multifaceted biological function and should be
therefore better classified as an intrinsically disordered protein
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