Evaluation of residue-residue contact prediction methods: From retrospective to prospective

Abstract

Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives. The results show that different methods have different application scenarios: (1) DL methods based on multi-categories of inputs and large training sets are the best choices for low-contact-density proteins such as the intrinsically disordered ones and proteins with shallow multi-sequence alignments (MSAs). (2) With at least 5L (L is sequence length) effective sequences in the MSA, all the methods show the best performance, and methods that rely only on MSA as input can reach comparable achievements as methods that adopt multi-source inputs. (3) For top L/5 and L/2 predictions, DL methods can predict more hydrophobic interactions while ECA methods predict more salt bridges and disulfide bonds. (4) ECA methods can detect more secondary structure interactions, while DL methods can accurately excavate more contact patterns and prune isolated false positives. In general, multi-input DL methods with large training sets dominate current approaches with the best overall performance. Despite the great success of current DL methods must be stated the fact that there is still much room left for further improvement: (1) With shallow MSAs, the performance will be greatly affected. (2) Current methods show lower precisions for inter-domain compared with intra-domain contact predictions, as well as very high imbalances in precisions for intra-domains. (3) Strong prediction similarities between DL methods indicating more feature types and diversified models need to be developed. (4) The runtime of most methods can be further optimized.

COMTOP: Protein Residue–Residue Contact Prediction through Mixed Integer Linear Optimization

“…These methods also rely on different input data types. As shown in our previous work [ 72 ], the prediction results of these methods show certain degrees of similarity and difference, and the differences of prediction results from methods in the different categories are larger than that in the same category. COMTOP selects the weight variables for each individual method through the MILP optimization-based approach.…”

Hybrid drug-screening strategy identifies potential SARS-CoV-2 cell-entry inhibitors targeting human transmembrane serine protease

“…We targeted the TMPRSS2 protein as a therapeutic target to inhibit viral entrance because of its crucial role in viral pathogenesis. We recently published several papers on recommending possible small molecular chemical compounds against the SARS-CoV-2 main protease and RdRp proteins using our novel pipeline that includes deep learning–based drug screening [ 12 , 72 ]. The resulting top three drugs and top nine drug-like compounds were used for further analysis.…”

Inter-Residue Distance Prediction From Duet Deep Learning Models

“…The test set is obtained from our previous work, containing 610 highly non-redundant protein chains ( Zhang et al, 2021 ). The training set is obtained through culling from the whole PDB with the following criteria: 1) with maximum sequence identity of 30% against each chain in the training set and test set; 3) with structure resolutions better than 2.5 Å; 4) released before 1 May 2018 (before the beginning of CASP13).…”