Alexander MacKerell

Weber Group

Contact

Email: amackerell@rx.umaryland.edu

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Education

  • Ph.D., Biochemistry, Rutgers University, 1985
  • B.S., Chemistry, University of Hawaii, 1981
  • A.S., Biology, Gloucester County College, 1979

Subsequent training involved postdoctoral fellowships in the Department of Medical Biophysics, Karolinska Intitutet, Stockholm, Sweden in experimental and theoretical biophysics and in the Department of Chemistry, Harvard University in theoretical chemistry. 

Profile

Dr. Alexander MacKerell’s research involves the development and application of computational methods to investigate the relationships of structure and dynamics to function in a range of biological and chemical systems. These efforts include empirical force field development, implementation of novel sampling methodologies, understanding the physical forces driving the structure and dynamics of proteins, nucleic acids, and carbohydrates, and computer-aided drug design (CADD) studies and methodology development. The MacKerell lab works closely with experimentalists in the area of drug development to provide detailed interpretation of experimental data while simultaneously refining and developing novel theoretical approaches.

The MacKerell lab is responsible for developing and maintaining the empirical force fields used in a number of simulation packages including the program CHARMM (Chemistry at HARvard Macromolecular Mechanics). CADD methodologies developed in the MacKerell lab include SILCS (Site-Identification by Ligand Competitive Saturation) and CSP (Conformationally Sampled Pharmacophore).

The Drude-2013 force field predicts differences in the X-ray scattering spectra of DNA duplex conformation in different environmental conditions, results that are not observed with the additive CHARMM36 force field (Savelyev and MacKerell, 2015, J Phys Chem Lett).
SILCS FragMaps overlaid on an inhibitor of the protein Factor Xa.  The FragMaps may be used to facilitate ligand design.

CURRENT RESEARCH

A central focus of Dr. MacKerell’s group is the continued development and extension of empirical force fields and enhanced sampling methods for use in the simulation of biological macromolecules. Such simulations are important in advancing the understanding of how intrinsic characteristics and environmental conditions contribute to the conformational properties of proteins, nucleic acids, lipids, carbohydrates, and small molecules.

In addition to widely used additive force fields, the MacKerell lab developed a polarizable force field for biomolecules, based on a classical Drude oscillator model. Drude-2013 extends the capabilities of molecular dynamics simulations, as in the example shown in the graph to the right.

CADD is a powerful tool that must contend with both the large conformational space of flexible macromolecular drug targets like proteins and the large chemical space of potential drug-like molecules to be screened against the target. Work in the MacKerell lab seeks to address these challenges with the development of new methodologies. For example, the SILCS (Site Identification by Ligand Competitive Saturation) methodology may be used for lead compound identification and optimization, evaluation of protein-protein interactions, and optimization of formulation via rational selection of excipients and buffer.

Image of the HIV envelope trimer including both the glycans and proteins obtained from an enhanced sampling MD simulation.

Additional activities in the MacKerell lab include structure-function studies of carbohydrates, proteins, and nucleic acids. Recently, the lab used enhanced sampling methods to explore the conformational heterogeneity HIV envelope protein glycans (Yang et al. 2017, Scientific Reports), resulting in the identification of novel glycan-antibody interactions that may play a role in neutralizing the virus.  See https://mackerell.umaryland.edu/ for additional information on the MacKerell lab.

Publications
2024
Balancing Group 1 Monoatomic Ion-Polar Compound Interactions in the Polarizable Drude Force Field: Application in Protein and Nucleic Acid Systems.
Revised 4-Point Water Model for the Classical Drude Oscillator Polarizable Force Field: SWM4-HLJ.
Refinement of the Drude Polarizable Force Field for Hexose Monosaccharides: Capturing Ring Conformational Dynamics with Enhanced Accuracy.
Exploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation.
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.
First-in-Class Mitogen-Activated Protein Kinase p38α: MAPK-Activated Protein Kinase-2 (MK2) Dual Signal Modulator with Anti-inflammatory and Endothelial-stabilizing Properties.
Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.
Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms.
FFParam-v2.0: A Comprehensive Tool for CHARMM Additive and Drude Polarizable Force-Field Parameter Optimization and Validation.
Identifying and Assessing Putative Allosteric Sites and Modulators for CXCR4 Predicted through Network Modeling and Site Identification by Ligand Competitive Saturation.
Balancing Group I Monatomic Ion-Polar Compound Interactions for Condensed Phase Simulation in the Polarizable Drude Force Field.
2023
Biomolecular dynamics in the 21st century.
Dendritic Cell-Mediated Cross-Priming by a Bispecific Neutralizing Antibody Boosts Cytotoxic T Cell Responses and Protects Mice against SARS-CoV-2.
Non-β Lactam Inhibitors of the Serine β-Lactamase blaCTX-M15 in Drug-Resistant Salmonella typhi.
Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.
Nitro-benzylideneoxymorphone, a bifunctional mu and delta opioid receptor ligand with high mu opioid receptor efficacy.
Identification of a novel transport system in Borrelia burgdorferi that links the inner and outer membranes.
Influence of Mg2+ Distribution on the Stability of Folded States of the Twister Ribozyme Revealed Using Grand Canonical Monte Carlo and Generative Deep Learning Enhanced Sampling.
Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation.
GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.