omics view markdown

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 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)
    • decomposition of an RNA secondary structure into structural elements. Energy contributions are assigned to the plane faces depending on their type and the nucleotides delimiting them. “Stem loops”, i.e., stacked pairs of base pairs, from the unit of helices RNA secondary structure elements
  • RNA-Seq - 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 (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 (rao et al. 2019) - 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 (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 (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 (stärk et al. 2022)

covid

  • The Coronavirus Nucleocapsid Is a Multifunctional Protein (mcbride et al. 2014)
    • coronavirus nucleocapsid = N protein

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