Protein Structure Prediction Methods Introduction

Protein structure prediction is the prediction of the three-dimensional structure of a protein from its amino acid sequence — that is, the prediction of its folding and its secondary, tertiary, and quaternary structure from its primary structure. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).

protein structure prediction introduction from: https://en.wikipedia.org/wiki/Protein_structure_prediction

There are three major theoretical methods for predicting the structure of proteins: comparative modelling, fold recognition, and ab initio prediction.

Comparative modelling

Comparative modelling exploits the fact that evolutionarily related proteins with similar sequences, as measured by the percentage of identical residues at each position based on an optimal structural superposition, have similar structures. The similarity of structures is very high in the so-called ``core regions'', which typically are comprised of a framework of secondary structure elements such as alpha-helices and beta-sheets. Loop regions connect these secondary structures and generally vary even in pairs of homologous structures with a high degree of sequence similarity.

Fold recognition or "threading"

Threading uses a database of known three-dimensional structures to match sequences without known structure with protein folds. This is accomplished by the aid of a scoring function that assesses the fit of a sequence to a given fold. These functions are usually derived from a database of known structures and generally include a pairwise atom contact and solvation terms. Threading methods compare a target sequence against a library of structural templates, producing a list of scores. The scores are then ranked and the fold with the best score is assumed to be the one adopted by the sequence. The methods to fit a sequence against a library of folds can be extremely elaborate computationally, such as those involving double dynamic programming, dynamic programming with frozen approximation, Gibbs Sampling using a database of ``threading'' cores, and branch and bound heuristics, or as ``simple'' as using sophisticated sequence alignment methods such as Hidden Markov Models.

Ab initio prediction

The ab initio approach is a mixture of science and engineering. The science is in understanding how the three-dimensional structure of proteins is attained. The engineering portion is in deducing the three-dimensional structure given the sequence. The biggest challenge with regards to the folding problem is with regards to ab initio prediction, which can be broken down into two components: devising a scoring function that can distinguish between correct (native or native-like) structures from incorrect (non-native) ones, and a search method to explore the conformational space. In many ab initio methods, the two components are coupled together such that a search function drives, and is driven by, the scoring function to find native-like structures.

protein structure prediction introduction from: http://www.iscb.org/cms_addon/conferences/ismb2000/tutorials/samudrala.html