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Michael G. Lerner

Below is a list of software projects I have either been in charge of or contributed to.

• I worked with Matt Perrone and Joel Bader on NetPert v1, the code to rank genes and their proteins in network pathways between adriver and response genes via perturbation theory methods, can be found on the Bader Group github page. You can read the paper for free at PLoS Comp Bio.
• Web interface for finite size corrections in membrane MD
  An implementation of the the model from Camley, Lerner, Pastor, and Brown, J. Chem. Phys. 143, 243113 (2015) is available here.
• PyMOL APBS Plugin
  Michael G. Lerner and Heather A. Carlson
  University of Michigan, Ann Arbor, 2006
  The PyMOL-APBS Plugin integrates the PyMOL molecular graphics system with APBS to enable rapid, easy calculation of electrostatic potential surfaces with minimal user intervention. It is included with recent versions of PyMOL and is available directly on the web at the PyMOL wiki. It is one of the most heavily used PyMOL plugin and is responsible for upwards of 80% of all calculations done with APBS.
  
• PyPAT: Python-based Protein Analysis Tools
  Michael G. Lerner, Steven A. Spronk and Heather A. Carlson
  University of Michigan, Ann Arbor, 2008 (Paper in preparation)
  PyPAT (Python-based Protein Analysis Tools) is a collection of tools that build upon the ptraj module of AMBER and the PyMOL visualization package to aid in the analysis of protein structures and molecular dynamics trajectories. They allow for the evaluation of the convergence of trajectories, as well as the examination of correlated dynamics, hydrogen bonds, and bridging-water molecules throughout a trajectory. Our tools are written in Python and released under an open-source license.
  Available here.
• Automated clustering of probe molecules from solvent mapping of protein surfaces
  Michael G. Lerner, Kristin L. Meagher and Heather A. Carlson. Automated clustering of probe molecules from solvent mapping of protein surfaces: New algorithms applied to hot-spot mapping and structure-based drug design. J Comput. Aided Mol. Des. 2008, 10, 727-736.
  In an effort to understand protein binding and function, researchers will often create a reciprocal map of a protein surface. Multiple-copy methods (MCM) use probe molecules to define these complementary maps. These techniques flood the protein surface with hundreds of small molecule probes. The probes are then simultaneously and independently minimized to the s potential energy surface. Clusters of probes on the protein surface can define the most important among these interactions. Most MCMs that cluster molecules in physical space do so via RMSD-based methods. We find that significantly improved results may be obtained by Jarvis-Patrick clustering in physical space. This package contains programs for flooding a protein surface with probe molecules, as well as programs to group the probe molecules into clusters.
  Available here.
• I also develop many smaller, standalone programs including a CHARMM mode for Emacs, several scripts for the automated setup of MD systems in GROMACS, CHARMM and AMBER, and a suite of tools for the analysis of membrane diffusion. This code is freely available, although it is not yet suitable for full public release. Please email me if interested.