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Ng TA, Rashid S, Kwoh CK. Virulence network of interacting domains of influenza a and mouse proteins. FRONTIERS IN BIOINFORMATICS 2023; 3:1123993. [PMID: 36875146 PMCID: PMC9982101 DOI: 10.3389/fbinf.2023.1123993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/03/2023] [Indexed: 02/19/2023] Open
Abstract
There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50 values) prominently displayed. The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.
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Affiliation(s)
- Teng Ann Ng
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Shamima Rashid
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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2
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Narykov O, Johnson NT, Korkin D. Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning. Cell Rep 2021; 37:110045. [PMID: 34818539 DOI: 10.1016/j.celrep.2021.110045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/21/2021] [Accepted: 11/02/2021] [Indexed: 10/19/2022] Open
Abstract
Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.
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Affiliation(s)
- Oleksandr Narykov
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Nathan T Johnson
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA; Harvard Program in Therapeutic Sciences, Harvard Medical School, and Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dmitry Korkin
- Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA.
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3
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Slater O, Miller B, Kontoyianni M. Decoding Protein-protein Interactions: An Overview. Curr Top Med Chem 2021; 20:855-882. [PMID: 32101126 DOI: 10.2174/1568026620666200226105312] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 12/24/2022]
Abstract
Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.
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Affiliation(s)
- Olivia Slater
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Bethany Miller
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Maria Kontoyianni
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
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4
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Narykov O, Bogatov D, Korkin D. DISPOT: a simple knowledge-based protein domain interaction statistical potential. Bioinformatics 2020; 35:5374-5378. [PMID: 31350874 PMCID: PMC6954640 DOI: 10.1093/bioinformatics/btz587] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 06/17/2019] [Accepted: 07/22/2019] [Indexed: 01/01/2023] Open
Abstract
MOTIVATION The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task. RESULTS Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions. AVAILABILITY AND IMPLEMENTATION DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: https://github.com/korkinlab/dispot and standalone docker images on DockerHub: https://hub.docker.com/r/korkinlab/dispot. The web server is freely available at http://dispot.korkinlab.org/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Oleksandr Narykov
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Dmytro Bogatov
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Dmitry Korkin
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.,Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
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5
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Pawar G, Madden JC, Ebbrell D, Firman JW, Cronin MTD. In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR. Front Pharmacol 2019; 10:561. [PMID: 31244651 PMCID: PMC6580867 DOI: 10.3389/fphar.2019.00561] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 05/03/2019] [Indexed: 12/14/2022] Open
Abstract
A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.
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Affiliation(s)
| | | | | | | | - Mark T. D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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6
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Peptide Derivatives of Erythropoietin in the Treatment of Neuroinflammation and Neurodegeneration. THERAPEUTIC PROTEINS AND PEPTIDES 2018; 112:309-357. [DOI: 10.1016/bs.apcsb.2018.01.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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7
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Kandoth PK, Liu S, Prenger E, Ludwig A, Lakhssassi N, Heinz R, Zhou Z, Howland A, Gunther J, Eidson S, Dhroso A, LaFayette P, Tucker D, Johnson S, Anderson J, Alaswad A, Cianzio SR, Parrott WA, Korkin D, Meksem K, Mitchum MG. Systematic Mutagenesis of Serine Hydroxymethyltransferase Reveals an Essential Role in Nematode Resistance. PLANT PHYSIOLOGY 2017; 175:1370-1380. [PMID: 28912378 PMCID: PMC5664460 DOI: 10.1104/pp.17.00553] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 09/11/2017] [Indexed: 05/27/2023]
Abstract
Rhg4 is a major genetic locus that contributes to soybean cyst nematode (SCN) resistance in the Peking-type resistance of soybean (Glycine max), which also requires the rhg1 gene. By map-based cloning and functional genomic approaches, we previously showed that the Rhg4 gene encodes a predicted cytosolic serine hydroxymethyltransferase (GmSHMT08); however, the novel gain of function of GmSHMT08 in SCN resistance remains to be characterized. Using a forward genetic screen, we identified an allelic series of GmSHMT08 mutants that shed new light on the mechanistic aspects of GmSHMT08-mediated resistance. The new mutants provide compelling genetic evidence that Peking-type rhg1 resistance in cv Forrest is fully dependent on the GmSHMT08 gene and demonstrates that this resistance is mechanistically different from the PI 88788-type of resistance that only requires rhg1 We also demonstrated that rhg1-a from cv Forrest, although required, does not exert selection pressure on the nematode to shift from HG type 7, which further validates the bigenic nature of this resistance. Mapping of the identified mutations onto the SHMT structural model uncovered key residues for structural stability, ligand binding, enzyme activity, and protein interactions, suggesting that GmSHMT08 has additional functions aside from its main enzymatic role in SCN resistance. Lastly, we demonstrate the functionality of the GmSHMT08 SCN resistance gene in a transgenic soybean plant.
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Affiliation(s)
- Pramod K Kandoth
- Division of Plant Sciences and Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Shiming Liu
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, Illinois 62901
| | - Elizabeth Prenger
- Division of Plant Sciences and Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Andrew Ludwig
- Division of Plant Sciences and Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Naoufal Lakhssassi
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, Illinois 62901
| | - Robert Heinz
- Division of Plant Sciences and Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Zhou Zhou
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, Illinois 62901
| | - Amanda Howland
- Division of Plant Sciences and Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Joshua Gunther
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, Illinois 62901
| | - Samantha Eidson
- Mathematics and Computer Science Department, Fontbonne University, St. Louis, Missouri 63105
| | - Andi Dhroso
- Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609
| | - Peter LaFayette
- Center for Applied Genetic Technologies, University of Georgia, Athens, Georgia 30602
| | - Donna Tucker
- Center for Applied Genetic Technologies, University of Georgia, Athens, Georgia 30602
| | - Sarah Johnson
- Center for Applied Genetic Technologies, University of Georgia, Athens, Georgia 30602
| | - James Anderson
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, Illinois 62901
| | - Alaa Alaswad
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, Illinois 62901
| | | | - Wayne A Parrott
- Center for Applied Genetic Technologies, University of Georgia, Athens, Georgia 30602
| | - Dmitry Korkin
- Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609
| | - Khalid Meksem
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, Illinois 62901
| | - Melissa G Mitchum
- Division of Plant Sciences and Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
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8
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Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T. Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. Sci Rep 2017; 7:10480. [PMID: 28874689 PMCID: PMC5585393 DOI: 10.1038/s41598-017-09654-8] [Citation(s) in RCA: 488] [Impact Index Per Article: 69.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 07/28/2017] [Indexed: 01/01/2023] Open
Abstract
Cellular processes often depend on interactions between proteins and the formation of macromolecular complexes. The impairment of such interactions can lead to deregulation of pathways resulting in disease states, and it is hence crucial to gain insights into the nature of macromolecular assemblies. Detailed structural knowledge about complexes and protein-protein interactions is growing, but experimentally determined three-dimensional multimeric assemblies are outnumbered by complexes supported by non-structural experimental evidence. Here, we aim to fill this gap by modeling multimeric structures by homology, only using amino acid sequences to infer the stoichiometry and the overall structure of the assembly. We ask which properties of proteins within a family can assist in the prediction of correct quaternary structure. Specifically, we introduce a description of protein-protein interface conservation as a function of evolutionary distance to reduce the noise in deep multiple sequence alignments. We also define a distance measure to structurally compare homologous multimeric protein complexes. This allows us to hierarchically cluster protein structures and quantify the diversity of alternative biological assemblies known today. We find that a combination of conservation scores, structural clustering, and classical interface descriptors, can improve the selection of homologous protein templates leading to reliable models of protein complexes.
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Affiliation(s)
- Martino Bertoni
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Florian Kiefer
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Marco Biasini
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Lorenza Bordoli
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Torsten Schwede
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland. .,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland.
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9
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Kuang X, Dhroso A, Han JG, Shyu CR, Korkin D. DOMMINO 2.0: integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:bav114. [PMID: 26827237 PMCID: PMC4733329 DOI: 10.1093/database/bav114] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2015] [Accepted: 11/16/2015] [Indexed: 11/14/2022]
Abstract
Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1,040,000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43,000 RNA-mediated interactions, and ∼12,000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org.
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Affiliation(s)
- Xingyan Kuang
- Informatics Institute, University of Missouri, Columbia, MO, USA
| | - Andi Dhroso
- Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Jing Ginger Han
- Informatics Institute, University of Missouri, Columbia, MO, USA
| | - Chi-Ren Shyu
- Informatics Institute, University of Missouri, Columbia, MO, USA, Department of Electrical and Computer Engineering, Department of Computer Science, University of Missouri, Columbia, MO, USA
| | - Dmitry Korkin
- Department of Computer Science and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA,
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10
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Cui H, Dhroso A, Johnson N, Korkin D. The variation game: Cracking complex genetic disorders with NGS and omics data. Methods 2015; 79-80:18-31. [PMID: 25944472 DOI: 10.1016/j.ymeth.2015.04.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2014] [Revised: 03/27/2015] [Accepted: 04/17/2015] [Indexed: 12/14/2022] Open
Abstract
Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.
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Affiliation(s)
- Hongzhu Cui
- Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, United States
| | - Andi Dhroso
- Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, United States
| | - Nathan Johnson
- Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, United States
| | - Dmitry Korkin
- Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, United States; Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, United States
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11
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Pang B, Schlessman D, Kuang X, Zhao N, Shyu D, Korkin D, Shyu CR. An Integrated Approach to Sequence-Independent Local Alignment of Protein Binding Sites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:298-308. [PMID: 26357218 DOI: 10.1109/tcbb.2014.2355208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Accurate alignment of protein-protein binding sites can aid in protein docking studies and constructing templates for predicting structure of protein complexes, along with in-depth understanding of evolutionary and functional relationships. However, over the past three decades, structural alignment algorithms have focused predominantly on global alignments with little effort on the alignment of local interfaces. In this paper, we introduce the PBSalign (Protein-protein Binding Site alignment) method, which integrates techniques in graph theory, 3D localized shape analysis, geometric scoring, and utilization of physicochemical and geometrical properties. Computational results demonstrate that PBSalign is capable of identifying similar homologous and analogous binding sites accurately and performing alignments with better geometric match measures than existing protein-protein interface comparison tools. The proportion of better alignment quality generated by PBSalign is 46, 56, and 70 percent more than iAlign as judged by the average match index (MI), similarity index (SI), and structural alignment score (SAS), respectively. PBSalign provides the life science community an efficient and accurate solution to binding-site alignment while striking the balance between topological details and computational complexity.
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12
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Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine. ACTA ACUST UNITED AC 2014. [DOI: 10.1155/2014/471836] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.
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13
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Zhao N, Han JG, Shyu CR, Korkin D. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning. PLoS Comput Biol 2014; 10:e1003592. [PMID: 24784581 PMCID: PMC4006705 DOI: 10.1371/journal.pcbi.1003592] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Accepted: 03/13/2014] [Indexed: 12/31/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/.
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Affiliation(s)
- Nan Zhao
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
| | - Jing Ginger Han
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
| | - Chi-Ren Shyu
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
| | - Dmitry Korkin
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
- Bond Life Science Center, University of Missouri, Columbia, Missouri, United States of America
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14
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Corominas R, Yang X, Lin GN, Kang S, Shen Y, Ghamsari L, Broly M, Rodriguez M, Tam S, Trigg SA, Fan C, Yi S, Tasan M, Lemmens I, Kuang X, Zhao N, Malhotra D, Michaelson JJ, Vacic V, Calderwood MA, Roth FP, Tavernier J, Horvath S, Salehi-Ashtiani K, Korkin D, Sebat J, Hill DE, Hao T, Vidal M, Iakoucheva LM. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism. Nat Commun 2014; 5:3650. [PMID: 24722188 PMCID: PMC3996537 DOI: 10.1038/ncomms4650] [Citation(s) in RCA: 108] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2013] [Accepted: 03/14/2014] [Indexed: 02/04/2023] Open
Abstract
Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.
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Affiliation(s)
- Roser Corominas
- Department of Psychiatry, University of California San Diego, La Jolla, California 92093, USA,These authors contributed equally to this work
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA,These authors contributed equally to this work
| | - Guan Ning Lin
- Department of Psychiatry, University of California San Diego, La Jolla, California 92093, USA,These authors contributed equally to this work
| | - Shuli Kang
- Department of Psychiatry, University of California San Diego, La Jolla, California 92093, USA,These authors contributed equally to this work
| | - Yun Shen
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Lila Ghamsari
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA,Present address: Columbia University, New York, New York 10032, USA
| | - Martin Broly
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Maria Rodriguez
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Stanley Tam
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Shelly A. Trigg
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA,Present address: Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | - Changyu Fan
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Song Yi
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Murat Tasan
- Donnelly Centre and Departments of Molecular Genetics & Computer Science, University of Toronto, and Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, Ontario, Canada M5S 3E1
| | - Irma Lemmens
- Department of Medical Protein Research, VIB, and Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent B-9000, Belgium
| | - Xingyan Kuang
- Department of Computer Science and Informatics Institute, University of Missouri, Columbia, Missouri 65203, USA
| | - Nan Zhao
- Department of Computer Science and Informatics Institute, University of Missouri, Columbia, Missouri 65203, USA
| | - Dheeraj Malhotra
- Beyster Center for Genomics of Psychiatric Diseases and Department of Psychiatry, University of California San Diego, La Jolla, California 92093, USA
| | - Jacob J. Michaelson
- Beyster Center for Genomics of Psychiatric Diseases and Department of Psychiatry, University of California San Diego, La Jolla, California 92093, USA,Present address: Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242, USA
| | | | - Michael A. Calderwood
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Frederick P. Roth
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA,Donnelly Centre and Departments of Molecular Genetics & Computer Science, University of Toronto, and Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, Ontario, Canada M5S 3E1
| | - Jan Tavernier
- Department of Medical Protein Research, VIB, and Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent B-9000, Belgium
| | - Steve Horvath
- Department of Human Genetics and Biostatistics, University of California, Los Angeles, California 90095, USA
| | - Kourosh Salehi-Ashtiani
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA,Present address: Division of Science and Math, and Center for Genomics and Systems Biology, New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi, United Arab Emirates
| | - Dmitry Korkin
- Department of Computer Science and Informatics Institute, University of Missouri, Columbia, Missouri 65203, USA
| | - Jonathan Sebat
- Beyster Center for Genomics of Psychiatric Diseases and Department of Psychiatry, University of California San Diego, La Jolla, California 92093, USA
| | - David E. Hill
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA,
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA,
| | - Lilia M. Iakoucheva
- Department of Psychiatry, University of California San Diego, La Jolla, California 92093, USA,
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15
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Warren S, Wan XF, Conant G, Korkin D. Extreme evolutionary conservation of functionally important regions in H1N1 influenza proteome. PLoS One 2013; 8:e81027. [PMID: 24282564 PMCID: PMC3839886 DOI: 10.1371/journal.pone.0081027] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Accepted: 10/08/2013] [Indexed: 12/31/2022] Open
Abstract
The H1N1 subtype of influenza A virus has caused two of the four documented pandemics and is responsible for seasonal epidemic outbreaks, presenting a continuous threat to public health. Co-circulating antigenically divergent influenza strains significantly complicates vaccine development and use. Here, by combining evolutionary, structural, functional, and population information about the H1N1 proteome, we seek to answer two questions: (1) do residues on the protein surfaces evolve faster than the protein core residues consistently across all proteins that constitute the influenza proteome? and (2) in spite of the rapid evolution of surface residues in influenza proteins, are there any protein regions on the protein surface that do not evolve? To answer these questions, we first built phylogenetically-aware models of the patterns of surface and interior substitutions. Employing these models, we found a single coherent pattern of faster evolution on the protein surfaces that characterizes all influenza proteins. The pattern is consistent with the events of inter-species reassortment, the worldwide introduction of the flu vaccine in the early 80's, as well as the differences caused by the geographic origins of the virus. Next, we developed an automated computational pipeline to comprehensively detect regions of the protein surface residues that were 100% conserved over multiple years and in multiple host species. We identified conserved regions on the surface of 10 influenza proteins spread across all avian, swine, and human strains; with the exception of a small group of isolated strains that affected the conservation of three proteins. Surprisingly, these regions were also unaffected by genetic variation in the pandemic 2009 H1N1 viral population data obtained from deep sequencing experiments. Finally, the conserved regions were intrinsically related to the intra-viral macromolecular interaction interfaces. Our study may provide further insights towards the identification of novel protein targets for influenza antivirals.
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Affiliation(s)
- Samantha Warren
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
| | - Xiu-Feng Wan
- Department of Basic Sciences, Mississippi State University, Mississippi State, Mississippi, United States of America
| | - Gavin Conant
- Division of Animal Sciences, University of Missouri, Columbia, Missouri, United States of America
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
| | - Dmitry Korkin
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
- Bond Life Science Center, University of Missouri, Columbia, Missouri, United States of America
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16
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Pang B, Kuang X, Zhao N, Korkin D, Shyu CR. PBSword: a web server for searching similar protein-protein binding sites. Nucleic Acids Res 2012; 40:W428-34. [PMID: 22689645 PMCID: PMC3394332 DOI: 10.1093/nar/gks527] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
PBSword is a web server designed for efficient and accurate comparisons and searches of geometrically similar protein–protein binding sites from a large-scale database. The basic idea of PBSword is that each protein binding site is first represented by a high-dimensional vector of ‘visual words’, which characterizes both the global and local shape features of the binding site. It then uses a scalable indexing technique to search for those binding sites whose visual words representations are similar to that of the query binding site. Our system is able to return ranked results of binding sites in short time from a database of 194 322 domain–domain binding sites. PBSword supports query by protein ID and by new structures uploaded by users. PBSword is a useful tool to investigate functional connections among proteins based on the local structures of binding site and has potential applications to protein–protein docking and drug discovery. The system is hosted at http://pbs.rnet.missouri.edu.
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Affiliation(s)
- Bin Pang
- Informatics Institute and Department of Computer Science, University of Missouri, Columbia, MO, USA
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17
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Pang B, Zhao N, Korkin D, Shyu CR. Fast protein binding site comparisons using visual words representation. ACTA ACUST UNITED AC 2012; 28:1345-52. [PMID: 22492639 DOI: 10.1093/bioinformatics/bts138] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Finding geometrically similar protein binding sites is crucial for understanding protein functions and can provide valuable information for protein-protein docking and drug discovery. As the number of known protein-protein interaction structures has dramatically increased, a high-throughput and accurate protein binding site comparison method is essential. Traditional alignment-based methods can provide accurate correspondence between the binding sites but are computationally expensive. RESULTS In this article, we present a novel method for the comparisons of protein binding sites using a 'visual words' representation (PBSword). We first extract geometric features of binding site surfaces and build a vocabulary of visual words by clustering a large set of feature descriptors. We then describe a binding site surface with a high-dimensional vector that encodes the frequency of visual words, enhanced by the spatial relationships among them. Finally, we measure the similarity of binding sites by utilizing metric space operations, which provide speedy comparisons between protein binding sites. Our experimental results show that PBSword achieves a comparable classification accuracy to an alignment-based method and improves accuracy of a feature-based method by 36% on a non-redundant dataset. PBSword also exhibits a significant efficiency improvement over an alignment-based method.
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Affiliation(s)
- Bin Pang
- Informatics Institute, University of Missouri, Columbia, MO 65211, USA
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