Toward structure prediction of cyclic peptides

2015 ◽  
Vol 17 (6) ◽  
pp. 4210-4219 ◽  
Author(s):  
Hongtao Yu ◽  
Yu-Shan Lin
Keyword(s):  
Structure Prediction ◽  
Cyclic Peptides ◽  
Computational Method ◽  
Structural Description

A computational method to provide a converged structural description for cyclic peptides.

scholarly journals APPTEST is an innovative new method for the automatic prediction of peptide tertiary structures

2021 ◽  
Author(s):  
Patrick Brendan Timmons ◽  
Chandralal M. Hewage
Keyword(s):  
Network Architecture ◽  
Structure Prediction ◽  
Cyclic Peptides ◽  
Tertiary Structure ◽  
Prediction Method ◽  
Computational Method ◽  
Computationally Efficient ◽  
Biological Targets ◽  
Benchmark Datasets ◽  
Natural Amino Acids

AbstractGood knowledge of a peptide’s tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational method that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5-40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and Peplook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more-native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

PREDICTION OF THE EXPOSURE STATUS OF TRANSMEMBRANE BETA BARREL RESIDUES FROM PROTEIN SEQUENCE

2011 ◽  
Vol 09 (01) ◽  
pp. 43-65 ◽  
Author(s):  
SIKANDER HAYAT ◽  
PETER WALTER ◽  
YUNGKI PARK ◽  
VOLKHARD HELMS
Keyword(s):  
Structure Prediction ◽  
Prediction Accuracy ◽  
3D Structure ◽  
Chemical Properties ◽  
Confidence Score ◽  
Computational Method ◽  
Test Dataset ◽  
Hydrophobic Residues ◽  
Beta Barrel ◽  
Physico Chemical

We present BTMX (Beta barrel TransMembrane eXposure), a computational method to predict the exposure status (i.e. exposed to the bilayer or hidden in the protein structure) of transmembrane residues in transmembrane beta barrel proteins (TMBs). BTMX predicts the exposure status of known TM residues with an accuracy of 84.2% over 2,225 residues and provides a confidence score for all predictions. Predictions made are in concert with the fact that hydrophobic residues tend to be more exposed to the bilayer. The biological relevance of the input parameters is also discussed. The highest prediction accuracy is obtained when a sliding window comprising three residues with similar Cα - Cβ vector orientations is employed. The prediction accuracy of the BTMX method on a separate unseen non-redundant test dataset is 78.1%. By employing out-pointing residues that are exposed to the bilayer, we have identified various physico-chemical properties that show statistically significant differences between the beta strands located at the oligomeric interfaces compared to the non-oligomeric strands. The BTMX web server generates colored, annotated snake-plots as part of the prediction results and is available under the BTMX tab at . Exposure status prediction of TMB residues may be useful in 3D structure prediction of TMBs.

scholarly journals Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

2017 ◽  
Vol 114 (13) ◽  
pp. E2662-E2671 ◽  
Author(s):  
Guido Uguzzoni ◽  
Shalini John Lovis ◽  
Francesco Oteri ◽  
Alexander Schug ◽  
Hendrik Szurmant ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Large Scale ◽  
Structural Models ◽  
Direct Coupling ◽  
Pfam Family ◽  
Sequence Information ◽  
Structural Description ◽  
Protein Families ◽  
Coupling Analysis ◽  
Direct Coupling Analysis

Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.

Toward accurately modeling N-methylated cyclic peptides

2017 ◽  
Vol 19 (7) ◽  
pp. 5377-5388 ◽  
Author(s):  
Diana P. Slough ◽  
Hongtao Yu ◽  
Sean M. McHugh ◽  
Yu-Shan Lin
Keyword(s):  
Structure Prediction ◽  
Cyclic Peptides ◽  
Sampling Methods ◽  
Enhanced Sampling ◽  
Cyclic Hexapeptides

Structure prediction of benchmark N-methylated cyclic hexapeptides using enhanced sampling methods.

scholarly journals TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhe Liu ◽  
Yingli Gong ◽  
Yihang Bao ◽  
Yuanzhao Guo ◽  
Han Wang ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Transmembrane Proteins ◽  
Secondary Structures ◽  
Computational Method ◽  
Transmembrane Region ◽  
Long Distance ◽  
Topology Structure ◽  
And Topology

Alpha transmembrane proteins (αTMPs) profoundly affect many critical biological processes and are major drug targets due to their pivotal protein functions. At present, even though the non-transmembrane secondary structures are highly relevant to the biological functions of αTMPs along with their transmembrane structures, they have not been unified to be studied yet. In this study, we present a novel computational method, TMPSS, to predict the secondary structures in non-transmembrane parts and the topology structures in transmembrane parts of αTMPs. TMPSS applied a Convolutional Neural Network (CNN), combined with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network, to extract the local contexts and long-distance interdependencies from primary sequences. In addition, a multi-task learning strategy was used to predict the secondary structures and the transmembrane helixes. TMPSS was thoroughly trained and tested against a non-redundant independent dataset, where the Q3 secondary structure prediction accuracy achieved 78% in the non-transmembrane region, and the accuracy of the transmembrane region prediction achieved 90%. In sum, our method showcased a unified model for predicting the secondary structure and topology structure of αTMPs by only utilizing features generated from primary sequences and provided a steady and fast prediction, which promisingly improves the structural studies on αTMPs.

On the crystallographic accuracy of structure prediction by implicit water models: Tests for cyclic peptides

2013 ◽  
Vol 415 ◽  
pp. 168-172 ◽  
Author(s):  
Yonathan Goldtzvik ◽  
Moshe Goldstein ◽  
R. Benny Gerber
Keyword(s):  
Structure Prediction ◽  
Cyclic Peptides ◽  
Water Models
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