omics

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some papers involving proteins and ml, especially predicting protein structure from dna/rna

overview

vocabulary

  • oligonucleotide = oligo = short single strands of synthetic DNA or RNA

data

code

  • rna
    • Galaxy RNA workbench - many tools including alignment, annotation, interaction
    • viennaRNA - incorporating constraints into predictions
    • neural nets
      • spot rna / spot rna2
        • uses an Ensemble of Two-dimensional Deep Neural Networks and Transfer Learning
      • mxfold2 (sato et al. 2021)

rna structure prediction

rna basics

  • rna does many things
    • certain structures of RNA lend themselves to catalytic activities
    • tRNA/mRNA convert DNA into proteins
    • RNA also serves as the information storage and replication agent in some viruses
  • rna components
    • 4 bases: adenine-uracil (instead of dna’s thymine), cytosine-guanine
    • ribose sugar in RNA is more flexible than DNA
  • RNA World Hypothesis (Walter Gilbert, 1986) - suggests RNA was precursor to modern life
    • later dna stepped in for info storage (more stable) and proteins took over for catalyzing reactions
  • rna structure
    • rna_structure
      • primary - sequence in which the bases are aligned - relatively simple, comes from sequencing
      • secondary - 2d analysis of hydrogen bonds between rna parts (double-strands, hairpins, loops) - most of stabilizing free energy comes from here (unlike proteins, where tertiary is most important)
        • most work on “RNA folding” predicts secondary structure from primary structure
        • a lot of this comes from hydrogen bonds between pairs (watson-crick edge)
          • other parts of the pairs (e.g. the Hoogsteen- / CH-edge and the sugar edge) can also form bonds
      • tertiary - complete 3d structure (e.g. bends, twists)
    • Screen Shot 2021-12-01 at 6.33.41 PM
  • RNA-Seq - Wikipedia - RNA-Seq uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, analyzing the continuously changing cellular transcriptome

algorithms

  • computational bio book ch 10 (kellis, 2016)
    • 2 main approaches to rna folding (i.e. predicting rna structure):
      • (1) thermodynamic stability of the molecule
      • (2) probabilistic models
      • note: can use evolutionary (i.e. phylogenetic) data to improve either of these
        • some RNA changes still result in similar structures
        • consistent mutations - something mutates but structure doesn’t change (e.g. AU -> G)
        • compensatory mutations - two mutations but structure doesn’t change (e.g. AU -> CU -> CG)
        • incorporate similarities to other RNAs along with something like zuker algorithm
    • thermodynamic stability - uses more domain knowledge
      • dynamic programming approach: given energy value for each pair, minimize total energy by pairing up appropriate base pairs
        • assumption: no crossings - given a subsequence $[i,j]$, there is either no edge connecting to the ith base (meaning it is unpaired) or there is some edge connecting the ith base to the kth base where $i < k \leq j$ (meaning the ith base is paired to the kth base)
          • this induces a tree-structure to theh problem
        • nussinov algorithm (1978)
          • ignores stacking interactions between neighboring pairs (i.e. assumes there are no pseudo-knots)
        • zuker algorithm (1981) - includes stacking energies
    • probabilistic approach - uses more statistical likelihood
      • stochastic context-free grammer (SCFG) is like an extension of HMM that incorporates some RNA constraints (e.g. bonds must happen between pairs)
  • Recent advances in RNA folding - ScienceDirect (fallmann et al. 2017)
    • algorithmic advances
      • algorithmic constraints
        • locality - restrict maximal span of base pairs
        • add in bonus energies as hard or soft constraints
      • rna-rna and rna-protein interactions - there are different algorithms for telling how 2 things will interact
      • newer algorithms take into account some aspects of tertiary structure to predict secondary structure (e.g. motifs, nonstandard base pairs, pseudo-knots)
    • representation
      • ...((((((((...)). .)). .((.((...)). )))))). “dot-parenthesis” notation - opening and closing represent bonded pairs
    • evaluation
      • computing alignments is non-obvious in these representations
      • centroids - structures with a minimum distance to all other structures in the ensemble of possible structures
      • consensus structures - given a good alignment of a collection of related RNA structures, can compute their consensus structure, (i.e., a set of base pairs at corresponding alignment positions)
  • Folding and Finding RNA Secondary Structure (matthews et al. 2010)
  • new work drops the assumption of no knots (e.g. crossings)
    • project to topology (e.g. low/high genus) - e.g. projecting to a torus can remove crossings but still allow us to use dynamic programming
  • RNA secondary structure prediction using deep learning with thermodynamic integration (sato et al. 2021)
    • RNA-folding scores learnt using a DNN are integrated together with Turner’s nearest-neighbor free energy parameters
      • DNN predicts scores that are fed into zuker-style dynamic programming

3d rna structure prediction

  • Geometric deep learning of RNA structure (townshend, …, dror 2021 - atomic ai + stanford, science)
    • uses neural network as scoring mode (called ARES = Atomic Rotationally Equivariant Scorer)
      • this gives energy function for (primary sequence, structure) pair
    • architecture
      • captures rotational + translational symmetries
    • startup atomic ai works on this
    • trained with only 18 known RNA structures
    • uses only atomic coordinates as inputs and incorporates no RNA-specific information
  • Review: RNA 3D Structure Prediction Using Coarse-Grained Model (li & chen, 2021)
    • all-atom - more precise, each nucleotide models ~20 heavy atoms + ~10 hydrogen atoms - relatively rare
    • coarse-grained - less precise
    • steps
      1. input - RNA sequence + optinal secondary structure + more (e.g. distance between some atom pairs)
      2. sampling - includes discriminator (=scoring function = (potential) energy function = force field) and generator
        • generator proposes new structures (e.g. MCMC)
        • discriminator decides which is the most stable - 3 types of energy to consider
      3. output - sometimes pick lowest energy structure, somestimes cluster low-energy clusters and pick centroid
      4. all-atom structure reconstruction - the fragment matching algorithm (Jonikas et al., 2009a) is often used, followed by structure refinement to remove steric clashes and chain breaks
    • 12 papers that differ slightly in these steps

protein structure prediction (dna)

protein basics

  • the standard way to obtain the 3D structure of a protein is X-ray crystallography
    • takes ~1 year & $120k to obtain the structure of a single protein through X-ray crystallography (source)
    • alternative: nuclear magnetic resonance (NMR) spectroscopy
  • on average, a protein is composed of 300 amino acids (residues)
    • 21 amino acid types
    • the first residue is fixed
  • proteins evolved via mutations (e.g. additions, substitutions)
    • fitness selects the best one

algorithm basics

  • multiple-sequence-alignment (MSA) - alignment of 3 or more amino acid (or nucleic acid) sequences, which show conserved regions within a protein family which are of structural and functional importance
    • matrix is (n_proteins x n_amino_acids)
    • independent model (PSSM) - model likelihood for each column independently
    • pairwise sites (potts model) - model likelihood for pairs of positions
  • prediction problems
    • contact prediction - binary map for physical contacts in the final protein
      • common to predict this, evaluation uses binary metrics
    • protein folding - predict 3d positions of all amino acids in the output structure
    • Evaluating Protein Transfer Learning with TAPE - given embeddings, 5 tasks to measure downstream performance (rao et al. 2019)
      • tasks: structure (SS + contact), evolutionary (homology), engineering (fluorescence + stability)
    • future: interactions between molecules (e.g. protein-protein, environment, highly designed proteins)

deep-learning papers

  • [De novo protein design by deep network hallucination Nature](https://www.nature.com/articles/s41586-021-04184-w) (anishchenko…baker, 2021)
    • deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins
  • [Highly accurate protein structure prediction with AlphaFold Nature](https://www.nature.com/articles/s41586-021-03819-2) (jumper, …, hassabis, 2021)
    • supp
    • best blog post (other blog post)
    • model overview
      • alphafold
    • preprocessing
      • MSA ($N_{seq} \times N_{res}$)
        • uses evolutionary information
      • pair representation ($N_{res} \times N_{res}$)
        • finding templates - find similar proteins to model “pairs of residues” - which residues are likely to interact with each other
        • residue is the same as an amino acid
    • evoformer
      • uses attention on graph network
      • iterative
    • structure model - converts msa/pair representations into set of (x,y,z) coordinates
      • “invariant point attention” - invariance to translations and rotations
      • predicts a global frame (residue gas)
  • MSA Transformer (rao et al. 2021) - predicts contact prediction
    • train via masking
    • output (contact prediction) is just linear combination of attention heads finetuned on relatively little data
    • some architecture tricks
      • attention is axial - applied to rows / columns rather than entire input
  • EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

covid

TIPs - using viruses for treatment

  • videos
  • 2 issues
    • mutation - viruses mutate, our drugs don’t
    • transmission - adherence / deployment - hard to give certain the drugs
  • solution: use modified versions of viruses as treatment
    • therapeutic interfering particles or TIPs are engineered deletion mutants designed to piggyback on a virus and deprive the virus of replication material
      • TIP is like a parasite of the virus - it clips off some of the virus DNA (via genetic engineering)
        • it doesn’t contain the code for replication, just for getting into the cell
        • since it’s shorter, it’s made more efficiently - thus it outcompetes the virus
    • how do TIPs spread?
      • mutations happen because the “copy machine” within a cell makes a particular mutation
        • when a new virus comes along, it makes some of the mutated parts
        • TIPs can’t replicate, so they take some of the mutated parts made by the new virus
        • then, the TIP gets copied with the same mutation as the virus and this now spreads
  • effect
    • viral load will immediately be lower
    • superspreaders can spread treatment to others
    • can take before exposure as well (although harder to get approval for this)
  • note: HIV mutates very fast so a TIP in a single person sees a lot of variants
    • HIP also enters the genome (so corresponding TIP does this as well)
  • Identification of a therapeutic interfering particle—A single-dose SARS-CoV-2 antiviral intervention with a high barrier to resistance (chaturvedi…weinberger, 2021)
    • DIP = defective interfering particle = wild-type virus
    • single administration of TIP RNA inhibits SARS-CoV-2 sustainably in continuous cultures
    • in hamsters, both prophylactic and therapeutic intranasal administration of lipid-nanoparticle TIPs durably suppressed SARS-CoV-2 by 100-fold in the lungs, reduced pro-inflammatory cytokine expression, and prevented severe pulmonary edema
    • TIP consists 1k - 2k bases
    • hard to actually look at structure here (requires cryoEM)

random papers