Download Instructions for FUEL-mLoc Web-server

Instructions for FUEL-mLoc Server
Instructions for FUEL-mLoc Web-server
Shibiao Wan∗,‡ and Man-Wai Mak‡
email: [email protected], [email protected]
∗
‡
Princeton University, NJ, USA
The Hong Kong Polytechnic University, Hong Kong SAR, China
August 2016
Back to FUEL-mLoc Server
Contents
1 Significance of Subcellular Localization Prediction
2
2 Major organelles in a typical eukaryotic cell
2
3 Specific information about FUEL-mLoc
4
4 Some Notes
7
1
Instructions for FUEL-mLoc Server
1
Significance of Subcellular Localization Prediction
Proteins must be transported to the correct organelles of a cell and folded into correct
3-D structures to properly perform their functions. Therefore, knowing the subcellular
localization is one step towards understanding its functions. Proteins can exist in different
locations within a cell, and some proteins can even simultaneously reside at, or move
between, two or more different subcellular locations. As an essential and indispensable
topic in proteomics research and molecular cell biology, protein subcellular localization
is critically important for protein function annotation, drug target discovery, and drug
design. Efficient and reliable computational methods are developed to assist the biological
experiments such as fluorescent microscopy imaging. Proteins with multiple locations play
important roles in some metabolic processes taking place in more than one compartment.
2
Major organelles in a typical eukaryotic cell
Most of the biological activities performed by proteins occur in organelles. An organelle
is a cellular component or subcellular location within a cell that has specific functions.
Fig. 1 illustrates some organelles in a typical eukaryotic cell. (Note that human cells
and plant cells are two subsets of eukaryotic cells.) In eukaryotic cells, major organelles
include cytoplasm, mitochondria, chloroplast, nucleus, extracellular space, endoplasmic
reticulum (ER), Golgi apparatus and plasma membrane. Cytoplasm takes up most of
the cell volume where most of the cellular activities, such as cell division and metabolic
pathways, occur. Mitochondrion is a membrane-bound organelle found in most eukaryotic
cells. It is mainly responsible for supplying energy for cellular activities. Chloroplast is
2
Instructions for FUEL-mLoc Server
Figure 1: Organelles or subcellular locations in a typical eukaryotic cell. Major eukaryotic organelles include cytoplasm, mitochondria, chloroplast, nucleus, extracellular space,
endoplasmic reticulum, Golgi apparatus and plasma membrane.
an organelle existing in plant or algal cells. Its role is to conduct photosynthesis to
store energy from sunlight. Nucleus is a membrane-enclosed organelle containing most
of the genetic materials for a cell. Its main function is to control the activities of the
cell by regulating gene expression. Extracellular space refers to the space outside the
plasma membrane, which is occupied by fluid. ER is a type of organelle that forms an
interconnected membranous network of cistemae which serves the functions of folding
protein molecules in cistemae and transporting synthesized proteins to Golgi apparatus.
3
Instructions for FUEL-mLoc Server
Golgi apparatus is an organelle which is particularly important in cell secretion. Plasma
membrane or cell membrane is a biological membrane that separates the intracellular
environment from extracellular space. Its basic function is to protect the cell from its
surroundings.
Some proteins locate in peroxisome, vacuole, cytoskeleton, nucleoplasm, lysosome,
acrosome, cell wall, centrosome, cyanelle, endosome, hydrogenosome, melanosome, microsome, spindle pole body, synapse, etc.
For Gram-positive bacteria species, their proteins are usually located in cell membrane,
cell wall, cytoplasm and extracellular space.
For Gram-negative bacteria species, their proteins are located in eight subcellular
locations, including cell inner membrane, cell outer membrane, cytoplasm, extracellular
space, fimbrium, flagellum, nucleus and periplasm.
For the virus species, viral proteins are usually located within host cells, which are distributed in subcellular locations such as host cytoplasm, host nucleus, host cell membrane,
host ER, host nucleus as well as viral capsid.
3
Specific information about FUEL-mLoc
FUEL-mLoc is an interpretable multi-label predictor which uses unified features to yield
sparse and interpretable solutions for large-scale prediction of both single-label and multilabel proteins of different species, including eukaryota, human, plant, Gram-positive bacteria. Gram-negative bacteria and virus. Given a query protein sequence in a particular species, a set of GO terms are retrieved from a newly created compact databases,
namely ProSeq-GO. The frequencies of GO occurrences are used to formulate frequency
4
Instructions for FUEL-mLoc Server
vectors with dimensionality of more than eight thousand. By using the one-vs-rest ENbased (elastic net-based) classifiers, much fewer GO terms are selected. Subsequently, the
dimension-reduced feature vectors are classified by a multi-label EN classifier. Based on
the selected essential GO terms, the user of FUEL-mLoc can not only determine where a
protein resides, but also explains why it is located there.
Similar to R3P-Loc [1, 2, 3, 4], instead of using the Swiss-Prot and GOA databases
as previous predictors [5, 6, 7, 8, 9, 10, 11], FUEL-mLoc uses two newly-created compact
databases, namely ProSeq and ProSeq-GO, for GO information transfer. The ProSeq
database is a sequence database in which each amino acid sequence has at least one GO
term annotated to it. The ProSeq-GO comprises GO terms annotated to the protein
sequences in the ProSeq database. An important property of the ProSeq and ProSeqGO databases is that they are much smaller than the Swiss-Prot and GOA databases,
respectively.
FUEL-mLoc is designed to be able to predict subcellular localization of proteins from
six species, namely eukaryota, human, plant, Gram-positive bacteria, Gram-negative bacteria and virus.
• For eukaryotic proteins, 22 subcellular locations can be predicted, including: (1)
acrosome; (2) cell membrane; (3) cell-wall; (4) centrosome; (5) chloroplast; (6)
cyanelle; (7) cytoplasm; (8) cytoskeleton; (9) endoplasmic reticulum; (10) endosome; (11) extracellular; (12) golgi apparatus; (13) hydrogenosome; (14) lysosome;
(15) melanosome; (16) microsome; (17) mitochondrion; (18) nucleus; (19) peroxisome; (20) spindle pole body; (21) synapse; and (22) vacuole. The predictor is
not designed for predicting the subcellular localization of non-eukaryotic proteins
5
Instructions for FUEL-mLoc Server
when selecting predicting the eukaryotic proteins. Therefore, the prediction results
of non-eukaryotic proteins in this case are arbitrary and meaningless.
• For human proteins, 14 subcellular locations can be predicted, including: (1) centrosome; (2) cytoplasm; (3) cytoskeleton; (4) endoplasmic reticulum; (5) endosome;
(6) extracellular; (7) Golgi apparatus; (8) lysosome; (9) microsome; (10) mitochondrion; (11) nucleus; (12) peroxisome; (13) plasma membrane; and (14) synapse. The
predictor is not designed for predicting the subcellular localization of non-human
proteins. Therefore, the prediction results of non-human proteins are arbitrary and
meaningless.
• For plant proteins, 12 subcellular locations can be predicted, including: (1) cell
membrane; (2) cell wall; (3) chloroplast; (4) cytoplasm; (5) endoplasmic reticulum;
(6) extracellular; (7) golgi apparatus; (8) mitochondrion; (9) nucleus; (10) peroxisome; (11) plastid; and (12) vacuole. Note (11) plastid here includes those plastid
groups except for (3) chloroplast. The predictor is not designed for predicting the
subcellular localization of non-plant proteins when selecting predicting the plant
proteins. Therefore, the prediction results of non-plant proteins in this case are
arbitrary and meaningless.
• For Gram-positive bacterial proteins, 4 subcellular locations can be predicted, including: (1) cell membrane; (2) cell wall; (3) cytoplasm; and (4) extracellular space.
The predictor is not designed for predicting the subcellular localization of non-plant
proteins when selecting predicting the plant proteins. Therefore, the prediction
results of non-plant proteins in this case are arbitrary and meaningless.
6
Instructions for FUEL-mLoc Server
• For Gram-negative bacterial proteins, 8 subcellular locations can be predicted, including: (1) cell inner membrane; (2) cell outer membrane; (3) cytoplasm; (4)
extracellular space; (5) fimbrium; (6) flagellum; (7) nucleus; and (8) periplasm. The
predictor is not designed for predicting the subcellular localization of non-Gramnegative-bacterial proteins when selecting predicting the plant proteins. Therefore,
the prediction results of non-plant proteins in this case are arbitrary and meaningless.
• For viral proteins, 6 subcellular locations can be predicted, including: (1) viral
capsid; (2) host cell membrane; (3) host endoplasmic reticulum; (4) host cytoplasm;
(5) host nucleus; (6) secreted. The predictor is not designed for predicting the
subcellular localization of non-viral proteins. Therefore, the prediction results of
non-viral proteins are arbitrary and meaningless.
4
Some Notes
The following notes for FUEL-mLoc web-server should be paid attention to:
1. FUEL-mLoc can make prediction for the input of amino acid sequences of proteins
in FASTA format.
2. Users can optionally provide email addresses to receive the prediction results. If
no (or wrongly-formatted) email address is provided, the results will be shown on
the webpage. On the other hand, if an email address is provided, the prediction
results will be sent via email. For users’ convenience, the prediction results are still
downloadable from the webpage even if the results are sent to their emails.
7
Instructions for FUEL-mLoc Server
3. FUEL-mLoc is an interpretable predictor and the interpretations for a particular
query protein (or a list of proteins) are presented in a formatted table in an HTML
file which can also be downloadable.
4. Because FUEL-mLoc uses two new compact databases, namely ProSeq and ProSeqGO, instead of Swiss-Prot and GOA, it consumes much less memory and predicts
faster.
References
[1] S. Wan, M. W. Mak, and S. Y. Kung, “Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins,” BMC Bioinformatics, vol.
17, no. 97, 2016.
[2] S. Wan, M. W. Mak, and S. Y. Kung, “Mem-ADSVM: a two-layer multi-label
predictor for identifying multi-functional types of membrane proteins,” Journal of
Theoretical Biology, vol. 398, pp. 32–42, 2016.
[3] S. Wan, M. W. Mak, and S. Y. Kung, “Mem-mEN: predicting multi-functional types
of membrane proteins by interpretable elastic nets,” IEEE/ACM Transactions on
Computational Biology and Bioinformatics, vol. 13, pp. 706–718, 2016.
[4] S. Wan, M. W. Mak, and S. Y. Kung, “R3P-Loc: A compact multi-label predictor
using ridge regression and random projection for protein subcellular localization,”
Journal of Theoretical Biology, vol. 360, pp. 34–45, 2014.
8
Instructions for FUEL-mLoc Server
[5] K. C. Chou, Z. C. Wu, and X. Xiao, “iLoc-Hum: using the accumulation-label scale
to predict subcellular locations of human proteins with both single and multiple
sites,” Molecular BioSystems, vol. 8, pp. 629–641, 2012.
[6] S. Wan, M. W. Mak, and S. Y. Kung, “mGOASVM: Multi-label protein subcellular
localization based on gene ontology and support vector machines,” BMC Bioinformatics, vol. 13, pp. 290, 2012.
[7] S. Wan, M. W. Mak, and S. Y. Kung, “GOASVM: A subcellular location predictor by
incorporating term-frequency gene ontology into the general form of Chou’s pseudoamino acid composition,” Journal of Theoretical Biology, vol. 323, pp. 40–48, 2013.
[8] S. Wan, M. W. Mak, and S. Y. Kung, “HybridGO-Loc: Mining hybrid features
on gene ontology for predicting subcellular localization of multi-location proteins,”
PLoS ONE, vol. 9, no. 3, pp. e89545, 2014.
[9] S. Wan, M. W. Mak, and S. Y. Kung, “mPLR-Loc: An adaptive decision multi-label
classifier based on penalized logistic regression for protein subcellular localization
prediction,” Analytical Biochemistry, vol. 473, pp. 14–27, 2015.
[10] S. Wan and M. W. Mak, Machine learning for protein subcellular localization prediction, De Gruyter, 2015.
[11] S. Wan and M. W. Mak, “Predicting subcellular localization of multi-location proteins by improving support vector machines with an adaptive-decision scheme,” International Journal of Machine Learning and Cybernetics, pp. 1–13.
9