RECCR Rensselaer Exploratory Center for Cheminformatics Research

Bioseparations

Modeling

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Homology Modeling

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Co-PI: Steven Cramer

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Bioseparations

Homology Modeling

Recent advances in the molecular modeling field have resulted in the development and refinement of homology modeling (Blomberg et al. 1999; Goldsmith-Fischman et al. 2003; Yao et al. 2004) and threading techniques (Madej et al. 1995; Panchenko et al. 1999) that can be employed to “estimate” the three-dimensional structure of a protein from its primary sequence information (Fig 4). These techniques offer an excellent opportunity to overcome the drawbacks of using 2D descriptors alone in QSPR model generation. Homology modeling relies on the identification of a structurally conserved region (SCR) for a family of homologous molecules. Once an SCR is identified, appropriate loops based on the unaccounted “gaps” in the primary sequence of the target molecule are identified from available databases and added onto the SCR. Finally, the side chains of all amino acid residues are incorporated into the structure followed by an energy minimization procedure to yield the final predicted structure of the protein. On the other hand, threading algorithms are based on the premise that there are a limited number of ‘unique’ folds found in proteins. It involves determination of the appropriate fold for a given sequence by comparing the query sequence against a database of folds. The degree of similarity is given by the Z-score calculated for each sequence/profile pair and the structure-sequence match is validated by energy calculations. Homology modeling and threading methods are often used together and may be combined with other protein folding algorithms that have been extensively researched by several groups (Sun et al. 1995; Yuan et al. 2003; Znamenskiy et al. 2003a; Znamenskiy et al. 2003b) .

The above discussion presents some of the options whereby the dependence on crystal structure data for generating predictive QSPR models for proteins may be circumvented. The development of efficient strategies for building QSPR models based on protein primary sequence information alone is perhaps one of the most important factors governing the applicability of the multiscale modeling protocol in an industrial setting

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