SGSikao Guo
Contact

Computational biophysics × scientific software

I turn molecular modeling methods into reliable scientific software.

Computational biophysicist and research engineer developing reusable C++ and Python systems for protein conformational sampling, biomolecular self-assembly, structural bioinformatics, and high-performance simulation.

View case studies View CVGitHub
Sampled protein backbone conformations.01Protein conformationsDiagram showing a Qt interface and CLI metadata transformed into JSON and then into VMD, PyMOL, and web applications.02Scientific interfacesEight simulation snapshots showing membrane-associated assemblies growing as molecular concentration increases, above a stylized membrane.03Mechanistic assembly

What I work on

Methods that survive contact with real scientific workflows.

My work spans algorithm design, scientific validation, scalable implementation, and the interfaces researchers use for research.

01

Molecular modeling algorithms

Inverse kinematics, ensemble sampling, stochastic reaction-diffusion, geometric constraints, and quantitative model validation.

02

Scalable scientific systems

C++ and Python infrastructure spanning MPI parallelization, reproducible workflows, structural data processing, and automated validation.

03

Scientist-facing software

Command-line tools, desktop plugins, web interfaces, molecular visualization, and code-generation systems built for maintainability.

1 → 3 targets

One specification to VMD, PyMOL, and web across 9 SBL applications

40k+ PDB structures

Benchmarking reported for ioNERDSS

96 MPI processes

Parallel NERDSS scaling benchmark

Selected work

Research methods developed as reusable infrastructure.

Each case study separates the scientific problem, the algorithm or architecture, my contribution, and the evidence used to validate the result.

Diagram showing a Qt interface and CLI metadata transformed into JSON and then into VMD, PyMOL, and web applications.Open source
Research software2025 – 2026

Cross-platform GUI generation

A Python framework that converts a designed interface and CLI metadata into a validated, platform-independent JSON specification, then generates consistent scientific applications for desktop molecular viewers and web environments.

My roleDesigned the framework architecture and implemented the specification, validation, and platform generators.

  • Python
  • Qt
  • JSON Schema
  • VMD
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Sampled protein backbone conformations.Ongoing work
Molecular modeling2026 – Present

Protein backbone sampling

An extension of tripeptide loop-closure sampling that releases terminal constraints, introduces SE(3) motions and shifted sampling frames, and couples broad backbone geometric exploration to all-atom structural validation.

My roleExtended and optimized the C++ sampler and built its all-atom validation workflow.

  • C++
  • Inverse kinematics
  • SE(3)
  • Hit-and-Run
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Conceptual path from particle-based reaction–diffusion models to parallel NERDSS simulation.Open source
Scientific infrastructure2022 – 2025

NERDSS + ioNERDSS infrastructure

A connected software stack that transforms PDB or mmCIF structures into simulation-ready coarse-grained models and runs explicit particle-based reaction-diffusion and self-assembly at larger spatial and computational scales.

My roleCo-developed ioNERDSS and led the MPI design, implementation, validation, and benchmarking for NERDSS.

  • C++
  • Python
  • MPI
  • PDB/mmCIF
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Eight simulation snapshots showing membrane-associated assemblies growing as molecular concentration increases, above a stylized membrane.Published
Computational biophysics2020 – 2024

Biomolecular assembly models

Structure-resolved stochastic and kinetic models of clathrin lattices, HIV-1 Gag assembly, and dynamin recruitment that integrate experimental constraints and produce testable system-level predictions.

My roleDesigned and analyzed the clathrin and HIV-1 models and contributed quantitative modeling to collaborative studies.

  • NERDSS
  • Stochastic simulation
  • Kinetic modeling
  • Membranes
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Selected publications

Mechanistic modeling, molecular structure and dynamics, and scientific computing.

A focused selection of work in protein assembly, structural workflows, high-performance simulation, and research software.

View publication list

Background

Physics training, biological questions, engineering execution.

I work where molecular modeling and software engineering meet: turning research algorithms into maintainable systems that scientists can validate, scale, and reuse.

Current

Research Engineer · Inria

Developing inverse-kinematics protein-backbone samplers and a cross-platform framework for automatically generating VMD, PyMOL, and web applications.

Previously

Johns Hopkins University · Biophysics

Built NERDSS and ioNERDSS infrastructure and led computational studies of clathrin, retroviral Gag, and membrane-associated assembly.

Training

Ph.D. in Condensed Matter Physics

Developed quantitative models connecting molecular conformational changes and chemical transitions to the emergent mechanics of kinesin motors.

Interests

Reusable research infrastructure

Scientific software, protein conformational search, hybrid physics/ML workflows, simulation platforms, and agent-usable tools for molecular discovery.

Contact

Building a molecular-modeling method or scientific platform?

I am interested in research and engineering roles that connect computational methods to reliable, scalable software used by scientists.

Email me