22 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
TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool.
BACKGROUND: Transmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics; how the structural information should be gained from a sequence. RESULTS: Here, we present a novel method, TMFoldRec, for fold prediction of membrane segments in transmembrane proteins. TMFoldRec based on statistical potentials was tested on a benchmark set containing 124 TMP chains from the PDBTM database. Using a 10-fold jackknife method, the native folds were correctly identified in 77 % of the cases. This accuracy overcomes the state-of-the-art methods. In addition, a key feature of TMFoldRec algorithm is the ability to estimate the reliability of the prediction and to decide with an accuracy of 70 %, whether the obtained, lowest energy structure is the native one. CONCLUSION: These results imply that the membrane embedded parts of TMPs dictate the TM structures rather than the soluble parts. Moreover, predictions with reliability scores make in this way our algorithm applicable for proteome-wide analyses. AVAILABILITY: The program is available upon request for academic use
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Recently exciting progress has been made on protein contact prediction, but
the predicted contacts for proteins without many sequence homologs is still of
low quality and not very useful for de novo structure prediction. This paper
presents a new deep learning method that predicts contacts by integrating both
evolutionary coupling (EC) and sequence conservation information through an
ultra-deep neural network formed by two deep residual networks. This deep
neural network allows us to model very complex sequence-contact relationship as
well as long-range inter-contact correlation. Our method greatly outperforms
existing contact prediction methods and leads to much more accurate
contact-assisted protein folding. Tested on three datasets of 579 proteins, the
average top L long-range prediction accuracy obtained our method, the
representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21
and 0.30, respectively; the average top L/10 long-range accuracy of our method,
CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding
using our predicted contacts as restraints can yield correct folds (i.e.,
TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and
CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively.
Further, our contact-assisted models have much better quality than
template-based models. Using our predicted contacts as restraints, we can (ab
initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast,
when the training proteins of our method are used as templates, homology
modeling can only do so for 10 of them. One interesting finding is that even if
we do not train our prediction models with any membrane proteins, our method
works very well on membrane protein prediction. Finally, in recent blind CAMEO
benchmark our method successfully folded 5 test proteins with a novel fold