scholarly journals A review of mathematical representations of biomolecular data

2020 ◽  
Vol 22 (8) ◽  
pp. 4343-4367 ◽  
Author(s):  
Duc Duy Nguyen ◽  
Zixuan Cang ◽  
Guo-Wei Wei
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Computational Biology ◽  
Structure Prediction ◽  
Critical Assessment ◽  
Drug Design Data Resource ◽  
Design Data ◽  
Data Resource ◽  
Mathematical Representations ◽  
Grand Challenges

Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges.

scholarly journals D3R Grand Challenge 4: Blind Prediction of Protein-Ligand Poses, Affinity Rankings, and Relative Binding Free Energies

2019 ◽  
Author(s):  
conor parks ◽  
Zied Gaieb ◽  
Michael Chiu ◽  
Huanwang Yang ◽  
Chenghua Shao ◽  
...  
Keyword(s):  
Drug Design ◽  
Crystal Structures ◽  
Best Practice ◽  
Grand Challenge ◽  
Pose Prediction ◽  
Free Energies ◽  
Design Data ◽  
Data Resource ◽  
Practice Methods ◽  
Stage 1

<div>The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.<br></div>

scholarly journals Protein structure prediction

2018 ◽  
Vol 32 (18) ◽  
pp. 1840009 ◽  
Author(s):  
Haiyou Deng ◽  
Ya Jia ◽  
Yang Zhang
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Computational Biology ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
3D Structure ◽  
Critical Assessment ◽  
Prediction Methods ◽  
Recent Effort

Predicting 3D structure of protein from its amino acid sequence is one of the most important unsolved problems in biophysics and computational biology. This paper attempts to give a comprehensive introduction of the most recent effort and progress on protein structure prediction. Following the general flowchart of structure prediction, related concepts and methods are presented and discussed. Moreover, brief introductions are made to several widely-used prediction methods and the community-wide critical assessment of protein structure prediction (CASP) experiments.

scholarly journals D3R Grand Challenge 4: Blind Prediction of Protein-Ligand Poses, Affinity Rankings, and Relative Binding Free Energies

2019 ◽  
Author(s):  
conor parks ◽  
Zied Gaieb ◽  
Michael Chiu ◽  
Huanwang Yang ◽  
Chenghua Shao ◽  
...  
Keyword(s):  
Drug Design ◽  
Crystal Structures ◽  
Best Practice ◽  
Grand Challenge ◽  
Pose Prediction ◽  
Free Energies ◽  
Design Data ◽  
Data Resource ◽  
Practice Methods ◽  
Stage 1

<div>The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.<br></div>

D3R Grand Challenge 4: Ligand Similarity and MM-GBSA-Based Pose Prediction and Affinity Ranking for BACE-1 Inhibitors

2019 ◽  
Author(s):  
Sukanya Sasmal ◽  
Léa El Khoury ◽  
David Mobley
Keyword(s):  
Binding Mode ◽  
Energy Estimates ◽  
Poor Correlation ◽  
Grand Challenge ◽  
Pose Prediction ◽  
Design Data ◽  
Data Resource ◽  
Affinity Ranking ◽  
Ligand Similarity ◽  
Bace 1

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4, which focused on predicting the binding poses and affinity ranking for compounds targeting the beta-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R Grand Challenge 4 suggest that: i) the generation of the macrocycles conformers is a key step for successful pose prediction, ii) the protonation states of the BACE-1 binding site should be treated carefully, iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

Computational Biology Tools for Identifying Specific Ligand Binding Residues for Novel Agrochemical and Drug Design

2015 ◽  
Vol 16 (8) ◽  
pp. 701-717 ◽  
Author(s):  
Izabella Pena Neshich ◽  
Leticia Nishimura ◽  
Fabio de Moraes ◽  
Jose Salim ◽  
Fabian Villalta-Romero ◽  
...  
Keyword(s):  
Drug Design ◽  
Computational Biology ◽  
Ligand Binding ◽  
Binding Residues ◽  
Specific Ligand
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