Recoord 07

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Christiane Riedinger

This is a presentation about a paper published in Proteins in 2005 by Nederveen et al. It's about a tool to standardise NMR structure calculations in order to decrease user - and software dependent bias (REcalculated COORdinates Database). RECOORD has been developed at the Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, The Netherlands and is really useful tool for anyone who has to face solving an NMR structure on their own (and other people, too). Those guys in Utrecht are doing a great job, I can specially recommend the NMR course they organise every year. Thank you!!!!

Technology

RECOORD

REcalculated COORdinates Database

Nederveen et al., Proteins 2005

Recalculation of > 500
NMR Structures

• decrease user - and software dependent bias
• better comparison
• provide database for development of…
- validation tools
- calculation protocols
- statistical approaches
- modelling

NMR Data?!?

Three types of restraints:

1. distance restraints (NOEs !!!!)

2. dihedral angle restraints (coupling constants or chemical shift)

3. orientational restaints (RDCs, relaxation data)

NMR Structure Calculation !?!?

Find a model that fits with your data and
the known chemistry of your protein!

XPLOR/CNS: Molecular Dynamics in Cartesian Space
Simulated Annealing
Refinement (put in solvent!)

DYANA: Torsion Angle Dynamics
Simulated Annealing
No force field, energy
minimisation necessary

How it was done….

• used CNS and CYANA for recalculation
• restraints obtained from BMRB database
• water refinement: restrained molecular dynamics (CNS)
• > 8000 processors used
• total CPU time: 31,169h
(~3.5 years on single computer!)
• extensive quality assessment
(PROCHECK and WHAT-CHECK)

Results

Quality-assessment of recalculated structures

o Packing
o Ramachandran appearance
One standard deviation closer to mean!

Results continued…

Quality-assessment of recalculated structures
o Number of restraints per residue
o Agreement with experimental restraints
o RMSD (positional root mean square deviation)
o Comparison CNS and CYANA
o Relation NMR data quality and structural quality

CNS vs. CYANA (no water ref.)

Z-score: standard deviation away from the mean

Why am I telling you this?

… you can use the scripts!

www.ebi.ac.uk/msd-srv/docs/NMR/recoord/main.html

The ionization state of a chemical, reflected in pKa values, affects lipophilicity, solubility, protein binding and the ability of a chemical to cross the plasma membrane. These properties govern the pharmacokinetic parameters such as absorption, distribution, metabolism, excretion and toxicity and thus pKa is a fundamental chemical property and is used in many models of chemical toxicity. Experimentally determining pKa is not feasible for high-throughput assays. Predicting pKa is challenging and existing models have been developed only using restricted chemical space (e.g., anilines, phenols, benzoic acids, primary amines) and lack of a generalized model impedes ADME modeling. No free and open source models exist for heterogeneous chemical classes, however, several proprietary programs exist. In this work, pKa open data bundled with DataWarrior (http://www.openmolecules.org/) were used to develop predictive models for pKa. After data cleaning, there were ~3100 and ~3900 monoprotic chemicals with an acidic or basic pKa, respectively. 1D and 2D chemical descriptors (AlogP, Topological polar surface area, etc) in addition to 12 fingerprints (presence or absence of a chemical group) were generated using PaDEL software. Three datasets were used: acidic, basic and acidic and basic combined. 13 datasets were examined, the 1D/2D descriptors and 12 fingerprints. Using the Extreme Gradient Boosting algorithm showed that the MACCS and Substructure Count fingerprints yielded the best results, with models showing an R-Squared of ~0.78 and a RMSE of 1.42. Recently, Deep Learning models have showed remarkable progress in image recognition and natural language processing. To determine if the Deep Learning algorithms would increase model performance we examined the datasets and found that the Deep Learning models were somewhat superior than Extreme Gradient Boosting with an R-Squared of ~0.80 and an RMSE of ~1.38. This work does not reflect U.S. EPA policy.

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"Optimizing Drug Discovery (ADMET) using Machine Learning" involves leveraging advanced algorithms to enhance the drug development process. By analyzing Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) data with ML models, researchers can predict a drug candidate's properties, safety, and efficacy. This approach accelerates the identification of potential drugs, reduces costs, and minimizes the likelihood of late-stage failures. Machine learning aids in the selection of promising compounds, ultimately improving the efficiency and success of drug discovery, benefiting both pharmaceutical companies and patients by delivering safer and more effective medications.

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NMR Random Coil Index & Protein Dynamics. Presentation is related to: biochemistry, bioinformatics, biology, biophysics, Mark Berjanskii, molecular biology, molecular dynamics, molecular modeling, nmr spectroscopy, protein nmr, public speaking, python programming, sparse data, structural biology, structure determination, teaching, web design, web development, web programming, web server, Wishart group, protein dynamics, NMR dynamics, protein flexibility, accessible surface area, RCI, random coil index, order parameter, bruker, jeol

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The validation of the performance of a neural network based 13C NMR prediction algorithm using a test set available from an open source publicly available database, NMRShiftDB, is described. The validation was performed using a version of the database containing ca. 214,000 chemical shifts as well as for two subsets of the database to compare performance when overlap with the training set is taken into account. The first subset contained ca. 93,000 chemical shifts that were absent from the ACD\CNMR DB, the “excluded shift set” used for training of the neural network and the ACD\CNMR prediction algorithm, while the second contained ca. 121,000 shifts that were present in the ACD\CNMR DB training set, the “included shift set”. This work has shown that the mean error between experimental and predicted shifts for the entire database is 1.59 ppm, while the mean deviation for the subset with included shifts is 1.47 ppm and 1.74 ppm for excluded shifts. Since similar work has been reported online for another algorithm we compared the results with the errors determined using Robien’s CNMR Neural Network Predictor using the entire NMRShiftDB for program validation.

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ChemSpider is a free access online database of over 26 million chemical compounds sourced from over 400 different sources including government laboratories, chemical vendors, public resources and publications. ChemSpider allows its users to deposit data including structures, properties, links to external resources and various forms of spectral data. ChemSpider has aggregated over 3000 high quality NMR spectra and continues to expand as the community deposits additional data. The majority of spectral data is licensed as Open Data allowing it to be downloaded and reused. The validation of the data can be performed by members of the community but an automated validation of the data was undertaken using ACD/Labs software using NMR prediction and verification routines. The dataset is a “real world” dataset containing the contributions of a number of laboratories around the world supplying data of varying quality including S/N issues, misreferencing, impurities etc. This work will report on the batch analysis of the ChemSpider spectral data including the identification of multiple errors in the spectra.

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Recoord 07

1. RECOORD REcalculated COORdinates Database Nederveen et al., Proteins 2005

2. Recalculation of > 500 NMR Structures • decrease user - and software dependent bias • better comparison • provide database for development of… - validation tools - calculation protocols - statistical approaches - modelling

3. NMR Data?!? Three types of restraints: 1. distance restraints (NOEs !!!!) 2. dihedral angle restraints (coupling constants or chemical shift) 3. orientational restaints (RDCs, relaxation data)

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6. Results Quality-assessment of recalculated structures o Packing o Ramachandran appearance One standard deviation closer to mean!

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8. CNS vs. CYANA (no water ref.) Z-score: standard deviation away from the mean

9. CNS before and after water ref.

10. Why am I telling you this? … you can use the scripts! www.ebi.ac.uk/msd-srv/docs/NMR/recoord/main.html

Recoord 07

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