CV

SUMMARY

David C. Wych is a Scientific Developer II focusing on cryoEM modeling for pharmaceutical drug discovery in the Structural Biology group at OpenEye Scientific Software (Cadence Molecular Sciences). He was recently a postdoctoral research associate in the Computer, Computational, and Statistical Sciences Division (CCS-3) and the Center for Non-Linear Studies (CNLS) at Los Alamos National Laboratory (LANL).

His research focus involves using molecular-dynamics (MD) and quantum mechanics(QM) simulations of proteins, in both the crystalline and solution state, to better understand and improve the modeling of protein/solvent structure and disorder in X-ray crystallography (specifically, the “diffuse scattering”) and cryo-electron microscopy, developing High-performance Computing methods for crystallographic modeling and refinement, and using QM-MD simulations to probe protein structure and dynamics beyond what is possible with classical MD.

EDUCATION

Ph.D. – Pharmacological Sciences – University of California, Irvine – 2021

• Dissertation: Insights into the Modeling of Crystallographic Structure and Disorder from Molecular Dynamics Simulations of Protein Crystals

• Advisor/PI: David L. Mobley (Chem. and Pharm. Sci.)

• Mentor: Michael E. Wall (CCS-3; LANL)

B.A. – Physics – Claremont McKenna College (CMC) – 2015

• Advisor: Scot Gould (Physics)

• Mentor: Yun “Lyna” Luo (Pharmacy; Western University of Health Sciences)

PROFESSIONAL EXPERIENCE

Scientific Developer II | CryoEM – OpenEye Scientific | Cadence Molecular Sciences – July 2023-present

• Scientific software development in C++ and python
• Developing methods for modeling cryoEM data for application to pharmaceutical drug discovery

Postdoctoral Research Associate – LANL – Computer, Computational and Statistical Sciences Division (CCS-3) and the Center for Non-linear Studies (CNLS) – 2021-2023

• Created new advanced methods for X-ray crystallographic (XRC) modeling & refinement using structure factors calculated from molecular-dynamics (MD) simulations of protein crystals (published in Acta Cryst. D).
• Developed software in C++ and Python for the modeling of serial femtosecond XRC data on next-generation supercomputers as part of the DOE Exascale Computing Project, allowing for fast refinement of parameters in the modeling of diffuse scattering.
• Developed software in FORTRAN for quantum mechanical simulations of large protein-solvent systems (thousands of atoms) using cutting-edge fast quantum molecular dynamics (QM-MD) algorithms (SCC-DFTB in LATTE & NAMD).

Graduate Student Researcher – UCI – Pharmaceutical Sciences – 2016-2021

• Published work in Structural Dynamics which used MD simulations to characterize correlated disorder in protein crystals, to predict the diffuse scattering in XRC, and to provide context to a long-standing debate about the main source of diffuse scattering (selected as a SciLight by the American Institute of Physics [AIP])
• Investigated the prediction of diffuse scattering using Markov State Models (MSMs)
• Developed methods for improving structural modeling and refinement in XRC with structure factors calculated from MD simulations of protein crystals.

Virtual Summer Intern – LANL – CNLS – Summer 2020

• Prepared, ran, and analyzed MD simulations of protein crystals, studying the effect of restraint strength on the prediction of avg. structure and harmonic disorder (B-factors).

Graduate Student Assistant – LANL – CCS-3 – Winter-Fall 2019

• Prepared, ran, and analyzed MD simulations, characterizing the nature of correlated disorder in protein crystals, and predicting diffuse scattering in XRC.

Professional Tutor – Revolution Prep

• One-on-one remote SAT/ACT, Physics, Chemistry, Mathematics, and Biology tutoring for high school and college students.

PUBLICATIONS

Peer-reviewed Publications

• Wych, D.C. and Wall, M.E. (2023) “Molecular-Dynamics Simulations of Macromolecular Diffraction, Part 1: Preparation of Protein Crystal Simulations.” Methods in Enzymology. In Press, Corrected Proof.

• Wych, D.C. and Wall, M.E. (2023) “Molecular-Dynamics Simulations of Macromolecular Diffraction, Part 2: Analysis of Protein Crystal Simulations.” Methods in Enzymology. In Press, Corrected Proof.

• Doyle, M. D., Bhowmick, A., Wych, D. C., Lassalle, L., Simon, P. S., Holton, J., … & Wall, M. E. (2023). “Water Networks in Photosystem II Using Crystalline Molecular Dynamics Simulations and Room-Temperature XFEL Serial Crystallography.” Journal of the American Chemical Society, 145(27), 14621-14635.

• Wych, D. C., Aoto, P. C., Vu, L., Wolff, A. M., Mobley, D. L., Fraser, J. S., Taylor, S. S. & Wall, M. E. (2023). “Molecular-dynamics simulation methods for macromolecular crystallography.” Acta Cryst. D79, 50–65.

• Ge, Y., Wych, D. C., Samways, M. L., Wall, M. E., Essex, J. W., & Mobley, D. L. (2022). “Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques.” Journal of Chemical Theory and Computation, 18(3), 1359-1381.

• Wych, D. C., Fraser, J. S., Mobley, D. L., & Wall, M. E. (2019). “Liquid-like and rigid-body motions in molecular-dynamics simulations of a crystalline protein.” Structural Dynamics, 6(6), 064704. [Selected as a Feature Article by the Editors of Structural Dynamics, and a SciLight by the AIP]

Pre-print / In Review

• Smith, N., Dasgupta, M., Wych, D. C., Dolamore, C., Sierra, R. G., Lisova, S., … & Wilson, M. A. (2023). “Changes in an Enzyme Ensemble During Catalysis Observed by High Resolution XFEL Crystallography.” bioRxiv, 2023-08.

TEACHING EXPERIENCE

Course Instructor:

Physical Biochemistry (300-level, Pharm. Sci.) – UCI – Fall 2020 – Virtual (COVID19)

• Created original course content (homework and exams)
• Remote course administration and instruction through Canvas and Zoom
• Evaluations (overall performance, n=35):
  • mean: 3.89/4; std. dev.: 0.28; median: 4; mode: 4.

Physical Biochemistry (300-level, Pharm. Sci.) – UCI – Fall 2019 – In Person

• Performed a full re-write of the course curriculum (lecture slides and notes, homework, and exams)
• Evaluations (overall performance, n=60):
• mean: 3.77/4; std. dev. 0.42; median: 4; mode: 4.

Course description and responsibilities:

• Upper-level Pharm. Sci. degree course (PHARMSCI 171): biochemistry studied from a physics-centered perspective, using Thermodynamics and Statistical Mechanics, Chemical Kinetics, and Quantum Mechanics; the only physics-based, and most math-heavy course among the degree requirements.
• Class size: 100-120 students
• Recorded, edited and published lectures twice weekly and released lecture notes for each
• Open office hours twice weekly, and one-on-one/group if needed
• Managed two TAs
• Handled all administrative tasks (emails, grading, reporting, etc.)
• Full Instructor Evaluations/Comments available upon request

Teaching Assistant:

Physical Biochemistry (PHARMSCI 171) – 2018

• Served as supplementary instructor twice weekly (20-30 students)

Introductory Chemistry Laboratory (CHEM 1L) – 2017

• Served as instructor for laboratory requirement for introductory chemistry (25 students)

Professional Tutor:

Revolution Prep – 2015 - 2016

• One-on-one remote tutor for 10-20 students at a time (rolling turnover) for SAT/ACT, and high-school Physics, Chemistry, Biology, and Mathematics.

Course Tutor:

Scripps College – 2014 - 2015

• Calculus I - III and Differential Equations

Keck Science Department of the Claremont Colleges – 2013 - 2015

• Physics (all major-required courses)

HONORS AND AWARDS

CNLS Postdoctoral Fellowship – LANL – 2021

• Competitive fellowship funding 50% time, allowing for continuation of graduate work in LANLs most collaborative, interdisciplinary division, and for exposure to the cutting edge in a wide variety of fields, from quantum information and machine learning to microbiology, genomics, and petroleum engineering.

Applied Machine Learning Summer Fellowship – LANL – 2018

• Applied Non-negative Matrix Factorization (NMF) to trajectories of three-dimensional electron density calculated from molecular dynamics (MD) simulations to isolate the structural/dynamical features important to the prediction of diffuse scattering.

PROFESSIONAL ACTIVITIES AND AFFILIATIONS

Invited Lecturer and Workshop Leader/Organizer at the 57th Course of the Erice International School of Crystallography on Diffuse Scattering: the crystallography of dynamics, defects, and disorder. (2022)

• Lecture:
  • Analysis of Molecular Dynamics Simulations of Protein Crystals
• Workshops:
  • Molecular Dynamics Simulations of Bio-molecular Crystals: System Preparation
  • Molecular Dynamics Simulations of Bio-molecular Crystals: Analysis

Reviewer for the Journal of Chemical Informatics and Modeling (JCIM) (2022)

Member of the American Chemical Society (ACS), Biophysical Society (BPS), and American Crystallographic Association (ACA)

COMPUTATIONAL EXPERTISE

⋅ GROMACS (9 yrs.)	⋅ AMBER (9 yrs.)	⋅ CHARMM (2 yrs.)	⋅ NAMD (1 yr.)	⋅ LATTE (1 yr.)
⋅ OpenEye toolkits	⋅ MDTraj	⋅ ParmEd	⋅ PyEMMA	⋅ ChimeraX
⋅ PyMOL	⋅ VMD	⋅ cctbx (4 yrs.)	⋅ phenix (3 yrs.)	⋅ ccp4 (3 yrs.)
⋅ coot	⋅ lunus	⋅ Python (10 yrs.)	⋅ NumPy	⋅ SciPy
⋅ pandas	⋅ Matplotlib	⋅ Jupyter	⋅ C++ (2 yrs.)	⋅ bash/zsh (10 yrs.)
⋅ slurm (9 yrs.)	⋅ LaTeX	⋅ Mathematica	⋅ Maple	⋅ MATLAB

PROFESSIONAL PRESENTATIONS

“Analysis of Molecular Dynamics Simulations of Protein Crystals”

• Invited lecture at the 57th Course of the Erice International School of Crystallography

“Molecular Dynamics Simulation Methods for Macromolecular Crystallography”

• Invited presentation at OpenEye CUP XXI (Technical Conference for OpenEye Scientific Software), Santa Fe, NM

“Molecular Dynamics Simulations of Protein X-ray Crystallographic Diffuse Scattering”

• Invited presentation at the 69th Annual Meeting of the American Crystallographic Association (ACA), July 20-24. Cincinnati/Northern Kentucky. Section 2.1.3.