Comparison of 1D and 3D protein alignment

U ovom radu bavimo se usporedbom strukturalnog poravnanja (3D) i poravnanja nizova (1D). Poznato je kako proteinske strukture evoluiraju mnogo sporije nego nizovi, stoga pronalazak “pravog” (strukturalnog) poravnanja iz primarne strukture postaje teže kada se bavimo udaljenim homolozima. Pokazano je kako Miyazawin algoritam1D poravnanje provodi točnije nego standardne metode poput Needleman-Wunsch, Smith-Waterman ili BLAST algoritma.This work is concerned with a comparison between structural and sequence alignment. It is well known that protein structures evolve more slowly than sequences, so detecting the “right” (i.e. structural) alignment from the sequence information only becomes increasingly difficult when dealing with distant homologues. It is shown that Miyazaw’s algorithm for sequence alignment performs this task more accurately than standard methods, such as Needleman-Wunsch, Smith-Waterman or BLAST

Analysis of the relationship between end-to-end distance and activity of single-chain antibody against colorectal carcinoma

We investigated the relationship of End-to-end distance between VH and VL with different peptide linkers and the activity of single-chain antibodies by computer-aided simulation. First, we developed (G(4)S)(n) (where n = 1-9) as the linker to connect VH and VL, and estimated the 3D structure of single-chain Fv antibody (scFv) by homologous modeling. After molecular models were evaluated and optimized, the coordinate system of every protein was built and unified into one coordinate system, and End-to-end distances calculated using 3D space coordinates. After expression and purification of scFv-n with (G(4)S)n as n = 1, 3, 5, 7 or 9, the immunoreactivity of purified ND-1 scFv-n was determined by ELISA. A multi-factorial relationship model was employed to analyze the structural factors affecting scFv: [Formula: see text]. The relationship between immunoreactivity and r-values revealed that fusion protein structure approached the desired state when the r-value = 3. The immunoreactivity declined as the r-value increased, but when the r-value exceeded a certain threshold, it stabilized. We used a linear relationship to analyze structural factors affecting scFv immunoreactivity

Exploring protein structural dissimilarity to facilitate structure classification

Background: Classification of newly resolved protein structures is important in understanding their architectural, evolutionary and functional relatedness to known protein structures. Among various efforts to improve the database of Structural Classification of Proteins (SCOP), automation has received particular attention. Herein, we predict the deepest SCOP structural level that an unclassified protein shares with classified proteins with an equal number of secondary structure elements (SSEs). Results: We compute a coefficient of dissimilarity (omega) between proteins, based on structural and sequence-based descriptors characterising the respective constituent SSEs. For a set of 1,661 pairs of proteins with sequence identity up to 35%, the performance of omega in predicting shared Class, Fold and Super-family levels is comparable to that of DaliLite Z score and shows a greater than four-fold increase in the true positive rate (TPR) for proteins sharing the Family level. On a larger set of 600 domains representing 200 families, the performance of Z score improves in predicting a shared Family, but still only achieves about half of the TPR of omega. The TPR for structures sharing a Superfamily is lower than in the first dataset, but omega performs slightly better than Z score. Overall, the sensitivity of omega in predicting common Fold level is higher than that of the DaliLite Z score. Conclusion: Classification to a deeper level in the hierarchy is specific and difficult. So the efficiency of omega may be attractive to the curators and the end-users of SCOP. We suggest omega may be a better measure for structure classification than the DaliLite Z score, with the caveat that currently we are restricted to comparing structures with equal number of SSEs

Comparison of 1D and 3D protein alignment

U ovom radu bavimo se usporedbom strukturalnog poravnanja (3D) i poravnanja nizova (1D). Poznato je kako proteinske strukture evoluiraju mnogo sporije nego nizovi, stoga pronalazak “pravog” (strukturalnog) poravnanja iz primarne strukture postaje teže kada se bavimo udaljenim homolozima. Pokazano je kako Miyazawin algoritam1D poravnanje provodi točnije nego standardne metode poput Needleman-Wunsch, Smith-Waterman ili BLAST algoritma.This work is concerned with a comparison between structural and sequence alignment. It is well known that protein structures evolve more slowly than sequences, so detecting the “right” (i.e. structural) alignment from the sequence information only becomes increasingly difficult when dealing with distant homologues. It is shown that Miyazaw’s algorithm for sequence alignment performs this task more accurately than standard methods, such as Needleman-Wunsch, Smith-Waterman or BLAST

Comparison of 1D and 3D protein alignment

U ovom radu bavimo se usporedbom strukturalnog poravnanja (3D) i poravnanja nizova (1D). Poznato je kako proteinske strukture evoluiraju mnogo sporije nego nizovi, stoga pronalazak “pravog” (strukturalnog) poravnanja iz primarne strukture postaje teže kada se bavimo udaljenim homolozima. Pokazano je kako Miyazawin algoritam1D poravnanje provodi točnije nego standardne metode poput Needleman-Wunsch, Smith-Waterman ili BLAST algoritma.This work is concerned with a comparison between structural and sequence alignment. It is well known that protein structures evolve more slowly than sequences, so detecting the “right” (i.e. structural) alignment from the sequence information only becomes increasingly difficult when dealing with distant homologues. It is shown that Miyazaw’s algorithm for sequence alignment performs this task more accurately than standard methods, such as Needleman-Wunsch, Smith-Waterman or BLAST

Diseño de un algoritmo para rendering eficiente de estructuras proteicas de gran escala

El software de gráficos por computadora en 3D de hoy en día nos da la capacidad de modelar y visualizar objetos en situaciones o tamaños que antes no habría sido posible, incluso nos dan la capacidad de que la visualización de estos objetos sea generada en tiempo real lo que otorga la posibilidad de crear aplicaciones que hagan uso de esta capacidad para agregar interactividad con los objetos modelados. Es muy importante la capacidad de poder dotar al usuario de una capacidad de interactividad con el gráfico generado, pero esto no se logra si es que el tiempo de respuesta de la aplicación es muy grande, por ejemplo una consola de videojuegos exigen como mínimo 30fps (cuadros por segundo) un valor menor ocasiona que los movimientos no fueran fluidos y se pierda la sensación de movimiento. Esto hace que la experiencia de usuario fluida sea una de las metas principales del rendering interactivo. Uno de los mayores problemas que se encuentran en esta área es el de visualizar gran cantidad de polígonos, debido a limitaciones de memoria o capacidad de procesamiento, mientras mayor sea la cantidad de polígonos que se desea dibujar en pantalla, mayor será el tiempo de procesamiento que será necesario para generar las imágenes. Una aplicación en particular es el de visualización de la estructura de proteínas. Existen proteínas que poseen una gran estructura, por la cantidad de polígonos que se requieren para representar todos los elementos y conexiones que poseen estas moléculas y adicionalmente la necesidad de visualizar grandes cantidades de moléculas simultáneamente, ocasiona que se disminuya el rendimiento y la interactividad al momento de la visualización. El presente proyecto plantea utilizar una estructura algorítmica para realizar rendering eficiente de gran cantidad de proteínas haciendo uso de un visualizador 3D, que muestre la estructura tridimensional de estas y permita la interacción en tiempo real con el modelo. La estructura propuesta en este proyecto hace uso de la aceleración por hardware presente en las tarjetas gráficas modernas a través de un API de generación de gráficos en tiempo real que es OpenGL con el cual se aplican optimizaciones que aprovechan la estructura planteada. Para que el proceso de renderizado sea más veloz, se mantiene un número bajo de polígonos en los modelos. Debido a que los elementos son repetitivos (esferas y cilindros) se reutiliza la geometría de estos elementos haciendo uso de una estructura como el Scene Graph de modo que el uso de memoria sea menor y de otra estructura como el Octree que permite discriminar los elementos que deben ser procesados durante el rendering. Combinando todo lo mencionado anteriormente, la estructura propuesta permite que se visualicen proteínas de gran estructura o gran cantidad de estas, manteniendo el grado necesario de interactividad para facilitar su estudio así como también manteniendo un aspecto estético que permita reconocer los elementos sin reducir el rendimiento.Tesi

Combined in silico approaches towards the identification of novel malarial cysteine protease inhibitors

Malaria an infectious disease caused by a group of parasitic organisms of the Plasmodium genus remains a severe public health problem in Africa, South America and parts of Asia. The leading causes for the persistence of malaria are the emergence of drug resistance to common antimalarial drugs, lack of effective vaccines and the inadequate control of mosquito vectors. Worryingly, accumulating evidence shows that the parasite has developed resistant to the current first-line treatment based on artemisinin. Hence, the identification and characterization of novel drug targets and drugs with unique mode of action remains an urgent priority. The successful sequencing and assembly of genomes from several Plasmodium species has opened an opportune window for the identification of new drug targets. Cysteine proteases are one of the major drug targets to be identified so far. The use of cysteine protease inhibitors coupled with gene manipulation studies has defined specific and putative roles of cysteine proteases which include hemoglobin degradation, erythrocyte rupture, immune evasion and erythrocyte invasion, steps which are central for the completion of the Plasmodium parasite life cycle. In an aim to discover potential novel antimalarials, this thesis focussed on falcipains (FPs), a group of four papain-like cysteine proteases from Plasmodium falciparum. Two of these enzymes, FP-2 and FP-3 are the major hemoglobinases and have been validated as drug targets. For the successful elimination of malaria, drugs must be safe and target both human and wild Plasmodium infective forms. Thus, an incipient aim was to identify protein homologs of these two proteases from other Plasmodium species and the host (human). From BLASTP analysis, up to 16 FP-2 and FP-3 homologs were identified (13 plasmodial proteases and 3 human cathepsins). Using in silico characterization approaches, the intra and inter group sequence, structural, phylogenetic and physicochemical differences were determined. To extend previous work (MSc student) involving docking studies on the identified proteins using known FP-2 and FP-3 inhibitors, a South African natural compound and its ZINC analogs, molecular dynamics and binding free energy studies were performed to determine the stabilities and quantification of the strength of interactions between the different protein-ligand complexes. From the results, key structural elements that regulate the binding and selectivity of non-peptidic compounds onto the different proteins were deciphered. Interaction fingerprints and energy decomposition analysis identified key residues and energetic terms that are central for effective ligand binding. This research presents novel insight essential for the structure-based molecular drug design of more potent antimalarial drugs

Retrieval of 3D protein structures

IEEE International Conference on Image Processing3III/933-III/93685QT