Research
The Merz lab develops theoretical and computational tools and explores their application to biological problems.
Research areas of most interest include:
· Computer-aided drug design (CADD)Metal ion force field design
· Metalloenzymes and metal ion homeostasis
· Development and application of quantum mechanical methods to biological problems
· Application of quantum computing to the health and life sciences
· Development of metabolomics workflows for NMR and CS computations
Biography
Kenneth M. Merz Jr., PhD, previously served as a University Distinguished Professor, the Joseph Zichis Chair in Chemistry and a professor of biochemistry and molecular biology at Michigan State University (MSU). He held the position of Director of MSU’s Institute for Cyber Enabled Research (iCER) from 2013-2019. He is currently the Editor-in-Chief of the Journal of Chemical Information and Modeling, which is part of the American Chemical Society suite of chemistry journals.
Dr. Merz published over 400 papers and gave more than 300 lectures worldwide. His experience spans across industry and academia. Prior to joining MSU, he served as a professor at the University of Florida and Pennsylvania State University. His roles in industry included serving as the first Senior Director of the Center for Informatics and Drug Discovery (CIDD) at Pharmacopeia, Inc. (now part of Ligand, Inc.) and Senior Director of the ADMET Research and Development Group in the Accelrys software division of Pharmacopeia (now part of Dassault Systémes and renamed BIOVIA).
Dr. Merz is the founder of the software company QuantumBio, Inc and the Cofounder of the software company Attmos, Inc.
Education & Professional Highlights
Fellowships & Awards
2010 ACS Award for Computers in Chemical and Pharmaceutical Research
American Chemical Society Fellow
2013 Chair of American Chemical Society’s Computers in Chemistry Division
Fellow the American Association for the Advancement of Science
John Simon Guggenheim Fellowship
Education
University of California, San Francisco
1989
Cornell University
1987
University of Texas at Austin
1985
Washington College
1981
Research
Simulating the chelate effect
Despite the rich history of experimental studies focusing on the thermochemistry and kinetics associated with the chelate effect, molecular-level computational studies on the chelate ring opening/ring closure are scarce. The challenge lies in an accurate description of both the metal ion and its aqueous environment.
We demonstrated that an optimized 12-6-4 Lennard-Jones (LJ) model can capture thermodynamics and provide detailed structural and mechanistic insights into the formation of ethylenediamine (en) complexes with metal ions. The water molecules in the first solvation shell of the metal ion were found to facilitate the chelate ring formation. The reported optimized parameters were further able to simulate the formation of bis and tris(en) complexes in solution representing the wide applicability of the 12-6-4 model to simulate coordination chemistry and self-assembly processes.
Random Forest refinement of a knowledge-based scoring function for protein decoy detection
Knowledge-based potentials have generally performed better than physics-based scoring functions in detecting the native structure from a collection of decoy protein structures. Through the use of a reference state, the pure interactions between atom/residue pairs can be obtained through the removal of contributions from ideal-gas state potentials. However, it is a challenge for conventional knowledge-based potentials to assign different importance factors to different atom/residue pairs.
In this project we used the “comparison” concept to generate Random Forest (RF) models that assign different importance factors to atom pair potentials to enhance their ability to identify native proteins from decoy proteins. Individual and combined data sets consisting of 12 decoy sets were used to test the performance of the RF models. We found that RF models increase the recognition of native structures without affecting their ability to identify the best decoy structures. We also created models using scrambled atom types, which create physically unrealistic probability functions. These models help to test the ability of the RF algorithm to create useful models based on input scrambled probability functions.
From this test, we found that we were unable to create models that are of similar quality relative to the unscrambled probability functions. We also created uniform probability functions where the peak positions are the same as in the original, but each interaction has the same peak height. Using these uniform potentials, we were able to recover models as good as the ones using the full potentials suggesting all that is important in these models are the experimental peak positions.
Our Team
Selected Publications
Find the latest publications on Pubmed: https://pubmed.ncbi.nlm.nih.gov/?term=Merz+KM+JR
Careers
Openings are available on a rolling basis. Areas of interest include expertise in MD simulations, potential function development, QM methodologies, structure-based drug design and software development.
Research News
The Merz lab explored the effectiveness of quantum machine learning on quantum hardware using its onsite IBM Quantum System One.
The project aims to increase access to computational models that can simulate the complex biophysics behind transition metal ion homeostasis.
Cleveland Clinic investigators explore error correction methods for quantum computing results, building the infrastructure to address biomedical research questions.