Protein structure search and local structure characterization

<p>Abstract</p> <p>Background</p> <p>Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.</p> <p>Results</p> <p>We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at <url>http://140.113.166.178/safast/</url>.</p> <p>Conclusion</p> <p>The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.</p

Metodología y algoritmo para la construcción de una matriz de sustitución generalizada para alfabetos arbitrarios que describen secuencias biológicas

En este documento se presenta una propuesta para desarrollar e implementar una metodología para la construcción de una matriz de sustitución generalizada para alfabetos arbitrarios que describen secuencias biológicas. El desarrollo de esta propuesta se realiza mediante el seguimiento de un conjunto de pasos que van desde el entendimiento conceptual del problema, el desarrollo de la metodología, y la implementación de los algoritmos correspondientes que permitan la construcción de una matriz de sustitución generalizada, la cual se utilizará en un algoritmo de alineamiento que sirva para probar algunos casos de estudio. A partir de la base de datos BLOCKS se obtuvieron nuevas bases de datos que representan tuplas y tripletas formadas a partir de las secuencias de proteínas pertenecientes a cada uno de los bloques. Mediante el uso del algoritmo BLOSUM se obtuvieron matrices de sustitución generalizadas mostrando así que es posible desarrollar una metodología y construirlas mientras se tenga claridad sobre el alfabeto que se va a utilizar.PregradoINGENIERO(A) DE SISTEMA

Multiple Alignment of Structures using Center of Proteins

There is a buzz among structural biologists about conducting a major portion of their future work in silico, due to progressively refined computational tools and an amazing quantity of digitized biological data. This masters thesis focusses on the area of computational methods for aligning multiple protein structures. As the problem under consideration is known to be np–complete, several ways for coming up with good approximations have been suggested over the years. A new approach for achieving better, or at least as good results as before, is presented here. We discuss the proposed algorithm and its constituent methods. Finally, we report the widely used root mean square deviation (RMSD) as measures of structural similarity, and the execution time. Some chosen results, from our extensive experimentation, and their significance have been discussed. A web server has also been implemented for trying out a pairwise alignment algorithm. This is hosted on the university website and the link has been provided in the contribution