VARUNA – Towards a Grid- based Molecular Modeling Environment CICC/MACE – Meeting May 22, 2006 Mookie Baik Department of Chemistry & School of Informatics

Chemical Informatics in Academic Research? Industrial Research: Target Oriented Not bound to a specific molecular system Not bound to a method Not concerned with generality Aware of Efficiency Aware of Overall Cost Aware of Toxicity Concerned about Formulations Cares about active MOLECULES Academic Research: Concept Oriented Specialized on few molecular families Method Development is important Obsessed with generality Does not care much about Efficiency Cost is unimportant Often cant even assess for Toxicity Formulation is a minor issue Cares mostly about REACTIONS, i.e. Ways to GET to a molecule

Consequences for ChemInfo Design for Academia TWO Strategies are needed: Making traditional ChemInfo tools that are often available in commercial research available to Academia is in principle straightforward. New ChemInfo Tools that are CONCEPT centered and include REACTIONS in addition to MOLECULES must be developed. Our approach: Development of (a) Quantum Chemical Database (b) Molecular Modeling Database Harness the power of recent advances in Molecular Modeling (QM, QM/MM, MM, MD) through information management. Data-depository for Quantum Chemical Data including both Properties & Mechanisms

Traditional Workflow of Molecular Modeling Supercomputer Researcher FORTRAN Code, Scripts, Visualization Code Hard Drive Directory Jungle Chemical Concepts Experiments Highly inefficient workflow (no automation) Knowledge is human bound (grad student leaves and projects dies) Incorporation with other DBs is done in Researchers head

Varuna – a new environment for molecular modeling QM Database Supercomputer Researcher Simulation Service FORTRAN Code, Scripts Chemical Concepts Experiments QM/MM Database PubChem, PDB, NCI, etc. Chem-Grid Reaction DB DB Service Queries, Clustering, Curation, etc.

Automatic Generator of ForceFields (AutoGeFF) Developing a Stand-Alone Software (in C) that can take ANY drug-like molecule (from PubChem, for example) metal complexes metalloenzymes (from PDB, for example) unnatural or functionalized amino acids, nucleobases (from in-house db) for which molecular mechanics force fields are not available and automatically generate FFs based on High level Quantum Simulations (using Varuna as a Webservice) for Sophisticated Molecular Mechanics Simulations Demo: Coding of a specialized Prototype that can reproduce our manually derived novel force fields for Cu-A Alzheimers Disease as a Demonstration Study. Mapping the reaction energy profile for the hydrogen peroxide hypothesis for AD. Interactions of redox active small molecules with the active Cu-center.

Building the fibril T = 325 K, P = 1 bar t sim = 5ns eq. + 5 ns sim.

Insertion of Cu(II) into the fibril T = 325 K, P 1 bar t sim = 5 ns eq + 5 ns His 6, Glu 3, 11 - Asp 7 - Tyr 10

QM Calculation Workflow

Automatic Quantum Mechanical Curation of Structure Data Chemical Research logic is often driven by molecular structure Large-scale, small molecule DBs (such as PubChem) have low- resolution structure data Often key properties are not consistently available: e.g.: Rotation-barriers, Redox Potentials, Polarizabilities, IR frequencies, reactivity towards nucleophiles QM web-services will provide tools for generating high-resolution data that will curate the results of traditional ChemInfo studies allow for combinatorial computational chemistry access a database of modeling data