• Corpus ID: 218486914

Customised fragment libraries for ab initio protein structure prediction using a structural alphabet

  title={Customised fragment libraries for ab initio protein structure prediction using a structural alphabet},
  author={Surbhi Dhingra and Ramanathan Sowdhamini and Yves‐Henri Sanejouand and Fr{\'e}d{\'e}ric Cadet and Bernard O. Offmann},
  journal={arXiv: Quantitative Methods},
Motivation: Computational protein structure prediction has taken over the structural community in past few decades, mostly focusing on the development of Template-Free modelling (TFM) or ab initio modelling protocols. Fragment-based assembly (FBA), falls under this category and is by far the most popular approach to solve the spatial arrangements of proteins. FBA approaches usually rely on sequence based profile comparison to generate fragments from a representative structural database. Here we… 

Figures and Tables from this paper


Customised fragments libraries for protein structure prediction based on structural class annotations
This work proposes to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process, and shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS.
Building a Better Fragment Library for De Novo Protein Structure Prediction
It is demonstrated that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used, which was used to develop a novel method, Flib.
Knowledge-based prediction of protein backbone conformation using a structural alphabet
The kPRED method was shown to be able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues, and a scoring function that gives a good estimate of the accuracy of prediction was further developed.
Detecting Protein Candidate Fragments Using a Structural Alphabet Profile Comparison Approach
It is shown that structural alphabet profile-profile comparison can be used efficiently to retrieve accurate structural fragments, and it is found it outperforms present state of the art approaches in terms of the accuracy of the fragments identified, the rate of true positives identified, and having a high coverage score.
Improved fragment-based protein structure prediction by redesign of search heuristics
This work has developed two new conformational sampling techniques, one employing a bilevel optimisation framework and the other employing iterated local search, combining strategies of forced structural perturbation and greedy local optimisation, allowing greater exploration of the available conformational space.
Designing succinct structural alphabets
This approach generates significantly more accurate and succinct structural alphabets with more than 50% improvement over the previous accuracies, and is able to construct more accurate protein structures than the state-of-art ab initio protein structure prediction programs such as ROSETTA.
General overview on structure prediction of twilight-zone proteins
In this mini review, the methods, progresses and challenges for the prediction of twilight-zone proteins were discussed and it was suggested that combination of different methods brings an improved success in the prediction.
Protein Structure Prediction Using Rosetta
Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction
This work describes a set of techniques that aim to reduce the impact of the energy function, and assess exploration in view of the search space defined by a given fragment library, using Rosetta and EdaFold to illustrate this approach.