27,187 research outputs found
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
Sequence-based protein homology detection has been extensively studied and so
far the most sensitive method is based upon comparison of protein sequence
profiles, which are derived from multiple sequence alignment (MSA) of sequence
homologs in a protein family. A sequence profile is usually represented as a
position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and
accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This
paper presents a new homology detection method MRFalign, consisting of three
key components: 1) a Markov Random Fields (MRF) representation of a protein
family; 2) a scoring function measuring similarity of two MRFs; and 3) an
efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning
two MRFs. Compared to HMM that can only model very short-range residue
correlation, MRFs can model long-range residue interaction pattern and thus,
encode information for the global 3D structure of a protein family.
Consequently, MRF-MRF comparison for remote homology detection shall be much
more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that
MRFalign outperforms several popular HMM or PSSM-based methods in terms of both
alignment accuracy and remote homology detection and that MRFalign works
particularly well for mainly beta proteins. For example, tested on the
benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM
succeed on 48% and 52% of proteins, respectively, at superfamily level, and on
15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign
succeeds on 57.3% and 42.5% of proteins at superfamily and fold level,
respectively. This study implies that long-range residue interaction patterns
are very helpful for sequence-based homology detection. The software is
available for download at http://raptorx.uchicago.edu/download/.Comment: Accepted by both RECOMB 2014 and PLOS Computational Biolog
DeepSF: deep convolutional neural network for mapping protein sequences to folds
Motivation
Protein fold recognition is an important problem in structural
bioinformatics. Almost all traditional fold recognition methods use sequence
(homology) comparison to indirectly predict the fold of a tar get protein based
on the fold of a template protein with known structure, which cannot explain
the relationship between sequence and fold. Only a few methods had been
developed to classify protein sequences into a small number of folds due to
methodological limitations, which are not generally useful in practice.
Results
We develop a deep 1D-convolution neural network (DeepSF) to directly classify
any protein se quence into one of 1195 known folds, which is useful for both
fold recognition and the study of se quence-structure relationship. Different
from traditional sequence alignment (comparison) based methods, our method
automatically extracts fold-related features from a protein sequence of any
length and map it to the fold space. We train and test our method on the
datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On
the independent testing dataset curated from SCOP2.06, the classification
accuracy is 77.0%. We compare our method with a top profile profile alignment
method - HHSearch on hard template-based and template-free modeling targets of
CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is
14.5%-29.1% higher than HHSearch on template-free modeling targets and
4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10
predicted folds. The hidden features extracted from sequence by our method is
robust against sequence mutation, insertion, deletion and truncation, and can
be used for other protein pattern recognition problems such as protein
clustering, comparison and ranking.Comment: 28 pages, 13 figure
Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction.
BackgroundOne of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.ResultsWe explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.ConclusionWhen attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used
Kernel methods in genomics and computational biology
Support vector machines and kernel methods are increasingly popular in
genomics and computational biology, due to their good performance in real-world
applications and strong modularity that makes them suitable to a wide range of
problems, from the classification of tumors to the automatic annotation of
proteins. Their ability to work in high dimension, to process non-vectorial
data, and the natural framework they provide to integrate heterogeneous data
are particularly relevant to various problems arising in computational biology.
In this chapter we survey some of the most prominent applications published so
far, highlighting the particular developments in kernel methods triggered by
problems in biology, and mention a few promising research directions likely to
expand in the future
Malware Detection Using Dynamic Analysis
In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach
- …