What is sbsp?

SBSP, or Sequence Based Structure Prediction, refers to computational methods that predict the three-dimensional structure of a protein solely or primarily from its amino acid sequence. This is a significant area in bioinformatics as determining protein structure is crucial to understanding its function.

Key aspects of SBSP include:

• Homology Modeling: This method relies on the existence of a known structure of a homologous protein (a protein with a similar sequence) to predict the structure of the target protein. A detailed concept can be found here: Homology Modeling

• Threading (Fold Recognition): Threading methods attempt to fit a query sequence into a library of known protein folds. These methods are useful when no close homolog with a known structure is available. More information here: Threading

• De Novo (Ab Initio) Prediction: These methods predict structure from sequence without relying on templates from known structures. They utilize physical principles and statistical potentials to guide the folding process. A detailed explanation is available here: De%20Novo%20Prediction

• Machine Learning: Modern methods increasingly utilize machine learning, especially deep learning, to predict aspects of protein structure such as secondary structure, contact maps (residues that are close in 3D space), and inter-residue distances. These predictions can then be used to guide the structure prediction process. Read more about it here: Machine%20Learning

• CASP (Critical Assessment of Structure Prediction): CASP is a community-wide experiment where researchers predict the structures of proteins before they are experimentally determined, and the predictions are then assessed. It is an important benchmark for evaluating and advancing SBSP methods. Details can be seen here: CASP