omics

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

## 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
• 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)
• 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 • 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

# 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)