AlphaFold protein structure prediction deep learning explained basic to advanced

AlphaFold protein structure prediction

AlphaFold protein structure prediction architecture diagram

https://310.ai/blog/alphafold2-alphafold-multimer-alphafold3

PMID: 40332289

PMID: 39125995

AlphaFold2 evoformer attention mechanism neural network architecture

AlphaFold2 neural network architecture pipeline diagram showing: 1) Input stage with amino acid sequence and MSA (Multiple Sequence Alignment) database search producing sequence + pairwise features, 2) Evoformer block with row-wise attention, column-wise attention, triangle multiplicative updates, and pair representation updates with bidirectional arrows between MSA representation and Pair representation, 3) Structure Module with Invariant Point Attention (IPA), backbone frame updates, and side chain prediction, 4) Output showing 3D protein structure with pLDDT confidence coloring. Use clear boxes, arrows, and labels. Scientific diagram style with blue and green color scheme.

protein folding problem amino acid sequence three dimensional structure secondary tertiary structure

AlphaFold3 diffusion model architecture improvements over AlphaFold2 2024

Timeline infographic of AlphaFold development history: 2018 AlphaFold1 at CASP13 winning with residue-residue distance predictions; 2020 AlphaFold2 at CASP14 achieving GDT score 92.4 solving the protein folding problem; 2021 AlphaFold2 paper published in Nature and AlphaFold Database launched with 200 million structures; 2021 AlphaFold-Multimer for protein complexes; 2024 AlphaFold3 adding diffusion model for DNA RNA ligand prediction. Each milestone shown as a node on a timeline arrow with key achievements. Blue scientific style.

AlphaFold2 architecture Evoformer structure module MSA pairwise features explained

AlphaFold3 diffusion model improvements over AlphaFold2 biomolecular interactions 2024

AlphaFold protein structure prediction review

AlphaFold2 neural network architecture Evoformer MSA pair representation

AlphaFold2 complete pipeline architecture diagram. Left to right flow: Box 1 INPUT showing DNA sequence converted to amino acid sequence (MKTAYIAKQRQISFV...). Arrows to Box 2 FEATURE EXTRACTION showing two branches: top branch MSA Search querying UniRef90 and BFD databases producing Multiple Sequence Alignment grid with N sequences x L residues, bottom branch Template Search querying PDB70 producing template structures. Both feed into Box 3 INPUT EMBEDDINGS showing MSA Representation (Nseq x Nres x 256) and Pair Representation (Nres x Nres x 128). Center section Box 4 EVOFORMER STACK (48 blocks repeated) containing: Row-wise gated self-attention, Column-wise gated self-attention, MSA transition feedforward, Outer product mean (MSA to Pair), Triangle multiplicative update outgoing, Triangle multiplicative update incoming, Triangle self-attention around starting node, Triangle self-attention around ending node, Pair transition feedforward - with bidirectional arrows between MSA and Pair tracks. Box 5 STRUCTURE MODULE showing IPA (Invariant Point Attention), backbone frame updates, side chain torsion prediction, recycling arrows looping back. Box 6 OUTPUT showing 3D protein ribbon structure colored by pLDDT confidence (blue=high confidence, orange=low confidence) and PAE matrix heatmap. Clean white background, blue/teal/green color scheme, professional scientific diagram style.

Scientific timeline infographic of AlphaFold history milestones on horizontal arrow. Node 1 at 1972: Anfinsen Dogma - sequence determines structure, Nobel Prize. Node 2 at 1994: CASP competition begins - biennial protein structure prediction challenge. Node 3 at 2018: AlphaFold1 - DeepMind enters CASP13, wins with GDT score 58.9 using residue-residue distance maps and deep ResNets. Node 4 at 2020: AlphaFold2 - CASP14 breakthrough, GDT score 92.4, protein folding problem solved, attention mechanism and Evoformer. Node 5 at 2021: AlphaFold2 paper in Nature (Jumper et al.), AlphaFold Database launched with 200 million+ structures, AlphaFold-Multimer for complexes. Node 6 at 2022: Nobel Prize in Chemistry awarded to David Baker, Demis Hassabis, John Jumper. Node 7 at 2024: AlphaFold3 - diffusion model, predicts proteins + DNA + RNA + small molecules + ions. Clean professional infographic style, blue and gold color scheme, white background.

AlphaFold confidence metrics explanation diagram. Top section showing pLDDT score color scale: Dark blue (90-100 very high confidence), Light blue (70-90 confident), Yellow (50-70 low confidence), Orange (below 50 very low confidence). Show a ribbon protein structure colored by this scheme. Middle section showing PAE (Predicted Aligned Error) matrix: NxN heatmap where dark green = low error (high confidence in relative position), white/yellow = high error. Example showing two domains with low PAE within domains but high PAE between them indicating flexible linker. Bottom section showing CASP metrics: GDT_TS score bar chart comparing AlphaFold1 (score 58), other methods (scores 50-70), AlphaFold2 (score 92.4, highlighted in gold). Labels explaining GDT = Global Distance Test, TM-score, lDDT. Clean educational diagram, white background, professional style.

Protein folding basics educational diagram. Panel A: Four levels of protein structure - Primary structure showing linear amino acid chain with labeled residues (Gly, Ala, Val, Leu, etc.). Secondary structure showing alpha-helix (coiled ribbon) and beta-sheet (flat arrows) with hydrogen bond dotted lines. Tertiary structure showing single protein chain folded into 3D globular structure with color gradient blue to red N to C terminus. Quaternary structure showing multiple subunits (hemoglobin tetramer example). Panel B: The protein folding problem - arrow from DNA sequence to mRNA to amino acid sequence to question mark to 3D structure, with text "10^300 possible conformations, Levinthal's paradox". Panel C: Experimental methods vs AlphaFold - X-ray crystallography (years, expensive), NMR spectroscopy (size limited), Cryo-EM (complex), vs AlphaFold (minutes, free). Clean educational illustration, pastel colors, white background.

Evoformer block detailed mechanism diagram. Central box labeled EVOFORMER BLOCK showing two parallel tracks side by side connected with arrows. LEFT TRACK labeled MSA Representation (Nseq x Nres matrix grid visualization): showing Row-wise Gated Self-Attention (attention across residues within each sequence, with attention weight visualization), Column-wise Gated Self-Attention (attention across sequences at each residue position), MSA Transition layer. RIGHT TRACK labeled Pair Representation (Nres x Nres matrix grid): showing Triangle Multiplicative Update diagram with three nodes A-B-C forming triangle, updating edge AB using edges AC and CB with formula m_AB = sigmoid(gate) x linear(m_AC concat m_CB), Triangle Self-Attention diagram, Pair Transition layer. Center connecting arrows: Outer Product Mean (blue arrow left to right showing how MSA row information flows into pair representation), Attention bias (green arrow right to left showing how pair representation biases MSA attention). Bottom showing N_blocks = 48 repetitions notation. Dark blue and teal color scheme, white background, professional neural network diagram style.