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Article: ZEPPI: proteome-scale sequence-based evaluation of protein-protein interaction models.

Zhao, Haiqing / Murray, Diana / Petrey, Donald / Honig, Barry

Research square

2023

Abstract: We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence co-evolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated ... ...

Abstract	We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence co-evolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. ZEPPI can be implemented on a proteome-wide scale as evidenced by calculations on millions of structural models of dimeric complexes in the
Language	English
Publishing date	2023-09-18
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-3289791/v1
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Article: PrePPI: A structure informed proteome-wide database of protein-protein interactions.

Petrey, Donald / Zhao, Haiqing / Trudeau, Stephen / Murray, Diana / Honig, Barry

bioRxiv : the preprint server for biology

2023

Language	English
Publishing date	2023-02-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.02.27.530276
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Article ; Online: PrePPI: A Structure Informed Proteome-wide Database of Protein–Protein Interactions

Petrey, Donald / Zhao, Haiqing / Trudeau, Stephen J / Murray, Diana / Honig, Barry

Journal of Molecular Biology. 2023 July, v. 435, no. 14 p.168052-

2023

Abstract: We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio ...

Abstract	We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein–protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.
Keywords	Bayesian theory ; Escherichia coli ; databases ; humans ; molecular biology ; protein-protein interactions ; proteome ; database ; alphafold models ; structural modeling ; non-structural evidence
Language	English
Dates of publication	2023-07
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2023.168052
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Article ; Online: PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions.

Petrey, Donald / Zhao, Haiqing / Trudeau, Stephen J / Murray, Diana / Honig, Barry

Journal of molecular biology

2023 Volume 435, Issue 14, Page(s) 168052

Abstract	We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.
MeSH term(s)	Humans ; Bayes Theorem ; Databases, Protein ; Escherichia coli/metabolism ; Protein Interaction Mapping ; Proteome/metabolism
Chemical Substances	Proteome
Language	English
Publishing date	2023-03-17
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2023.168052
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Article ; Online: Integrating 3D structural information into systems biology.

Murray, Diana / Petrey, Donald / Honig, Barry

The Journal of biological chemistry

2021 Volume 296, Page(s) 100562

Abstract: Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in ...

Abstract	Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus-host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.
MeSH term(s)	Databases, Protein ; Protein Conformation ; Protein Interaction Maps ; Proteins/chemistry ; Systems Biology
Chemical Substances	Proteins
Language	English
Publishing date	2021-03-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	2997-x
ISSN	1083-351X ; 0021-9258
ISSN (online)	1083-351X
ISSN	0021-9258
DOI	10.1016/j.jbc.2021.100562
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Article ; Online: PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.

Trudeau, Stephen J / Hwang, Howook / Mathur, Deepika / Begum, Kamrun / Petrey, Donald / Murray, Diana / Honig, Barry

Protein science : a publication of the Protein Society

2023 Volume 32, Issue 4, Page(s) e4594

Abstract: We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural ... ...

Abstract	We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.
MeSH term(s)	Humans ; Bayes Theorem ; Databases, Chemical ; Proteins/chemistry ; Algorithms ; Databases, Protein
Chemical Substances	Proteins
Language	English
Publishing date	2023-02-09
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1106283-6
ISSN	1469-896X ; 0961-8368
ISSN (online)	1469-896X
ISSN	0961-8368
DOI	10.1002/pro.4594
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Article ; Online: Structural bioinformatics of the interactome.

Petrey, Donald / Honig, Barry

Annual review of biophysics

2014 Volume 43, Page(s) 193–210

Abstract: The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate ... ...

Abstract	The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.
MeSH term(s)	Animals ; Computational Biology/methods ; Humans ; Protein Interaction Maps ; Proteins/chemistry ; Proteins/metabolism
Chemical Substances	Proteins
Language	English
Publishing date	2014-04-17
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2434725-5
ISSN	1936-1238 ; 1936-122X
ISSN (online)	1936-1238
ISSN	1936-122X
DOI	10.1146/annurev-biophys-051013-022726
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Article ; Online: Structure-based prediction of ligand-protein interactions on a genome-wide scale.

Hwang, Howook / Dey, Fabian / Petrey, Donald / Honig, Barry

Proceedings of the National Academy of Sciences of the United States of America

2017 Volume 114, Issue 52, Page(s) 13685–13690

Abstract: We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly ...

Abstract	We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner's performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.
MeSH term(s)	Databases, Protein ; Genome ; Ligands ; Protein Kinase Inhibitors/chemistry ; Proteins/chemistry ; Proteins/genetics ; Proteins/metabolism
Chemical Substances	Ligands ; Protein Kinase Inhibitors ; Proteins
Language	English
Publishing date	2017--26
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.1705381114
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Article ; Online: A hybrid method for protein-protein interface prediction.

Hwang, Howook / Petrey, Donald / Honig, Barry

Protein science : a publication of the Protein Society

2016 Volume 25, Issue 1, Page(s) 159–165

Abstract: The growing structural coverage of proteomes is making structural comparison a powerful tool for function annotation. Such template-based approaches are based on the observation that structural similarity is often sufficient to infer similar function. ... ...

Abstract	The growing structural coverage of proteomes is making structural comparison a powerful tool for function annotation. Such template-based approaches are based on the observation that structural similarity is often sufficient to infer similar function. However, it seems clear that, in addition to structural similarity, the specific characteristics of a given protein should also be taken into account in predicting function. Here we describe PredUs 2.0, a method to predict regions on a protein surface likely to bind other proteins, that is, interfacial residues. PredUs 2.0 is based on the PredUs method that is entirely template-based and uses known binding sites in structurally similar proteins to predict interfacial residues. PredUs 2.0 uses a Bayesian approach to combine the template-based scoring of PredUs with a score that reflects the propensities of individual amino acids to be in interfaces. PredUs 2.0 includes a novel protein size dependent metric to determine the number of residues that should be reported as interfacial. PredUs 2.0 significantly outperforms PredUs as well as other published interface prediction methods.
MeSH term(s)	Amino Acids/chemistry ; Computational Biology ; Protein Conformation ; Protein Interaction Domains and Motifs ; Protein Interaction Mapping ; Proteins/chemistry
Chemical Substances	Amino Acids ; Proteins
Language	English
Publishing date	2016-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1106283-6
ISSN	1469-896X ; 0961-8368
ISSN (online)	1469-896X
ISSN	0961-8368
DOI	10.1002/pro.2744
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Article ; Online: Predicting peptide-mediated interactions on a genome-wide scale.

Chen, T Scott / Petrey, Donald / Garzon, Jose Ignacio / Honig, Barry

PLoS computational biology

2015 Volume 11, Issue 5, Page(s) e1004248

Abstract: We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes ...

Abstract	We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI's coverage of the human protein interactome.
MeSH term(s)	Algorithms ; Bayes Theorem ; Computational Biology ; Databases, Protein/statistics & numerical data ; Genome, Human ; Humans ; Likelihood Functions ; Models, Biological ; Protein Interaction Domains and Motifs ; Protein Interaction Mapping/statistics & numerical data ; Proteomics/statistics & numerical data ; Support Vector Machine
Language	English
Publishing date	2015-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1004248
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