LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins

Abstract

The LOMETS2 server (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is an online meta-threading server system for template-based protein structure prediction. Although the server has been widely used by the community over the last decade, the previous LOMETS server no longer represents the state-of-the-art due to aging of the algorithms and unsatisfactory performance on distant-homology template identification. An extension of the server built on cutting-edge methods, especially techniques developed since the recent CASP experiments, is urgently needed. In this work, we report the recent advancements of the LOMETS2 server, which comprise a number of major new developments, including (i) new state-of-the-art threading programs, including contact-map-based threading approaches, (ii) deep sequence search-based sequence profile construction and (iii) a new web interface design that incorporates structure-based function annotations. Large-scale benchmark tests demonstrated that the integration of the deep profiles and new threading approaches into LOMETS2 significantly improve its structure modeling quality and template detection, where LOMETS2 detected 176% more templates with TM-scores >0.5 than the previous LOMETS server for Hard targets that lacked homologous templates. Meanwhile, the newly incorporated structure-based function prediction helps extend the usefulness of the online server to the broader biological community.

Figure 1.

The flowchart of the LOMETS2 server pipeline consists of four steps: generation of deep profiles, fold-recognition by 11 individual threading programs, template selection and full-length model construction.

Figure 2.

The TM-scores of the first templates (A) and full-length models (B) predicted by LOMETS and LOMETS2 for 614 test proteins. Red and blue points correspond to Hard and Easy targets, respectively. The red numbers represent the number of targets above or below the diagonal line, while the black numbers correspond to the number of targets in each sub-square.

Figure 3.

Average TM-scores of the first templates detected by LOMETS2 and its component threading programs based on default profiles versus those based on deep profiles for 211 Hard targets. N_st represents the number of targets whose first template has a TM-score >0.5.

Figure 4.

Illustration of the LOMETS2 server output using FokI restriction endonuclease (PDB ID: 2fokA) as an example. The three main sections of the LOMETS2 output are (A) summary of the top 10 templates identified by LOMETS2, (B) five models predicted by LOMETS2 and (C) the top templates identified by each threading program.