Protein loop structure prediction with flexible stem geometries

2005 ◽  
Vol 61 (4) ◽  
pp. 748-762 ◽  
Author(s):  
M. Mönnigmann ◽  
C.A. Floudas
Keyword(s):  
Structure Prediction ◽  
Loop Structure ◽  
Flexible Stem

Protein Loop Structure Prediction Using Conformational Space Annealing

2017 ◽  
Vol 57 (5) ◽  
pp. 1068-1078 ◽  
Author(s):  
Seungryong Heo ◽  
Juyong Lee ◽  
Keehyoung Joo ◽  
Hang-Cheol Shin ◽  
Jooyoung Lee
Keyword(s):  
Structure Prediction ◽  
Conformational Space ◽  
Loop Structure

Protein Loop Structure Prediction Methods

2008 ◽  
pp. 3100-3105
Author(s):  
Martin Mönnigmann ◽  
Christodoulos A. Floudas
Keyword(s):  
Structure Prediction ◽  
Prediction Methods ◽  
Loop Structure

Smotifs as structural local descriptors of supersecondary elements: classification, completeness and applications

2014 ◽  
Vol 10 (4) ◽  
Author(s):  
Jaume Bonet ◽  
Andras Fiser ◽  
Baldo Oliva ◽  
Narcis Fernandez-Fuentes
Keyword(s):  
Protein Design ◽  
Structure Prediction ◽  
Protein Structures ◽  
Regular Structure ◽  
Loop Structure ◽  
Apparent Lack ◽  
Knowledge Based ◽  
Limits Of Knowledge ◽  
Folding Dynamics ◽  
And Function

AbstractProtein structures are made up of periodic and aperiodic structural elements (i.e., α-helices, β-strands and loops). Despite the apparent lack of regular structure, loops have specific conformations and play a central role in the folding, dynamics, and function of proteins. In this article, we reviewed our previous works in the study of protein loops as local supersecondary structural motifs or Smotifs. We reexamined our works about the structural classification of loops (ArchDB) and its application to loop structure prediction (ArchPRED), including the assessment of the limits of knowledge-based loop structure prediction methods. We finalized this article by focusing on the modular nature of proteins and how the concept of Smotifs provides a convenient and practical approach to decompose proteins into strings of concatenated Smotifs and how can this be used in computational protein design and protein structure prediction.

scholarly journals Computational design of structured loops for new protein functions

2019 ◽  
Vol 400 (3) ◽  
pp. 275-288 ◽  
Author(s):  
Kale Kundert ◽  
Tanja Kortemme
Keyword(s):  
Protein Design ◽  
Protein Function ◽  
Structure Prediction ◽  
Computational Design ◽  
Loop Structure ◽  
Functional Sites ◽  
Loop Design ◽  
Routine Design ◽  
Protein Functions ◽  
New Protein

Abstract The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

scholarly journals Including Functional Annotations and Extending the Collection of Structural Classifications of Protein Loops (ArchDB)

2007 ◽  
Vol 1 ◽  
pp. 117793220700100 ◽  
Author(s):  
Antoni Hermoso ◽  
Jordi Espadaler ◽  
E Enrique Querol ◽  
Francesc X. Aviles ◽  
Michael J.E. Sternberg ◽  
...  
Keyword(s):  
Protein Function ◽  
Structure Prediction ◽  
Sequence Similarity ◽  
Protein Structures ◽  
Fold Increase ◽  
Loop Structure ◽  
Biological Databases ◽  
Loop Modeling ◽  
And Function

Loops represent an important part of protein structures. The study of loop is critical for two main reasons: First, loops are often involved in protein function, stability and folding. Second, despite improvements in experimental and computational structure prediction methods, modeling the conformation of loops remains problematic. Here, we present a structural classification of loops, ArchDB, a mine of information with application in both mentioned fields: loop structure prediction and function prediction. ArchDB ( http://sbi.imim.es/archdb ) is a database of classified protein loop motifs. The current database provides four different classification sets tailored for different purposes. ArchDB-40, a loop classification derived from SCOP40, well suited for modeling common loop motifs. Since features relevant to loop structure or function can be more easily determined on well-populated clusters, we have developed ArchDB-95, a loop classification derived from SCOP95. This new classification set shows a ~40% increase in the number of subclasses, and a large 7-fold increase in the number of putative structure/function-related subclasses. We also present ArchDB-EC, a classification of loop motifs from enzymes, and ArchDB-KI, a manually annotated classification of loop motifs from kinases. Information about ligand contacts and PDB sites has been included in all classification sets. Improvements in our classification scheme are described, as well as several new database features, such as the ability to query by conserved annotations, sequence similarity, or uploading 3D coordinates of a protein. The lengths of classified loops range between 0 and 36 residues long. ArchDB offers an exhaustive sampling of loop structures. Functional information about loops and links with related biological databases are also provided. All this information and the possibility to browse/query the database through a web-server outline an useful tool with application in the comparative study of loops, the analysis of loops involved in protein function and to obtain templates for loop modeling.

scholarly journals ABlooper: Fast accurate antibody CDR loop structure prediction with accuracy estimation

2021 ◽  
Author(s):  
Brennan Abanades ◽  
Guy Georges ◽  
Alexander Bujotzek ◽  
Charlotte M Deane
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Structural Variation ◽  
Protein Structures ◽  
Step Change ◽  
Antigen Binding ◽  
Loop Structure ◽  
Accurate Knowledge ◽  
Complementarity Determining Regions ◽  
Confidence Estimate

Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their function. The key area for antigen binding and the main area of structural variation in antibodies are concentrated in their six complementarity determining regions (CDRs), with the most variable being the CDR-H3 loop. The sequence and structure variability of CDR-H3 make it particularly challenging to model. Recently, deep learning methods have offered a step change in our ability to predict protein structures. In this work we present ABlooper, an end-toend equivariant deep-learning based CDR loop structure prediction tool. ABlooper predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average H3 RMSD of 2.45Å, which drops to 2.02Å when considering only its 76% most confident predictions.

scholarly journals ArchPRED: a template based loop structure prediction server

2006 ◽  
Vol 34 (Web Server) ◽  
pp. W173-W176 ◽  
Author(s):  
N. Fernandez-Fuentes ◽  
J. Zhai ◽  
A. Fiser
Keyword(s):  
Structure Prediction ◽  
Loop Structure ◽  
Prediction Server
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