Puerto Rico

A trip to Puerto Rico

Mount Lafayette

Hiking to the Mount Lafayette with friends from UMass Amherst.

Riverview Hiking

Hiking to the Northfield Mountain Reservoir with a friend.

Paper Title Number 1

This paper is about the number 1. The number 2 is left for future work. Read more

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf

Paper Title Number 2

This paper is about the number 2. The number 3 is left for future work. Read more

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf

Paper Title Number 3

This paper is about the number 3. The number 4 is left for future work. Read more

Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf

Prediction of infectivity-enhancing mutations of the SARS-CoV-2 spike protein

In silico affinity maturation identifies single point mutations on the SARS-CoV-2 RBD that can enhance binding to the hACE2 receptor, with 6 out of 9 predictions confirmed by SPR assay.

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TMEM16F: Activation Mechanism and Dual Ion/Lipid Permeation Pathways

How does TMEM16F open its pore to conduct both ions and lipids?

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AI-powered modeling for generative ensembles

How do we generate conformational ensembles of proteins using generative AI models?

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TRPV4: Gating Mechanisms and Lipid Regulation

Dissecting hydrophobic gating and PIP2-mediated regulation in the TRPV4 ion channel using atomistic simulations.

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Dynamic Domain Interactions Encode CheA Autophosphorylation Mechanisms

Coarse-grained simulations reveal how the phosphorylatable P1 domain docks onto the kinase P4 domain in multiple productive and nonproductive modes to regulate CheA autophosphorylation.

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Physics-Informed Deep Learning for Protein Variant Effect Prediction

Incorporating biophysical features into deep learning overcomes extrapolation failures in protein sequence-function modeling.

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Contact analysis

Contact frequency analysis and visualization for MD simulation trajectories Read more

Secondary structure analysis using DSSP

use the STRIDE algorithm to assign helix, sheet and coil to protein structures or trajectories Read more

Secondary structure analysis using STRIDE in VMD

use the STRIDE algorithm to assign helix, sheet and coil to protein structures or trajectories Read more

Intrinsically disordered protein trajectory analysis

This is a short tutorial of analyzing IDP (intrinsically disordered protein) trajectories. Prerequisites are the psf file (structural file) of your IDP system and the corresponding xtc (trajectory file). Read more

Important VMD tricks

In this article, I want to include some VMD tricks based on my personal experience. VMD provides an official documentation, which can be found here: VMD-documentation. Readers are suggested to refer to the VMD User’s Guide for more detailed information. Read more

Markov State Models for biomolecule conformational transitions sampled from MD

MD simulations allow us to characterize protein structure, dynamics and function in the all-atom and femto-second spatiotemporal resolution. However, interpreting data of high dimensionality, such as MD, to infer the inherent transitions between different metastable conformational states, can be tricky. Markov state model (MSM) provides a generally accepted, state-of-the-art dynamics analysis method. Read more

Machine Learning Basics

Contact frequency analysis and visualization for MD simulation trajectories Read more

Matplotlib practices

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Obsidian Practices

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Conda environment settings

This is a tutorial to:

1. download and install conda – the environment and package management tool
2. build a conda environment for computational biophysicist

Quick Gnuplot tricks

Define your own line style

# example
set style line 1 lc rgb "#8b0000" lw 2
set style line 2 lc -1 pt 7 ps 0.9