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Xiaopeng Xu, Ph.D.

Research Scientist @ KAUST · Agentic AI for scientific discovery, protein design & synthetic biology

Recent News

  • Sep 23, 2026, my Co-I Shaheen III Grand Challenge Project proposal on “Multi-Scale AI-MD-QM/MM Platform for Next-Generation Biologics Design” was appproved on Shaheen III GPU! Cheers!
  • Aug 2, 2026, my Co-I Translational Research Grants (TRG2026) on AI-based protein design solutions for industrial biologics and medical applications was funded by KAUST! ~1 million USD fund to support our research! Cheers!
  • Jun 23, 2026, started as a Research Scientist at SFB@KAUST.
  • Jun 8, 2026, Mater-trends — to track researches on material science — up and alive.
  • Jun 3, 2026, AI-trends — to track research trends on artificial intelligence — and Bio-trends — to track researches on bioscience — up and alive.
  • Mar 25, 2026, the first tool buddle in MacromNex, ProteinMCP — an agentic AI framework for autonomous protein engineering, published in Protein Science!
  • Mar 24, 2026, SegDesign — a framework for protein segment engineering — published in Protein Science.
  • Jan 22, 2025, AggNet — a Deep Learning method for protein aggregation prediction — published in Protein Science.
  • Oct 3, 2024, AutoBA — for multi-omics analysis with Agentic AI — published in Advanced Science.
  • Jul 18, 2024, SANTO — a method for alignment and stitching in spatial omics — published in Nature Communications.
  • Jun 12, 2024, HELM-GPT — de novo macrocyclic peptide design — published in Bioinformatics!
  • Jun 23, 2024, started as a Postdoctorate Research Fellow at SFB@KAUST.
  • May 13, 2024, my Co-I Shaheen III proposal on “Molecular dynamics simulations-enhanced AI for enzyme design and optimization” was appproved on Shaheen III CPU! Cheers!
  • May 5, 2024, defended my Ph.D Dissertation — “A ChatGPT-like solution for protein design: combining generative and predictive AI to design functional proteins”! Cheers!
  • Jun 28, 2023, SGPT-RL — for chemical drug design with GPT and RL — published in F1000Research!
  • Feb 20, 2023, AB-Gen — for antibody with GPT and RL — published in Genomics, Proteomics & Bioinformatics!
  • Nov 11, 2021, an AI guided review of 17 000 studies in COVID-19 computational research published in Briefings in Bioinformatics

Recent Notes

杰青/优青项目申请

项目简介

评审标准

海外优青评审标准

参考:https://www.sdxz2050.com/17570.html, https://blog.sciencenet.cn/blog-64212-1291802.html

今年新增了国家自然科学基金优青(海外)项目,旨在引进优秀海外人才。在函评本子过程中发现存在几个方面的问题,希望能引起相关学者和依托单位的重视,增加命中率。

Cloud 常用命令

Google cloud 操作

增加 ssh key

ssh-keygen # -t rsa -b 4096 -C account_name
# copy the content in *.pub to google cloud user keys.

VM 相关

创建和删除 VM

在 web 界面中按步骤操作即可。

注意:

  1. 在高级选项中的 availability policies,课选择 spot。可大幅减少计算成本。 使用 Admin 创建的 image,可以使用 NFS,已有 environment。

登录 VM

使用 gcloud 登录较为方便,命令如下

Ig-seq 操作说明

数据下载

从 NCBI SRA 平台下载对应的 Ig seq 项目数据。

先导入 google cloud 的 bucket 中,然后通过 gsutils 下载到本地或 ibex 文件夹中。

MiXCR 数据分析

安装 MiXCR

conda install -c imperial-college-research-computing mixcr

Ig-seq 快速使用

# Align
Align: mixcr align–library my_library -t 8 -r align_log.txt R1 R2 alignments.vdjca -s hs
# What is the my_library used here

# Assemble
Assemble: mixcr assemble -r assemble_log.txt -OseparateByV = true -OseparateByJ = true -OseparateByC = true align-
ments.vdjca clones.clns

使用介绍

典型的 MiXCR 工作流程主要由三个部分构成:

Nanopore 分析流程

基础的分析流程:

  1. Base calling 从电信号中识别碱基序列。原始电信号是Fast5/Pod5格式,需要转为 Fastq 格式。 Fast5/Pod5-> Fastq。

  2. Alignment 比对到参考基因组。常用工具是 minimap2。

  3. Call variants 获取突变数据,包括 GATK,VarScan 等。

Rosetta 使用

核心思想是 Rotamer

安装

Rosetta 可以从 RosettaCommons 上面下载源代码。但需要在本地做 Compile 后才能使用。相关介绍见:https://www.rosettacommons.org/demos/latest/tutorials/install_build/install_build。

tar -xvzf rosetta[releasenumber].tar.gz # 解压压缩包
cd rosetta*/main/source # 打开源代码文件夹
./scons.py -j 20 mode=release bin # 用 scons 来生成 binary 文件,20为核数

默认 build 好后在 ./bin/ 文件夹中。