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Protein engineering

Protein engineering is the process of developing useful or valuable proteins. It is a young discipline, with much research taking place into the understanding of protein folding and recognition for protein design principles. It is also a product and services market, with an estimated value of $168 billion by 2017. Protein engineering is the process of developing useful or valuable proteins. It is a young discipline, with much research taking place into the understanding of protein folding and recognition for protein design principles. It is also a product and services market, with an estimated value of $168 billion by 2017. There are two general strategies for protein engineering: rational protein design and directed evolution. These methods are not mutually exclusive; researchers will often apply both. In the future, more detailed knowledge of protein structure and function, and advances in high-throughput screening, may greatly expand the abilities of protein engineering. Eventually, even unnatural amino acids may be included, via newer methods, such as expanded genetic code, that allow encoding novel amino acids in genetic code. In rational protein design, a scientist uses detailed knowledge of the structure and function of a protein to make desired changes. In general, this has the advantage of being inexpensive and technically easy, since site-directed mutagenesis methods are well-developed. However, its major drawback is that detailed structural knowledge of a protein is often unavailable, and, even when available, it can be very difficult to predict the effects of various mutations since structural information most often provide a static picture of a protein structure. However, programs such as Folding@home and Foldit have utilized crowdsourcing techniques in order to gain insight into the folding motifs of proteins. Computational protein design algorithms seek to identify novel amino acid sequences that are low in energy when folded to the pre-specified target structure. While the sequence-conformation space that needs to be searched is large, the most challenging requirement for computational protein design is a fast, yet accurate, energy function that can distinguish optimal sequences from similar suboptimal ones. Without structural information about a protein, sequence analysis is often useful in elucidating information about the protein. These techniques involve alignment of target protein sequences with other related protein sequences. This alignment can show which amino acids are conserved between species and are important for the function of the protein. These analyses can help to identify hot spot amino acids that can serve as the target sites for mutations. Multiple sequence alignment utilizes data bases such as PREFAB, SABMARK, OXBENCH, IRMBASE, and BALIBASE in order to cross reference target protein sequences with known sequences. Multiple sequence alignment techniques are listed below. This method begins by performing pair wise alignment of sequences using k-tuple or Needleman–Wunsch methods. These methods calculate a matrix that depicts the pair wise similarity among the sequence pairs. Similarity scores are then transformed into distance scores that are used to produce a guide tree using the neighbor joining method. This guide tree is then employed to yield a multiple sequence alignment. This method is capable of aligning up to 190,00 sequences by utilizing the k-tuple method. Next sequences are clustered using the mBed and k-means methods. A guide tree is then constructed using the UPGMA method that is used by the HH align package. This guide tree is used to generate multiple sequence alignments. This method utilizes fast Fourier transform (FFT) that converts amino acid sequences into a sequence composed of volume and polarity values for each amino acid residue. This new sequence is used to find homologous regions. This method utilizes the Wu-Manber approximate string matching algorithm to generate multiple sequence alignments.

[ "Enzyme", "Xylose binding", "Phenylacetaldehyde reductase", "Engineering protein", "Anticalin", "Affitin" ]
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