You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Protein tertiary structure defines its functions, classification and binding sites. Similar structural characteristics between two proteins often lead to the similar characteristics thereof. Determining structural similarity accurately in real time is a crucial research issue. In this paper, we present a novel and effective scoring scheme that is dependent on novel features extracted from protein alpha carbon distance matrices. Our scoring scheme is inspired from pattern recognition and computer vision. Our method is significantly better than the current state of the art methods in terms of family match of pairs of protein structures and other statistical measurements. The effectiveness of our method is tested on standard benchmark structures. A web service is available at this http URL where you can get the similarity measurement score between two protein structures based on our method.

Cited Abstract(s):
[1]: A new algorithm is reported which builds an alignment between two protein structures. The algorithm involves a combinatorial extension (CE) of an alignment path defined by aligned fragment pairs (AFPs) rather than the more conventional techniques using dynamic programming and Monte Carlo optimization. AFPs, as the name suggests, are pairs of fragments, one from each protein, which confer structure similarity. AFPs are based on local geometry, rather than global features such as orientation of secondary structures and overall topology. Combinations of AFPs that represent possible continuous alignment paths are selectively extended or discarded thereby leading to a single optimal alignment. The algorithm is fast and accurate in finding an optimal structure alignment and hence suitable for database scanning and detailed analysis of large protein families. The method has been tested and compared with results from Dali and VAST using a representative sample of similar structures. Several new structural similarities not detected by these other methods are reported. Specific one-on-one alignments and searches against all structures as found in the Protein Data Bank (PDB) can be performed via the Web at http: cl.sdsc.edu ce.html.
CE or method [1] is similar to DALI in that it too breaks each structure in the query set into a series of fragments that it then attempts to reassemble into a complete alignment. A series of pairwise combinations of fragments called aligned fragment pairs, or AFPs, are used to define a similarity matrix through which an optimal path is generated to identify the final alignment. Only AFPs that meet given criteria for local similarity are included in the matrix as a means of reducing the necessary search space and thereby increasing efficiency [1] . However, in spite of having good accuracy it is impossible to implement these two methods as a real time web service due to their huge computational cost.