1
|
Guo X, Zheng Z, Cheong KH, Zou Q, Tiwari P, Ding Y. Sequence homology score-based deep fuzzy network for identifying therapeutic peptides. Neural Netw 2024; 178:106458. [PMID: 38901093 DOI: 10.1016/j.neunet.2024.106458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 06/22/2024]
Abstract
The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923).
Collapse
Affiliation(s)
- Xiaoyi Guo
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Quzhou People's Hospital, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000, PR China; Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore.
| | - Ziyu Zheng
- Department of Mathematical Sciences, University of Nottingham Ningbo, Ningbo, 315100, PR China.
| | - Kang Hao Cheong
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore; College of Computing and Data Science, Nanyang Technological University, S639798, Singapore.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, PR China.
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden.
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, PR China.
| |
Collapse
|
2
|
Thota V, Puddu V, Perry CC. Phage Display Panning on Silica: Optimization of Elution Conditions for Selection of Strong Binders. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2024; 40. [PMID: 39014914 PMCID: PMC11295192 DOI: 10.1021/acs.langmuir.4c01108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 07/10/2024] [Accepted: 07/10/2024] [Indexed: 07/18/2024]
Abstract
Phage display panning is a powerful tool to select strong peptide binders to a given target, and when applied to inorganic materials (e.g., silica) as a target, it provides information on binding events and molecular recognition at the peptide-mineral interface. The panning process has limitations with the phage chemical elution being affected by bias toward positively charged binders, resulting in the potential loss of information on binder diversity; the presence of fast growing phages with an intrinsic growth advantage; and the presence of false positives from target unrelated peptides. To overcome some of these limitations, we developed a panning approach based on the sequential use of different eluents (Gly-HCl, pH-2.2; MgCl2, pH-6.1; and TEA, pH-11.0), or pH conditions (Gly-HCl 2.2 < pH < 11.0) that allows the identification of a diverse and comprehensive pool of strong binders. We have assessed and tested the authenticity of the identified silica binders via a complementary experimental (in vivo phage recovery rates and TEM imaging) and bioinformatics approach. We provide experimental evidence of the nonspecificity of the Gly-HCl eluent as typically used. Using a fluorimetric assay, we investigate in vitro binding of two peptides that differ by pI-S4 (HYIDFRW, pI 7.80) and S5 (YSLKQYQ, pI 9.44)─modified at the C terminal with an amide group to simulate net charges in the phage display system, confirming the vital role of electrostatic interactions as driving binding forces in the phage panning process. The presented optimized phage panning approach provides an opportunity to match known surface interactions at play with suitable elution conditions; to select only sequences relevant to a particular interfacial system. The approach has the potential to open up avenues to design interfacial systems to advance our understanding of peptide-assisted mineral growth, among other possibilities.
Collapse
Affiliation(s)
- Veeranjaneyulu Thota
- Interdisciplinary Biomedical Research
Centre, School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, U.K.
| | - Valeria Puddu
- Interdisciplinary Biomedical Research
Centre, School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, U.K.
| | - Carole C. Perry
- Interdisciplinary Biomedical Research
Centre, School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, U.K.
| |
Collapse
|
3
|
Mao M, Ahrens L, Luka J, Contreras F, Kurkina T, Bienstein M, Sárria Pereira de Passos M, Schirinzi G, Mehn D, Valsesia A, Desmet C, Serra MÁ, Gilliland D, Schwaneberg U. Material-specific binding peptides empower sustainable innovations in plant health, biocatalysis, medicine and microplastic quantification. Chem Soc Rev 2024; 53:6445-6510. [PMID: 38747901 DOI: 10.1039/d2cs00991a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Material-binding peptides (MBPs) have emerged as a diverse and innovation-enabling class of peptides in applications such as plant-/human health, immobilization of catalysts, bioactive coatings, accelerated polymer degradation and analytics for micro-/nanoplastics quantification. Progress has been fuelled by recent advancements in protein engineering methodologies and advances in computational and analytical methodologies, which allow the design of, for instance, material-specific MBPs with fine-tuned binding strength for numerous demands in material science applications. A genetic or chemical conjugation of second (biological, chemical or physical property-changing) functionality to MBPs empowers the design of advanced (hybrid) materials, bioactive coatings and analytical tools. In this review, we provide a comprehensive overview comprising naturally occurring MBPs and their function in nature, binding properties of short man-made MBPs (<20 amino acids) mainly obtained from phage-display libraries, and medium-sized binding peptides (20-100 amino acids) that have been reported to bind to metals, polymers or other industrially produced materials. The goal of this review is to provide an in-depth understanding of molecular interactions between materials and material-specific binding peptides, and thereby empower the use of MBPs in material science applications. Protein engineering methodologies and selected examples to tailor MBPs toward applications in agriculture with a focus on plant health, biocatalysis, medicine and environmental monitoring serve as examples of the transformative power of MBPs for various industrial applications. An emphasis will be given to MBPs' role in detecting and quantifying microplastics in high throughput, distinguishing microplastics from other environmental particles, and thereby assisting to close an analytical gap in food safety and monitoring of environmental plastic pollution. In essence, this review aims to provide an overview among researchers from diverse disciplines in respect to material-(specific) binding of MBPs, protein engineering methodologies to tailor their properties to application demands, re-engineering for material science applications using MBPs, and thereby inspire researchers to employ MBPs in their research.
Collapse
Affiliation(s)
- Maochao Mao
- Lehrstuhl für Biotechnologie, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
| | - Leon Ahrens
- Lehrstuhl für Biotechnologie, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
| | - Julian Luka
- Lehrstuhl für Biotechnologie, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
| | - Francisca Contreras
- Lehrstuhl für Biotechnologie, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
| | - Tetiana Kurkina
- Lehrstuhl für Biotechnologie, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
| | - Marian Bienstein
- Lehrstuhl für Biotechnologie, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
| | | | | | - Dora Mehn
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Andrea Valsesia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Cloé Desmet
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | | | - Ulrich Schwaneberg
- Lehrstuhl für Biotechnologie, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany.
| |
Collapse
|
4
|
Li B, Chen H, Huang J, He B. CD47Binder: Identify CD47 Binding Peptides by Combining Next-Generation Phage Display Data and Multiple Peptide Descriptors. Interdiscip Sci 2023; 15:578-589. [PMID: 37389722 DOI: 10.1007/s12539-023-00575-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 07/01/2023]
Abstract
CD47/SIRPα pathway is a new breakthrough in the field of tumor immunity after PD-1/PD-L1. While current monoclonal antibody therapies targeting CD47/SIRPα have demonstrated some anti-tumor effectiveness, there are several inherent limitations associated with these formulations. In the paper, we developed a predictive model that combines next-generation phage display (NGPD) and traditional machine learning methods to distinguish CD47 binding peptides. First, we utilized NGPD biopanning technology to screen CD47 binding peptides. Second, ten traditional machine learning methods based on multiple peptide descriptors and three deep learning methods were used to build computational models for identifying CD47 binding peptides. Finally, we proposed an integrated model based on support vector machine. During the five-fold cross-validation, the integrated predictor demonstrated specificity, accuracy, and sensitivity of 0.755, 0.764, and 0.772, respectively. Furthermore, an online bioinformatics tool called CD47Binder has been developed for the integrated predictor. This tool is readily accessible on http://i.uestc.edu.cn/CD47Binder/cgi-bin/CD47Binder.pl .
Collapse
Affiliation(s)
- Bowen Li
- Medical College, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China
| | - Heng Chen
- Medical College, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China.
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 6173001, Sichuan, China.
| | - Bifang He
- Medical College, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China.
- State Key Laboratory of Public Big Data, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China.
| |
Collapse
|
5
|
Keller AP, Huemer M, Chang CC, Mairpady Shambat S, Bjurnemark C, Oberortner N, Santschi MV, Zinsli LV, Röhrig C, Sobieraj AM, Shen Y, Eichenseher F, Zinkernagel AS, Loessner MJ, Schmelcher M. Systemic application of bone-targeting peptidoglycan hydrolases as a novel treatment approach for staphylococcal bone infection. mBio 2023; 14:e0183023. [PMID: 37768041 PMCID: PMC10653945 DOI: 10.1128/mbio.01830-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/08/2023] [Indexed: 09/29/2023] Open
Abstract
IMPORTANCE The rising prevalence of antimicrobial resistance in S. aureus has rendered treatment of staphylococcal infections increasingly difficult, making the discovery of alternative treatment options a high priority. Peptidoglycan hydrolases, a diverse group of bacteriolytic enzymes, show high promise as such alternatives due to their rapid and specific lysis of bacterial cells, independent of antibiotic resistance profiles. However, using these enzymes for the systemic treatment of local infections, such as osteomyelitis foci, needs improvement, as the therapeutic distributes throughout the whole host, resulting in low concentrations at the actual infection site. In addition, the occurrence of intracellularly persisting bacteria can lead to relapsing infections. Here, we describe an approach using tissue-targeting to increase the local concentration of therapeutic enzymes in the infected bone. The enzymes were modified with a short targeting moiety that mediated accumulation of the therapeutic in osteoblasts and additionally enables targeting of intracellularly surviving bacteria.
Collapse
Affiliation(s)
- Anja P. Keller
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Markus Huemer
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Chun-Chi Chang
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Srikanth Mairpady Shambat
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Nicole Oberortner
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | | | - Léa V. Zinsli
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Christian Röhrig
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Anna M. Sobieraj
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Yang Shen
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Fritz Eichenseher
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Annelies S. Zinkernagel
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin J. Loessner
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Mathias Schmelcher
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
6
|
Jirwankar Y, Dighe V. Identification and validation of Sertoli cell homing peptides as molecular steering for testis targeted drug delivery. J Drug Target 2023; 31:390-401. [PMID: 36604336 DOI: 10.1080/1061186x.2022.2164007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The testicle, an organ privileged with immunity because of Blood-Testis Barrier (BTB), poses a major impediment to developing and delivering drugs to the testes. These problems can be prevented by targeting testicular cells using specific ligands, such as homing peptides. This is the first study to demonstrate the successful selection of Sertoli cell homing peptides using a phage display peptide library. The identification of peptides is performed with Sanger sequencing and high-throughput NGS. The Sertoli cell and testis targeting potential of the SCHP1 and SCHP2 was confirmed using confocal microscopy and flow cytometry of the FITC-labelled peptides and in vivo bio-distribution of the corresponding Cy5.5-tagged peptides. Secondary structures were predicted in the setting of different polarity by circular dichroism. The results suggest that SCHP1 and SCHP2 can effectively target Sertoli cells. In vivo bio-distribution in mouse models indicated significantly higher uptake of SCHP1 and SCHP2 by testes compared with the heart, brain, and spleen. SCHP1 and SCHP2 can be adopted as molecular steering for targeted male contraceptive delivery, treatment of testicular cancer, and male infertility. Further development of the peptides into peptidomimetics may increase their stability, and information on the molecular targets of these peptides may reveal their therapeutic potential.
Collapse
Affiliation(s)
- Yugandhara Jirwankar
- National Centre for Preclinical Reproductive and Genetic Toxicology, ICMR - National Institute for Research in Reproductive and Child Health, Mumbai, India
| | - Vikas Dighe
- National Centre for Preclinical Reproductive and Genetic Toxicology, ICMR - National Institute for Research in Reproductive and Child Health, Mumbai, India
| |
Collapse
|
7
|
Lin J, Wen L, Zhou Y, Wang S, Ye H, Su J, Li J, Shu J, Huang J, Zhou P. PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships. Amino Acids 2023; 55:235-242. [PMID: 36474016 DOI: 10.1007/s00726-022-03219-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022]
Abstract
Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB .
Collapse
Affiliation(s)
- Jing Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
| | - Li Wen
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
| | - Yuwei Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
| | - Shaozhou Wang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
| | - Haiyang Ye
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
| | - Jun Su
- College of Music, Chengdu Normal University, Chengdu, 611130, China
| | - Juelin Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
| | - Jianping Shu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
| | - Jian Huang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China.
| | - Peng Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), No. 2006 Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China.
| |
Collapse
|
8
|
Bakhshinejad B, Sadeghizadeh M. Identification of a novel colon adenocarcinoma cell targeting peptide using phage display library biopanning. Biotechnol Appl Biochem 2022; 69:2753-2765. [PMID: 35103339 DOI: 10.1002/bab.2320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 09/19/2021] [Indexed: 12/27/2022]
Abstract
Phage display is well recognized as a promising high-throughput screening tool for the discovery of novel cancer-targeting peptides. Here, we screened a phage display library of 7-mer random peptides through in vitro biopanning to isolate peptide ligands binding to SW480 human colon adenocarcinoma cells. Three rounds of negative and positive selection caused a remarkable enrichment of colon cancer cell-binding phage clones with a significant enhancement of phage recovery efficiency (about 157-fold). A number of phage clones were picked out from the eluted phages of last selection round and sequenced. According to the results of cell binding assay and phage cell-based ELISA, one of the isolated peptides denoted as CCBP1 (with the sequence HAMRAQP) was indicated to have the highest binding efficiency, selectivity, and specificity toward colon cancer cells with no significant binding to control cells. Peptide competitive inhibition assay revealed that binding of the phage-displayed CCBP1 is competitively inhibited by the same free peptide, suggesting that CCBP1 specific binding to the target cell is independent of the phage context. Taken together, our findings provide support for the notion that CCBP1 binds specifically to colon cancer cells and might be a potential lead candidate for targeted delivery of imaging agents or therapeutic genes/drugs to colon tumors.
Collapse
Affiliation(s)
- Babak Bakhshinejad
- Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Majid Sadeghizadeh
- Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| |
Collapse
|
9
|
Sloth AB, Bakhshinejad B, Jensen M, Stavnsbjerg C, Liisberg MB, Rossing M, Kjaer A. Analysis of Compositional Bias in a Commercial Phage Display Peptide Library by Next-Generation Sequencing. Viruses 2022; 14:v14112402. [PMID: 36366500 PMCID: PMC9697088 DOI: 10.3390/v14112402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 01/31/2023] Open
Abstract
The principal presumption of phage display biopanning is that the naïve library contains an unbiased repertoire of peptides, and thus, the enriched variants derive from the affinity selection of an entirely random peptide pool. In the current study, we utilized deep sequencing to characterize the widely used Ph.DTM-12 phage display peptide library (New England Biolabs). The next-generation sequencing (NGS) data indicated the presence of stop codons and a high abundance of wild-type clones in the naïve library, which collectively result in a reduced effective size of the library. The analysis of the DNA sequence logo and global and position-specific frequency of amino acids demonstrated significant bias in the nucleotide and amino acid composition of the library inserts. Principal component analysis (PCA) uncovered the existence of four distinct clusters in the naïve library and the investigation of peptide frequency distribution revealed a broad range of unequal abundances for peptides. Taken together, our data provide strong evidence for the notion that the naïve library represents substantial departures from randomness at the nucleotide, amino acid, and peptide levels, though not undergoing any selective pressure for target binding. This non-uniform sequence representation arises from both the M13 phage biology and technical errors of the library construction. Our findings highlight the paramount importance of the qualitative assessment of the naïve phage display libraries prior to biopanning.
Collapse
Affiliation(s)
- Ane Beth Sloth
- Department of Clinical Physiology and Nuclear Medicine & Cluster for Molecular Imaging, Copenhagen University Hospital—Rigshospitalet & Department of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Babak Bakhshinejad
- Department of Clinical Physiology and Nuclear Medicine & Cluster for Molecular Imaging, Copenhagen University Hospital—Rigshospitalet & Department of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Malte Jensen
- Department of Clinical Physiology and Nuclear Medicine & Cluster for Molecular Imaging, Copenhagen University Hospital—Rigshospitalet & Department of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Camilla Stavnsbjerg
- Department of Clinical Physiology and Nuclear Medicine & Cluster for Molecular Imaging, Copenhagen University Hospital—Rigshospitalet & Department of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Mikkel Baldtzer Liisberg
- Nano-Science Center, Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark
| | - Maria Rossing
- Center for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Andreas Kjaer
- Department of Clinical Physiology and Nuclear Medicine & Cluster for Molecular Imaging, Copenhagen University Hospital—Rigshospitalet & Department of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Correspondence:
| |
Collapse
|
10
|
Sánchez-Navarro M, Giralt E. Peptide Shuttles for Blood–Brain Barrier Drug Delivery. Pharmaceutics 2022; 14:pharmaceutics14091874. [PMID: 36145622 PMCID: PMC9505527 DOI: 10.3390/pharmaceutics14091874] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/24/2022] [Accepted: 08/28/2022] [Indexed: 11/29/2022] Open
Abstract
The blood–brain barrier (BBB) limits the delivery of therapeutics to the brain but also represents the main gate for nutrient entrance. Targeting the natural transport mechanisms of the BBB offers an attractive route for brain drug delivery. Peptide shuttles are able to use these mechanisms to increase the transport of compounds that cannot cross the BBB unaided. As peptides are a group of biomolecules with unique physicochemical and structural properties, the field of peptide shuttles has substantially evolved in the last few years. In this review, we analyze the main classifications of BBB–peptide shuttles and the leading sources used to discover them.
Collapse
Affiliation(s)
- Macarena Sánchez-Navarro
- Department of Molecular Biology, Instituto de Parasitología y Biomedicina ‘‘López Neyra” (CSIC), 18016 Granada, Spain
- Correspondence: (M.S.-N.); (E.G.)
| | - Ernest Giralt
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Baldiri Reixac 10, 08028 Barcelona, Spain
- Department of Inorganic and Organic Chemistry, University of Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain
- Correspondence: (M.S.-N.); (E.G.)
| |
Collapse
|
11
|
He B, Li B, Chen X, Zhang Q, Lu C, Yang S, Long J, Ning L, Chen H, Huang J. PDL1Binder: Identifying programmed cell death ligand 1 binding peptides by incorporating next-generation phage display data and different peptide descriptors. Front Microbiol 2022; 13:928774. [PMID: 35910615 PMCID: PMC9335124 DOI: 10.3389/fmicb.2022.928774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
Monoclonal antibody drugs targeting the PD-1/PD-L1 pathway have showed efficacy in the treatment of cancer patients, however, they have many intrinsic limitations and inevitable drawbacks. Peptide inhibitors as alternatives might compensate for the drawbacks of current PD-1/PD-L1 interaction blockers. Identifying PD-L1 binding peptides by random peptide library screening is a time-consuming and labor-intensive process. Machine learning-based computational models enable rapid discovery of peptide candidates targeting the PD-1/PD-L1 pathway. In this study, we first employed next-generation phage display (NGPD) biopanning to isolate PD-L1 binding peptides. Different peptide descriptors and feature selection methods as well as diverse machine learning methods were then incorporated to implement predictive models of PD-L1 binding. Finally, we proposed PDL1Binder, an ensemble computational model for efficiently obtaining PD-L1 binding peptides. Our results suggest that predictive models of PD-L1 binding can be learned from deep sequencing data and provide a new path to discover PD-L1 binding peptides. A web server was implemented for PDL1Binder, which is freely available at http://i.uestc.edu.cn/pdl1binder/cgi-bin/PDL1Binder.pl.
Collapse
Affiliation(s)
- Bifang He
- Medical College, Guizhou University, Guiyang, China
| | - Bowen Li
- Medical College, Guizhou University, Guiyang, China
| | - Xue Chen
- Medical College, Guizhou University, Guiyang, China
| | | | - Chunying Lu
- Medical College, Guizhou University, Guiyang, China
| | | | - Jinjin Long
- Medical College, Guizhou University, Guiyang, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Heng Chen
- Medical College, Guizhou University, Guiyang, China
- *Correspondence: Heng Chen,
| | - Jian Huang
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Jian Huang,
| |
Collapse
|
12
|
Pashova S, Balabanski L, Elmadjian G, Savov A, Stoyanova E, Shivarov V, Petrov P, Pashov A. Restriction of the Global IgM Repertoire in Antiphospholipid Syndrome. Front Immunol 2022; 13:865232. [PMID: 35493489 PMCID: PMC9043687 DOI: 10.3389/fimmu.2022.865232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 03/21/2022] [Indexed: 11/22/2022] Open
Abstract
The typical anti-phospholipid antibodies (APLA) in the anti-phospholipid syndrome (APS) are reactive with the phospholipid-binding protein β2GPI as well as a growing list of other protein targets. The relation of APLA to natural antibodies and the fuzzy set of autoantigens involved provoked us to study the changes in the IgM repertoire in APS. To this end, peptides selected by serum IgM from a 7-residue linear peptide phage display library (PDL) were deep sequenced. The analysis was aided by a novel formal representation of the Igome (the mimotope set reflecting the IgM specificities) in the form of a sequence graph. The study involved women with APLA and habitual abortions (n=24) compared to age-matched clinically healthy pregnant women (n=20). Their pooled Igomes (297 028 mimotope sequences) were compared also to the global public repertoire Igome of pooled donor plasma IgM (n=2 796 484) and a set of 7-mer sequences found in the J regions of human immunoglobulins (n=4 433 252). The pooled Igome was represented as a graph connecting the sequences as similar as the mimotopes of the same monoclonal antibody. The criterion was based on previously published data. In the resulting graph, identifiable clusters of vertices were considered related to the footprints of overlapping antibody cross-reactivities. A subgraph based on the clusters with a significant differential expression of APS patients’ mimotopes contained predominantly specificities underrepresented in APS. The differentially expressed IgM footprints showed also an increased cross-reactivity with immunoglobulin J regions. The specificities underexpressed in APS had a higher correlation with public specificities than those overexpressed. The APS associated specificities were strongly related also to the human peptidome with 1 072 mimotope sequences found in 7 519 human proteins. These regions were characterized by low complexity. Thus, the IgM repertoire of the APS patients was found to be characterized by a significant reduction of certain public specificities found in the healthy controls with targets representing low complexity linear self-epitopes homologous to human antibody J regions.
Collapse
Affiliation(s)
- Shina Pashova
- Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Lubomir Balabanski
- Department of Medical Genetics, Medical University-Sofia, Sofia, Bulgaria.,Genomics Laboratory, Hospital "Malinov", Sofia, Bulgaria
| | - Gabriel Elmadjian
- Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Alexey Savov
- Department of Medical Genetics, Medical University-Sofia, Sofia, Bulgaria
| | - Elena Stoyanova
- Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | | | - Peter Petrov
- Institute Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Anastas Pashov
- Institute of Microbiology, Bulgarian Academy of Sciences, Sofia, Bulgaria
| |
Collapse
|
13
|
Zambrano N, Froechlich G, Lazarevic D, Passariello M, Nicosia A, De Lorenzo C, Morelli MJ, Sasso E. High-Throughput Monoclonal Antibody Discovery from Phage Libraries: Challenging the Current Preclinical Pipeline to Keep the Pace with the Increasing mAb Demand. Cancers (Basel) 2022; 14:cancers14051325. [PMID: 35267633 PMCID: PMC8909429 DOI: 10.3390/cancers14051325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/25/2022] [Accepted: 03/02/2022] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Monoclonal antibodies are increasingly used for a broad range of diseases. Rising demand must face with time time-consuming and laborious processes to isolate novel monoclonal antibodies. Next-generation sequencing coupled to phage display provides timely and sustainable high throughput selection strategy to rapidly access novel target. Here, we describe the current NGS-guided strategies to identify potential binders from enriched sub-libraires by applying a user-friendly informatic pipeline to identify and discard false positive clones. Rescue step and strategies to boost mAb yield are also discussed to improve the limiting selection and screening steps. Abstract Monoclonal antibodies are among the most powerful therapeutics in modern medicine. Since the approval of the first therapeutic antibody in 1986, monoclonal antibodies keep holding great expectations for application in a range of clinical indications, highlighting the need to provide timely and sustainable access to powerful screening options. However, their application in the past has been limited by time-consuming and expensive steps of discovery and production. The screening of antibody repertoires is a laborious step; however, the implementation of next-generation sequencing-guided screening of single-chain antibody fragments has now largely overcome this issue. This review provides a detailed overview of the current strategies for the identification of monoclonal antibodies from phage display-based libraries. We also discuss the challenges and the possible solutions to improve the limiting selection and screening steps, in order to keep pace with the increasing demand for monoclonal antibodies.
Collapse
Affiliation(s)
- Nicola Zambrano
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
- Correspondence: (N.Z.); (E.S.)
| | - Guendalina Froechlich
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
| | - Dejan Lazarevic
- Center for Omics Sciences Ospedale San Raffaele, Via Olgettina 58, 20132 Milano, Italy; (D.L.); (M.J.M.)
| | - Margherita Passariello
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
| | - Alfredo Nicosia
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
| | - Claudia De Lorenzo
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
| | - Marco J. Morelli
- Center for Omics Sciences Ospedale San Raffaele, Via Olgettina 58, 20132 Milano, Italy; (D.L.); (M.J.M.)
| | - Emanuele Sasso
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Degli Studi di Napoli Federico II, Via Pansini 5, 80131 Napoli, Italy; (G.F.); (M.P.); (A.N.); (C.D.L.)
- CEINGE—Biotecnologie Avanzate s.c. a.r.l., Via Gaetano Salvatore 486, 80145 Naples, Italy
- Correspondence: (N.Z.); (E.S.)
| |
Collapse
|
14
|
Yang YH, Wang JS, Yuan SS, Liu ML, Su W, Lin H, Zhang ZY. A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods. Curr Med Chem 2021; 29:789-806. [PMID: 34514982 DOI: 10.2174/0929867328666210910125802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 11/22/2022]
Abstract
Protein-ligand interactions are necessary for majority protein functions. Adenosine-5'-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.
Collapse
Affiliation(s)
- Yu-He Yang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Jia-Shu Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Shi-Shi Yuan
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Meng-Lu Liu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Wei Su
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Zhao-Yue Zhang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| |
Collapse
|
15
|
He B, Yang S, Long J, Chen X, Zhang Q, Gao H, Chen H, Huang J. TUPDB: Target-Unrelated Peptide Data Bank. Interdiscip Sci 2021; 13:426-432. [PMID: 33993461 DOI: 10.1007/s12539-021-00436-5] [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: 12/21/2020] [Revised: 04/29/2021] [Accepted: 05/06/2021] [Indexed: 11/29/2022]
Abstract
The isolation of target-unrelated peptides (TUPs) through biopanning remains as a major problem of phage display selection experiments. These TUPs do not have any actual affinity toward targets of interest, which tend to be mistakenly identified as target-binding peptides. Therefore, an information portal for storing TUP data is urgently needed. Here, we present a TUP data bank (TUPDB), which is a comprehensive, manually curated database of approximately 73 experimentally verified TUPs and 1963 potential TUPs collected from TUPScan, the BDB database, and public research articles. The TUPScan tool has been integrated in TUPDB to facilitate TUP analysis. We believe that TUPDB can help identify and remove TUPs in future reports in the biopanning community. The database is of great importance to improving the quality of phage display-based epitope mapping and promoting the development of vaccines, diagnostics, and therapeutics. The TUPDB database is available at http://i.uestc.edu.cn/tupdb .
Collapse
Affiliation(s)
- Bifang He
- School of Medicine, Guizhou University, Guiyang, 550025, China. .,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Shanshan Yang
- School of Medicine, Guizhou University, Guiyang, 550025, China
| | - Jinjin Long
- School of Medicine, Guizhou University, Guiyang, 550025, China
| | - Xue Chen
- School of Medicine, Guizhou University, Guiyang, 550025, China
| | - Qianyue Zhang
- School of Medicine, Guizhou University, Guiyang, 550025, China
| | - Hui Gao
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Heng Chen
- School of Medicine, Guizhou University, Guiyang, 550025, China.
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| |
Collapse
|
16
|
Wang J, Tan Y, Ling J, Zhang M, Li L, Liu W, Huang M, Song J, Li A, Song Y, Yang C, Zhu Z. Highly paralleled emulsion droplets for efficient isolation, amplification, and screening of cancer biomarker binding phages. LAB ON A CHIP 2021; 21:1175-1184. [PMID: 33554995 DOI: 10.1039/d0lc01146k] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Based on the linkage of genotype and phenotype, display technology has been widely used to generate specific ligands for profiling, imaging, diagnosis and therapy applications. However, due to the lack of effective monoclonal manipulation and affinity evaluation methods, traditional display technology has to undergo tedious steps of selection, clone isolation, amplification, sequencing, synthesis and characterization to obtain the binding sequences. To directly acquire high-affinity clones, we propose a double monoclonal display approach (dm-Display) for peptide screening based on highly paralleled monoclonal manipulation in emulsion droplets. dm-Display can monoclonally link the genotype, phenotype and affinity to realize integrated monoclonal separation, amplification, recognition and staining in one droplet so that discrete high-affinity clones can be quickly extracted. Monoclonal manipulations highly-parallelly occur in millions of droplets so that molecular screening of a highly diverse phage library is achieved. We have screened specific peptide ligands against CD71 and GPC1, proving the feasibility and generality of dm-Display. As a highly efficient ligand screening platform, dm-Display will promote the further development of molecular screening.
Collapse
Affiliation(s)
- Junxia Wang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
17
|
Mishra R, Guo Y, Kumar P, Cantón PE, Tavares CS, Banerjee R, Kuwar S, Bonning BC. Streamlined phage display library protocols for identification of insect gut binding peptides highlight peptide specificity. CURRENT RESEARCH IN INSECT SCIENCE 2021; 1:100012. [PMID: 36003592 PMCID: PMC9387513 DOI: 10.1016/j.cris.2021.100012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 02/09/2021] [Accepted: 02/14/2021] [Indexed: 05/10/2023]
Abstract
Phage display libraries have been used to isolate insect gut binding peptides for use as pathogen transmission blocking agents, and to provide artificial anchors for increased toxicity of bacteria-derived pesticidal proteins. Previously, phage clones displaying enriched peptides were sequenced by Sanger sequencing. Here we present a streamlined protocol for identification of insect gut binding peptides, using insect-appropriate feeding strategies, with next generation sequencing and tailored bioinformatics analyses. The bioinformatics pipeline is designed to eliminate poorly enriched and false positive peptides, and to identify peptides predicted to be stable and hydrophilic. In addition to developing streamlined protocols, we also sought to address whether candidate gut binding peptides can bind to insects from more than one order, which is an important consideration for safe, practical use of peptide-modified pesticidal proteins. To this end, we screened phage display libraries for peptides that bind to the gut epithelia of two pest insects, the Asian citrus psyllid, Diaphorina citri (Hemiptera) and beet armyworm, Spodoptera exigua (Lepidoptera), and one beneficial insect, the western honey bee, Apis mellifera (Hymenoptera). While unique peptide sequences totaling 13,427 for D. citri, 89,561 for S. exigua and 69,053 for A. mellifera were identified from phage eluted from the surface of the insect guts, final candidate pools were comprised of 53, 107 and 1423 peptides respectively. The benefits of multiple rounds of biopanning, along with peptide binding properties in relation to practical use of peptide-modified pesticidal proteins for insect pest control are discussed.
Collapse
|
18
|
Braun R, Schönberger N, Vinke S, Lederer F, Kalinowski J, Pollmann K. Application of Next Generation Sequencing (NGS) in Phage Displayed Peptide Selection to Support the Identification of Arsenic-Binding Motifs. Viruses 2020; 12:E1360. [PMID: 33261041 PMCID: PMC7759992 DOI: 10.3390/v12121360] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/16/2020] [Accepted: 11/24/2020] [Indexed: 11/21/2022] Open
Abstract
Next generation sequencing (NGS) in combination with phage surface display (PSD) are powerful tools in the newly equipped molecular biology toolbox for the identification of specific target binding biomolecules. Application of PSD led to the discovery of manifold ligands in clinical and material research. However, limitations of traditional phage display hinder the identification process. Growth-based library biases and target-unrelated peptides often result in the dominance of parasitic sequences and the collapse of library diversity. This study describes the effective enrichment of specific peptide motifs potentially binding to arsenic as proof-of-concept using the combination of PSD and NGS. Arsenic is an environmental toxin, which is applied in various semiconductors as gallium arsenide and selective recovery of this element is crucial for recycling and remediation. The development of biomolecules as specific arsenic-binding sorbents is a new approach for its recovery. Usage of NGS for all biopanning fractions allowed for evaluation of motif enrichment, in-depth insight into the selection process and the discrimination of biopanning artefacts, e.g., the amplification-induced library-wide reduction in hydrophobic amino acid proportion. Application of bioinformatics tools led to the identification of an SxHS and a carboxy-terminal QxQ motif, which are potentially involved in the binding of arsenic. To the best of our knowledge, this is the first report of PSD combined with NGS of all relevant biopanning fractions.
Collapse
Affiliation(s)
- Robert Braun
- Department of Biotechnology, Helmholtz Institute Freiberg for Resource Technology, Helmholtz Center Dresden-Rossendorf, 01328 Dresden, Germany; (N.S.); (F.L.); (K.P.)
| | - Nora Schönberger
- Department of Biotechnology, Helmholtz Institute Freiberg for Resource Technology, Helmholtz Center Dresden-Rossendorf, 01328 Dresden, Germany; (N.S.); (F.L.); (K.P.)
| | - Svenja Vinke
- Microbial Genomics and Biotechnology, CeBiTec–Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany; (S.V.); (J.K.)
| | - Franziska Lederer
- Department of Biotechnology, Helmholtz Institute Freiberg for Resource Technology, Helmholtz Center Dresden-Rossendorf, 01328 Dresden, Germany; (N.S.); (F.L.); (K.P.)
| | - Jörn Kalinowski
- Microbial Genomics and Biotechnology, CeBiTec–Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany; (S.V.); (J.K.)
| | - Katrin Pollmann
- Department of Biotechnology, Helmholtz Institute Freiberg for Resource Technology, Helmholtz Center Dresden-Rossendorf, 01328 Dresden, Germany; (N.S.); (F.L.); (K.P.)
| |
Collapse
|
19
|
Wang Y, Kang J, Li N, Zhou Y, Tang Z, He B, Huang J. NeuroCS: A Tool to Predict Cleavage Sites of Neuropeptide Precursors. Protein Pept Lett 2020; 27:337-345. [PMID: 31721688 DOI: 10.2174/0929866526666191112150636] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 07/16/2019] [Accepted: 09/24/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Neuropeptides are a class of bioactive peptides produced from neuropeptide precursors through a series of extremely complex processes, mediating neuronal regulations in many aspects. Accurate identification of cleavage sites of neuropeptide precursors is of great significance for the development of neuroscience and brain science. OBJECTIVE With the explosive growth of neuropeptide precursor data, it is pretty much needed to develop bioinformatics methods for predicting neuropeptide precursors' cleavage sites quickly and efficiently. METHODS We started with processing the neuropeptide precursor data from SwissProt and NueoPedia into two sets of data, training dataset and testing dataset. Subsequently, six feature extraction schemes were applied to generate different feature sets and then feature selection methods were used to find the optimal feature subset of each. Thereafter the support vector machine was utilized to build models for different feature types. Finally, the performance of models were evaluated with the independent testing dataset. RESULTS Six models are built through support vector machine. Among them the enhanced amino acid composition-based model reaches the highest accuracy of 91.60% in the 5-fold cross validation. When evaluated with independent testing dataset, it also showed an excellent performance with a high accuracy of 90.37% and Area under Receiver Operating Characteristic curve up to 0.9576. CONCLUSION The performance of the developed model was decent. Moreover, for users' convenience, an online web server called NeuroCS is built, which is freely available at http://i.uestc.edu.cn/NeuroCS/dist/index.html#/. NeuroCS can be used to predict neuropeptide precursors' cleavage sites effectively.
Collapse
Affiliation(s)
- Ying Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Juanjuan Kang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ning Li
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuwei Zhou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhongjie Tang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bifang He
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Medical College, Guizhou University, Guiyang, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
20
|
Ambaye ND, Yu HE. Novel anti-cancer candidates from a combinatorial peptide library. Chem Biol Drug Des 2020; 97:87-96. [PMID: 32659860 DOI: 10.1111/cbdd.13763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 06/21/2020] [Accepted: 07/05/2020] [Indexed: 12/26/2022]
Abstract
STAT3 is attractive target for development of anti-cancer therapeutics as it is implicated in nearly all forms of human tumors. To identify novel leads, we screened a combinatorial peptide library displayed on the surface of M13 bacteriophage. After three rounds of biopanning, a dodecapeptide with the YYVSWPPDMMHY sequence was found to be enriched by 36% while another with a short consensus motif was displayed in 20% of the phages. Binding analysis by isothermal titration calorimetry shows the most displayed peptide interacted with a Kd of 1.79 μM, which on modification of its structure to mimic the natural binding partners of STAT3 brought the affinity to high nanomolar range (Kd = 500 nM). Using a panel of tumor cell lines, we show that the peptides prevented the proliferation of triple-negative breast cancer cells with a moderate activity (GI50 = 50 μM). Furthermore, gene expression analysis shows the peptide reduced the expression of oncoproteins critical for tumor cell proliferation, angiogenesis, and metastasis. To find novel STAT3-interacting proteins, we searched the non-redundant sequences of the National Center for Biotechnology Information database which allowed us to identify potential binding partners of the protein. In sum, our data show the identified agents could serve as useful therapeutics candidates for further development.
Collapse
Affiliation(s)
- Nigus D Ambaye
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Hua E Yu
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| |
Collapse
|
21
|
Feng P, Wang Z. Recent Advances in Computational Methods for Identifying Anticancer Peptides. Curr Drug Targets 2020; 20:481-487. [PMID: 30068270 DOI: 10.2174/1389450119666180801121548] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 05/28/2018] [Accepted: 05/28/2018] [Indexed: 01/10/2023]
Abstract
Anticancer peptide (ACP) is a kind of small peptides that can kill cancer cells without damaging normal cells. In recent years, ACP has been pre-clinically used for cancer treatment. Therefore, accurate identification of ACPs will promote their clinical applications. In contrast to labor-intensive experimental techniques, a series of computational methods have been proposed for identifying ACPs. In this review, we briefly summarized the current progress in computational identification of ACPs. The challenges and future perspectives in developing reliable methods for identification of ACPs were also discussed. We anticipate that this review could provide novel insights into future researches on anticancer peptides.
Collapse
Affiliation(s)
- Pengmian Feng
- School of Public Health, North China University of Science and Technology, Tangshan, 063000, China
| | - Zhenyi Wang
- Center for Genomics and Computational Biology, School of Life Science, North China University of Science and Technology, Tangshan, 063000, China
| |
Collapse
|
22
|
Tan JX, Lv H, Wang F, Dao FY, Chen W, Ding H. A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods. Curr Drug Targets 2020; 20:540-550. [PMID: 30277150 DOI: 10.2174/1389450119666181002143355] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 08/17/2018] [Accepted: 09/04/2018] [Indexed: 12/13/2022]
Abstract
Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.
Collapse
Affiliation(s)
- Jiu-Xin Tan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Chen
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.,Gordon Life Science Institute, Boston, MA 02478, United States
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
23
|
SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences. BIOMED RESEARCH INTERNATIONAL 2020; 2020:3508107. [PMID: 32596302 PMCID: PMC7288208 DOI: 10.1155/2020/3508107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 04/28/2020] [Accepted: 05/13/2020] [Indexed: 12/31/2022]
Abstract
Therapeutic antibodies are one of the most important parts of the pharmaceutical industry. They are widely used in treating various diseases such as autoimmune diseases, cancer, inflammation, and infectious diseases. Their development process however is often brought to a standstill or takes a longer time and is then more expensive due to their hydrophobicity problems. Hydrophobic interactions can cause problems on half-life, drug administration, and immunogenicity at all stages of antibody drug development. Some of the most widely accepted and used technologies for determining the hydrophobic interactions of antibodies include standup monolayer adsorption chromatography (SMAC), salt-gradient affinity-capture self-interaction nanoparticle spectroscopy (SGAC-SINS), and hydrophobic interaction chromatography (HIC). However, to measure SMAC, SGAC-SINS, and HIC for hundreds of antibody drug candidates is time-consuming and costly. To save time and money, a predictor called SSH is developed. Based on the antibody's sequence only, it can predict the hydrophobic interactions of monoclonal antibodies (mAbs). Using the leave-one-out crossvalidation, SSH achieved 91.226% accuracy, 96.396% sensitivity or recall, 84.196% specificity, 87.754% precision, 0.828 Mathew correlation coefficient (MCC), 0.919 f-score, and 0.961 area under the receiver operating characteristic (ROC) curve (AUC).
Collapse
|
24
|
He B, Dzisoo AM, Derda R, Huang J. Development and Application of Computational Methods in Phage Display Technology. Curr Med Chem 2020; 26:7672-7693. [PMID: 29956612 DOI: 10.2174/0929867325666180629123117] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 02/08/2018] [Accepted: 03/20/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display. METHODS We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses. RESULTS We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data. CONCLUSION The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.
Collapse
Affiliation(s)
- Bifang He
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China.,School of Medicine, Guizhou University, Guiyang 550025, China
| | - Anthony Mackitz Dzisoo
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ratmir Derda
- Department of Chemistry, University of Alberta, Edmonton T6G 2G2, Alberta, Canada
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| |
Collapse
|
25
|
Jiang L, Yu M, Zhou Y, Tang Z, Li N, Kang J, He B, Huang J. AGONOTES: A Robot Annotator for Argonaute Proteins. Interdiscip Sci 2019; 12:109-116. [PMID: 31741225 DOI: 10.1007/s12539-019-00349-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 10/06/2019] [Accepted: 10/30/2019] [Indexed: 12/01/2022]
Abstract
The argonaute protein (Ago) exists in almost all organisms. In eukaryotes, it functions as a regulatory system for gene expression. In prokaryotes, it is a type of defense system against foreign invasive genomes. The Ago system has been engineered for gene silencing and genome editing and plays an important role in biological studies. With an increasing number of genomes and proteomes of various microbes becoming available, computational tools for identifying and annotating argonaute proteins are urgently needed. We introduce AGONOTES (Argonaute Notes). It is a web service especially designed for identifying and annotating Ago. AGONOTES uses the BLASTP similarity search algorithm to categorize all submitted proteins into three groups: prokaryotic argonaute protein (pAgo), eukaryotic argonaute protein (eAgo), and non-argonaute protein (non-Ago). Argonaute proteins can then be aligned to the corresponding standard set of Ago sequences using the multiple sequence alignment program MUSCLE. All functional domains of Ago can further be curated from the alignment results and visualized easily through Bio::Graphic modules in the BioPerl bundle. Compared with existing tools such as CD-Search and available databases such as UniProt and AGONOTES showed a much better performance on domain annotations, which is fundamental in studying the new Ago. AGONOTES can be freely accessed at http://i.uestc.edu.cn/agonotes/. AGONOTES is a friendly tool for annotating Ago domains from a proteome or a series of protein sequences.
Collapse
Affiliation(s)
- Lixu Jiang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 637111, China
| | - Min Yu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 637111, China
| | - Yuwei Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 637111, China
| | - Zhongjie Tang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 637111, China
| | - Ning Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 637111, China
| | - Juanjuan Kang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 637111, China
| | - Bifang He
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 637111, China.,School of Medicine, Guizhou University, Guiyang, China
| | - Jian Huang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 637111, China.
| |
Collapse
|
26
|
Wang F, Guan ZX, Dao FY, Ding H. A Brief Review of the Computational Identification of Antifreeze Protein. CURR ORG CHEM 2019. [DOI: 10.2174/1385272823666190718145613] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Lots of cold-adapted organisms could produce antifreeze proteins (AFPs) to counter the freezing of cell fluids by controlling the growth of ice crystal. AFPs have been found in various species such as in vertebrates, invertebrates, plants, bacteria, and fungi. These AFPs from fish, insects and plants displayed a high diversity. Thus, the identification of the AFPs is a challenging task in computational proteomics. With the accumulation of AFPs and development of machine meaning methods, it is possible to construct a high-throughput tool to timely identify the AFPs. In this review, we briefly reviewed the application of machine learning methods in antifreeze proteins identification from difference section, including published benchmark dataset, sequence descriptor, classification algorithms and published methods. We hope that this review will produce new ideas and directions for the researches in identifying antifreeze proteins.
Collapse
Affiliation(s)
- Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
27
|
From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling. Future Med Chem 2019; 11:2313-2331. [PMID: 31581914 DOI: 10.4155/fmc-2018-0365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.
Collapse
|
28
|
Combinatorial Avidity Selection of Mosaic Landscape Phages Targeted at Breast Cancer Cells-An Alternative Mechanism of Directed Molecular Evolution. Viruses 2019; 11:v11090785. [PMID: 31454976 PMCID: PMC6784196 DOI: 10.3390/v11090785] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 08/19/2019] [Accepted: 08/22/2019] [Indexed: 02/08/2023] Open
Abstract
Low performance of actively targeted nanomedicines required revision of the traditional drug targeting paradigm and stimulated the development of novel phage-programmed, self-navigating drug delivery vehicles. In the proposed smart vehicles, targeting peptides, selected from phage libraries using traditional principles of affinity selection, are substituted for phage proteins discovered through combinatorial avidity selection. Here, we substantiate the potential of combinatorial avidity selection using landscape phage in the discovery of Short Linear Motifs (SLiMs) and their partner domains. We proved an algorithm for analysis of phage populations evolved through multistage screening of landscape phage libraries against the MDA-MB-231 breast cancer cell line. The suggested combinatorial avidity selection model proposes a multistage accumulation of Elementary Binding Units (EBU), or Core Motifs (CorMs), in landscape phage fusion peptides, serving as evolutionary initiators for formation of SLiMs. Combinatorial selection has the potential to harness directed molecular evolution to create novel smart materials with diverse novel, emergent properties.
Collapse
|
29
|
Dong H, Fan Q, Luo G, Zhang T, Yang H, Liu Q, Zhao G, Yang J. Three‐Component Mannich Reaction Catalyzed by Mesostructured Cellular Foam Silica Immobilized H
3
PW
12
O
40. ChemistrySelect 2019. [DOI: 10.1002/slct.201901146] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Hao Dong
- Key Laboratory of Low Carbon Energy and Chemical EngineeringCollege of Chemical and Environmental EngineeringShandong University of Science and Technology Qingdao 266590 Shandong China
| | - Qinzhen Fan
- College of Chemical EngineeringGuangdong University of Petrochemical Technology, Maoming 525000, Guangdong China
| | - Guanwei Luo
- Department of Production TechnologyQingyang Petrochemical CompanyChina National Petroleum Corporation Qingyang 745115 Gansu China
| | - Tengfei Zhang
- Key Laboratory of Low Carbon Energy and Chemical EngineeringCollege of Chemical and Environmental EngineeringShandong University of Science and Technology Qingdao 266590 Shandong China
| | - Hongyuan Yang
- Key Laboratory of Low Carbon Energy and Chemical EngineeringCollege of Chemical and Environmental EngineeringShandong University of Science and Technology Qingdao 266590 Shandong China
| | - Qing Liu
- Key Laboratory of Low Carbon Energy and Chemical EngineeringCollege of Chemical and Environmental EngineeringShandong University of Science and Technology Qingdao 266590 Shandong China
| | - Guoming Zhao
- Key Laboratory of Low Carbon Energy and Chemical EngineeringCollege of Chemical and Environmental EngineeringShandong University of Science and Technology Qingdao 266590 Shandong China
| | - Jing Yang
- Key Laboratory of Low Carbon Energy and Chemical EngineeringCollege of Chemical and Environmental EngineeringShandong University of Science and Technology Qingdao 266590 Shandong China
| |
Collapse
|
30
|
Chai G, Yu M, Jiang L, Duan Y, Huang J. HMMCAS: A Web Tool for the Identification and Domain Annotations of CAS Proteins. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1313-1315. [PMID: 28186905 DOI: 10.1109/tcbb.2017.2665542] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
The CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins) adaptive immune systems are discovered in many bacteria and most archaea. These systems are encoded by cas (CRISPR-associated) operons that have an extremely diverse architecture. The most crucial step in the depiction of cas operons composition is the identification of cas genes or Cas proteins. With the continuous increase of the newly sequenced archaeal and bacterial genomes, the recognition of new Cas proteins is becoming possible, which not only provides candidates for novel genome editing tools but also helps to understand the prokaryotic immune system better. Here, we describe HMMCAS, a web service for the detection of CRISPR-associated structural and functional domains in protein sequences. HMMCAS uses hmmscan similarity search algorithm in HMMER3.1 to provide a fast, interactive service based on a comprehensive collection of hidden Markov models of Cas protein family. It can accurately identify the Cas proteins including those fusion proteins, for example the Cas1-Cas4 fusion protein in Candidatus Chloracidobacterium thermophilum B (Cab. thermophilum B). HMMCAS can also find putative cas operon and determine which type it belongs to. HMMCAS is freely available at http://i.uestc.edu.cn/hmmcas.
Collapse
|
31
|
He B, Chen H, Huang J. PhD7Faster 2.0: predicting clones propagating faster from the Ph.D.-7 phage display library by coupling PseAAC and tripeptide composition. PeerJ 2019; 7:e7131. [PMID: 31245183 PMCID: PMC6585900 DOI: 10.7717/peerj.7131] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 05/15/2019] [Indexed: 01/08/2023] Open
Abstract
Selection from phage display libraries empowers isolation of high-affinity ligands for various targets. However, this method also identifies propagation-related target-unrelated peptides (PrTUPs). These false positive hits appear because of their amplification advantages. In this report, we present PhD7Faster 2.0 for predicting fast-propagating clones from the Ph.D.-7 phage display library, which was developed based on the support vector machine. Feature selection was performed against PseAAC and tripeptide composition using the incremental feature selection method. Ten-fold cross-validation results show that PhD7Faster 2.0 succeeds a decent performance with the accuracy of 81.84%, the Matthews correlation coefficient of 0.64 and the area under the ROC curve of 0.90. The permutation test with 1,000 shuffles resulted in p < 0.001. We implemented PhD7Faster 2.0 into a publicly accessible web tool (http://i.uestc.edu.cn/sarotup3/cgi-bin/PhD7Faster.pl) and constructed standalone graphical user interface and command-line versions for different systems. The standalone PhD7Faster 2.0 is able to detect PrTUPs within small datasets as well as large-scale datasets. This makes PhD7Faster 2.0 an enhanced and powerful tool for scanning and reporting faster-growing clones from the Ph.D.-7 phage display library.
Collapse
Affiliation(s)
- Bifang He
- School of Medicine, Guizhou University, Guiyang, Guizhou, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Heng Chen
- School of Medicine, Guizhou University, Guiyang, Guizhou, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| |
Collapse
|
32
|
He B, Chen H, Li N, Huang J. SAROTUP: a suite of tools for finding potential target-unrelated peptides from phage display data. Int J Biol Sci 2019; 15:1452-1459. [PMID: 31337975 PMCID: PMC6643146 DOI: 10.7150/ijbs.31957] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 04/09/2019] [Indexed: 01/13/2023] Open
Abstract
SAROTUP (Scanner And Reporter Of Target-Unrelated Peptides) 3.1 is a significant upgrade to the widely used SAROTUP web server for the rapid identification of target-unrelated peptides (TUPs) in phage display data. At present, SAROTUP has gathered a suite of tools for finding potential TUPs and other purposes. Besides the TUPScan, the motif-based tool, and three tools based on the BDB database, i.e., MimoScan, MimoSearch, and MimoBlast, three predictors based on support vector machine, i.e., PhD7Faster, SABinder and PSBinder, are integrated into SAROTUP. The current version of SAROTUP contains 27 TUP motifs and 823 TUP sequences. We also developed the standalone SAROTUP application with graphical user interface (GUI) and command line versions for processing deep sequencing phage display data and distributed it as an open source package, which can perform perfectly locally on almost all systems that support C++ with little or no modification. The web interfaces of SAROTUP have also been redesigned to be more self-evident and user-friendly. The latest version of SAROTUP is freely available at http://i.uestc.edu.cn/sarotup3.
Collapse
Affiliation(s)
- Bifang He
- School of Medicine, Guizhou University, Guiyang 550025, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Heng Chen
- School of Medicine, Guizhou University, Guiyang 550025, China
| | - Ning Li
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| |
Collapse
|
33
|
Ning L, He B, Zhou P, Derda R, Huang J. Molecular Design of Peptide-Fc Fusion Drugs. Curr Drug Metab 2019; 20:203-208. [DOI: 10.2174/1389200219666180821095355] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 01/18/2018] [Accepted: 05/29/2018] [Indexed: 12/11/2022]
Abstract
Background:Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed.Methods:We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and methods that had been used in the rational design of peptide-Fc fusion drugs. We also raised open questions about the computer-aided molecular design of peptide-Fc.Results:The design of peptibody consists of four steps. First, identify peptide leads from native ligands, biopanning, and computational design or prediction. Second, select the proper Fc region from different classes or subclasses of immunoglobulin. Third, fuse the peptide leads and Fc together properly. At last, evaluate the immunogenicity of the constructs. At each step, there are quite a few useful resources and computational tools.Conclusion:Reviewing the molecular design of peptibody will certainly help make the transition from peptide leads to drugs on the market quicker and cheaper.
Collapse
Affiliation(s)
- Lin Ning
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bifang He
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ratmir Derda
- Department of Chemistry, University of Alberta, Alberta, Canada
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
34
|
Khan YD, Batool A, Rasool N, Khan SA, Chou KC. Prediction of Nitrosocysteine Sites Using Position and Composition Variant Features. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180802122953] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
S-nitrosylation is one of the most prominent posttranslational modification among proteins. It involves the addition of nitrogen oxide group to cysteine thiols forming S-nitrosocysteine. Evidence suggests that S-nitrosylation plays a foremost role in numerous human diseases and disorders. The incorporation of techniques for robust identification of S-nitrosylated proteins is highly anticipated in biological research and drug discovery. The proposed system endeavors a novel strategy based on a statistical and computational intelligent methods for the identification of S-nitrosocystiene sites within a given primary protein sequence. For this purpose, 5-step rule was approached comprising of benchmark dataset creation, mathematical modelling, prediction, evaluation and web-server development. For position relative feature extraction, statistical moments were used and a multilayer neural network was trained adapting Gradient Descent and Adaptive Learning algorithms. The results were comparatively analyzed with existing techniques using benchmark datasets. It is inferred through conclusive experimentation that the proposed scheme is very propitious, accurate and exceptionally effective for the prediction of S-nitrosocystiene in protein sequences.
Collapse
Affiliation(s)
- Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Aroosa Batool
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Nouman Rasool
- Department of Life Sciences, School of Science, University of Management and Technology, Lahore, Pakistan
| | - Sher Afzal Khan
- Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, 21577, Saudi Arabia
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
| |
Collapse
|
35
|
Yun S, Lee S, Park JP, Choo J, Lee E. Modification of phage display technique for improved screening of high-affinity binding peptides. J Biotechnol 2019; 289:88-92. [DOI: 10.1016/j.jbiotec.2018.11.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 10/17/2018] [Accepted: 11/26/2018] [Indexed: 12/11/2022]
|
36
|
Dao FY, Lv H, Wang F, Ding H. Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics. Front Genet 2018; 9:613. [PMID: 30619452 PMCID: PMC6295579 DOI: 10.3389/fgene.2018.00613] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/21/2018] [Indexed: 01/01/2023] Open
Abstract
The initiate site of DNA replication is called origins of replication (ORI) which is regulated by a set of regulatory proteins and plays important roles in the basic biochemical process during cell growth and division in all living organisms. Therefore, the study of ORIs is essential for understanding the cell-division cycle and gene expression regulation so that scholars can develop a new strategy against genetic diseases by using the knowledge of DNA replication. Thus, the accurate identification of ORIs will provide key clues for DNA replication research and clinical medicine. Although, the conventional experiments could provide accurate results, they are time-consuming and cost ineffective. On the contrary, bioinformatics-based methods can overcome these shortcomings. Especially, with the emergence of DNA sequences in the post-genomic era, it is highly expected to develop high throughput tools to identify ORIs based on sequence information. In this review, we will summarize the current progress in computational prediction of eukaryotic ORIs including the collection of benchmark dataset, the application of machine learning-based techniques, the results obtained by these methods, and the construction of web servers. Finally, we gave the future perspectives on ORIs prediction. The review provided readers with a whole background of ORIs prediction based on machine learning methods, which will be helpful for researchers to study DNA replication in-depth and drug therapy of genetic defect.
Collapse
Affiliation(s)
- Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lv
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
37
|
Andrieu J, Re F, Russo L, Nicotra F. Phage-displayed peptides targeting specific tissues and organs. J Drug Target 2018; 27:555-565. [DOI: 10.1080/1061186x.2018.1531419] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Josu Andrieu
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Francesca Re
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Laura Russo
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Francesco Nicotra
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| |
Collapse
|
38
|
Shao L, Gao H, Liu Z, Feng J, Tang L, Lin H. Identification of Antioxidant Proteins With Deep Learning From Sequence Information. Front Pharmacol 2018; 9:1036. [PMID: 30294271 PMCID: PMC6158654 DOI: 10.3389/fphar.2018.01036] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Accepted: 08/27/2018] [Indexed: 01/26/2023] Open
Abstract
Antioxidant proteins have been found closely linked to disease control for its ability to eliminate excess free radicals. Because of its medicinal value, the study of identifying antioxidant proteins is on the upsurge. Many machine-learning classifiers have performed poorly owing to the nonlinear and unbalanced nature of biological data. Recently, deep learning techniques showed advantages over many state-of-the-art machine learning methods in various fields. In this study, a deep learning based classifier was proposed to identify antioxidant proteins based on mixed g-gap dipeptide composition feature vector. The classifier employed deep autoencoder to extract nonlinear representation from raw input. The t-Distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction. Support vector machine was finally performed for classification. The classifier achieved F 1 score of 0.8842 and MCC of 0.7409 in 10-fold cross validation. Experimental results show that our proposed method outperformed the traditional machine learning methods and could be a promising tool for antioxidant protein identification. For the convenience of others' scientific research, we have developed a user-friendly web server called IDAod for antioxidant protein identification, which can be accessed freely at http://bigroup.uestc.edu.cn/IDAod/.
Collapse
Affiliation(s)
- Lifen Shao
- Center for Informational Biology, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Gao
- Center for Informational Biology, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhen Liu
- Center for Informational Biology, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Juan Feng
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lixia Tang
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
39
|
Kalmykova SD, Arapidi GP, Urban AS, Osetrova MS, Gordeeva VD, Ivanov VT, Govorun VM. In Silico Analysis of Peptide Potential Biological Functions. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2018. [DOI: 10.1134/s106816201804009x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
40
|
Lin H, Peng S, Huang J. Special issue on Computational Resources and Methods in Biological Sciences. Int J Biol Sci 2018; 14:807-810. [PMID: 29989106 PMCID: PMC6036761 DOI: 10.7150/ijbs.27554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/03/2018] [Indexed: 12/11/2022] Open
Abstract
This special issue covers a wide range of topics in computational biology, such as database construction, sequence analysis and function prediction with machine learning methods, disease-related diagnosis, drug-target and drug discovery, and electronic health record system construction.
Collapse
Affiliation(s)
- Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Shaoliang Peng
- School of Computer Science, National University of Defense Technology, Changsha 410073, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| |
Collapse
|
41
|
Bhatnager R, Dang AS. Comprehensive in-silico prediction of damage associated SNPs in Human Prolidase gene. Sci Rep 2018; 8:9430. [PMID: 29930383 PMCID: PMC6013436 DOI: 10.1038/s41598-018-27789-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 06/04/2018] [Indexed: 12/05/2022] Open
Abstract
Prolidase is cytosolic manganese dependent exopeptidase responsible for the catabolism of imido di and tripeptides. Prolidase levels have been associated with a number of diseases such as bipolar disorder, erectile dysfunction and varied cancers. Single nucleotide polymorphism present in coding region of proteins (nsSNPs) has the potential to alter the primary structure as well as function of the protein. Hence, it becomes necessary to differentiate the potential harmful nsSNPs from the neutral ones. 19 nsSNPs were predicted as damaging by in-silico analysis of 298 nsSNPs retrieved from dbSNP database. Consurf analysis showed 18 out of 19 substitutions were present in the conserved regions. 4 substitutions (D276N, D287N, E412K, and G448R) that observed to have damaging effect are present in catalytic pocket. Four SNPs listed in splice site region were found to affect splicing of mRNA by altering acceptor site. On 3′UTR scan of 77 SNPs listed in SNP database, 9 SNPs were lead to alter miRNA target sites. These results provide a filtered data to explore the effect of uncharacterized nsSNP and SNP related to UTRs and splice site of prolidase to find their association with the disease susceptibility and to design the target dependent drugs for therapeutics.
Collapse
Affiliation(s)
- Richa Bhatnager
- Centre For Medical Biotechnology, M. D. University, Rohtak, 124001, India
| | - Amita S Dang
- Centre For Medical Biotechnology, M. D. University, Rohtak, 124001, India.
| |
Collapse
|
42
|
Wu Y, He Z, Lin H, Zheng Y, Zhang J, Xu D. A Fast Projection-Based Algorithm for Clustering Big Data. Interdiscip Sci 2018; 11:360-366. [PMID: 29882026 DOI: 10.1007/s12539-018-0294-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/18/2018] [Accepted: 03/22/2018] [Indexed: 01/01/2023]
Abstract
With the fast development of various techniques, more and more data have been accumulated with the unique properties of large size (tall) and high dimension (wide). The era of big data is coming. How to understand and discover new knowledge from these data has attracted more and more scholars' attention and has become the most important task in data mining. As one of the most important techniques in data mining, clustering analysis, a kind of unsupervised learning, could group a set data into objectives(clusters) that are meaningful, useful, or both. Thus, the technique has played very important role in knowledge discovery in big data. However, when facing the large-sized and high-dimensional data, most of the current clustering methods exhibited poor computational efficiency and high requirement of computational source, which will prevent us from clarifying the intrinsic properties and discovering the new knowledge behind the data. Based on this consideration, we developed a powerful clustering method, called MUFOLD-CL. The principle of the method is to project the data points to the centroid, and then to measure the similarity between any two points by calculating their projections on the centroid. The proposed method could achieve linear time complexity with respect to the sample size. Comparison with K-Means method on very large data showed that our method could produce better accuracy and require less computational time, demonstrating that the MUFOLD-CL can serve as a valuable tool, at least may play a complementary role to other existing methods, for big data clustering. Further comparisons with state-of-the-art clustering methods on smaller datasets showed that our method was fastest and achieved comparable accuracy. For the convenience of most scholars, a free soft package was constructed.
Collapse
Affiliation(s)
- Yun Wu
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China.
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
| | - Zhiquan He
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
- College of Information Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Hao Lin
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Yufei Zheng
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China
| | - Jingfen Zhang
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Dong Xu
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
| |
Collapse
|
43
|
Jiang J, Xing F, Zeng X, Zou Q. RicyerDB: A Database For Collecting Rice Yield-related Genes with Biological Analysis. Int J Biol Sci 2018; 14:965-970. [PMID: 29989091 PMCID: PMC6036756 DOI: 10.7150/ijbs.23328] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Accepted: 12/25/2017] [Indexed: 11/16/2022] Open
Abstract
The Rice Yield-related Database (RicyerDB) was created to complement with related research of influence rice (Oryza sativa L.) yield in multiple traits by manually curating the related databases and literature, and genomics and proteomics information that could be useful for comprehensive understanding of the rice biology. RicyerDB provides a more valuable resource in which to efficiently investigate, browse and analyze yield-related genes. The whole data set can be easily queried and downloaded through the webpage. In addition, RicyerDB also constructed a protein-protein interaction network with biological analysis. The combined rice database opens a new path to facilitate researchers achieving information on rice gene in terms of their effects on traits important for rice breeding. The web server is freely available at: http://server.malab.cn/Ricyer/index.html.
Collapse
Affiliation(s)
- Jing Jiang
- School of Aerospace Engineering, Xiamen University, Xiamen, 361001, China
| | - Fei Xing
- School of Aerospace Engineering, Xiamen University, Xiamen, 361001, China
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Xiamen University, Xiamen 361001, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, 300354, China
| |
Collapse
|
44
|
Yang H, Qiu WR, Liu G, Guo FB, Chen W, Chou KC, Lin H. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int J Biol Sci 2018; 14:883-891. [PMID: 29989083 PMCID: PMC6036749 DOI: 10.7150/ijbs.24616] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 02/04/2018] [Indexed: 02/06/2023] Open
Abstract
Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called "recombination hotspot", while the latter the "recombination coldspot". Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called "iRSpot-Pse6NC" was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.
Collapse
Affiliation(s)
- Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wang-Ren Qiu
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, 333403, China
| | - Guoqing Liu
- School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Feng-Biao Guo
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Chen
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.,Gordon Life Science Institute, Boston, MA 02478, USA
| | - Kuo-Chen Chou
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Gordon Life Science Institute, Boston, MA 02478, USA
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Gordon Life Science Institute, Boston, MA 02478, USA
| |
Collapse
|
45
|
Tang H, Zhao YW, Zou P, Zhang CM, Chen R, Huang P, Lin H. HBPred: a tool to identify growth hormone-binding proteins. Int J Biol Sci 2018; 14:957-964. [PMID: 29989085 PMCID: PMC6036759 DOI: 10.7150/ijbs.24174] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 01/15/2018] [Indexed: 12/19/2022] Open
Abstract
Hormone-binding protein (HBP) is a kind of soluble carrier protein and can selectively and non-covalently interact with hormone. HBP plays an important role in life growth, but its function is still unclear. Correct recognition of HBPs is the first step to further study their function and understand their biological process. However, it is difficult to correctly recognize HBPs from more and more proteins through traditional biochemical experiments because of high experimental cost and long experimental period. To overcome these disadvantages, we designed a computational method for identifying HBPs accurately in the study. At first, we collected HBP data from UniProt to establish a high-quality benchmark dataset. Based on the dataset, the dipeptide composition was extracted from HBP residue sequences. In order to find out the optimal features to provide key clues for HBP identification, the analysis of various (ANOVA) was performed for feature ranking. The optimal features were selected through the incremental feature selection strategy. Subsequently, the features were inputted into support vector machine (SVM) for prediction model construction. Jackknife cross-validation results showed that 88.6% HBPs and 81.3% non-HBPs were correctly recognized, suggesting that our proposed model was powerful. This study provides a new strategy to identify HBPs. Moreover, based on the proposed model, we established a webserver called HBPred, which could be freely accessed at http://lin-group.cn/server/HBPred.
Collapse
Affiliation(s)
- Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Ya-Wei Zhao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ping Zou
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Chun-Mei Zhang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Rong Chen
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Po Huang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Hao Lin
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
46
|
Zhu Y, Xie Z, Li Y, Zhu M, Chen YPP. Research on folding diversity in statistical learning methods for RNA secondary structure prediction. Int J Biol Sci 2018; 14:872-882. [PMID: 29989089 PMCID: PMC6036747 DOI: 10.7150/ijbs.24595] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 02/21/2018] [Indexed: 12/24/2022] Open
Abstract
How to improve the prediction accuracy of RNA secondary structure is currently a hot topic. The existing prediction methods for a single sequence do not fully consider the folding diversity which may occur among RNAs with different functions or sources. This paper explores the relationship between folding diversity and prediction accuracy, and puts forward a new method to improve the prediction accuracy of RNA secondary structure. Our research investigates the following: 1. The folding feature based on stochastic context-free grammar is proposed. By using dimension reduction and clustering techniques, some public data sets are analyzed. The results show that there is significant folding diversity among different RNA families. 2. To assign folding rules to RNAs without structural information, a classification method based on production probability is proposed. The experimental results show that the classification method proposed in this paper can effectively classify the RNAs of unknown structure. 3. Based on the existing prediction methods of statistical learning models, an RNA secondary structure prediction framework is proposed, namely "Cluster - Training - Parameter Selection - Prediction". The results show that, with information on folding diversity, prediction accuracy can be significantly improved.
Collapse
Affiliation(s)
- Yu Zhu
- College of Computer Science, Sichuan University, China
| | - ZhaoYang Xie
- College of Computer Science, Sichuan University, China
| | - YiZhou Li
- College of Chemistry, Sichuan University, China
| | - Min Zhu
- Vice Dean of College of Computer Science, Sichuan University
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Australia
| |
Collapse
|
47
|
Poonam, Gupta Y, Gupta N, Singh S, Wu L, Chhikara BS, Rawat M, Rathi B. Multistage inhibitors of the malaria parasite: Emerging hope for chemoprotection and malaria eradication. Med Res Rev 2018; 38:1511-1535. [DOI: 10.1002/med.21486] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 12/09/2017] [Accepted: 12/26/2017] [Indexed: 12/13/2022]
Affiliation(s)
- Poonam
- Department of Chemistry; Miranda House, University of Delhi; India
| | - Yash Gupta
- National Institute of Malaria Research (ICMR); New Delhi India
| | - Nikesh Gupta
- Special Centre for Nanosciences; Jawaharlal Nehru University; New Delhi India
| | - Snigdha Singh
- Laboratory for Translational Chemistry and Drug Discovery, Department of Chemistry; Hansraj College University Enclave, University of Delhi; Delhi India
| | - Lidong Wu
- Department of Chemistry; Massachusetts Institute of Technology; Cambridge MA USA
- Key Laboratory of Control of Quality and Safety for Aquatic Products; Ministry of Agriculture, Chinese Academy of Fishery Sciences; Beijing China
| | | | - Manmeet Rawat
- Department of Internal Medicine; University of New Mexico School of Medicine; Albuquerque NM USA
| | - Brijesh Rathi
- Laboratory for Translational Chemistry and Drug Discovery, Department of Chemistry; Hansraj College University Enclave, University of Delhi; Delhi India
- Department of Chemistry; Massachusetts Institute of Technology; Cambridge MA USA
| |
Collapse
|
48
|
He B, Tjhung KF, Bennett NJ, Chou Y, Rau A, Huang J, Derda R. Compositional Bias in Naïve and Chemically-modified Phage-Displayed Libraries uncovered by Paired-end Deep Sequencing. Sci Rep 2018; 8:1214. [PMID: 29352178 PMCID: PMC5775325 DOI: 10.1038/s41598-018-19439-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 01/02/2018] [Indexed: 01/09/2023] Open
Abstract
Understanding the composition of a genetically-encoded (GE) library is instrumental to the success of ligand discovery. In this manuscript, we investigate the bias in GE-libraries of linear, macrocyclic and chemically post-translationally modified (cPTM) tetrapeptides displayed on the M13KE platform, which are produced via trinucleotide cassette synthesis (19 codons) and NNK-randomized codon. Differential enrichment of synthetic DNA {S}, ligated vector {L} (extension and ligation of synthetic DNA into the vector), naïve libraries {N} (transformation of the ligated vector into the bacteria followed by expression of the library for 4.5 hours to yield a "naïve" library), and libraries chemically modified by aldehyde ligation and cysteine macrocyclization {M} characterized by paired-end deep sequencing, detected a significant drop in diversity in {L} → {N}, but only a minor compositional difference in {S} → {L} and {N} → {M}. Libraries expressed at the N-terminus of phage protein pIII censored positively charged amino acids Arg and Lys; libraries expressed between pIII domains N1 and N2 overcame Arg/Lys-censorship but introduced new bias towards Gly and Ser. Interrogation of biases arising from cPTM by aldehyde ligation and cysteine macrocyclization unveiled censorship of sequences with Ser/Phe. Analogous analysis can be used to explore library diversity in new display platforms and optimize cPTM of these libraries.
Collapse
Affiliation(s)
- Bifang He
- Department of Chemistry and Alberta Glycomics Centre, University of Alberta, Edmonton, AB T6G 2G2, Canada
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Katrina F Tjhung
- Department of Chemistry and Alberta Glycomics Centre, University of Alberta, Edmonton, AB T6G 2G2, Canada
- The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA, 92037, USA
- The Salk Institute, 10010 N. Torrey Pines Rd., La Jolla, CA, 92037, USA
| | - Nicholas J Bennett
- Department of Chemistry and Alberta Glycomics Centre, University of Alberta, Edmonton, AB T6G 2G2, Canada
| | - Ying Chou
- Department of Chemistry and Alberta Glycomics Centre, University of Alberta, Edmonton, AB T6G 2G2, Canada
| | - Andrea Rau
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Jian Huang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- Center for Information in Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ratmir Derda
- Department of Chemistry and Alberta Glycomics Centre, University of Alberta, Edmonton, AB T6G 2G2, Canada.
| |
Collapse
|
49
|
He B, Jiang L, Duan Y, Chai G, Fang Y, Kang J, Yu M, Li N, Tang Z, Yao P, Wu P, Derda R, Huang J. Biopanning data bank 2018: hugging next generation phage display. Database (Oxford) 2018; 2018:4955852. [PMID: 29688378 PMCID: PMC7206649 DOI: 10.1093/database/bay032] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 02/07/2018] [Accepted: 03/07/2018] [Indexed: 12/12/2022]
Abstract
Database URL The BDB database is available at http://immunet.cn/bdb.
Collapse
Affiliation(s)
- Bifang He
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Lixu Jiang
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Yaocong Duan
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Guoshi Chai
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Yewei Fang
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Juanjuan Kang
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Min Yu
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Ning Li
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Zhongjie Tang
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Pengcheng Yao
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Pengcheng Wu
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| | - Ratmir Derda
- Department of Chemistry, University of Alberta, 11227 Saskatchewan Drive, Edmonton, AB T6G 2G2, Canada
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
| |
Collapse
|
50
|
PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5761517. [PMID: 29445741 PMCID: PMC5763211 DOI: 10.1155/2017/5761517] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 11/02/2017] [Indexed: 11/18/2022]
Abstract
Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology community.
Collapse
|