Navigating Among Known Structures in Protein Space

2018 ◽  
pp. 233-249 ◽  
Author(s):  
Aya Narunsky ◽  
Nir Ben-Tal ◽  
Rachel Kolodny
Keyword(s):  
Protein Space

scholarly journals BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

2021 ◽  
Vol 22 (15) ◽  
pp. 7773
Author(s):  
Neann Mathai ◽  
Conrad Stork ◽  
Johannes Kirchmair
Keyword(s):  
Large Scale ◽  
Computational Approach ◽  
Large Sets ◽  
Compound Libraries ◽  
Wide Range ◽  
Protein Space ◽  
High Chance ◽  
Large Scale Screening ◽  
Early Drug ◽  
Selection Of

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).

Protein space: A natural method for realizing the nature of protein universe

2013 ◽  
Vol 318 ◽  
pp. 197-204 ◽  
Author(s):  
Chenglong Yu ◽  
Mo Deng ◽  
Shiu-Yuen Cheng ◽  
Shek-Chung Yau ◽  
Rong L. He ◽  
...  
Keyword(s):  
Protein Universe ◽  
Protein Space ◽  
Natural Method

Probing the protein space for extending the detection of weak homology folds

2013 ◽  
Vol 320 ◽  
pp. 152-158 ◽  
Author(s):  
Danilo Gullotto ◽  
Mario Salvatore Nolassi ◽  
Andrea Bernini ◽  
Ottavia Spiga ◽  
Neri Niccolai
Keyword(s):  
Protein Space

scholarly journals Pspace: a program that assesses protein space

2007 ◽  
Vol 2 (1) ◽  
Author(s):  
Mihaly Mezei ◽  
Ming-Ming Zhou
Keyword(s):  
Protein Space

scholarly journals Meta-Align: A Novel HMM-based Algorithm for Pairwise Alignment of Error-Prone Sequencing Reads

2020 ◽  
Author(s):  
Kentaro Tomii ◽  
Shravan Kumar ◽  
Degui Zhi ◽  
Steven E. Brenner
Keyword(s):  
Dna Sequences ◽  
Viterbi Algorithm ◽  
Query Sequence ◽  
Pairwise Alignment ◽  
Next Generation Sequencing Data ◽  
Alignment Algorithm ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Insertion And Deletion ◽  
Protein Space

AbstractBackgroundInsertion and deletion sequencing errors are relatively common in next-generation sequencing data and produce long stretches of mistranslated sequence. These frameshifting errors can cause very serious damages to downstream data analysis of reads. However, it is possible to obtain more precise alignment of DNA sequences by taking into account both coding frame and sequencing errors estimated by quality scores.ResultsHere we designed and proposed a novel hidden Markov model (HMM)-based pairwise alignment algorithm, Meta-Align, that aligns DNA sequences in the protein space, incorporating quality scores from the DNA sequences and allowing frameshifts caused by insertions and deletions. Our model is based on both an HMM transducer of a pair HMM and profile HMMs for all possible amino acid pairs. A Viterbi algorithm over our model produces the optimal alignment of a pair of metagenomic reads taking into account all possible translating frames and gap penalties in both the protein space and the DNA space. To reduce the sheer number of states of this model, we also derived and implemented a computationally feasible model, leveraging the degeneracy of the genetic code. In a benchmark test on a diverse set of simulated reads based on BAliBASE we show that Meta-Align outperforms TBLASTX which compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database using the BLAST algorithm. We also demonstrate the effects of incorporating quality scores on Meta-Align.ConclusionsMeta-Align will be particularly effective when applied to error-prone DNA sequences. The package of our software can be downloaded at https://github.com/shravan-repos/Metaalign.

scholarly journals Touring Protein Space with Matt

2012 ◽  
Vol 9 (1) ◽  
pp. 286-293 ◽  
Author(s):  
Noah Daniels ◽  
Anoop Kumar ◽  
Lenore Cowen ◽  
Matt Menke
Keyword(s):  
Protein Space

scholarly journals Fishing With (Proto)Net—A Principled Approach to Protein Target Selection

2003 ◽  
Vol 4 (5) ◽  
pp. 542-548
Author(s):  
Michal Linial
Keyword(s):  
Structural Genomics ◽  
Target Selection ◽  
Structural Diversity ◽  
Structural Data ◽  
Agglomerative Clustering ◽  
Homologous Proteins ◽  
Hierarchical Agglomerative Clustering ◽  
Global Classification ◽  
Protein Space

Structural genomics strives to represent the entire protein space. The first step towards achieving this goal is by rationally selecting proteins whose structures have not been determined, but that represent an as yet unknown structural superfamily or fold. Once such a structure is solved, it can be used as a template for modelling homologous proteins. This will aid in unveiling the structural diversity of the protein space. Currently, no reliable method for accurate 3D structural prediction is available when a sequence or a structure homologue is not available. Here we present a systematic methodology for selecting target proteins whose structure is likely to adopt a new, as yet unknown superfamily or fold. Our method takes advantage of a global classification of the sequence space as presented by ProtoNet-3D, which is a hierarchical agglomerative clustering of the proteins of interest (the proteins in Swiss-Prot) along with all solved structures (taken from the PDB). By navigating in the scaffold of ProtoNet-3D, we yield a prioritized list of proteins that are not yet structurally solved, along with the probability of each of the proteins belonging to a new superfamily or fold. The sorted list has been self-validated against real structural data that was not available when the predictions were made. The practical application of using our computational–statistical method to determine novel superfamilies for structural genomics projects is also discussed.

Molecular replacement: the approach of the programREMO

2006 ◽  
Vol 39 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Rocco Caliandro ◽  
Benedetta Carrozzini ◽  
Giovanni Luca Cascarano ◽  
Liberato De Caro ◽  
Carmelo Giacovazzo ◽  
...  
Keyword(s):  
Fourier Transforms ◽  
Reciprocal Lattice ◽  
Correlation Factor ◽  
Test Problems ◽  
Model Structure ◽  
Molecular Replacement ◽  
Space Group ◽  
Model Molecule ◽  
Translation Function ◽  
Protein Space

A new program for molecular replacement,REMO, has been written. In the rotation step, the orientation of the model molecule is found by rotating the weighted reciprocal lattice of the protein with respect to the calculated transform of the model structure: the fitting is searched in the reciprocal space. The space group of the model structure is assumed to be the symmorphic variant of the protein space group. The algebra necessary to optimize the correlation factor between protein and model structure-factor moduli is described. The oriented model molecule is located by using the correlation function coupled with a translation function calculated by fast Fourier transforms.REMOhas been successfully applied to a variety of test problems and extensively compared with other currently available molecular replacement programs.

scholarly journals Searching protein space for ancient sub-domain segments

2021 ◽  
Vol 68 ◽  
pp. 105-112
Author(s):  
Rachel Kolodny
Keyword(s):  
Protein Space
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