[email protected] (ORCID: 0000-0002-2761-3291)

A lab rotation project. Under development from 2021.12 to 2022.1.

• accept variable input size
  • protein of any length
  • any number of homologous sequences
• does not rely on paired MSA, thus it can predict cross-species protein interaction

• Sequence database e.g. UniRef30_2020_06_hhsuite.tar.gz
• PDB structures

• Protein Sequence * 2
  • build MSA via HHblits if possible
  • onehot encoding of amino acid type (20+gap+X)*2 + (hydrophoblic+hydrophilic)*2

Fitting Targets (i.e. Inter-chain Cβ-Cβ Distance Map) Examples

NOTE: Cα for GLY

PDB	Inter-chain Cβ-Cβ Distance Map	pdb_id	human chain	virus chain	len(human chain)	len(virus chain)
		3wwt	A	B	123	107
		1im3	E	H	275	95
		6bvv	A	B	416	24
		4rf1	B	A	75	321
		6e5x	B	A	13	127

Real-valued distances are discretely binned:

• from 2Å to 20Å
• with bin size of 0.5Å
• 36 bins + 1 bin for $[20, +\infty)$

• ResNet1D
  • 1d residual block: ((Conv1d + BatchNorm1d) + ELU + (Conv1d + BatchNorm1d)) (+) ELU
  • 8 1d-blocks (64 channels)
• ResNet2D
  • 2d residual block: ((Conv2d + BatchNorm2d) + ELU + (Conv2d + BatchNorm2d)) (+) ELU
  • 16 2d-blocks (96 channels), cycling through dilations $(1,2,4,8)$
• (mini-)batch size: 1
• Cross-entropy Loss

• for each protein sequence (of length $L$) search homologous sequences and input the MSA (of size $K$) if possible, otherwise input the single sequence
  • use original MSA to calculate the weight $w_k$ for each homologous sequence, $w_k=\frac{1}{\text{count}(\text{sequence with identity}\ge 0.8)}$
  • calculate $M_\text{eff}=\sum_{k}^{K}w_k$
• MSA Encoding: perform onehot-encoding for each pairwise alignment ($2\times L_k$, consider both insertion and deletion: $\rightarrow 48\times L_k$)
  • onehot-encoding including 22+2 channels for the reference sequence, 22+2 channels for the homologous sequence
  • 22: 20 amino acid types + 1 gap + 1 unknown type
  • 2: 1 hydrophoblic + 1 hydrophilic
  • for the single sequence input, all the homologous related channels are filled with the reference sequence's corresponding values
  • hence we get ${48\times L_k, k\in K}$
• MSA Embedding: for each encoded pairwise alignment, feed into the ResNet1D and get embedded pairwise alignment ($64\times L_k$)
  • hence we get ${64\times L_k, k\in K}$
  • omit the insertion region of the homologous sequences, thus we can get a $K\times 64 \times L$ tensor
    • $x_k\in R^{64\times L}$
    • $x_k(i) \in R^{64}$
• Paired Evolution Aggregation
  • calculate one body term
    • $f_1(i)=\frac{1}{M_{\text{eff}{1}}}\sum{k}^{K_1}w_{1_k} x_{1_k}(i)$
    • $f_2(j)=\frac{1}{M_{\text{eff}{2}}}\sum{k}^{K_2}w_{2_k} x_{2_k}(j)$
  • apply max function
    • $A(i,c) = \max{x_k(i,c), k \in K}$, c: channel; $A \in R^{64\times L}$
  • calculate two body term
    • $s(i,j) = \frac{1}{\sqrt{M_{\text{eff}{1}}\cdot M{\text{eff}_{2}}}}[A_1(i)\otimes A_2(j)], s(i,j)\in R^{64\times 64}$
  • concatenate
    • $h(i,j) = \text{concat}(f_1(i), f_2(j), s(i,j)), h(i,j)\in R^{4224}$
• Inter-chain Distance Estimation
  • feed the $R^{4224 \times L_1\times L_2}$ tensor into the ResNet2D
  • convert the ResNet2D outputs into the discrete distance distribution $37 \times L_1 \times L_2$ throught a (Conv2d+BatchNorm2d+ELU) layer

• randomly sample 1000 homologous sequences if $K > 1000$
• randomly crop the distance matrix into $64 \times 64$ shape
  • for those input sequences of length $\le 64$, keep their original length
• loss function ignore those missing residues of PDB structure
  • definition of missing
    1. complete without any modeled atoms for a residue
    2. GLY without Cα atom
    3. non-GLY without Cβ atom and without any anchoring atoms (Cα, N, C) that can infer Cβ atom

• training dataset (362)
• validation dataset (100)

NOTE: the datasets have not been carefully curated, they just serve as demo inputs for training and validation. Thus the training and validation results are just a demonstration of the model's learning ability.

• increase mini-batch size
• multiple GPU training
• improve archi

• Hardware (tested)
  • GPU: NVIDIA Tesla T4 (16G)
  • CUDA Version: 11.2
• Software
  • Python 3.8 or later in a conda environment
  • PyTorch and other Python packages: see requirements

I would like to thank Jinyuan Guo and Prof. Huaiqiu Zhu for their helpful discussions and provide me with devices to develop this project.

base on these papers

Apache-2.0 License