proteins

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

# 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
• 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
• 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

# 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
• 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)

## data

•  [Accurate SHAPE-directed RNA structure determination PNAS](https://www.pnas.org/content/106/1/97) - chemical probing

## 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 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)
• loosing 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
•  [RNA secondary structure prediction using deep learning with thermodynamic integration Nature Communications](https://www.nature.com/articles/s41467-021-21194-4) (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

• startup atomic ai works on this
•  [Geometric deep learning of RNA structure science](https://www.science.org/doi/10.1126/science.abe5650) (townshend, …, dror 2021)
• scoring model - gives energy function for (primary sequence, structure) pair
•  [Frontiers RNA 3D Structure Prediction Using Coarse-Grained Models Molecular Biosciences](https://www.frontiersin.org/articles/10.3389/fmolb.2021.720937/full) (li & chen, 2021)
• older

# protein structure prediction (dna)

## protein basics

• the standard way to obtain the 3D structure of a protein is X-ray crystallography. It takes about a year and costs about \$120,000 to obtain the structure of a single protein through X-ray crystallography [source]
• 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
• Evaluating Protein Transfer Learning with TAPE - given embeddings, five tasks to measure downstream performance (rao et al. 2019)
• 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, and such networks and methods should contribute alongside traditional physics-based models to the de novo design of proteins with new functions.
•  [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
• finding templates - find similar proteins to model “pairs of residues” - which residues are likely to interact with each other
• 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
• MSA Transformer (rao et al. 2021) - predicts contact prediction