OPUS-Rota4: A Gradient-Based Protein Side-Chain Modeling Framework Assisted by Deep Learning-Based Predictors

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs; and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. In summary, OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the constraints derived from OPUS-RotaCM. In this case, we convert the protein side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms into its side-chain modeling procedure. In other words, OPUS-Rota4 provides a platform in which the protein side-chain conformation can be dynamically adjusted under the influence of other processes, such as protein-protein interaction. We apply OPUS-Rota4 on 15 FM predictions submitted by Alphafold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than the side chains predicted by Alphafold2.

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Log-Linear Modelling of Protein Dipeptide Structure Reveals Interesting Patterns of Side-Chain-Backbone Interactions

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1579 ◽

2011 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Kerstin Hommola ◽

Walter R. Gilks ◽

Kanti V. Mardia

Keyword(s):

Chain Conformation ◽

Dimensional Structure ◽

Side Chain ◽

Dihedral Angles ◽

Parameter Estimates ◽

Side Chain Conformation ◽

Backbone Conformation ◽

Linear Modelling ◽

Side Interaction ◽

Log Linear

It has long been known that the amino-acid sequence of a protein determines its 3-dimensional structure, but accurate ab initio prediction of structure from sequence remains elusive. We gain insight into local protein structure conformation by studying the relationship of dihedral angles in pairs of residues in protein sequences (dipeptides). We adopt a contingency table approach, exploring a targeted set of hypotheses through log-linear modelling to detect patterns of association of these dihedral angles in all dipeptides considered. Our models indicate a substantial association of the side-chain conformation of the first residue with the backbone conformation of the second residue (side-to-back interaction) as well as a weaker but still significant association of the backbone conformation of the first residue with the side-chain conformation of the second residue (back-to-side interaction). To compare these interactions across different dipeptides, we cluster the parameter estimates for the corresponding association terms. This reveals a striking pattern. For the side-to-back term, all dipeptides which have the same first residue jointly appear in distinct clusters whereas for the back-to-side term, we observe a much weaker pattern. This suggests that the conformation of the first residue affects the conformation of the second.

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