Summer 2021

Group meeting/trip schedule

Quabbin Reservoir & Beaver Lake, MA

• June 3, 2021 (Thursday). Summer research kickoff
• June 8, 2021 (Tuesday). 11-2
  • Algorithm development (Brian)
  • NLP models of protein structure (Eamen, Roman, Edgar)
  • Bb transcriptomics (Niemah & Jackie)
  • HIV compartmentalization (Lily)
• June 10, 2021 (Thursday). No meeting. Field day
  • Location: Hackscher State Park, Long Island
  • Participants: Desiree, Lily, Lia, John, Weigang
  • Outcome: ~60 nymph ticks
• June 15, 2021 (Tuesday). 11-2. Lab meeting
• June 17, 2021 (Thursday). No meeting. Field day
  • Location: Quabbin Reservoir, MA
  • Participant: Saymon, Brian, Lia, Weigang
  • Outcome: ~130 nymph ticks
• June 22, 2021 (Tuesday).
  • Niemah & Jackie will work on collecting transcriptome data into Excel sheets
  • Lily presented latest trees on HIV compartmentalization
  • Roman will work on env gene embedding using Facebook pretrained ESM model

Project 1. Borrelia genomics

• Participants: Niemah, Jackie
• Questions & Goals:
  • Upgrade database, genome pipeline, and website (Lia)
  • Phylogeography & evolutionary maintenance of divided genome (Saymon)
  • vls evolution (with simulation) & development of immunoflorescence microsopy methods(Lily). Live imaging.
• Reading list
  • Latest review book Lyme Disease and Relapsing Fever Spirochetes: Genomics, Molecular Biology, Host Interactions and Disease Pathogenesis. The chapter on gene regulation and transcriptomics (notice Fig 1, Fig 2, and Table 1)
  • Schward et al (2021). Multipartite Genome of Lyme Disease Borrelia: Structure, Variation and Prophages
  • Stevenson & Seshu (2018). Regulation of Gene and Protein Expression in the Lyme Disease Spirochete
  • The bowtie model of gene regulation: Yan et al (2017). PLoS Comp Biol.

Project 2. Design algorithms for vaccines

• Participants: Dr Saad Mneimneih (CS Department), Brian
• Questions & Goals:
  • Generalized algorithms for antigen with arbitrary tree shape
    • Data set 1. Neutral evolution (with exponentially distributed branch lengths). Binary strings (L=100 bits) evolved from a coalescent tree of 20 leaves. Simulated with rcoal(20); rTraitDisc; simSeq(). code from previous work
    • Data set 2. Two major clades. HA sequences from fluB
    • Data set 3. Four major clades. Dengue
    • Data set 4. Star-shaped tree, driven by recombination. OspC
    • Data set 5. Multiple major clades. vls cassette in Lyme species
  • Combination algorithms
  • Naive Bayes models to integrate immunogenicity data
  • Natural language models to improve structural stability (see Project 4 below)
• Reading list
  • Di et al (2021). Maximum antigen divergence in Lyme bacterial population

Project 3. HIV compartmentalized evolution

by Lily

• Participants: Lily
• Questions and goals
  • Do HIV evolve cell type tropisms within the host? Specifically, the Neural(N)-tropism vs T-cell(T)-tropism?
  • Build a classifier of N-tropism HIV subtypes
  • A presentation for an HIV conference in October
• Reading list
  • HIV compartmentalized evolution: Evering et al (2014)
• Data sets
  • ~500 sequences of env genes from 15 patients
  • 2nd time point single-cell genome sequences for some of the patients
  • Experimentally verified N-tropism subtypes
• Approach
  • Evolutionary mechanisms: mutation, recombination, and adaptive selection
  • Homoplasy index as a measure of compartmentalization? Randomization to obtain p-values of HI.
  • Evolutionary rates & signature (BEAST)
  • Tests of natural selection (PAML site models, branch-site models & MK analysis)
  • Phylogenetic analysis: tree per individual; supertree; haplotype networks (per individual)
  • Simulated compartmentalization

Project 4. Natural Language models of proteins

• Participants: Eamen, Roman, and Edgar
• Questions & Goals

1. Learn, implement, and compare the existing tools
2. Fine-tuning for OspC, to be integrated with the centroid algorithm
3. 2nd-generation centroid design: k-means algorithm (with applications to vls, Dengue, flu B)

• Tools & Reading list
  • Facebook ESM: pre-trained language models (for feature extraction)
    • Step 1. implementation with colab
    • Step 2. Fine-tuning with OspC seqs; extract embedding
    • Step 3. Applications: classify (OspC vs VlsE), contact map (native vs synthetics), solubility
  • Strodthoff et al (2020). Bioinformatics. UDSMProt: universal deep sequence models for protein classification. Source code on Github
  • Rives et al (2021). PNAS. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Github repository
  • Transformer: Vaswani et al (2017). Attention is All You Need Github repository for Huggingface/Transformer