Q.27 Which one of the following is used for global alignment of two protein sequences?
(A) Chou–Fasman method
(B) Garnier–Osguthorpe–Robson (GOR) method
(C) Needleman–Wunsch algorithm
(D) Smith–Waterman algorithm

Needleman-Wunsch Algorithm for Global Alignment of Protein Sequences

The correct answer to the question is (C) Needleman-Wunsch algorithm, as it performs global alignment of two protein sequences by finding the optimal alignment across their entire lengths using dynamic programming.​

Option Analysis

Chou-Fasman method predicts protein secondary structures like alpha-helices and beta-sheets based on amino acid propensities, not sequence alignment.​

Garnier-Osguthorpe-Robson (GOR) method also focuses on secondary structure prediction through statistical analysis of sequence windows.​

Needleman-Wunsch algorithm uses a scoring matrix and traceback to align entire sequences end-to-end, ideal for global protein comparisons.​

Smith-Waterman algorithm handles local alignments by identifying the highest-scoring subsequences, differing from global methods.​

Introduction to Sequence Alignment

Global alignment of two protein sequences compares entire proteins to reveal evolutionary relationships and functional similarities. The Needleman-Wunsch algorithm excels here by optimizing matches across full lengths.​

Detailed Algorithm Breakdown

Needleman-Wunsch employs dynamic programming with a matrix where each cell scores alignment possibilities, penalizing gaps. Traceback from the bottom-right yields the best global alignment path.​

Why Other Methods Fail

• Chou-Fasman and GOR predict secondary structures, analyzing local propensities rather than pairwise alignments.​

• Smith-Waterman suits local alignments for similar domains within dissimilar sequences.​

Exam Relevance

In competitive exams like IIT JAM or GATE Biotechnology, distinguishing global (Needleman-Wunsch) from local (Smith-Waterman) alignment is crucial for bioinformatics sections.​