High accuracy prediction of transmembrane inter helix

MemBrain Improving the Accuracy of Predicting Transmembrane

An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins TMpro an algorithm for high accuracy TM helix prediction we previously developed is coupled with active learning

MemBrain Transmembrane protein structure prediction SJTU

Our method derives the highest topology accuracy than any other individual predictors and consensus predictors at the same time the TMHs are more accurately predicted in their length and locations where both the false positives FPs and the false negatives FNs decreased dramatically

BIOINFORMATICS ORIGINAL PAPER doi 10 1093 bioinformatics btt440

We present a novel method MemBrain to derive inter TMH contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers

High Accuracy Prediction Of Transmembrane Inter Helix

Active machine learning for transmembrane helix prediction

A novel method to derive transmembrane inter helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers is presented demonstrating significant progress in contact prediction and a potential for contact driven structure modeling of trans Membrane proteins

brane inter helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers Tested on 60 non redundant polytopic proteins using a strict leave one out cross validation protocol MemBrain achieves an average accuracy of 62 which is 12 5 higher than the current best method from the

Jing Yang Richard Jang Yang Zhang and Hong Bin Shen High accuracy prediction of transmembrane inter helix contacts and application to GPCR 3D structure modeling Bioinformatics

High accuracy prediction of transmembrane inter helix dblp

New insights into protein protein interaction modulators in

Results We present a novel method MemBrain to derive transmembrane inter helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers Tested on 60 non redundant polytopic proteins using a strict leave one out cross validation protocol MemBrain achieves an average accuracy of 62

We present a novel method MemBrain to derive transmembrane inter helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers

High accuracy prediction of transmembrane inter helix contacts

Prediction of transmembrane helices TMH in α helical membrane proteins provides valuable information about the protein topology when the high resolution structures are not available Many predictors have been developed based on either amino acid hydrophobicity scale or pure statistical approaches

Enhanced Inter helical Residue Contact Prediction in

Protein structure modeling of 13 GPCRs by I TASSER with or without using MemBrain contact predictions with RMSD and TM score calculated in the transmembrane regions a

Topology Prediction Improvement of α helical Transmembrane

Using an enlarged set of transmembrane alpha helical proteins in PDB we enhance the model by building new probability sets to satisfy the demand of high quality inter helical contact predictions in transmembrane proteins

High Accuracy Prediction Of Transmembrane Inter Helix Image Results

High accuracy prediction of transmembrane inter helix

c Myc is an oncogenic transcription factor that is characterized by a basic helix loop helix leucine zipper bHLH ZIP domain 239 c Myc regulation involves tightly controlled expression and post

MemBrain demonstrates an overall improvement of about 20 in prediction accuracy particularly in predicting the ends of TMHs and TMHs that are shorter than 15 residues It also has the capability to detect N terminal signal peptides

Improving transmembrane protein consensus topology prediction

Videos for High Accuracy Prediction Of Transmembrane Inter Helix

Transmembrane helices predicted at 95 accuracy Rost 1995

High accuracy prediction of transmembrane inter helix

Bibliographic details on High accuracy prediction of transmembrane inter helix contacts and application to GPCR 3D structure modeling

High Accuracy Prediction Of Transmembrane Inter Helix

The first module is transmembrane helix TMH prediction which features the capability of accurately predicting TMH with the tail part through the incorporation of tail modeling The prediction engine contains a multiscale deep learning model and a dynamic threshold strategy

PDF High accuracy prediction of transmembrane inter helix

MemBrain Improving the Accuracy of Predicting Transmembrane

Tested on 60 non redundant polytopic proteins using a strict leave one out cross validation protocol MemBrain achieves an average accuracy of 62 which is 12 5 higher than the current best method from the literature

DeepHelicon accurate prediction of inter helical residue

A rigorous cross validation test on 69 proteins with experimentally determined locations of transmembrane segments yielded an overall two state per residue accuracy of 95 About 94 of all segments were predicted correctly