Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.

Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image processing algorithms, building accurate structure models for cryo-EM maps remains a challenge. Protein secondary structure information, which can be extracted from EM maps, is beneficial for cryo-EM structure modeling. Here, we present a novel secondary structure annotation framework for cryo-EM maps at both intermediate and high resolutions, named EMNUSS. EMNUSS adopts a three-dimensional (3D) nested U-net architecture to assign secondary structures for EM maps. Tested on three diverse datasets including simulated maps, middle resolution experimental maps, and high-resolution experimental maps, EMNUSS demonstrated its accuracy and robustness in identifying the secondary structures for cyro-EM maps of various resolutions. The EMNUSS program is freely available at http://huanglab.phys.hust.edu.cn/EMNUSS.

TRScore: a 3D RepVGG-based scoring method for ranking protein docking models.

MOTIVATION: Protein-protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately, traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. RESULTS: We present a new deep learning-based scoring method for ranking protein-protein docking models based on a 3D RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein-protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein-protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set and overall obtained a significant improvement over existing methods in cross-validation and independent evaluations. AVAILABILITY AND IMPLEMENTATION: Codes available at: https://github.com/BioinformaticsCSU/TRScore.

Neoadjuvant chemotherapy weakens the prognostic value of the pathological tumor burden score for colorectal cancer liver metastases.

BACKGROUND: The pathological tumor burden score (TBS) has been proven to be a better risk stratification tool for liver metastasis of colorectal cancer than the traditional clinical risk score (CRS). The aim of this study was to evaluate the prognostic value of the pathological tumor burden score in patients with or without neoadjuvant chemotherapy (NAC). METHODS: A total of 348 patients with colorectal liver metastases (CRLM) who underwent curative hepatic resection were retrospectively enrolled from September 1999 to December 2016. Univariable and multivariable Cox regression analyses were conducted to identify the independent predictors of prognosis. Kaplan-Meier curves and log-rank tests were used to determine whether TBS has enough discriminatory ability under certain grouping. RESULTS: Patients who received NAC had a higher median TBS than patients who did not receive NAC (4.07 vs. 2.69, P < 0.001). Among patients who did not receive NAC, those with TBS > 3 showed a significantly worse 3-year RFS (41.1% vs. 63.6%, P < 0.001) and 3-year OS rate (73.3% vs. 84.1%, P = 0.003) than those with TBS ≤ 3. Among the patients who received NAC, those with TBS ≤ 3 or TBS > 3 showed comparable 3-year RFS (33.3% vs. 26.4%, P = 0.400) and 3-year OS rates (76.5% vs. 58.2%, P = 0.064) to those who did not. Regardless of the regimen and response to NAC, there was no significant difference about 3-year RFS and 3-year OS rates between the TBS ≤ 3 and TBS > 3 groups. CONCLUSION: Pathological TBS can be applied to predict the RFS and OS of patients suffering from CRLM who did not receive NAC. However, pathological TBS might not be regard as prognosis in patients who did receive NAC.

Full-length de novo protein structure determination from cryo-EM maps using deep learning.

MOTIVATION: Advances in microscopy instruments and image processing algorithms have led to an increasing number of Cryo-electron microscopy (cryo-EM) maps. However, building accurate models for the EM maps at 3-5 Å resolution remains a challenging and time-consuming process. With the rapid growth of deposited EM maps, there is an increasing gap between the maps and reconstructed/modeled three-dimensional (3D) structures. Therefore, automatic reconstruction of atomic-accuracy full-atom structures from EM maps is pressingly needed. RESULTS: We present a semi-automatic de novo structure determination method using a deep learning-based framework, named as DeepMM, which builds atomic-accuracy all-atom models from cryo-EM maps at near-atomic resolution. In our method, the main-chain and Cα positions as well as their amino acid and secondary structure types are predicted in the EM map using Densely Connected Convolutional Networks. DeepMM was extensively validated on 40 simulated maps at 5 Å resolution and 30 experimental maps at 2.6-4.8 Å resolution as well as an Electron Microscopy Data Bank-wide dataset of 2931 experimental maps at 2.6-4.9 Å resolution, and compared with state-of-the-art algorithms including RosettaES, MAINMAST and Phenix. Overall, our DeepMM algorithm obtained a significant improvement over existing methods in terms of both accuracy and coverage in building full-length protein structures on all test sets, demonstrating the efficacy and general applicability of DeepMM. AVAILABILITY AND IMPLEMENTATION: http://huanglab.phys.hust.edu.cn/DeepMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Advances in Bi2WO6-Based Photocatalysts for Degradation of Organic Pollutants.

With the rapid development of modern industries, water pollution has become an urgent problem that endangers the health of human and wild animals. The photocatalysis technique is considered an environmentally friendly strategy for removing organic pollutants in wastewater. As an important member of Bi-series semiconductors, Bi2WO6 is widely used for fabricating high-performance photocatalysts. In this review, the recent advances of Bi2WO6-based photocatalysts are summarized. First, the controllable synthesis, surface modification and heteroatom doping of Bi2WO6 are introduced. In the respect of Bi2WO6-based composites, existing Bi2WO6-containing binary composites are classified into six types, including Bi2WO6/carbon or MOF composite, Bi2WO6/g-C3N4 composite, Bi2WO6/metal oxides composite, Bi2WO6/metal sulfides composite, Bi2WO6/Bi-series composite, and Bi2WO6/metal tungstates composite. Bi2WO6-based ternary composites are classified into four types, including Bi2WO6/g-C3N4/X, Bi2WO6/carbon/X, Bi2WO6/Au or Ag-based materials/X, and Bi2WO6/Bi-series semiconductors/X. The design, microstructure, and photocatalytic performance of Bi2WO6-based binary and ternary composites are highlighted. Finally, aimed at the existing problems in Bi2WO6-based photocatalysts, some solutions and promising research trends are proposed that would provide theoretical and practical guidelines for developing high-performance Bi2WO6-based photocatalysts.

Topology-independent and global protein structure alignment through an FFT-based algorithm.

MOTIVATION: Protein structure alignment is one of the fundamental problems in computational structure biology. A variety of algorithms have been developed to address this important issue in the past decade. However, due to their heuristic nature, current structure alignment methods may suffer from suboptimal alignment and/or over-fragmentation and thus lead to a biologically wrong alignment in some cases. To overcome these limitations, we have developed an accurate topology-independent and global structure alignment method through an FFT-based exhaustive search algorithm, which is referred to as FTAlign. RESULTS: Our FTAlign algorithm was extensively tested on six commonly used datasets and compared with seven state-of-the-art structure alignment approaches, TMalign, DeepAlign, Kpax, 3DCOMB, MICAN, SPalignNS and CLICK. It was shown that FTAlign outperformed the other methods in reproducing manually curated alignments and obtained a high success rate of 96.7 and 90.0% on two gold-standard benchmarks, MALIDUP and MALISAM, respectively. Moreover, FTAlign also achieved the overall best performance in terms of biologically meaningful structure overlap (SO) and TMscore on both the sequential alignment test sets including MALIDUP, MALISAM and 64 difficult cases from HOMSTRAD, and the non-sequential sets including MALIDUP-NS, MALISAM-NS, 199 topology-different cases, where FTAlign especially showed more advantage for non-sequential alignment. Despite its global search feature, FTAlign is also computationally efficient and can normally complete a pairwise alignment within one second. AVAILABILITY AND IMPLEMENTATION: http://huanglab.phys.hust.edu.cn/ftalign/.

HNADOCK: a nucleic acid docking server for modeling RNA/DNA-RNA/DNA 3D complex structures.

Interactions between nuclide acids (RNA/DNA) play important roles in many basic cellular activities like transcription regulation, RNA processing, and protein synthesis. Therefore, determining the complex structures between RNAs/DNAs is crucial to understand the molecular mechanism of related RNA/DNA-RNA/DNA interactions. Here, we have presented HNADOCK, a user-friendly web server for nucleic acid (NA)-nucleic acid docking to model the 3D complex structures between two RNAs/DNAs, where both sequence and structure inputs are accepted for RNAs, while only structure inputs are supported for DNAs. HNADOCK server was tested through both unbound structure and sequence inputs on the benchmark of 60 RNA-RNA complexes and compared with the state-of-the-art algorithm SimRNA. For structure input, HNADOCK server achieved a high success rate of 71.7% for top 10 predictions, compared to 58.3% for SimRNA. For sequence input, HNADOCK server also obtained a satisfactory performance and gave a success rate of 83.3% when the bound RNA templates are included or 53.3% when excluding those bound RNA templates. It was also found that inclusion of the inter-RNA base-pairing information from RNA-RNA interaction prediction can significantly improve the docking accuracy, especially for the top prediction. HNADOCK is fast and can normally finish a job in about 10 minutes. The HNADOCK web server is available at http://huanglab.phys.hust.edu.cn/hnadock/.

Challenges and opportunities of automated protein-protein docking: HDOCK server vs human predictions in CAPRI Rounds 38-46.

Protein-protein docking plays an important role in the computational prediction of the complex structure between two proteins. For years, a variety of docking algorithms have been developed, as witnessed by the critical assessment of prediction interactions (CAPRI) experiments. However, despite their successes, many docking algorithms often require a series of manual operations like modeling structures from sequences, incorporating biological information, and selecting final models. The difficulties in these manual steps have significantly limited the applications of protein-protein docking, as most of the users in the community are nonexperts in docking. Therefore, automated docking like a web server, which can give a comparable performance to human docking protocol, is pressingly needed. As such, we have participated in the blind CAPRI experiments for Rounds 38-45 and CASP13-CAPRI challenge for Round 46 with both our HDOCK automated docking web server and human docking protocol. It was shown that our HDOCK server achieved an "acceptable" or higher CAPRI-rated model in the top 10 submitted predictions for 65.5% and 59.1% of the targets in the docking experiments of CAPRI and CASP13-CAPRI, respectively, which are comparable to 66.7% and 54.5% for human docking protocol. Similar trends can also be observed in the scoring experiments. These results validated our HDOCK server as an efficient automated docking protocol for nonexpert users. Challenges and opportunities of automated docking are also discussed.

Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models.

MOTIVATION: Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB). RESULTS: Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions. AVAILABILITY AND IMPLEMENTATION: http://huanglab.phys.hust.edu.cn/mprdock/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PepBDB: a comprehensive structural database of biological peptide-protein interactions.

Summary: A structural database of peptide-protein interactions is important for drug discovery targeting peptide-mediated interactions. Although some peptide databases, especially for special types of peptides, have been developed, a comprehensive database of cleaned peptide-protein complex structures is still not available. Such cleaned structures are valuable for docking and scoring studies in structure-based drug design. Here, we have developed PepBDB-a curated Peptide Binding DataBase of biological complex structures from the Protein Data Bank (PDB). PepBDB presents not only cleaned structures but also extensive information about biological peptide-protein interactions, and allows users to search the database with a variety of options and interactively visualize the search results. Availability and implementation: PepBDB is available at http://huanglab.phys.hust.edu.cn/pepbdb/.

Visual test for the presence of the illegal additive ethyl anthranilate by using a photonic crystal test strip.

A test strip has been developed for the rapid detection of the illegal additive ethyl anthranilate (EA) in wine. The detection scheme is based on a combination of photonic crystal based detection and molecular imprinting based recognition. The resulting molecularly imprinted photonic crystal (MIPC) undergoes a gradual color change from green to yellow to red upon binding of EA. A semi-quantitative colorimetric card can be used to estimate the content of EA, either visually or by making use of an optical fiber spectrometer. A linear relationship was found between the Bragg diffraction peak shift and the concentration of EA in the range from 0.1 mM to 10 mM. The detection limit is 10 µM. The test has been successfully used to screening for the presence of EA in grape wine. The test strip is selective, and can be re-used after re-activation. Graphical abstract Schematic representation of the fabrication and application of the molecularly imprinted photonic crystal (MIPC) based test trip. The resulting MIPC undergoes a gradual color change from green to yellow to red upon binding of the illegal wine additive ethyl anthranilate (EA).

All-atom RNA structure determination from cryo-EM maps.

Many methods exist for determining protein structures from cryogenic electron microscopy maps, but this remains challenging for RNA structures. Here we developed EMRNA, a method for accurate, automated determination of full-length all-atom RNA structures from cryogenic electron microscopy maps. EMRNA integrates deep learning-based detection of nucleotides, three-dimensional backbone tracing and scoring with consideration of sequence and secondary structure information, and full-atom construction of the RNA structure. We validated EMRNA on 140 diverse RNA maps ranging from 37 to 423 nt at 2.0-6.0 Å resolutions, and compared EMRNA with auto-DRRAFTER, phenix.map_to_model and CryoREAD on a set of 71 cases. EMRNA achieves a median accuracy of 2.36 Å root mean square deviation and 0.86 TM-score for full-length RNA structures, compared with 6.66 Å and 0.58 for auto-DRRAFTER. EMRNA also obtains a high residue coverage and sequence match of 93.30% and 95.30% in the built models, compared with 58.20% and 42.20% for phenix.map_to_model and 56.45% and 52.3% for CryoREAD. EMRNA is fast and can build an RNA structure of 100 nt within 3 min.

Combination of DNA ploidy, stroma, and nucleotyping predicting prognosis and tailoring adjuvant chemotherapy duration in stage III colon cancer.

Introduction: DNA ploidy (P), stroma fraction (S), and nucleotyping (N) collectively known as PSN, have proven prognostic accuracy in stage II colorectal cancer (CRC). However, few studies have reported on the prognostic value of the PSN panel in stage III colon cancer patients receiving capecitabine and oxaliplatin adjuvant chemotherapy. Objectives: This study aimed to validate PSN's prognostic impact on stage III colon cancer, identifying candidates for optimized adjuvant chemotherapy duration. Design: A retrospective analysis was conducted on a cohort of stage III colon cancer patients from April 2008 to June 2020. Methods: Postoperative pathological samples from stage III colon cancer patients who underwent radical surgery and postoperative adjuvant chemotherapy at Sun Yat-sen University Cancer Center were retrospectively collected. Automated digital imaging assessed PSN, categorizing risk groups. Kaplan-Meier, Cox regression, and time-dependent receiver operating characteristic analysis compared model validity. Results: Significant differences in 5-year disease-free survival (DFS) and overall survival (OS) were noted among PSN-based low-, moderate-, and high-risk groups (DFS: 92.10% versus 83.62% versus 79.80%, p = 0.029; OS: 96.69% versus 93.99% versus 90.12%, p = 0.016). PSN emerged as an independent prognostic factor for DFS [hazard ratio (HR) = 1.409, 95% confidence interval (CI): 1.002-1.981, p = 0.049] and OS (HR = 1.720, 95% CI: 1.127-2.624, p = 0.012). The PSN model, incorporating perineural invasion and tumor location, displayed superior area under the curve for 5-year (0.692 versus 0.553, p = 0.020) and 10-year (0.694 versus 0.532, p = 0.006) DFS than TNM stage. In the PSN high-risk group, completing eight cycles of adjuvant chemotherapy significantly improved 5-year DFS and OS compared to four to seven cycles (DFS: 89.43% versus 71.52%, p = 0.026; OS: 96.77% versus 85.46%, p = 0.007). Conclusion: The PSN panel effectively stratifies stage III colon cancer, aiding in optimized adjuvant chemotherapy duration determination.

Tumor-derived lactate promotes resistance to bevacizumab treatment by facilitating autophagy enhancer protein RUBCNL expression through histone H3 lysine 18 lactylation (H3K18la) in colorectal cancer.

Bevacizumab plays an important role in the first and second line treatment for metastatic colorectal cancer (CRC). And induction of hypoxia and the tumors response to it plays an important role in determining the efficacy of antiangiogenic therapy while the connection between them remains unclear. Here, we found that lactate accumulated in the tumor environment of CRC and acted as substrates for histone lactylation, and this process was further induced by cellular enhanced glycolysis in hypoxia. We determined that CRC patients resistant to bevacizumab treatment presented with elevated levels of histone lactylation and inhibition of histone lactylation efficiently suppressed CRC tumorigenesis, progression and survival in hypoxia. Histone lactylation promoted the transcription of RUBCNL/Pacer, facilitating autophagosome maturation through interacting with BECN1 (beclin 1) and mediating the recruitment and function of the class III phosphatidylinositol 3-kinase complex, which had a crucial role in hypoxic cancer cells proliferation and survival. Moreover, combining inhibition of histone lactylation and macroautophagy/autophagy with bevacizumab treatment demonstrated remarkable treatment efficacy in bevacizumab-resistance patients-derived pre-clinical models. These findings delivered a new exploration and important supplement of metabolic reprogramming-epigenetic regulation, and provided a new strategy for improving clinical efficacy of bevacizumab in CRC by inhibition of histone lactylation.Abbreviations: 2-DG: 2-deoxy-D-glucose; BECN1: beclin 1; CQ: chloroquine; CRC: colorectal cancer; DMOG: dimethyloxalylglycine; H3K18la: histone H3 lysine 18 lactylation; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; Nala: sodium lactate; PDO: patient-derived orgnoid; PDX: patient-derived xenograft; RUBCNL/Pacer: rubicon like autophagy enhancer; SQSTM1/p62: sequestosome 1.

Improvement of cryo-EM maps by simultaneous local and non-local deep learning.

Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0-6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building.

Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly.

Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0-8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7-9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building.

Effect of Ho Addition on the Glass-Forming Ability and Crystallization Behaviors of Zr54Cu29Al10Ni7 Bulk Metallic Glass.

The effect of holmium (Ho) addition on the glass-forming ability (GFA) and crystallization behaviors of Zr54Cu29Al10Ni7 bulk metallic glass (BMGs) were studied by employing differential scanning calorimetry (DSC), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The characteristic temperatures and activation energies of crystallization were obtained from DSC data. Classical kinetic modes were used to evaluate the crystallization processes of Zr54Cu29Al10Ni7 and Zr48Cu29Ni7Al10Ho6 BMGs. The results showed that Ho addition reduces the activation energy in the original crystallization period of Zr-based BMG and improves the nucleation, which is due to the formation of simpler compounds, such as CuZr2, Cu2Ho, and Al3Zr5.

Docking and scoring for nucleic acid-ligand interactions: Principles and current status.

Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

The Pathologic Complete Response Ratio of Liver Metastases Represents a Valuable Prognostic Indicator.

Background and Objectives: The aim of this study was to evaluate the role of the pathologic complete response ratio of liver metastases (PCRRLM) in predicting the prognosis and recurrence of colorectal cancer liver metastases (CRLM). Methods: A total of 305 CRLM patients who underwent preoperative chemotherapy followed by hepatectomy were included. PCRRLM was defined as the number of liver metastases exhibiting pathologic complete response (PCR) divided by the number of total resected liver metastases. The Kaplan-Meier method was used to calculate survival, and differences were examined by the log-rank test. Univariate and multivariate analyses were performed to identify the predictors of PCRRLM, recurrence-free survival (RFS) and overall survival (OS). Results: Among the 305 included patients, 44 (14.4%) achieved a PCRRLM ≥0.50 (including PCRRLM = 1), and 261 (85.6%) achieved a PCRRLM <0.50 (including PCRRLM = 0). Patients of an older age (≥55 years old) and those with higher carcinoembryonic antigen (CEA) levels (≥5 ng/ml) were less likely to achieve a PCRRLM ≥0.50. In the multivariate analysis, PCRRLM≥ 0.50 (vs. < 0.50, HR [95% CI]: 0.67 [0.46-0.99], p = 0.043) was associated with better RFS. Positive lymph node status (vs. negative, HR [95% CI]: 1.46 [1.04-2.05], p = 0.028) and TBS ≥5 (vs. < 5, HR [95% CI]: 1.44 [1.02-2.04], p = 0.038) were associated with worse RFS. Conclusion: PCRRLM was significantly associated with long-term RFS after preoperative chemotherapy and CRLM resection. Thus, it may be a valuable indicator of recurrence in CRLM patients.