BLAST: a more efficient report with usability improvements

Boratyn, Grzegorz M.; Camacho, Christiam; Cooper, P. S.; Coulouris, George; Fong, Amelia C.; Ma, Ning; Madden, Thomas; Matten, Wayne T.; McGinnis, Scott D.; Merezhuk, Yuri; Raytselis, Yan; Sayers, Eric W; Tao, Tao; Ye, Jian; Zaretskaya, Irena

doi:10.1093/nar/gkt282

Cited by 1,049 publications

(835 citation statements)

References 9 publications

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“…1 C and D and 3A). Furthermore, a protein BLAST search (18), using the SNAP25 interacting regions of Rph3A as a reference, revealed that only DOC2B conserves these contacting residues (Fig. 3B).…”

Section: Resultsmentioning

confidence: 99%

“…The Rph3A C2B residues interacting with the SNAP25 are marked with olive blocks, and that of syt1 C2B with salmon blocks. (B) BLAST alignment (18), using the sequences around the C2B α1-and α2-helices (red rectangles) that form the main interface of the C2B-SNAP25 complex. Blue rectangles show residues interacting with SNAP25, with red stars indicating amino acids that contact the Nt-region II (D51, E52, E55, and R59), and yellow stars highlighting residues that contact Nt-region I (E38, K40, and D41).…”

Section: Methodsmentioning

confidence: 99%

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Structural characterization of the Rabphilin-3A–SNAP25 interaction

Ferrer-Orta

Perez-Sanchez

Coronado-Parra

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Membrane fusion is essential in a myriad of eukaryotic cell biological processes, including the synaptic transmission. Rabphilin-3A is a membrane trafficking protein involved in the calcium-dependent regulation of secretory vesicle exocytosis in neurons and neuroendocrine cells, but the underlying mechanism remains poorly understood. Here, we report the crystal structures and biochemical analyses of Rabphilin-3A C2B-SNAP25 and C2B-phosphatidylinositol 4,5-bisphosphate (PIP 2 ) complexes, revealing how Rabphilin-3A C2 domains operate in cooperation with PIP 2 /Ca 2+ and SNAP25 to bind the plasma membrane, adopting a conformation compatible to interact with the complete SNARE complex. Comparisons with the synaptotagmin1-SNARE show that both proteins contact the same SNAP25 surface, but Rabphilin-3A uses a unique structural element. Data obtained here suggest a model to explain the Ca 2+ -dependent fusion process by membrane bending with a myriad of variations depending on the properties of the C2 domain-bearing protein, shedding light to understand the fine-tuning control of the different vesicle fusion events.C2 domains | membrane fusion | Rabphilin-3A | SNAP-25 | X-ray crystallography N eurons are able to communicate with others through the liberation of neurotransmitters during synapses. Numerous proteins are recruited to the presynaptic space to execute a highly controlled and precise mechanism, resulting in the liberation of neurotransmitters to the synaptic cleft. Central components of the process are the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins: syntaxin-1A (STX1A), VAMP2 (synaptobrevin-2), and synaptosome-associated protein of 25 kDa (SNAP25), which fold into a stable four-helix bundle that brings together vesicle and plasma membranes, driving membrane fusion (1-4). Other important players are complexin, Munc18-1, Munc13, and a collection of proteins that share a common structural motif: the C2 domain (5, 6). These domains are regulated by their ability to bind Ca 2+ , phospholipids, and protein interactions, endowing them with properties to fine-tune the wide variety of vesicle release modes (7). Nowadays, we have detailed information on many of the steps contributing to docking, priming, and fusion of these vesicles, but a clear picture on how these regulators contribute in each particular state still remains under debate, mainly due to the lack of high-resolution structural data.Rabphilin-3A (Rph3A) is a membrane trafficking protein involved in the Ca 2+ -dependent regulation of secretory vesicle exocytosis in neurons and neuroendocrine cells; however, its exact role in the process still remains under debate. The protein is targeted to synaptic or secretory vesicles through the interaction with the small G-proteins Rab3 or Rab27, via its N-terminal Rabbinding domain (8-10). Its C-terminal domain consists of tandem C2 domains that are responsible for the Ca 2+ -and phospholipidsdependent membrane specificity of the protein (11, 12) and also participate in oth...

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Structural characterization of the Rabphilin-3A–SNAP25 interaction

Ferrer-Orta

Perez-Sanchez

Coronado-Parra

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Las muestras positivas fueron enviadas a la compañía Macrogen-USA, para ser secuenciadas con los primers 16SrRNAF518 (CCAGCAGCCGCGGTAATACG) y 16SrRNAR800 (TACCAGGGTATCTAATCC). Las secuencias de ARNr 16S fueron reconstruidas utilizando el programa Mega6 (9) y analizadas con BLAST (10) . Basic Local Alignment Search Tool) de NCBI (National Center for Biotechnology Information).…”

Section: Cultivo Microbiológico Estándarunclassified

Caracterización molecular de bacterias patógenas de las vías respiratorias de pacientes peruanos con fibrosis quística

Aquino

Gonzáles

Samaniego³

et al. 2017

Rev Peru Med Exp Salud Publica

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“…These include sequence similarity [27,28] Among these in-silico methods [52], the basic local alignment search tool (BLAST) [53] revealing protein functions based on excess sequence similarity [54] demonstrated great capacity and attracted substantial interests from the researchers of this field [55,56]. Apart from BLAST, the methods based on the machine learning algorithm (a specific type of artificial intelligence) were frequently used in recent years to predict protein function [57][58][59][60][61][62], and various types of software together with several web-based tools integrating these methods were developed to predict the protein function from sequences irrespective of sequence or structural similarity [36,63].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

Yu¹,

Li²,

Yang³

et al. 2017

Preprint

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The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction.Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research. KEYWORDSfalse discovery rate; machine learning; protein function prediction; support vector machine; BLAST Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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BLAST: a more efficient report with usability improvements

Cited by 1,049 publications

References 9 publications

Structural characterization of the Rabphilin-3A–SNAP25 interaction

Structural characterization of the Rabphilin-3A–SNAP25 interaction

Caracterización molecular de bacterias patógenas de las vías respiratorias de pacientes peruanos con fibrosis quística

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

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