Protein Structure Prediction with AI

Module: Healthcare AI | Difficulty: Advanced

RMSD (Root Mean Square Deviation)

GDT-TS (Global Distance Test)

where is the number of C-alpha atoms within distance Angstrom of the native structure.

Self-Attention in AlphaFold

where is the relative positional bias.

Protein Structure Prediction Metrics

| Method | GDT-TS | RMSD (A) | TM-Score | Speed | |--------|--------|----------|----------|-------| | AlphaFold2 | 92.4 | 1.1 | 0.94 | Hours | | RoseTTAFold | 85.2 | 2.1 | 0.88 | Hours | | ESMFold | 83.5 | 2.5 | 0.86 | Minutes | | TrRosetta | 72.1 | 4.2 | 0.75 | Minutes | | I-TASSER | 75.3 | 3.8 | 0.80 | Days |

import torch
import torch.nn as nn

class EvoformerBlock(nn.Module):
    def __init__(self, dim=256, num_heads=8):
        super().__init__()
        self.row_attn = nn.MultiheadAttention(dim, num_heads, batch_first=True)
        self.col_attn = nn.MultiheadAttention(dim, num_heads, batch_first=True)
        self.mlp = nn.Sequential(
            nn.LayerNorm(dim), nn.Linear(dim, dim * 4),
            nn.GELU(), nn.Linear(dim * 4, dim))
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)

    def forward(self, x):
        B, N, M, D = x.shape
        row = x.reshape(B * N, M, D)
        row, _ = self.row_attn(row, row, row)
        row = row.reshape(B, N, M, D)
        x = x + row
        col = x.permute(0, 2, 1, 3).reshape(B * M, N, D)
        col, _ = self.col_attn(col, col, col)
        col = col.reshape(B, M, N, D).permute(0, 2, 1, 3)
        x = x + col
        x = x + self.mlp(x)
        return x

class StructureModule(nn.Module):
    def __init__(self, dim=256, num_layers=8):
        super().__init__()
        self.ipa = nn.ModuleList([
            nn.MultiheadAttention(dim, 4, batch_first=True)
            for _ in range(num_layers)])
        self.norm = nn.LayerNorm(dim)
        self.coordinate_head = nn.Linear(dim, 3)

    def forward(self, representations, positions):
        x = representations + positions
        for layer in self.ipa:
            residual = x
            x = self.norm(x)
            x, _ = layer(x, x, x)
            x = x + residual
        return self.coordinate_head(x)

model = StructureModule(dim=256)
repr = torch.randn(1, 100, 256)
pos = torch.randn(1, 100, 256)
coords = model(repr, pos)
print(f'Predicted coordinates shape: {coords.shape}')

Research Insight: AlphaFold2's breakthrough came from the Evoformer, which processes MSA and pair representations simultaneously. The key insight is that evolutionary co-evolution patterns and spatial proximity provide complementary information for structure prediction. The method achieves experimental-structure-level accuracy for most single-domain proteins.